Academic literature on the topic 'Radiomics analysis'
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Journal articles on the topic "Radiomics analysis"
Hu, Shuyi, Xiajie Lyu, Weifeng Li, Xiaohan Cui, Qiaoyu Liu, Xiaoliang Xu, Jincheng Wang, Lin Chen, Xudong Zhang, and Yin Yin. "Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)." Contrast Media & Molecular Imaging 2022 (June 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/7693631.
Full textYin, Yunchao, Derya Yakar, Rudi A. J. O. Dierckx, Kim B. Mouridsen, Thomas C. Kwee, and Robbert J. de Haas. "Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging." Diagnostics 12, no. 2 (February 21, 2022): 550. http://dx.doi.org/10.3390/diagnostics12020550.
Full textGelardi, Fabrizia, Lara Cavinato, Rita De Sanctis, Gaia Ninatti, Paola Tiberio, Marcello Rodari, Alberto Zambelli, et al. "The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort." Diagnostics 14, no. 20 (October 17, 2024): 2312. http://dx.doi.org/10.3390/diagnostics14202312.
Full textCinarer, Gokalp, and Bulent Gursel Emiroglu. "Statistical analysis of radiomic features in differentiation of glioma grades." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 68–79. http://dx.doi.org/10.18844/gjpaas.v0i12.4988.
Full textChilaca-Rosas, Maria-Fatima, Melissa Garcia-Lezama, Sergio Moreno-Jimenez, and Ernesto Roldan-Valadez. "Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation." Diagnostics 13, no. 5 (February 23, 2023): 849. http://dx.doi.org/10.3390/diagnostics13050849.
Full textHu, Yumin, Qiaoyou Weng, Haihong Xia, Tao Chen, Chunli Kong, Weiyue Chen, Peipei Pang, Min Xu, Chenying Lu, and Jiansong Ji. "A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer." Abdominal Radiology 46, no. 6 (June 2021): 2384–92. http://dx.doi.org/10.1007/s00261-021-03120-w.
Full textLei, Chu-qian, Wei Wei, Zhen-yu Liu, Qian-Qian Xiong, Ci-Qiu Yang, Teng Zhu, Liu-Lu Zhang, Mei Yang, Jie Tian, and Kun Wang. "Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e13055-e13055. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13055.
Full textWei, Zhi-Yao, Zhe Zhang, Dong-Li Zhao, Wen-Ming Zhao, and Yuan-Guang Meng. "Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer." World Journal of Clinical Cases 12, no. 26 (September 16, 2024): 5908–21. http://dx.doi.org/10.12998/wjcc.v12.i26.5908.
Full textKalasauskas, Darius, Michael Kosterhon, Naureen Keric, Oliver Korczynski, Andrea Kronfeld, Florian Ringel, Ahmed Othman, and Marc A. Brockmann. "Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors." Cancers 14, no. 3 (February 7, 2022): 836. http://dx.doi.org/10.3390/cancers14030836.
Full textHuang, Yen-Cho, Shih-Ming Huang, Jih-Hsiang Yeh, Tung-Chieh Chang, Din-Li Tsan, Chien-Yu Lin, and Shu-Ju Tu. "Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy." Diagnostics 14, no. 9 (April 30, 2024): 941. http://dx.doi.org/10.3390/diagnostics14090941.
Full textDissertations / Theses on the topic "Radiomics analysis"
Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.
Full textOrtiz, Ramón Rafael. "Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/119118.
Full text[CAT] En els últims anys, els investigadors han intentat explotar la informació de les imatges mèdiques a través de l'avaluació de nombrosos paràmetres quantitatius per ajudar els clínics amb el diagnòstic i la valoració de malalties. Aquesta pràctica ha sigut batejada com radiomics,. L'anàlisi de textures proporciona una gran varietat de paràmetres que permeten quantificar l'heterogeneïtat característica de diferents teixits, especialment quan s'obtenen a partir d'imatge per ressonància magnètica (IRM). Basant-nos en aquests fets, vam decidir estudiar les possibilitats dels paràmetres texturals extrets d'IRM per caracteritzar diversos trastorns cerebrals. El potencial de les textures es va analitzar amb mètodes d'aprenentatge automàtic, usant diferents classificadors i mètodes de selecció de característiques per trobar el model òptim per a cada tasca específica. En aquesta tesi, la metodologia radiomics es va emprar per realitzar quatre projectes independents. En el primer projecte, vam estudiar la diferenciació entre glioblastomes multiformes (GBMs) i metàstasis cerebrals (MCs) en IRM convencional. Aquests tipus de tumors cerebrals poden confondre's al diagnosticar-se ja que presenten un perfil radiològic similar i les dades clíniques poden no ser concloents. Per tal d'evitar procediments exhaustius i invasius, vam estudiar el poder discriminatori de textures 2D extretes d'imatges de referència T1 filtrades i sense filtrar. Els resultats suggereixen que els paràmetres texturals proporcionen informació sobre l'heterogeneïtat dels GBMs i les MCs que pot servir per distingir amb precisió ambdues lesions quan s'utilitza una aproximació d'aprenentatge automàtic adequada. En el segon projecte, vam analitzar la classificació de MCs segons el seu origen primari en IRM de referència. En un percentatge de pacients, les MCs són diagnosticades com la primera manifestació d'un tumor primari desconegut. Per tal de detectar el tumor primari d'una forma no invasiva i més ràpida, vam examinar la capacitat de l'anàlisi de textura 2D i 3D per diferenciar les MCs derivades dels tumors primaris més propensos a metastatitzar (càncer de pulmó, càncer de mama i melanoma) en imatges T1. Els resultats van mostrar que s'aconsegueix una alta precisió quan s'utilitza un conjunt reduït de textures 3D per diferenciar les MCs de càncer de pulmó de les MCs de càncer de mama i melanoma. En el tercer projecte, vam avaluar les propietats de l'hipocamp en la IRM per identificar les diferents etapes de la malaltia d'Alzheimer (MA). Els criteris actuals per diagnosticar la MA requereixen la presència de dèficits cognitius severs. Amb la idea d'establir nous biomarcadors per detectar la MA en les seues primeres etapes, vam avaluar un conjunt de textures 2D i 3D extretes d'IRM de l'hipocamp de pacients amb MA avançada, deteriorament cognitiu lleu i normalitat cognitiva. Molts paràmetres de textura 3D van resultar ser estadísticament significatius per diferenciar entre pacients amb MA i individus de les altres dues poblacions. En combinar aquests paràmetres amb tècniques d'aprenentatge automàtic, es va obtenir una alta precisió. En el quart projecte, vam intentar caracteritzar els patrons d'heterogeneïtat de l'ictus cerebral isquèmic en la IRM estructural. En la IRM cerebral d'individus d'edat avançada, alguns processos patològics presenten característiques similars, com les lesions per ictus i les hiperintensitats de la substància blanca (HSBs). Atès que els ictus tenen efecte també en teixit adjacent, vam decidir estudiar la viabilitat de textures 3D extretes de les HSBs, la substància blanca no afectada i les estructures subcorticals per diferenciar individus afectats per ictus llacunars o corticals visibles en IRM convencional (imatges T1, T2 i FLAIR) d'individus sense ictus. Les textures no foren útils per diferenciar ictus corticals i llacunars, però es van obtenir resultats prometedors per disce
[EN] Over the last years, researchers have attempted to exploit the information provided by medical images through the evaluation of numerous imaging quantitative parameters in order to help clinicians with the diagnosis and assessment of many lesions and diseases. This practice has been recently named as radiomics. Texture analysis supply a wide range of features that allow quantifying the distinctive heterogeneity of different tissues, especially when obtained from magnetic resonance imaging (MRI). With this in mind, we decided to study the possibilities of texture features from MRI in order to characterize several disorders that affect the human brain. The potential of texture features was analyzed with various machine learning approaches, involving different classifiers and feature selection methods so as to find the optimal model to accomplish each specific task. In this thesis, the radiomics methodology was used to perform four independent projects. In the first project, we studied the differentiation between glioblastomas (GBMs) and brain metastases (BMs) in conventional MRI. Sometimes these types of brain tumors can be misdiagnosed since they may present a similar radiological profile and the clinical data may be inconclusive. With the aim of avoiding exhaustive and invasive procedures, we studied the discriminatory power of a large amount of 2D texture features extracted from baseline original and filtered T1-weighted images. The results suggest that 2D texture features provide some heterogeneity information of GBMs and BMs that can help in their accurate discernment when using the proper machine learning approach. In the second project, we analyzed the classification of BMs by their primary site of origin in baseline MRI. A percentage of patients are diagnosed with BM as the first manifestation of an unknown primary tumor. In order to detect the primary tumor in a faster non-invasive way, we examined the capability of 2D and 3D texture analysis to differentiate BMs derived from the most common primary tumors (lung cancer, breast cancer and melanoma) in T1-weighted images. The results showed that high accuracy was achieved when using a reduced set of 3D descriptors to differentiate lung cancer BMs from breast cancer and melanoma BMs. In the third project, we evaluated the hippocampus MRI profile of Alzheimer's disease (AD) patients to identify the different stages of the disease. The current criteria for diagnosing AD require the presence of relevant cognitive deficits. With the purpose of establishing new biomarkers to detect AD in its early stages, we evaluated a set of 2D and 3D texture features extracted from MRI scans of the hippocampus of patients with advanced AD, early mild cognitive impairment and cognitive normality. Many 3D texture parameters resulted to be statistically significant to differentiate between AD patients and subjects from the other two populations. When combining these 3D parameters with machine learning techniques, high accuracy was obtained. In the fourth project, we attempted to characterize the heterogeneity patterns of ischemic stroke in structural MRI. In brain MRI of older individuals, some pathological processes present similar imaging characteristics, like in the case of stroke lesions and white matter hyperintensities (WMH) of diverse natures. Given that stroke effects are present not only in the affected region, but also in unaffected tissue, we investigated the feasibility of 3D texture features from WMH, normal-appearing white matter and subcortical structures to differentiate individuals who had a lacunar or cortical stroke visible on conventional brain MRI (T1-weighted, T2-weighted and FLAIR images) from subjects who did not. Texture features were not useful to differentiate between post-acute cortical and lacunar strokes, but promising results were achieved for discerning between patients presenting an old stroke and normal-ageing patients who never had a stroke.
Ortiz Ramón, R. (2019). Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/119118
TESIS
Iyer, Sukanya Raj. "Deformation heterogeneity radiomics to predict molecular sub-types and overall survival in pediatric Medulloblastoma." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1588601774292049.
Full textWang, Dingqian. "Quantitative analysis with machine learning models for multi-parametric brain imaging data." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22245.
Full textBoughdad, Sarah. "Contributions of radiomics in ¹⁸F-FDG PET/CT and in MRI in breast cancer." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS500.
Full textBreast cancer is a common disease for which ¹⁸F-FDG PET/CT and breast MRI are frequently performed in routine practice. However, the different information provided by each of these imaging techniques are currently under-exploited. Indeed, in routine the interpretation of these scans is mainly based on visual analysis whereas the « quantitative » analysis of PET/CT data is generally limited to the sole use of the SUVmax while in breast MRI, simple parameters to characterize tumor enhancement after injection of contrast medium are used. The advent of PET/MRI machines, calls for an evaluation of the contribution of a more advanced quantification of each of the modalities separately and in combination in the setting of breast cancer. This is along with the concept of « Radiomics » a field currently expanding and which consists in extracting many quantitative characteristics from medical images used in clinical practice to decipher tumor heterogeneity or improve prediction of prognosis. The aim of our work was to study the contribution of radiomic data extracted from ¹⁸F-FDG PET and MRI imaging with contrast injection to characterize tumor heterogeneity in breast cancer taking into account the different molecular subtypes of breast cancer, namely luminal (Lum A, Lum B HER2- and Lum B HER2 +), triple-negative and HER2 + tumors. In this context, we focused on the prediction of prognosis in patients treated with neo-adjuvant chemotherapy. The influence of physiological variations such as age on the calculation of radiomic data in normal breast and breast tumors separately was also explored, as well as the multi-center variability of radioman features. Radiomic features were extracted using the LiFex software developed within IMIV laboratory. The patient database used for the studies were all retrospective data. We reported for the first time the influence of age on the values of radiomic features in healthy breast tissue in patients recruited from 2 different institutions but also in breast tumors especially those with a triple-negative subtype. Similarly, significant associations between the radiomic tumor phenotype in PET and MRI imaging and well-established prognostic factors in breast cancer have been identified. In addition, we showed a large variability in the PET « radiomic profile » of breast tumors with similar breast cancer subtype suggesting complementary information within their metabolic phenotype defined by radiomic features. Moreover, taking into account this variability has been shown to be of particular interest in improving the prediction of pathological response in patients with triple-negative tumors treated with neoadjuvant chemotherapy. A peri-tumoral breast tissue region satellite to the breast tumor was also investigated and appeared to bear some prognostic information in patients with Lum B HER2- tumors treated with neoadjuvant chemotherapy. In MR, we demonstrated the need to harmonize the methods for radiomic feature calculation. Overall, we observed that radiomic features derived from MR were less informative about the molecular features of the tumors than radiomic features extracted from PET data and were of lower prognostic value. Yet, the combination of the enhanced tumor volume in MR with a PET radiomic feature and the tumor molecular subtype yielded enhanced the accuracy with which response to neoadjuvant therapy could be predicted compared to features from one modality only or molecular subtype only
Mahon, Rebecca N. "Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5516.
Full textOliver, Jasmine Alexandria. "Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6123.
Full textPrasanna, Prateek. "NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.
Full textChirra, Prathyush V. Chirra. "EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.
Full textBasu, Satrajit. "Developing Predictive Models for Lung Tumor Analysis." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/3963.
Full textBooks on the topic "Radiomics analysis"
Ma, Xuelei, Lei Deng, Rong Tian, and Chunxiao Guo, eds. Novel Methods for Oncologic Imaging Analysis: Radiomics, Machine Learning, and Artificial Intelligence. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-347-9.
Full textBook chapters on the topic "Radiomics analysis"
Veeraraghavan, Harini. "Radiomics analysis for gynecologic cancers." In Radiomics and Radiogenomics, 319–35. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2019] |: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351208277-19.
Full textGhosh, Adarsh, and Suraj D. Serai. "Radiomics and Texture Analysis." In Advanced Clinical MRI of the Kidney, 407–18. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40169-5_27.
Full textChen, Qingfeng. "Fusion and Radiomics Study of Multimodal Medical Images." In Association Analysis Techniques and Applications in Bioinformatics, 301–24. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8251-6_10.
Full textYang, Jiancheng, Rongyao Fang, Bingbing Ni, Yamin Li, Yi Xu, and Linguo Li. "Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis." In Lecture Notes in Computer Science, 658–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32226-7_73.
Full textMorvan, Ludivine, Cristina Nanni, Anne-Victoire Michaud, Bastien Jamet, Clément Bailly, Caroline Bodet-Milin, Stephane Chauvie, et al. "Learned Deep Radiomics for Survival Analysis with Attention." In Predictive Intelligence in Medicine, 35–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59354-4_4.
Full textEl Naqa, Issam. "Computerized Prediction of Treatment Outcomes and Radiomics Analysis." In Image-Based Computer-Assisted Radiation Therapy, 357–75. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2945-5_14.
Full textKlontzas, Michail E., and Renato Cuocolo. "Machine Learning Methods for Radiomics Analysis: Algorithms Made Easy." In Imaging Informatics for Healthcare Professionals, 69–85. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25928-9_4.
Full textShi, Zhenwei, Chong Zhang, Inge Compter, Maikel Verduin, Ann Hoeben, Danielle Eekers, Andre Dekker, and Leonard Wee. "A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma." In Radiomics and Radiogenomics in Neuro-oncology, 70–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40124-5_8.
Full textPiantadosi, Gabriele, Giampaolo Bovenzi, Giuseppe Argenziano, Elvira Moscarella, Domenico Parmeggiani, Ludovico Docimo, and Carlo Sansone. "Skin Lesions Classification: A Radiomics Approach with Deep CNN." In New Trends in Image Analysis and Processing – ICIAP 2019, 252–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30754-7_26.
Full textAli, Muhammad, Viviana Benfante, Giuseppe Cutaia, Leonardo Salvaggio, Sara Rubino, Marzia Portoghese, Marcella Ferraro, et al. "Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI." In Image Analysis and Processing - ICIAP 2023 Workshops, 83–92. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51026-7_8.
Full textConference papers on the topic "Radiomics analysis"
Filos, Dimitris, Dimitris Fotopoulos, Maria Anastasia Rouni, and Ioanna Chouvarda. "Machine Learning-Based Whole Gland Radiomics Analysis for Prostate Cancer Classification." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635588.
Full textSmith, R. L., K. Al-Battat, R. John, M. Li, I. Ackerley, E. Spezi, K. Wells, N. Morley, and C. Marshall. "From Radiomics to Deep Learning: Leveraging Gramian Matrix Features in CNNs for NSCLC Survival Analysis." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657697.
Full textAhmadyar, Y., R. Samimi, A. Kamali-Asl, J. Majidpour, H. Arabi, and H. Zaidi. "Predicting Neoadjuvant Therapy Response in Breast Cancer Patients via Radiomics Analysis of Dynamic Contrast-Enhanced MRI Imaging Features." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655295.
Full textDuman, A., J. Powell, S. Thomas, and E. Spezi. "Evaluation of Radiomic Analysis over the Comparison of Machine Learning Approach and Radiomic Risk Score on Glioblastoma." In Cardiff University Engineering Research Conference 2023. Cardiff University Press, 2024. http://dx.doi.org/10.18573/conf1.f.
Full textColter, L., J. Kohlhammer, S. Wesarg, F. Jung, I. Stenin, C. Plettenberg, J. Schipper, and K. Scheckenbach. "Multimodal "Radiomics" data analysis and visualization." In Abstract- und Posterband – 89. Jahresversammlung der Deutschen Gesellschaft für HNO-Heilkunde, Kopf- und Hals-Chirurgie e.V., Bonn – Forschung heute – Zukunft morgen. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1639818.
Full textChaddad, Ahmad. "Stability in Radiomics Analysis: Advancements and Challenges." In 2023 IEEE International Conference on E-health Networking, Application & Services (Healthcom). IEEE, 2023. http://dx.doi.org/10.1109/healthcom56612.2023.10472376.
Full textHaniff, Nurin Syazwina Mohd, Muhammad Khalis Bin Abdul Karim, Nur Syafina Ali, Mohd Amiruddin Abdul Rahman, Nurul Huda Osman, and M. Iqbal Saripan. "Magnetic Resonance Imaging Radiomics Analysis for Predicting Hepatocellular Carcinoma." In 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE, 2021. http://dx.doi.org/10.1109/icoten52080.2021.9493533.
Full textLuna, Eduardo Almeda, José María Luna, and Sebastián Ventura. "Radiomics Software Tools: A comparative Analysis on Breast Cancer." In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2023. http://dx.doi.org/10.1109/cbms58004.2023.00276.
Full textS, Sherly Angel, Nidhi N. Nishanimath, and Nandish S. "Radiomics Features Analysis From Lung Cancer Using CT Images." In 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021. http://dx.doi.org/10.1109/conecct52877.2021.9622697.
Full textOyibo, P., P. Brynolfsson, and E. Spezi. "Integrating Radiomic Image Analysis in the Hero Imaging Platform." In Cardiff University School of Engineering Research Conference, 23–27. Cardiff University Press, 2024. http://dx.doi.org/10.18573/conf3.g.
Full textReports on the topic "Radiomics analysis"
Ouyang, Zhiqiang, Qian Li, Guangrong Zheng, Tengfei Ke, Jun Yang, and Chengde Liao. Radiomics for predicting tumor microenvironment phenotypes in non-small cell lung cance: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0060.
Full textChen, Jie, Xinyue Zhang, Chi Xu, and Kefu Liu. Diagnostic Performance of Radiomics Analysis for Pulmonary Cancer Airway Spread: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2024. http://dx.doi.org/10.37766/inplasy2024.10.0103.
Full textWang, Chih-Keng, Ting-Wei Wang, Chia-Fung Lu, and Yu-Te Wu. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2024. http://dx.doi.org/10.37766/inplasy2024.2.0101.
Full textChang, Ke-Vin. Ultrasound Radiomics for Diagnosing Carpal Tunnel Syndrome: a Protocol for Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2023. http://dx.doi.org/10.37766/inplasy2023.9.0069.
Full textYang, Jiawen, Shuzong You, Limin Zhang, Huangqi Zhang, Binhao Zhang, Xue Dong, Wenting Pan, Shaofeng Duan, and Wenbin Ji. Prediction Power of Radiomics in Early Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0099.
Full textWang, Yingxuan, Cheng Yan, and Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.
Full textzheng, xiushan. CT-based radiomics for prediction of lymph node metastasis in lung cancer A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0167.
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