Relevant bibliographies by topics / The Cancer Imaging Archive (TCIA)

Academic literature on the topic 'The Cancer Imaging Archive (TCIA)'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "The Cancer Imaging Archive (TCIA)"

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Zanfardino, Pane, Mirabelli, Salvatore, and Franzese. "TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review." International Journal of Molecular Sciences 20, no. 23 (November 29, 2019): 6033. http://dx.doi.org/10.3390/ijms20236033.

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In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular ‘omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular ‘omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular ‘omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.
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Sanada, Takahiro, Shota Yamamoto, Mio Sakai, Toru Umehara, Hirotaka Sato, Masato Saito, Nobuyuki Mitsui, et al. "NI-13 THE RATIO OF T1-WEIGHTED TO T2-WEIGHTED SIGNAL INTENSITY AND IDH MUTATION IN GLIOMA." Neuro-Oncology Advances 4, Supplement_3 (December 1, 2022): iii17—iii18. http://dx.doi.org/10.1093/noajnl/vdac167.066.

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Abstract Purpose The current study aims to test the hypothesis that the ratio of T1-weighted image to T2-weighted image signal intensity (T1w/T2w-Ratio: rT1/T2), an imaging surrogate developed for myelin integrity, is predictive of histologically lower-grade glioma's IDH mutation status. Materials and Methods 25 histologically and molecularly confirmed lower-grade glioma patients with eight IDH-wildtype (IDHwt) and 17 IDH-mutant (IDHmt) tumors at Asahikawa Medical University Hospital (AMUH) were used as a test cohort. Twenty-nine patients (IDHwt: 13, IDHmt: 16) from Osaka International Cancer Institute (OICI) and 101 patients from the Cancer Imaging Archive (TCIA) / Cancer Genome Atlas (TCGA) dataset (IDHwt: 19, IDHmt: 82) were used as external cohorts. rT1/T2 images were calculated from T1- and T2-weighted images using a recommended signal correction. The relationship between the mean rT1/T2 (mrT1/T2) and the IDH mutation status was investigated. Moreover, t-Distributed Stochastic Neighbor Embedding (t-SNE) investigated the difference in MRI qualities and characteristics between the three cohorts. Results The test cohort at AMUH revealed that mrT1/T2 of IDHwt tumors was significantly higher than that of IDHmt tumors (p < 0.05) and that the optimal cut-off of mrT1/T2 for discriminating IDHmt was 0.666-0.677, (AUC = 0.75, p < 0.05), which finding was validated by the external domestic cohort at OICI (AUC = 0.75, p = 0.02). However, the external international cohort deriving from TCIA/TCGA could not validate this (AUC = 0.63, p = 0.08). t-SNE analysis revealed that the difference in image characteristics within the cohort was more diverse for the TCIA/TCGA than for the two domestic cohorts. Conclusion The current study revealed that mrT1/T2 was able to discriminate IDHwt and IDHmt tumors in two domestic cohorts significantly. This was not validated by the TCIA/TCGA cohort due to the wide variety in the original imaging characteristics of the TCIA/TCGA cohort.
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Clark, Kenneth, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, et al. "The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository." Journal of Digital Imaging 26, no. 6 (July 25, 2013): 1045–57. http://dx.doi.org/10.1007/s10278-013-9622-7.

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Sharma, Ashish, Lawrence Tarbox, Tahsin Kurc, Jonathan Bona, Kirk Smith, Pradeeban Kathiravelu, Erich Bremer, Joel H. Saltz, and Fred Prior. "PRISM: A Platform for Imaging in Precision Medicine." JCO Clinical Cancer Informatics, no. 4 (September 2020): 491–99. http://dx.doi.org/10.1200/cci.20.00001.

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PURPOSE Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community METHODS A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA’s efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
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Castaldo, Rossana, Katia Pane, Emanuele Nicolai, Marco Salvatore, and Monica Franzese. "The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status." Cancers 12, no. 2 (February 24, 2020): 518. http://dx.doi.org/10.3390/cancers12020518.

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In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naïve Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.
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Fedorov, Andrey, Reinhard Beichel, Jayashree Kalpathy-Cramer, David Clunie, Michael Onken, Jörg Riesmeier, Christian Herz, et al. "Quantitative Imaging Informatics for Cancer Research." JCO Clinical Cancer Informatics, no. 4 (September 2020): 444–53. http://dx.doi.org/10.1200/cci.19.00165.

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PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.
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Treiber, Jeffrey M., Tyler C. Steed, Michael G. Brandel, Kunal S. Patel, Anders M. Dale, Bob S. Carter, and Clark C. Chen. "Molecular physiology of contrast enhancement in glioblastomas: An analysis of The Cancer Imaging Archive (TCIA)." Journal of Clinical Neuroscience 55 (September 2018): 86–92. http://dx.doi.org/10.1016/j.jocn.2018.06.018.

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Luo, Yangkun, Lu Li, Gang Yin, and Jin Yi Lang. "Predicting tumor mutational burden in head and neck squamous cell carcinoma based on CT imaging features: A TCGA/TCIA study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e15254-e15254. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e15254.

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e15254 Background: Immunotherapy has substantially changed the therapeutic strategies for cancers. Unfortunately, only 20–50% of patients with advanced solid tumours respond to treatment. There is therefore a need for the development of methods to identify patients who are most likely to respond to immunotherapy. Tumor mutation burden (TMB) have been served as the most prevalent biomarkers to predict immunotherapy response. This study was designed to investigate the ability of radiomics to predict TMB status in patients with head and neck squamous cell carcinoma (HNSCC). Methods: TMB values were calculated using genomic data obtained from the HNSCC dataset in The Cancer Genome Atlas (TCGA).We identified matching patients (n = 100) who underwent contrast-enhanced CT scan prior to treatment from The Cancer Imaging Archive (TCIA),and patients were grouped based on the cutoff value; high group(>4.2 mutations/Mb) and low group(≤4.2 mutations/Mb). A total of 249 radiomics features(9 non-texture features and 240 scan-texture-parameter features) were extracted from CT images of the tumor. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. The performance was evaluated in terms of area under the curve (AUC), sensitivity, and specificity. Results: Among all the features, twenty features were found to have the most impact on the predictive value; the two top texture parameters were GLCM-Variance and GLCM-Sum Average. In multivariable analysis, the best performance was obtained using a combination of seven texture features that can discriminate between high mutation burden versus low mutation burden. The AUC, sensitivity, and specificity of this model were 0.97 ± 0.01, 0.92 ± 0.04, and 0.92 ±0.01, respectively. Conclusions: The proposed CT-derived predictive model can accurately predict TMB status in patients with HNSCC. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.
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Sanada, Takahiro, Shota Yamamoto, Hirotaka Sato, Mio Sakai, Masato Saito, Nobuyuki Mitsui, Satoru Hiroshima, et al. "NI-12 The ratio of T1-Weighted to T2-Weighted Signal Intensity and IDH mutation in glioma." Neuro-Oncology Advances 3, Supplement_6 (December 1, 2021): vi20. http://dx.doi.org/10.1093/noajnl/vdab159.074.

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Abstract Introduction: Prediction of IDH mutation status for Lower-grade glioma (LrGG) is clinically significant. The purpose of this study is to test the hypothesis that the T1-weighted image/T2-weighted image ratio (rT1/T2), an imaging surrogate developed for myelin integrity, is a useful MRI biomarker for predicting the IDH mutation status of LrGG. Methods: Twenty-five LrGG patients (IDHwt: 8, IDHmt: 17) at Asahikawa Medical University Hospital (AMUH) were used as an exploratory cohort. Twenty-nine LrGG patients (IDHwt: 13, IDHmt: 16) from Osaka International Cancer Institute (OICI) and 103 patients from the Cancer Imaging Archive (TCIA) / Cancer Genome Atlas (TCGA) dataset (IDHwt: 19, IDHmt: 84) were used as validation cohorts. rT1/T2 images were calculated from T1- and T2-weighted images using a recommended signal correction. The region-of-interest was defined on T2-weighted images, and the relationship between the mean rT1/T2 (mrT1/T2) and the IDH mutation status was investigated. Results: The mrT1/T2 was able to significantly predict the IDH mutation status for the AMUH exploratory cohort (AUC = 0.75, p = 0.048). The ideal cut-off for detecting mutant IDH was mrT1/T2 < 0.666 ~ 0.677, with a sensitivity of 58.8% and a specificity of 87.5%. This result was further validated by the OICI validation cohort (AUC = 0.75, p = 0.023) with a sensitivity of 56.3% and a specificity of 69.2%. On the other hand, the sensitivity was 42.9% and the specificity was 68.4 % for the TCIA validation cohort (AUC = 0.63, p = 0.068). Conclusion: Our results supported the hypothesis that mrT1/T2 could be a useful image surrogate to predict the IDH mutation status of LrGG using two domestic cohorts. The decline of the accuracy for the TCIA cohort should be further investigated.
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Kawaguchi, Risa K., Masamichi Takahashi, Mototaka Miyake, Manabu Kinoshita, Satoshi Takahashi, Koichi Ichimura, Ryuji Hamamoto, Yoshitaka Narita, and Jun Sese. "Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals." Cancers 13, no. 14 (July 19, 2021): 3611. http://dx.doi.org/10.3390/cancers13143611.

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Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.
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Book chapters on the topic "The Cancer Imaging Archive (TCIA)"

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Lamere, Alicia Taylor. "Cluster Analysis in R With Big Data Applications." In Open Source Software for Statistical Analysis of Big Data, 111–36. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2768-9.ch004.

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This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical and model-based clustering methods on larger datasets. These methods are illustrated through an application to a dataset of RNA-sequencing expression data for cancer patients obtained from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) data collection from The Cancer Imaging Archive (TCIA).
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Krishnamoorthy, Gayathri Devi, and Kishore B. "Certain Investigation Titles on the Segmentation of Colon and Removal of Opacified Fluid for Virtual Colonoscopy." In Medical Image Processing for Improved Clinical Diagnosis, 114–38. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5876-7.ch006.

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Colorectal cancer (CRC) is a most important type of cancer that can be detected by virtual colonoscopy (VC) in the colon or rectum, and it is the major cause of death prevailing in the world. The CAD technique requires the segmentation of the colon to be accurate and can be implemented by two approaches. The first approach focuses on the segmentation of lungs in the computed tomography (CT) images downloaded from The Cancer Imaging Archive (TCIA) using clustering approach. The second method focused on the automatic segmentation of colon, removal of opacified fluid and bowels for all the slices in a dataset in a sequential order using MATLAB. The second approach requires more computational time, and hence, in order to reduce, the semiautomatic segmentation of colon was implemented in 3D seeded region growing and fuzzy clustering approach in MEVISLAB software. The approaches were implemented in multiple datasets and the accuracy were verified with manual segmentation by radiologist, and the importance of removing opacified fluid were shown for improving the accuracy of colon segments.
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Sukheja, Deepak, T. Sunil Kumar, B. V. Kiranmayee, Malaya Nayak, and Durgesh Mishra. "Prediction of Skin lesions (Melanoma) using Convolutional Neural Networks." In Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape, 43–69. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815079272122010005.

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Nowadays, computational technology is given great importance in the health care system to understand the importance of advanced computational technologies. Skin cancer or skin disease (melanoma) has been considered in this chapter. As we know, the detection of skin lesions caused by exposure to UV rays over the human body would be a difficult task for doctors to diagnose in the initial stages due to the low contrast of the affected portion of the body. Early prediction campaigns are expected to diminish the incidence of new instances of melanoma by lessening the populace's openness to sunlight. While beginning phase forecast campaigns have ordinarily been aimed at whole campaigns or the public, regardless of the real dangers of disease among people, most specialists prescribe that melanoma reconnaissance be confined to patients who are in great danger of disease. The test for specialists is the way to characterise a patient's real danger of melanoma since none of the rules, in actuality, throughout the communities offer an approved algorithm through which melanoma risk may be assessed. The main objective of this chapter is to describe the employment of the deep learning (DL) approach to predict melanoma at an early stage. The implemented approach uses a novel hair removal algorithm for preprocessing. The k.means clustering technique and the CNN architecture are then used to differentiate between normal and abnormal skin lesions. The approach is tested using the ISIC International Skin Imaging Collaboration Archive set, which contains different images of melanoma and non-melanoma.
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Conference papers on the topic "The Cancer Imaging Archive (TCIA)"

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Diniz, João Otávio Bandeira, Aristófanes Corrêa Silva, and Anselmo Cardoso de Paiva. "Methods for segmentation of spinal cord and esophagus in radiotherapy planning computed tomography." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sibgrapi.est.2021.20009.

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Organs at Risk (OARs) are healthy tissues around cancer that must be preserved in radiotherapy (RT). The spinal cord and esophagus are crucial OARs. In this work, we proposed methods for the segmentation of these OARs from the CT using image processing techniques and deep convolutional neural network (CNN). For spinal cord segmentation, two methods are proposed, the first using techniques such as template matching, superpixel, and CNN. The second method, use adaptive template matching and CNN. In the esophagus segmentation, we proposed a method composed of registration techniques, atlas, pre-processing, U-Net, and post-processing. The methods were applied to 36 planning CT images provided by The Cancer Imaging Archive. The first method for spinal cord segmentation obtained 78.20% Dice. The second method for spinal cord segmentation obtained 81.69% Dice. The esophagus segmentation method obtained an accuracy of 82.15% Dice.
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