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M, Jerin Jose, Janani B.R, Janani Priya K, Jeevitha J, and Swathi S. "Lung Cancer Detection using Artificial Neural Netw." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 06 (December 27, 2017): 01–05. http://dx.doi.org/10.9756/sijcsea/v5i6/05010050101.
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Reddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar, and Dr Syed Siraj Ahmed. "Lung Cancer Detection using YOLO CNN Algorithm." International Journal of Research Publication and Reviews 4, no. 5 (June 2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.
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M, Albertini. "Canine Scent Detection of Lung Cancer: Preliminary Results." Open Access Journal of Veterinary Science & Research 1, no. 4 (2016): 1–5. http://dx.doi.org/10.23880/oajvsr-16000118.
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Several researches have evidenced that cancer cells can produce volatile organic compounds (VOCs) which are released not only in breath but also in other organic fluids, such as blood and urine. This study has evaluated the olfactory capability of trained dogs to detect human lung cancer VOCs in urine. We recruited 150 subjects from European Institute of Oncology (IEO) divided into three groups: 57 patients with lung cancer (group 1); 38 patients with lung disease, other than cancer (group 2); 55 healthy co ntrol subjects (group 3).The results are referred to the last 45 days of training, and evidenced that dogs reached a mean success rate that exceeded 80%, with a sensitivity of 0,72 and a specificity of 0,94 for two out of three dogs enrolled. The important novelty is that dogs can discriminate lung cancer not only from healthy subjects, but also from patients with other lung diseases. The results obtained so far are encouraging and lead us to persevere with the training session in order to improve the succe ss rate, reaching values as close as possible to 100%. If so, we believe that, in the future, dogs may be used to perform early diagnostic tests, useful in improving the chances of survival in cases of human lung cancer.
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Patel, Bindiya, Dr Pankaj Kumar Mishra, and Prof Amit Kolhe. "Lung Cancer Detection on CT Images by using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.
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Choe, Wonho, Jeong Don Chae, Byoung-Hoon Lee, Sang-Hoon Kim, So Young Park, Satish Balasaheb Nimse, Junghoon Kim, et al. "9G TestTM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening." Cancers 12, no. 11 (October 30, 2020): 3192. http://dx.doi.org/10.3390/cancers12113192.
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A complimentary biomarker test that can be used in combination with LDCT for lung cancer screening is highly desirable to improve the diagnostic capacity of LDCT and reduce the false-positive rates. Most importantly, the stage I lung cancer detection rate can be dramatically increased by the simultaneous use of a biomarker test with LDCT. The present study was conducted to evaluate 9G testTM Cancer/Lung’s sensitivity and specificity in detecting Stage 0~IV lung cancer. The obtained results indicate that the 9G testTM Cancer/Lung can detect lung cancer with overall sensitivity and specificity of 75.0% (69.1~80.3) and 97.3% (95.0~98.8), respectively. The detection of stage I, stage II, stage III, and stage IV cancers with sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3% have never been reported before. The receiver operating characteristic curve analysis allowed us to determine the population-weighted AUC of 0.93 (95% CI, 0.91–0.95). These results indicate that the 9G testTM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Furthermore, obtained results indicate that the use of 9G testTM Cancer/Lung with LDCT for lung cancer screening can increase stage I cancer detection, which is crucial to improve the currently low 5-year survival rates.
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Yektaei, Homayoon, and Mohammad Manthouri. "DIAGNOSIS OF LUNG CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK." Biomedical Engineering: Applications, Basis and Communications 32, no. 05 (August 12, 2020): 2050030. http://dx.doi.org/10.4015/s1016237220500301.
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Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. This study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the multiscale convolution. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The use of image processing techniques and identifying patterns in the detection of lung cancer from CT images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect lung cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents CT picture to several deep convolutional neural networks with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state of the art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches a diagnosis accuracy of [Formula: see text] using multiscale convolution technique, which reveals the efficiency of the proposed method.
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Kaur, Jatinder, Ravi Prakash Chaturvedi, and Sameer Asthana. "Lung cancer detection a machine learning approach." YMER Digital 21, no. 03 (March 23, 2022): 337–48. http://dx.doi.org/10.37896/ymer21.03/37.
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In the United States, lung cancer is the largest cause of cancer-related death. Patients with early discovery and diagnosis have a better chance of surviving. Our main focus in this project is on developing a computer-aided diagnostic (CAD) tool for early cancer detection utilizing CT scans as information. The LUNA 16 data set and the kaggle data set are two different data sets that we introduce. In this research, we provide a methodology for predicting whether a patient has cancer using information learned from the LUNA data set on the kaggle data. We discuss pre-processing, lung segmentation, lung nodule segmentation, and finally data classification. We use GPU-enabled clusters to run the methods provided in each step above due to the large amount of data. The results were positive, and the pipeline project is functional that can be used in real-world applications
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Inage, Terunaga, Takahiro Nakajima, Ichiro Yoshino, and Kazuhiro Yasufuku. "Early Lung Cancer Detection." Clinics in Chest Medicine 39, no. 1 (March 2018): 45–55. http://dx.doi.org/10.1016/j.ccm.2017.10.003.
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Smith, Robert A., and Thomas J. Glynn. "Early lung cancer detection." Cancer 89, S11 (December 1, 2000): 2327–28. http://dx.doi.org/10.1002/1097-0142(20001201)89:11+<2327::aid-cncr1>3.0.co;2-r.
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Thanzeem Mohamed Sheriff, S., J. Venkat Kumar, S. Vigneshwaran, Aida Jones, and Jose Anand. "Lung Cancer Detection using VGG NET 16 Architecture." Journal of Physics: Conference Series 2040, no. 1 (October 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2040/1/012001.
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Abstract Cancer is one of the main reason for loss of human life across the world. All the medical practitioners and researchers are dealing with the demanding situations to fight against cancer. Based on the report in 2019 from American Cancer Society, 96,480 deaths are anticipated due to skin cancers, 142,670 deaths are from lung cancers, 42,260 deaths are from breast cancers, 31,620 deaths are from prostate cancers, and 17,760 deaths are from mind cancers. Initial detection of most cancers has the pinnacle precedence for saving the lives. This paper proposed a lung cancer detection using Deep Learning based on VEE NET architecture. This was one of the famous models submitted to ILSVRC-2014. Visual checkup and manual practices are used on this venture for the various types of cancer diagnoses. This guide interpretation of scientific images that needs massive time intake and is notably susceptible to mistakes. Thus, in this project, we apply deep learning algorithms to identify lung cancer and its presence without the need for several consultations from different doctors. This leads to an earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences in an effective and cheap manner avoiding human error rate. In this project lung cancer and its presence is determined. A web application is developed as a hospital application where an input x-ray image is given to detect lung cancer.
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Shobha Rani N, Rakshitha B S, and Rohith V. "Patch analysis based lung cancer classification." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (July 19, 2019): 2163–73. http://dx.doi.org/10.26452/ijrps.v10i3.1443.
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Lung Cancer may be a variety of Cancer that begins in the Lungs because of those that smokes often. However, there Area unit rare probabilities those area unit non-smokers get Affected because of unhealthy pollution and Harmful gasses. The detection of tumor is incredibly vital that helps to detect affected neoplasm areas in the lungs. Computed tomography help us to understand the cancer positions in patients. The detection of cancer tumours are performed by scanning the images of computed tomography. Lung cancer identification system goes with a method of Morphological opening and Gray level co-occurrence matrix (GLCM) feature extraction and Normalized cross-correlation with patches Analysis. Lung cancer classification using Linear Discriminant Analysis (LDA) gives good results of Accuracy of 81.81%. Patch Analysis is a new method to find lung cancer.
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Lucke-Wold, Brandon. "Principles of Lung Cancer Metastasis to Brain." Journal of Skeleton System 1, no. 1 (December 18, 2022): 01–04. http://dx.doi.org/10.58489/2836-2284/003.
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Lung cancer is a disease associated with significant morbidity and mortality on a global setting. This form of cancer commonly gives raise to metastatic lesions the brain, which can further worsen outcomes. In this focused review, we discuss an overview of lung cancers that metastasize to the brain: known risk factors; means of detection and diagnosis; and options for treatment including a comparison between surgical resection, stereotactic radiosurgery, and whole-brain radiation therapy. These interventions are still being assessed by clinical trials and continue to be modified through evidence-based practice.
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Anand, Rameshwari. "LUNG CANCER DETECTION AND PREDICTION USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 7, no. 1 (May 1, 2022): 313–20. http://dx.doi.org/10.33564/ijeast.2022.v07i01.048.
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Lung cancer is a malignant tumor of the lungs that is characterized by uncontrolled cell proliferation. Early identification of lung cancer can improve lung cancer patient’s chances of survival and save many lives. Because cancer diagnosis is one of the most difficult tasks for radiologists due to the shape of cancer cells, a computer- aided diagnosis method can be beneficial. As a result, early detection and prediction of lung cancer should play a critical role in the diagnosis process, as well as increase patient survival rates. This project presents lung cancer detection based on CT images using efficient lung cancer classification and prediction using deep learning models for improved accuracy. The architecture is trained using preprocessed CT images. The patient input photos are then tested using deep learning models. The primary goal of this research is to determine whether a patient's lung containsa cancer tumor. By creating a handcrafted CNN model and using Transfer Learning-based VGG-16 and Inception V3 architectures to train the model, we present a convolution neural network-based classification technique. The performance of these models is compared in this study. To achieve the best outcomes, hyper parameter optimization was performed.
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Mate, Prof G. S., Karan Shirsat, Aakash Panditha, Kaumodaki Koul, and Sejal Rayou. "Lung Cancer Detection Using Neural Net." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 940–45. http://dx.doi.org/10.22214/ijraset.2023.48530.
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Abstract: Leukemia is a sort of blood threatening development which happens due touncommon development in WBCs (white platelets) in bone marrow of human body. Leukemia can be appointed serious leukemia and progressing leukemia, affected severely and causes malignant growth. In the vast majority of the cases early in which serious leukemia turns out to be astoundingly fast however steady leukemia creates slowly .Among all kinds of kinds of developments, cell breakdown in the lungs is the most overwhelming contamination having the most critical passing rate. Dealt with tomography investigates are utilized for ID of lung hurt as it gives unmistakable image of harmful development in the body and tracks its development. Picture dealing with systems are utilized generally in clinical fields for beginning stage affirmation of cell breakdown in the lungs. The calculation for cell breakdown in the lungs disclosure is proposed utilizing strategies, for example, focus disconnecting for picture pre-dealing with followed by division of lung area of interest utilizing numerical morphological endeavors. Mathematical parts are dealt with from the eliminated district of interest and used to bundle CT channel pictures into typical and abnormal by utilizing support vector machine. Further both the sorts have two sub arrangements lymphocytic and myeloid. In this paper, we will explore different picture dealing with and Man-made intelligence procedures used for request of leukemia area and endeavor to focus in on advantages and limitations of different similar investigates to summarize an result which will be valuable for various experts.
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Balannolla, Sneha, Dr A. Kousar Nikhath, and Dr Sagar Yeruva. "Detection and Classification of Lung Carcinoma using CT scans." Journal of Physics: Conference Series 2286, no. 1 (July 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2286/1/012011.
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Abstract Lung carcinoma, generally known as lung cancer, is the most common cause of cancer which is related to mortality worldwide. Lung carcinoma is an extremely complex problem to solve and Lung cancer patients appear to be the most vulnerable to SARS-CoOVID-19 infection early discovery, on the other hand, has a high rate of survivability. Lung carcinoma detection in computed tomography (CT) has emerged as an emerging research subject in the field of medical imaging systems in recent years. The ability to accurately detect the size and location of lung cancer plays a critical role in lung cancer diagnosis. As a result, there is a requirement to rapidly read, detect, classify and evaluate CT scans. In this paper, we suggest a method for detecting and classifying lung nodules (or lesions) using a multi-strategy system. It has two parts: nodule detection (finding nodules) and classification (classifying nodules into Benign / non-cancerous or Malignant / cancerous). Lung CT scan images are utilized to detect and classify lung nodules in this work. U-Net architecture is used to segment CT scans, while VGG Net is tested on 3D images derived from LUNA 16 and LIDC - IDRI. The U-Net and the VGG-Net results are combined in the final findings.
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T., Manikandan. "Synthesis and Characterisation of Magnetic Nanoparticles for Lung Cancer Detection and Therapy." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 2730–40. http://dx.doi.org/10.37200/ijpr/v24i5/pr201976.
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Sadhin, MD Ismail Hossain, Methila Farzana Woishe, Nila Sultana, and Tamanna Zaman Bristy. "Identifying Lung Cancer Using CT Scan Images Based On Artificial Intelligence." International Journal of Computer and Information System (IJCIS) 3, no. 1 (March 18, 2022): 38–44. http://dx.doi.org/10.29040/ijcis.v3i1.64.
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Lung cancer appears to be the common reason behind the death of human beings at some stage on the planet. Early detection of lung cancers can growth the possibility of survival amongst human beings. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected in time. Although computerized tomography (CT) is frequently more efficient than X-ray. However, the problem regarded to merge way to time constraints in detecting this lung cancer concerning the numerous diagnosing strategies used. Hence, a lung cancer detection system that usage of image processing is hired to categorize lung cancer in CT images. In image processing procedures, procedures like image pre-processing, segmentation, and have extraction are mentioned intimately. This paper is pointing to set off the extra precise comes approximately through making use of distinctive improve and department procedures. In this proposal paper, the proposed method is built in some filter and segmentation that pre-process the data and classify the trained data. After the classification and trained WONN-MLB method is used to reduce the time complexity of finding result. Therefore, our research goal is to get the maximum result of lung cancer detection.
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Khan, Adeel, Irfan Tariq, Haroon Khan, Sifat Ullah Khan, Nongyue He, Li Zhiyang, and Faisal Raza. "Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network." Journal of Oncology 2022 (September 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/5682451.
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Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model’s good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.
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Narayanan, Vidyul, Nithya P., and Sathya M. "Effective lung cancer detection using deep learning network." Journal of Cognitive Human-Computer Interaction 5, no. 2 (2023): 15–23. http://dx.doi.org/10.54216/jchci.050202.
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The use of a computer-assisted diagnosis system was crucial to the results of the clinical study conducted to determine the nature of the human illness. When compared to other disorders, lung cancer requires extra caution during the examination process. This is because the mortality rate from lung cancer is higher because it affects both men and women. Poor image resolution has hampered previous lung cancer detection technologies, preventing them from achieving the requisite degree of dependability. Therefore, in this study, we provide a unique approach to lung cancer prognosis that makes use of improved machine learning and processing of images. Images of lung disease from CT scan databases created using quasi cells are used for diagnosis. Multilayer illumination was used to analyse the generated images, which improved the precision of the lungs' depiction by probing each and every one of their pixels while simultaneously decreasing the amount of background noise. Lung CT images are pre-processed to remove noise, and then a more advanced deep learning network is used to isolate the affected region. The territory is partitioned into subnetworks according to the number of existing networks, from which different features are subsequently extracted. Next, an ensemble classifier should be used to correctly diagnose lung diseases. Using MATLAB simulation, the authors examine how the provided technique improves the rate at which lung cancer could be diagnosed.
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Smeltzer, Matthew, Walter Stevens, Nicholas Ryan Faris, Jennifer Kethireddy, Meghan Brooke Meadows, Meredith Ray, Amanda Epperson, et al. "Lung cancer diagnosed by an incidental lung nodule program or lung cancer screening." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 8546. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.8546.
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8546 Background: Early detection reduces lung cancer (LC) mortality. We prospectively evaluated LC patients diagnosed through Lung Cancer Screening (LCS) or an Incidental Lung Nodule Program (ILNP) (‘early detection’ programs) compared to routinely diagnosed LC patients in a multidisciplinary program (MDP). Methods: We compare demographics, tumor characteristics, and survival between the three groups diagnosed within the same healthcare system from 2015-2018. The ILNP prospectively tracks patients with suspicious lung lesions on routinely-performed studies flagged by radiologists using a standard macro text. LCS used Medicare eligibility criteria. Statistical methods include the chi-square test, Kruskal-Wallis test, and proportional hazards models with hazard ratios (HR) and 95% confidence intervals. Results: ILNP detected 201 lung cancers from 4713 scans (4.3%), LCS yielded 35 lung cancers from 1540 low-dose CT scans (2.3%), while MDC had 926 LC cases not detected by LCS or ILNP. Mean age at diagnosis for ILNP/LCS/MDC was 70/69/67 years (p = 0.0083); African Americans were under-represented in LCS (25%/11%/32%, p = 0.0104). LCS had the highest proportion with commercial insurance (46%/54%/43%, p = 0.3442). Early detection groups were more likely to have adenocarcinoma histology (ILNP/LCS/MDC: 61%/57%/49%, p = 0.0113). Smoking exposure was highest in LCS cohort (mean pack years: 48/64/52, p = 0.0500); 11% of ILNP, 8% MDC patients were never-smokers. Only 36% ILNP and 39% MDC patients were eligible for LCS by NLST criteria and 30%/40% by NELSON criteria. Reasons for ineligibility included smoking status in 73-90% and age in 7-27% of patients. Stage I/II distribution was (66%/58%/21%, p < 0.0001), stage IV 15%/20%/36%; surgical resection rates were (56%/55%/31%, p < 0.0001). Overall survival was longer in early detection groups (LCS HR: 0.31 [0.11,0.82]; ILNP HR: 0.51[0.33,0.81]) compared to MDC (p = 0.0011). Conclusions: The majority of LC patients were ineligible for LCS, but the ILNP identified LC in a high proportion of such patients, with similar stage re-distribution, curative-intent treatment, and survival rates. Structured ILNP complement LCS for early LC detection, such programs need to be built out.
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Nasir, Muflah, Muhammad Shahid Farid, Zobia Suhail, and Muhammad Hassan Khan. "Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer." Applied Sciences 13, no. 12 (June 18, 2023): 7256. http://dx.doi.org/10.3390/app13127256.
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Lung cancer is the world’s second-largest cause of cancer mortality. Patients’ lives can be saved if this malignancy is detected early. Doctors, however, encounter difficulties in detecting cancer in computed tomography (CT) images. In recent years, significant research has been devoted to producing automated lung nodule detection methods that can help radiologists. Most of them use only the lung window in their analysis and generally do not consider the mediastinal windows, which, according to recent research, carry important information. In this paper, we propose a simple yet effective algorithm to analyze multi-window CT images for lung nodules. The algorithm works in three steps. First, the CT image is preprocessed to suppress any noise and improve the image quality. Second, the lungs are extracted from the preprocessed image. Based on the histogram analysis of the lung windows, we propose a multi-Otsu-based approach for lung segmentation in lung windows. The case of mediastinal windows is rather difficult due to irregular patterns in the histograms. To this end, we propose a global–local-mean-based thresholding technique for lung detection. In the final step, the nodule candidates are extracted from the segmented lungs using simple intensity-based thresholding. The radius of the extracted objects is computed to separate the nodule from the bronchioles and blood vessels. The proposed algorithm is evaluated on the benchmark LUNA16 dataset and achieves accuracy of over 94% for lung tumor detection, surpassing that of existing similar methods.
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A. Abd Al-Ameer, Asraa, Ghufran Abdulameer Hussien, and Hajer A. Al Ameri. "Lung cancer detection using image processing and deep learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (November 1, 2022): 987. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp987-993.
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This project is about the detection of lung cancer by training a model of deep neural networks using histopathological lung cancer tissue images. Deferent models have been proposed for detecting lung cancer cells automatically involving Inception V3, Random Forest, and convolutional neural network (CNN). The deep convolutional neural network has been trained to extract important features that facilitate build detection and diagnosis of lung cancer cells more efficiently and accurately. The proposed method in this project has accomplished promising and satisfactory results in terms of accuracy, precision, recall, F-score, and specificity measure in lung cancer detection. Furthermore, it has been applied on dataset which contains 178,000 photos. The accuracy values that are obtained are accuracy 97.09%, precision 96.89%, recall 97.31%, F-score measure 97.09%, and specificity measure 96.88%.
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Midthun, David E. "Early detection of lung cancer." F1000Research 5 (April 25, 2016): 739. http://dx.doi.org/10.12688/f1000research.7313.1.
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Most patients with lung cancer are diagnosed when they present with symptoms, they have advanced stage disease, and curative treatment is no longer an option. An effective screening test has long been desired for early detection with the goal of reducing mortality from lung cancer. Sputum cytology, chest radiography, and computed tomography (CT) scan have been studied as potential screening tests. The National Lung Screening Trial (NLST) demonstrated a 20% reduction in mortality with low-dose CT (LDCT) screening, and guidelines now endorse annual LDCT for those at high risk. Implementation of screening is underway with the desire that the benefits be seen in clinical practice outside of a research study format. Concerns include management of false positives, cost, incidental findings, radiation exposure, and overdiagnosis. Studies continue to evaluate LDCT screening and use of biomarkers in risk assessment and diagnosis in attempt to further improve outcomes for patients with lung cancer.
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Hameed, Rana Majeed. "PLACENTALALKALINE PHOSPHATASE AS A THRESHOLD CONCEPT FOR EARLY DETECTION OF PRIMARY LUNG CANCER." Era's Journal of Medical Research 9, no. 2 (December 2022): 143–49. http://dx.doi.org/10.24041/ejmr2022.24.
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The biochemical existing tool of diagnostic methods to lung cancer cases need to be improved. In order to validate an early screening of primary tumor patients, a developed a simple procedure or technique was demanded. The aims of this study were to provide an overview of alkaline Placental Alkaline Phosphatase activity in lung cancer. Using heating inactivation method regarding the measurement of Placental Alkaline Phosphatase activity as an early diagnosis marker in lung cancer cases. Total alkaline phosphatase and Placental alkaline phosphatase activity were measured in patients of Lung cancer patients who were classified according to the site of tumor by histological picture. ALP isoenzymes were identified by heat inactivation, and compared with the most frequently applied method (ELISA). Monitoring of the Total ALPand Placental ALPactivity in the studied groups using two different methods were shown a highly performance of heating method by an experimental assessment to confirm the accuracy and validity of the proposed method. The distribution of serum placental ALP isoenzyme activity in patients and control groups which was measured by two different methods were found to be (20.2-43.1) IU/Lrespectively (measured by heating method) and (394.3- 454.5) pg/mLmeasured by ELISA method) respectively. Placental ALP isoenzyme showed a high significant activity in lung cancer patients than healthy control with p value less than (0.05). That application of the heat inactivation method yields similar indication to the ones obtained by the highly and specific enzyme-linked immunosorbent assay. The results of detection Placental alkaline phosphatase in serum were in excellent agreement and could have a potentially extensive application for Placental alkaline phosphatase quantification.
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Rubins, Jeffrey B. "Lung cancer detection and prevention." Postgraduate Medicine 114, no. 2 (August 2003): 19. http://dx.doi.org/10.3810/pgm.2003.08.1465.
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Bechtel, Joel J., and Thomas L. Petty. "Strategies in lung cancer detection." Postgraduate Medicine 114, no. 2 (August 2003): 20–26. http://dx.doi.org/10.3810/pgm.2003.08.1469.
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Lee, H. W., and K. H. Lee. "Early Detection of Lung Cancer." Yeungnam University Journal of Medicine 15, no. 2 (1998): 195. http://dx.doi.org/10.12701/yujm.1998.15.2.195.
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Porter JC, Spiro, S. "Detection of early lung cancer." Thorax 55, no. 90001 (August 1, 2000): 56S—62. http://dx.doi.org/10.1136/thorax.55.suppl_1.s56.
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Wolpaw, Daniel R. "EARLY DETECTION IN LUNG CANCER." Medical Clinics of North America 80, no. 1 (January 1996): 63–82. http://dx.doi.org/10.1016/s0025-7125(05)70427-2.
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Burki, Talha Khan. "Late detection of lung cancer." Lancet Oncology 15, no. 13 (December 2014): e590. http://dx.doi.org/10.1016/s1470-2045(14)70371-7.
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Pastorino, Ugo. "Early Detection of Lung Cancer." Respiration 73, no. 1 (2006): 5–13. http://dx.doi.org/10.1159/000090990.
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s, Avinash, K. Manjunath, and S. Senthil Kumar. "Lung Cancer Detection using Modified Gabor filter, Gradient operators and Morphological segmentation tool." International Journal of Innovative Technology and Exploring Engineering 8, no. 10 (August 30, 2019): 1488–94. http://dx.doi.org/10.35940/ijitee.a1023.0881019.
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Lung Cancer is a disease of irregular cells multiplying and growing into a tumour. It’s hard to believe, but lung cancer is the primary cause of cancer deaths among both women and men. Every year more people die of lung cancer than of colon, prostate and Breast cancers. Some important facts of lung cancer are Excluding Skin cancer, lung cancer is the second most common cancer in both women and men. Statistics from Indian Council of Medical Research (ICMR) recommend that lung cancer is fast turning into a plague in India. It is a high mortality cancer due to poor access to affordable health care and diagnosis at late stage. At the time of diagnosis only 15 percent of the cases of lung cancer are curable. Due to the nature of the disease patients with lung cancer present themselves for diagnosis at a much later stage than other cancers. Globally lung cancer accounts for 8.4% percent of all cancers in women and 14.5% in men. For lung cancer Smoking is the single largest contributor. Other causes are exposure to carcinogenic toxins like radon, asbestos, radiation and air pollutants. Exposure to women to smoke from the burning of charcoal for cooking is also a cause of lung cancer. About 20 percent we can reduce the risk of lung cancer by doing physical activities and exercise is known to improve lung function. In recent times, image processing measures are frequently used in a number of medical areas for enlargement of the image in preceding recognition and managing periods, where the instant aspect is really important to determine the abnormality problems in objective figures, mainly in a variety of malignancy tumours such as lung cancer, breast cancer etc.
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Song, Keum-Soo, Satish Balasaheb Nimse, Shrikant Dashrath Warkad, Jung-Hoon Kim, Hey-Jin Kim, and Taisun Kim. "Detection and Quantification of Tp53 and p53-Anti-p53 Autoantibody Immune Complex: Promising Biomarkers in Early Stage Lung Cancer Diagnosis." Biosensors 12, no. 2 (February 16, 2022): 127. http://dx.doi.org/10.3390/bios12020127.
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Lung cancer is a leading cause of death worldwide, claiming nearly 1.80 million lives in 2020. Screening with low-dose computed tomography (LDCT) reduces lung cancer mortality by about 20% compared to standard chest X-rays among current or heavy smokers. However, several reports indicate that LDCT has a high false-positive rate. In this regard, methods based on biomarker detection offer excellent potential for developing noninvasive cancer diagnostic tests to complement LDCT for detecting stage 0∼IV lung cancers. Herein, we have developed a method for detecting and quantifying a p53-anti-p53 autoantibody complex and the total p53 antigen (wild and mutant). The LOD for detecting Tp53 and PIC were 7.41 pg/mL and 5.74 pg/mL, respectively. The detection ranges for both biomarkers were 0–7500 pg/mL. The known interfering agents in immunoassays such as biotin, bilirubin, intra-lipid, and hemoglobin did not detect Tp53 and PIC, even at levels that were several folds higher levels than their normal levels. Furthermore, the present study provides a unique report on this preliminary investigation using the PIC/Tp53 ratio to detect stage I–IV lung cancers. The presented method detects lung cancers with 81.6% sensitivity and 93.3% specificity. These results indicate that the presented method has high applicability for the identification of lung cancer patients from the healthy population.
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Gupta, Ankit, Priyaraj Priyaraj, and Yashi Agarwal. "DETECTION OF LUNG CANCER USING IMAGE PROCESSING." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (December 22, 2021): 323–27. http://dx.doi.org/10.53633/ijmasri.2021.1.10.012.
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This project constructs and assesses an image processing approach for lung cancer diagnosis in this study. Image processing techniques are frequently utilized for picture improvement in the detection phase to enable early medical therapy in a variety of medical issues. We suggested a lung cancer detection approach based on picture segmentation in this study. Image segmentation is a level of image processing that is intermediate. To segment a CT scan image, a marker control watershed and region growth technique is applied. Following the detection phases, picture augmentation with the Gabor filter, image segmentation, and feature extraction is performed. We discovered the efficiency of our strategy based on the experimental results. The results demonstrate that the watershed with the masking method, which has great accuracy and robustness, is the best strategy for detecting major features. Keywords: Lung cancer, MATLAB, CT images, Distortion removal, Segmentation, Mortality rate.
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Hayabuchi, N., W. J. Russell, and J. Murakami. "Problems in Radiographic Detection and Diagnosis of Lung Cancer." Acta Radiologica 30, no. 2 (March 1989): 163–67. http://dx.doi.org/10.1177/028418518903000209.
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All chest radiographs of 107 proven lung cancer patients who received consecutive biennial chest radiography were reviewed to elucidate problems detecting their cancers, and diagnosing them when initially radiographically detected. Subjects, members of a fixed population sample, originally numbered 20000 persons, 17000 of whom consistently received consecutive biennial chest radiography during examinations for late effects of atomic-bomb radiation. Among the 107 subjects, 64 had radiographic manifestations of cancer; 47 were initially correctly diagnosed; 17 were not. Eleven of the 17 were initially equivocal, diagnosable only after subsequent radiography and retrospective review of serial radiographs. Diagnostic problems consisted of 1) six detection errors with cancer images superimposed on musculoskeletal and cardiovascular structures, reducible by stereoscopic p.a. instead of single p.a. radiography; immediate tentative interpretations; and by comparing earlier with current radiographs. 2) Eight decision errors, wherein cancers mimicked other diseases, were reducible by greater index of suspicion and scrutiny during interpretations.
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Đekić Malbaša, Jelena, Darijo Bokan, Bojan Zarić, Tomi Kovačević, Goran Stojanović, Dragan Dragišić, and Ivan Kuhajda. "Skrining karcinoma pluća u AP Vojvodini - Lung cancer screening in Vojvodina province." Respiratio 13, no. 1-2 (August 31, 2023): 95–101. http://dx.doi.org/10.26601/rsp.aprs.23.9.
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Introduction Better survival of lung cancer (LC) patients is possible if the diagnosis is made early in the disease course. Low-dose CT in a high-risk population for developing LC enables cancer detection in the earlier stages of the disease and improves survival. Method LC screening with low-dose CT was performed at the Institute for Pulmonary Diseases of Vojvodina (IPBV) from September 2020 to December 2022. People aged 50-74 years, ex-smokers (started smoking 10 years ago) and active smokers with 30 pack-years, or 20 pack-years with additional risk factors, were included. We retrospectively analyzed screen population characteristics, screening results (according to Lung-RADS score) and definite findings of suspicious nodules by histological type and LC stage by gender. Results During the observed period, a total of 3432 LDCT scans were performed, of which 62.3% (2138) were baseline LDCT. Suspicious and highly suspicious nodule findings (Lung-RADS 4) were observed in 196 (9.2%) subjects. The emphysema was registered in 709 (33.2%) subjects. The LC detection rate was 1.8% (40/2138). 88.5% (23/26) of confirmed LC were detected after the initial LDCT, while the remaining 11.5% (3/26) were established after the follow-up period. 75% (30/40) of LC were in stages I to IIIA. Non-small cell lung cancer (adenocarcinoma 60.0% (24/40), squamous cancers 22.5% (9/40) and other non-small cell cancers (NOS) 5.0% (2/40)) accounted for 85.7% (35/40), while small cells lung cancer was present in 12.5% (5/40). Conclusion Most LC cases were detected in the early stage of the disease. Therefore, screening for LC should cover the high-risk population in the territory of the entire Republic.
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Dennison, Jennifer, Ehsan Irajizad, Edwin Ostrin, Myrna Godoy, Olena Weaver, Jessica Leung, and Samir Hanash. "Abstract P057: Application of multi-cancer early detection to lung and breast cancer screening populations." Cancer Prevention Research 16, no. 1_Supplement (January 1, 2023): P057. http://dx.doi.org/10.1158/1940-6215.precprev22-p057.
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Abstract The Center for Global Early Detection at MD Anderson has initiated two longitudinal early detection studies: LEAP (Lung Cancer Early Detection and Prevention, 2013-2019) for lung cancer screening and MERIT (Mammography Early Detection Risk Assessment and Imaging Technologies, 2017-ongoing) for breast cancer screening. Both cohorts enrolled subjects based on US standard-of-care guidelines at the time of enrollment for low-dose CT screening and mammography, respectively. The cohorts collected detailed health data from questionnaires, imaging data, and blood specimens at the time of screening. In addition to information on the screened-detected cancers, follow-up information was collected on any other types of cancers that are diagnosed after enrollment. Here, we evaluate the incidence of other cancers without available screening to assess the potential impact of multi-cancer early detection studies for these two types of screening populations. The two studies enrolled approximately 9,300 subjects (N = 2853 for LEAP; N = 6,400 for MERIT) with median follow up times of 2.99 years (IQR: 1.09-4.08) for LEAP and 2.05 years (IQR: 0.09-3.01) for MERIT. Currently, there are more than 800,000 aliquots of banked biospecimens from both cohorts. The incidence of lung cancer for the LEAP cohort undergoing low-dose CT screening was 0.91% per year. Cancers other than lung cancer were also diagnosed after enrollment including prostate, breast, bladder, head, and neck, colorectal, lymphoma, endocrine, gynecologic, and kidney cancers. Overall, the incidence of other cancers in LEAP was high for those cancers without available screening - 0.49% per year excluding breast, prostate, and colorectal cancers. The incidence of breast cancer for women in LEAP was lower than expected, 0.17% per year, likely a result of underscreening by mammography. For the MERIT cohort, the incidence of breast cancer was 0.89% per year. Cancers other than breast were diagnosed in MERIT after enrollment including gynecologic, melanoma, lung, colorectal, endocrine, bladder, and gastrointestinal cancers. The incidence of other cancers in MERIT was 0.55% per year, excluding colorectal cancer. We conclude that the incidence of cancers without a current screening modality in LEAP and MERIT is sufficiently high (0.49-0.55% per year) to justify the application of multi-cancer screening. Even for cancers with standard-of-care screening, a multi-cancer blood-based biomarker test could be useful given the possibility of underscreening, as we observed for breast cancer in the LEAP cohort. Citation Format: Jennifer Dennison, Ehsan Irajizad, Edwin Ostrin, Myrna Godoy, Olena Weaver, Jessica Leung, Samir Hanash. Application of multi-cancer early detection to lung and breast cancer screening populations. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P057.
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Mubashir Ali. "Lung Cancer Detection using Supervised Machine Learning Techniques." Lahore Garrison University Research Journal of Computer Science and Information Technology 6, no. 1 (March 30, 2022): 49–68. http://dx.doi.org/10.54692/lgurjcsit.2022.0601276.
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In recent times, Lung cancer is the most common cause of mortality in both men and women around the world. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. The possibility of a patient's survival rate increasing if lung cancer is identified early. To detect and diagnose lung cancer in its early stages, a variety of data analysis and machine learning techniques have been applied. In this paper, we applied supervised machine learning algorithms like SVM (Support vector machine), ANN (Artificial neural networks), MLR (Multiple linear regression), and RF (random forest), to detect the early stages of lung tumors. The main purpose of this study is to examine the success of machine learning algorithms in detecting lung cancer at an early stage. When compared to all other supervised machine learning algorithms, the Random forest model produces a high result, with a 99.99% accuracy rate
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Mathios, Dimitrios, Jakob Sidenius Johansen, Stephen Cristiano, Jamie Medina, Jillian Phallen, Klaus Richter Larsen, Daniel Bruhm, et al. "Early detection of lung cancer using cfDNA fragmentation." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 8519. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.8519.
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8519 Background: Lung cancer incidence and mortality are increasing worldwide despite more effective treatments. This is primarily due to the late stage of diagnosis when treatments are less effective. Although large randomized trials have demonstrated a significant decrease in lung cancer mortality through screening of high-risk individuals with chest low dose computed tomography (LDCT), LDCT has made little impact in the community, mainly due to lack of accessibility. There is therefore an unmet clinical need for development of cost-effective and easily implemented tests for early lung cancer detection. Methods: We have previously shown that altered genome-wide fragmentation of cell free DNA (cfDNA) is a common characteristic of many cancers. In this study, we leverage this knowledge to increase the sensitivity of lung cancer detection by interrogating characteristics of the size distribution of cfDNA fragments across the genome using machine learning methods. The approach we present, called DELFI (DNA evaluation of fragments for early interception) generates a score that reflects the presence of tumor-derived DNA in plasma based on a multi-feature genomic analysis that assesses millions of cfDNA fragments for tumor-derived genomic and epigenomic changes in a small amount of blood (2-4 mls) via inexpensive low coverage (1-2x) whole genome sequencing. We applied this methodology in a prospectively collected cohort of 365 individuals under investigation for lung cancer and we prospectively validated it in a separate case-control cohort of patients with newly diagnosed early stage lung cancer as well as individuals without cancer (n=427). Results: These analyses revealed high performance for detection of early and late stage disease (Table). When DELFI was used as a prescreen for LDCT it increased specificity from 58% with CT imaging alone to 80% using the combined approach. The DELFI score was significantly associated with T and N stage in lung cancer cases (p<0.0001) as well as with overall survival (p=0.003). In a multivariable analysis including age, histology and stage, DELFI score was an independent prognostic factor of overall survival (HR=2.53; p=0.0003). Finally, we determined that genome-wide fragmentation profiles can be used to distinguish small cell lung cancer from non-small cell lung cancer with high accuracy (AUC 0.98). Conclusions: These findings provide key insights into cfDNA fragmentation in patients with cancer and a new and easily accessible avenue for non-invasive diagnosis and molecular profiling of lung cancer.[Table: see text]
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New, Melissa, and Robert Keith. "Early Detection and Chemoprevention of Lung Cancer." F1000Research 7 (January 16, 2018): 61. http://dx.doi.org/10.12688/f1000research.12433.1.
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Despite advances in targeted treatments, lung cancer remains a common and deadly malignancy, in part owing to its typical late presentation. Recent developments in lung cancer screening and ongoing efforts aimed at early detection, treatment, and prevention are promising areas to impact the mortality from lung cancer. In the past several years, lung cancer screening with low-dose chest computed tomography (CT) was shown to have mortality benefit, and lung cancer screening programs have been implemented in some clinical settings. Biomarkers for screening, diagnosis, and monitoring of response to therapy are under development. Prevention efforts aimed at smoking cessation are as crucial as ever, and there have been encouraging findings in recent clinical trials of lung cancer chemoprevention. Here we review advancements in the field of lung cancer prevention and early malignancy and discuss future directions that we believe will result in a reduction in the mortality from lung cancer.
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Abdualjabar, Rana Dhia’a, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (December 1, 2021): 1610. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.
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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.
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Dawalkar, Krishna, Omkar Joshi, Priti Mantri, Vaishnao Wankar, and Prof M. S. Bhosale. "Early Diagnosis of Lung Cancer with Advanced ALCDC System and Deep Learning-Based CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1874–78. http://dx.doi.org/10.22214/ijraset.2023.51926.
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Abstract: This research paper presents a study on the use of an advanced ALCDC system, which utilizes a deep learning-based convolutional neural network (CNN) algorithm, for early detection of lung cancer. The purpose of this study is to evaluate the effectiveness of this system in detecting lung cancer at an early stage and to compare its performance with traditional detection methods.Lung cancer is the leading cause of cancer-related deaths worldwide, with a high mortality rate due to late diagnosis. Therefore, early detection of lung cancer is crucial for successful treatment and improving patient outcomes. Traditional methods of lung cancer detection, such as chest X-rays and CT scans, have limitations in terms of accuracy and efficiency. Recent advancements in AI and deep learning algorithms, such as CNNs, have shown promise in improving the accuracy and efficiency of lung cancer detection.In comparison to traditional detection methods, the ALCDC system showed a significant improvement in accuracy and efficiency. The system was able to detect lung nodules at an earlier stage, which is critical for successful treatment and improving patient outcomes.
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ANWAR, MR, MA BAKAR, HM AWAIS, MU DIN, M. MOHSIN, MA NAZIR, I. SAQIB, and MM KHALID. "EARLY DETECTION OF LUNGS CANCER USING MACHINE LEARNING ALGORITHMS." Biological and Clinical Sciences Research Journal 2023, no. 1 (January 26, 2023): 187. http://dx.doi.org/10.54112/bcsrj.v2023i1.187.
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Medical healthcare systems store a large amount of clinical data about patients related to their biographies and disease information. Doctors use clinical data for the early detection of diseases that helps with proper patients’ treatments to save their lives. These clinical systems are helpful in detecting cancer diseases at early stages to save people's lives. Lung cancer is the third largely spreading disease in human beings all over the globe, which may lead so many people to death because of inaccurate detection of their disease at the initial stages. Therefore, this study will help doctors and radiologists in the detection of lung cancerous and non-cancerous patients at early stages with a random forest algorithm to save patients’ lives. In this research work, a new and novel model based on random forest algorithm was employed to detect lung cancer from the Wisconsin data set. Lung cancer was detected at early stages, and it was decided whether targeted patient was cancerous or non-cancerous. This experimental outcome showed that the proposed methodology achieved an accuracy rate that was batter compared to previous studies for early detection of lung cancer.
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Arun, Ramaiah, and Shanmugasundaram Singaravelan. "Automated communication system for detection of lung cancer using catastrophe features." Informatologia 53, no. 3-4 (December 30, 2020): 184–90. http://dx.doi.org/10.32914/i.53.3-4.5.
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One of the biggest challenges the world face today is the mortality due to Cancer. One in four of all diagnosed cancers involve the lung cancer, where the mortality rate is high, even after so much of technical and medical advances. Most lung cancer cases are diagnosed either in the third or fourth stage, when the disease is not treatable. The main reason for the highest mortality, due to lung cancer is because of non availability of prescreening system which can analyze the cancer cells at early stages. So it is necessary to develop a prescreening system which helps doctors to find and detect lung cancer at early stages. Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate. The reason is mainly attributed to the increased rate of smoking - both active and passive. In the present work, a system for the classification of lung glandular cells for early detection of Cancer using multiple color spaces is developed. For segmentation, various clustering techniques like K-Means clustering and Fuzzy C-Means clustering on various Color spaces such as HSV, CIELAB, CIEXYy and CIELUV are used. Features are Extracted and classified using Support Vector Machine (SVM).
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C, SHANKARA, HARIPRASAD S A, and Gururaj H L. "Artifact removal techniques for lung CT images in lung cancer detection." International Journal of Data Informatics and Intelligent Computing 1, no. 1 (September 23, 2022): 21–29. http://dx.doi.org/10.59461/ijdiic.v1i1.14.
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Lung Cancer in today’s world is one of the major widespread dangerous diseases which is the subject of maximum deaths every year. Accurate detection of lung cancer could boost the endurance rate. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. Images from a CT scan are widely used because they provide comprehensive imaging of tumor progression inside the lungs. Although different types of noise might be experienced while doing CT scans, producing it a monotonous task for recognizing tumors in the lung. Elimination of noise in CT images is a challenging task for medical diagnoses. The presence of noise in an image is inevitable. Hence reducing noise from the CT scan image is critical for further analysis. Hence various filtering techniques have been used that denoise and enhance the image and help in further evaluation of CT images for accurate lung cancer detection. This paper analyses the noises of different kinds in the CT images and different noise removal techniques which help in improving the accuracy of segmentation and feature extraction as they remove unwanted noise and contribute to the accurate detection of lung cancer. The various filtering methods are analyzed with salt along with pepper noise and speckle noise. The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR. The experimental results show that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer MSE values of 214.8522, high SNR value of 19.36304, SSIM value 0.595997, and high PSNR value of 24.80941.
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Petersen, Rebecca P., and David H. Harpole. "Computed Tomography Screening for the Early Detection of Lung Cancer." Journal of the National Comprehensive Cancer Network 4, no. 6 (July 2006): 591–94. http://dx.doi.org/10.6004/jnccn.2006.0048.
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Although lung cancer is the leading cause of cancer-related death in the world and has an increased chance of cure if detected at an earlier stage, routine lung cancer screening is currently not recommended in the United States. Unfortunately, most patients with lung cancer present only after the onset of symptoms and have advanced disease that cannot be surgically resected. The overall 5-year survival rate for all patients with lung cancer is only 15%. When the cancer is detected at its earliest stage (pathologic stage IA), however, the 5-year survival rate is more than 70%. Although past randomized screening trials evaluating the use of standard chest radiography or sputum cytology have not resulted in lower mortality, recent studies suggest that computed tomography (CT) may have promise as a screening tool. This article summarizes experience over the past decade of using low-dose spiral CT imaging as a screening tool to detect early lung cancers in asymptomatic, high-risk individuals.
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Abdullah, M. F., S. N. Sulaiman, M. K. Osman, S. Setumin, N. K. A. Karim, F. A. Sahimi, and A. I. C. Ani. "Image segmentation and feature extraction method for lung lesion detection in computed tomography images." Journal of Physics: Conference Series 2559, no. 1 (August 1, 2023): 012001. http://dx.doi.org/10.1088/1742-6596/2559/1/012001.
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Abstract Lung cancer is a form of cancer that causes uncontrollable cell growth in the lungs. Patients with lung cancer frequently miss a treatment, face higher health care costs, and get the worst outcomes. The detection of the existence of lung cancer can be performed in a variety of ways, such as computed tomography (CT), magnetic resonance imaging (MRI), and radiography. Many researchers have developed ways of automating lung cancer diagnosis using image processing techniques because of the noise and low image quality between the cancer cells, the lung, and the background. This study develops an image processing technique that uses image segmentation algorithms to segment lung nodules in computed tomography images using feature extraction. In the initial phase, it is essential to establish a rigorous image processing framework with the following sequential steps: (i) object edge identification and (ii) lesion boundary recognition. The architecture includes image processing techniques, thresholding, and morphological detections (erosion and dilation). Lesions can have various sizes and shapes, both regular and irregular. The new method has been applied to find the lesions using their roundness size. In addition to learning purely from CT scans, the previously studied lesion characteristics are also integrated. Data was collected from the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Penang. The manual segmentation was used image segmented in the MATLAB software function to remove the background of the images. The perimeter evaluates such as accuracy, recall, and F-score. Based on the analysis the performance of lung lesion segmentation of accuracy is 99.95, recall at 45.76%, and the F-score is 60.67%. For lung lesion detection, the results shows it consist of 3-5 slices with the value of roundness. Besides, lesion detection also have continuity for the roundness value. The experiment results found clear support for the next step of this research for classifications of lesions.
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Wiesel, Ory, Sook-Whan Sung, Amit Katz, Raya Leibowitz, Jair Bar, Iris Kamer, Itay Berger, Inbal Nir-Ziv, and Michal Mark Danieli. "A Novel Urine Test Biosensor Platform for Early Lung Cancer Detection." Biosensors 13, no. 6 (June 6, 2023): 627. http://dx.doi.org/10.3390/bios13060627.
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Lung cancer is the leading cause of cancer-related mortality worldwide. Early detection is essential to achieving a better outcome and prognosis. Volatile organic compounds (VOCs) reflect alterations in the pathophysiology and body metabolism processes, as shown in various types of cancers. The biosensor platform (BSP) urine test uses animals’ unique, proficient, and accurate ability to scent lung cancer VOCs. The BSP is a testing platform for the binary (negative/positive) recognition of the signature VOCs of lung cancer by trained and qualified Long–Evans rats as biosensors (BSs). The results of the current double-blind study show high accuracy in lung cancer VOC recognition, with 93% sensitivity and 91% specificity. The BSP test is safe, rapid, objective and can be performed repetitively, enabling periodic cancer monitoring as well as an aid to existing diagnostic methods. The future implementation of such urine tests as routine screening and monitoring tools has the potential to significantly increase detection rate as well as curability rates with lower healthcare expenditure. This paper offers a first instructive clinical platform utilizing VOC’s in urine for detection of lung cancer using the innovative BSP to deal with the pressing need for an early lung cancer detection test tool.
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Yin, Hong-Run, Ming Ye, Yang Wu, Kai Liu, Hua-Ping Pan, and Jia-Feng Yao. "Biological tissue detection based on electrical impedance spectroscopic tomograsphy." Acta Physica Sinica 71, no. 4 (2022): 048706. http://dx.doi.org/10.7498/aps.71.20211600.
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A bioimpedance spectroscopic imaging method for detecting the biological tissue based on electrical impedance tomography (EIT) and bioimpedance spectroscopy (BIS) is proposed. This method visualizes the target area and accurately recognizes the target type, which can be used for detecting the early lung cancer, assist clinicians in accurately detecting the early lung cancer, and improving the cure rate of early lung cancer. In this paper the bioimpedance spectroscopic imaging method is verified to be feasible and effective in detecting the early lung cancer through numerical simulation. The simulation results show that 1) the bioimpedance spectroscopic imaging method can realize the visualization of the early lung cancer area and accurately distinguish the type of early lung cancer, and 2) the optimal number of acquisitions of impedance spectroscopy is 4, and the best classifier is Linear-SVM, and the average classification accuracy of 5-fold cross-validation can reach 99.9%. In order to verify the simulation results, three biological tissues with different electrical characteristics are selected to simulate cancerous regions used for detection. The experimental results show that the method can visualize the biological tissue area and distinguish the type of biological tissue. This method can integrate the advantages of electrical impedance imaging and bioimpedance spectroscopy, and is very promising way of detecting early lung cancer.
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Jeon, Ok Hwa, Byeong Hyeon Choi, Jiyun Rho, Kyungsu Kim, Jun Hee Lee, Jinhwan Lee, Beop-Min Kim, and Hyun Koo Kim. "Optimization of Indocyanine Green for Intraoperative Fluorescent Image-Guided Localization of Lung Cancer; Analysis Based on Solid Component of Lung Nodule." Cancers 15, no. 14 (July 16, 2023): 3643. http://dx.doi.org/10.3390/cancers15143643.
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ICG fluorescence imaging has been used to detect lung cancer; however, there is no consensus regarding the optimization of the indocyanine green (ICG) injection method. The aim of this study was to determine the optimal dose and timing of ICG for lung cancer detection using animal models and to evaluate the feasibility of ICG fluorescence in lung cancer patients. In a preclinical study, twenty C57BL/6 mice with footpad cancer and thirty-three rabbits with VX2 lung cancer were used. These animals received an intravenous injection of ICG at 0.5, 1, 2, or 5 mg/kg, and the cancers were detected using a fluorescent imaging system after 3, 6, 12, and 24 h. In a clinical study, fifty-one patients diagnosed with lung cancer and scheduled to undergo surgery were included. Fluorescent images of lung cancer were obtained, and the fluorescent signal was quantified. Based on a preclinical study, the optimal injection method for lung cancer detection was 2 mg/kg ICG 12 h before surgery. Among the 51 patients, ICG successfully detected 37 of 39 cases with a consolidation-to-tumor (C/T) ratio of >50% (TNR: 3.3 ± 1.2), while it failed in 12 cases with a C/T ratio ≤ 50% and 2 cases with anthracosis. ICG injection at 2 mg/kg, 12 h before surgery was optimal for lung cancer detection. Lung cancers with the C/T ratio > 50% were successfully detected using ICG with a detection rate of 95%, but not with the C/T ratio ≤ 50%. Therefore, further research is needed to develop fluorescent agents targeting lung cancer.