Mengjuan Li1, Tong Chen1, Wenlu Zhao1, Chaogang Wei1, Xiaobo Li2, Shaofeng Duan2, Libiao Ji3, Zhihua Lu3, Junkang Shen1,4. 1. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China. 2. GE Healthcare Life Science, Shanghai 200000, China. 3. Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China. 4. Institute of Radiation Oncology Therapeutics of Soochow University, Suzhou 215000, China.
Abstract
BACKGROUND: To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa). METHODS: In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 6:4 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa: a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively. RESULTS: Both the radiomics model (AUC: 0.98) and the clinical-radiomics combined model (AUC: 0.98) achieved greater predictive efficacy than the clinical model (AUC: 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model. CONCLUSIONS: Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa). METHODS: In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 6:4 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa: a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively. RESULTS: Both the radiomics model (AUC: 0.98) and the clinical-radiomics combined model (AUC: 0.98) achieved greater predictive efficacy than the clinical model (AUC: 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model. CONCLUSIONS: Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: David Bonekamp; Simon Kohl; Manuel Wiesenfarth; Patrick Schelb; Jan Philipp Radtke; Michael Götz; Philipp Kickingereder; Kaneschka Yaqubi; Bertram Hitthaler; Nils Gählert; Tristan Anselm Kuder; Fenja Deister; Martin Freitag; Markus Hohenfellner; Boris A Hadaschik; Heinz-Peter Schlemmer; Klaus H Maier-Hein Journal: Radiology Date: 2018-07-31 Impact factor: 11.105
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: Nicolas Mottet; Joaquim Bellmunt; Michel Bolla; Erik Briers; Marcus G Cumberbatch; Maria De Santis; Nicola Fossati; Tobias Gross; Ann M Henry; Steven Joniau; Thomas B Lam; Malcolm D Mason; Vsevolod B Matveev; Paul C Moldovan; Roderick C N van den Bergh; Thomas Van den Broeck; Henk G van der Poel; Theo H van der Kwast; Olivier Rouvière; Ivo G Schoots; Thomas Wiegel; Philip Cornford Journal: Eur Urol Date: 2016-08-25 Impact factor: 20.096
Authors: Simpa S Salami; Manish A Vira; Baris Turkbey; Mathew Fakhoury; Oksana Yaskiv; Robert Villani; Eran Ben-Levi; Ardeshir R Rastinehad Journal: Cancer Date: 2014-06-10 Impact factor: 6.860
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919
Authors: Rodrigo Delgadillo; John C Ford; Matthew C Abramowitz; Alan Dal Pra; Alan Pollack; Radka Stoyanova Journal: Strahlenther Onkol Date: 2020-08-21 Impact factor: 3.621