Jing Wang1, Chen-Jiang Wu2, Mei-Ling Bao3, Jing Zhang2, Xiao-Ning Wang2, Yu-Dong Zhang4. 1. Center for Medical Device Evaluation, CFDA, Beijing, China, 100044. 2. Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009. 3. Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 210009. 4. Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009. njmu_zyd@163.com.
Abstract
OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). METHODS: This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. RESULTS: For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). CONCLUSION: Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. KEY POINTS: • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.
OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). METHODS: This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. RESULTS: For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). CONCLUSION: Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. KEY POINTS: • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.
Entities:
Keywords:
Machine learning; Multi-parametric MRI; Prostate Imaging Reporting and Data System v2; Prostate cancer; Support vector machine
Authors: Philipp Kickingereder; David Bonekamp; Martha Nowosielski; Annekathrin Kratz; Martin Sill; Sina Burth; Antje Wick; Oliver Eidel; Heinz-Peter Schlemmer; Alexander Radbruch; Jürgen Debus; Christel Herold-Mende; Andreas Unterberg; David Jones; Stefan Pfister; Wolfgang Wick; Andreas von Deimling; Martin Bendszus; David Capper Journal: Radiology Date: 2016-09-16 Impact factor: 11.105
Authors: Thomas Hambrock; Diederik M Somford; Henkjan J Huisman; Inge M van Oort; J Alfred Witjes; Christina A Hulsbergen-van de Kaa; Thomas Scheenen; Jelle O Barentsz Journal: Radiology Date: 2011-05 Impact factor: 11.105
Authors: Duc Fehr; Harini Veeraraghavan; Andreas Wibmer; Tatsuo Gondo; Kazuhiro Matsumoto; Herbert Alberto Vargas; Evis Sala; Hedvig Hricak; Joseph O Deasy Journal: Proc Natl Acad Sci U S A Date: 2015-11-02 Impact factor: 11.205
Authors: Daniel Junker; Michael Quentin; Udo Nagele; Michael Edlinger; Jonathan Richenberg; Georg Schaefer; Michael Ladurner; Werner Jaschke; Wolfgang Horninger; Friedrich Aigner Journal: World J Urol Date: 2014-08-01 Impact factor: 4.226
Authors: Ji Eun Park; Donghyun Kim; Ho Sung Kim; Seo Young Park; Jung Youn Kim; Se Jin Cho; Jae Ho Shin; Jeong Hoon Kim Journal: Eur Radiol Date: 2019-07-26 Impact factor: 5.315
Authors: Armando Stabile; Francesco Giganti; Andrew B Rosenkrantz; Samir S Taneja; Geert Villeirs; Inderbir S Gill; Clare Allen; Mark Emberton; Caroline M Moore; Veeru Kasivisvanathan Journal: Nat Rev Urol Date: 2019-07-17 Impact factor: 14.432