Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images

He, Xiaofeng; Yi, Wei; Zhang, Hanmei; Zhang, Tong; Yuan, Fang; Huang, Zixing; Han, Fei; Song, Bin

doi:10.1016/j.acra.2019.05.004

Cited by 26 publications

(31 citation statements)

References 33 publications

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“…However, corticomedullary phase or nephrographic phase alone could achieve a higher diagnostic efficiency (AUC 0.975 and 0.963, respectively). In another study of He et al, 34 artificial neural networks were used to model the same samples. Their results showed that the combined model of conventional CECT features and nephrographic texture features still had the highest accuracy (94.06%), and the accuracy of corticomedullary phase or nephrographic phase alone was more than 90%.…”

Section: Discussionmentioning

confidence: 99%

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

Han

Yan

et al. 2020

The British Journal of Radiology

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Objectives: Comparing the prediction models for the ISUP/WHO grade of clear cell renal cell carcinoma (ccRCC) based on CT radiomics and conventional contrast-enhanced CT (CECT). Methods: The corticomedullary Phase images of 119 cases of low-grade (I and II) and high-grade (III and IV) ccRCC based on 2016 ISUP/WHO pathological grading criteria were analyzed retrospectively. The patients were randomly divided into training and validation set by stratified sampling according to 7:3 ratio.Prediction models of ccRCC differentiation were constructed using CT radiomics and conventional CECT findings in the training setandwere validated using validation set.The discrimination, calibration, net reclassification index (NRI) and integrated discrimination improvement index (IDI) of the two prediction models were further compared.The decision curve was used to analyze the net benefit of patients under different probability thresholds of the two models. Results: In the training set, the C-statistics of radiomics prediction model was statistically higher than that of CECT (p < 0.05), with NRI of 9.52% and IDI of 21.6%, both with statistical significance (p < 0.01).In the validation set, the C-statistics of radiomics prediction model was also higher but did not show statistical significance (p = 0.07). The NRI and IDI was 14.29 and 33.7%, respectively, both statistically significant (p < 0.01). Validation setdecision curve analysis showed the net benefit improvement of CT radiomics prediction model in the range of 3–81% over CECT. Conclusion: The prediction model using CT radiomics in corticomedullary phaseis more effective for ccRCC ISUP/WHO grade than conventional CECT. Advances in knowledge: As a non-invasive analysis method, radiomics can predict the ISUP/WHO grade of ccRCC more effectively than traditional enhanced CT.

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Section: Discussionmentioning

confidence: 99%

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

Han

Yan

et al. 2020

The British Journal of Radiology

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“…The top-ranked models were reported by He et al with a predictive mean value of 92.5% ± 1.83% using ANN-based radiomics. The best accuracy (94.1% ± 1.14%) was achieved by combining texture features from conventional image which were calculated from manually selected regions of interest (ROI), such as mean attenuation, parenchyma attenuation and absolute enhance attenuation, and CMP [45,[48][49][50][51].…”

Section: Nuclear Grade Predictionmentioning

confidence: 99%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.

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“…If aggressive features could be reliably identified in advance of surgery, either through more targeted biopsies or non-invasive assessment, it could have immediate clinical impact on treatment decisions and prognostication. Recently, multiple groups have used machine learning analysis applied to radiomics features in an attempt to improve performance in identification of aggressive features such as high nuclear grade on imaging, with some success [3,11,23,24,8,5,13]. Identification of sarcomatoid features has remained challenging from CT imaging.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)

Gurbani

Morgan²,

Jog

et al. 2021

Abdom Radiol

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I would like to thank my supervisor, Professor Varun Jog, for introducing me to the fascinating world of machine learning in medical imaging and for giving me the opportunity and freedom to explore as much of it as possible. His encouragement and scientific supervision helped me to move forward for further success. Special thanks go to Prof. Dane Morgan, Prof.Meghan Lubner and Mingren Shen for their endless amount of patience, support, insight and guidance. This thesis would not have been possible without all of their advice. I would also like to thank my parents and my brother for their never-ending support.iii

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Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images

Cited by 26 publications

References 33 publications

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)

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