Novel Radiomic Analysis on Bi-parametric MRI for Characterizing Differences Between MR Non-visible and Visible Clinically Significant Prostate Cancer

Overview

Journal Eur J Radiol Open

Specialty Radiology

Date 2023 Jul 3

PMID 37396490

Authors

Lin Li

Rakesh Shiradkar

Sree Harsha Tirumani

Leonardo Kayat Bittencourt

Pingfu Fu

Amr Mahran

Christina Buzzy

Phillip D Stricker

Ardeshir R Rastinehad

Cristina Magi-Galluzzi

Lee Ponsky

Eric Klein

Andrei S Purysko

Anant Madabhushi

Affiliations

Soon will be listed here.

Abstract

Background: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─).

Objective: To quantify the differences between MR visible (MRI+) and MRI CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI).

Methods: This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (D) comprised 52 patients from a single institution, the remaining 112 patients were used for training (D). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on D to identify radiomic features associated with MRI and MRI CsPCa to generate corresponding risk scores and . was further generated by integrating and . Statistical significance was determined using the Wilcoxon signed-rank test.

Results: Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI and MRI CsPCa (p < 0.05). yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on D. correctly reclassified 10 out of 14 MRI CsPCa on D.

Conclusion: Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI CsPCa. These features could assist in CsPCa identification on bpMRI.

Citing Articles

Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.

Mendes B, Domingues I, Santos J J Clin Med. 2024; 13(13).

PMID: 38999473 PMC: 11242211. DOI: 10.3390/jcm13133907.

References

Norris J, Carmona Echeverria L, Bott S, Brown L, Burns-Cox N, Dudderidge T . What Type of Prostate Cancer Is Systematically Overlooked by Multiparametric Magnetic Resonance Imaging? An Analysis from the PROMIS Cohort. Eur Urol. 2020; 78(2):163-170. PMC: 7397509. DOI: 10.1016/j.eururo.2020.04.029. View

Sled J, Zijdenbos A, Evans A . A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998; 17(1):87-97. DOI: 10.1109/42.668698. View

Ahmed H, El-Shater Bosaily A, Brown L, Gabe R, Kaplan R, Parmar M . Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017; 389(10071):815-822. DOI: 10.1016/S0140-6736(16)32401-1. View

Prasanna P, Tiwari P, Madabhushi A . Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep. 2016; 6:37241. PMC: 5118705. DOI: 10.1038/srep37241. View

Bernatz S, Ackermann J, Mandel P, Kaltenbach B, Zhdanovich Y, Harter P . Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features. Eur Radiol. 2020; 30(12):6757-6769. PMC: 7599168. DOI: 10.1007/s00330-020-07064-5. View

Schelb P, Kohl S, Radtke J, Wiesenfarth M, Kickingereder P, Bickelhaupt S . Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment. Radiology. 2019; 293(3):607-617. DOI: 10.1148/radiol.2019190938. View

Li L, Pahwa S, Penzias G, Rusu M, Gollamudi J, Viswanath S . Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation. Sci Rep. 2017; 7(1):8717. PMC: 5562695. DOI: 10.1038/s41598-017-08969-w. View

McHugh M . Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012; 22(3):276-82. PMC: 3900052. View

Park K, Choi S, Lee J, Kim J, Kim M . Interreader Agreement with Prostate Imaging Reporting and Data System Version 2 for Prostate Cancer Detection: A Systematic Review and Meta-Analysis. J Urol. 2020; 204(4):661-670. DOI: 10.1097/JU.0000000000001200. View

10.

Algohary A, Shiradkar R, Pahwa S, Purysko A, Verma S, Moses D . Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel). 2020; 12(8). PMC: 7465024. DOI: 10.3390/cancers12082200. View

11.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

12.

Sathianathen N, Omer A, Harriss E, Davies L, Kasivisvanathan V, Punwani S . Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in the Detection of Clinically Significant Prostate Cancer in the Prostate Imaging Reporting and Data System Era: A Systematic Review and Meta-analysis. Eur Urol. 2020; 78(3):402-414. DOI: 10.1016/j.eururo.2020.03.048. View

13.

Woo S, Suh C, Kim S, Cho J, Kim S, Moon M . Head-to-Head Comparison Between Biparametric and Multiparametric MRI for the Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol. 2018; 211(5):W226-W241. DOI: 10.2214/AJR.18.19880. View

14.

Rosenkrantz A, Lim R, Haghighi M, Somberg M, Babb J, Taneja S . Comparison of interreader reproducibility of the prostate imaging reporting and data system and likert scales for evaluation of multiparametric prostate MRI. AJR Am J Roentgenol. 2013; 201(4):W612-8. DOI: 10.2214/AJR.12.10173. View

15.

Cuocolo R, Stanzione A, Ponsiglione A, Romeo V, Verde F, Creta M . Clinically significant prostate cancer detection on MRI: A radiomic shape features study. Eur J Radiol. 2019; 116:144-149. DOI: 10.1016/j.ejrad.2019.05.006. View

16.

Hiremath A, Shiradkar R, Fu P, Mahran A, Rastinehad A, Tewari A . An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. Lancet Digit Health. 2021; 3(7):e445-e454. PMC: 8261599. DOI: 10.1016/S2589-7500(21)00082-0. View

17.

Bass E, Pantovic A, Connor M, Gabe R, Padhani A, Rockall A . A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk. Prostate Cancer Prostatic Dis. 2020; 24(3):596-611. DOI: 10.1038/s41391-020-00298-w. View

18.

Bonekamp D, Kohl S, Wiesenfarth M, Schelb P, Radtke J, Gotz M . Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. Radiology. 2018; 289(1):128-137. DOI: 10.1148/radiol.2018173064. View

19.

Fedorov A, Vangel M, Tempany C, Fennessy F . Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification. Invest Radiol. 2017; 52(9):538-546. PMC: 5544576. DOI: 10.1097/RLI.0000000000000382. View

20.

Li L, Shiradkar R, Leo P, Algohary A, Fu P, Tirumani S . A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI. EBioMedicine. 2020; 63:103163. PMC: 7744939. DOI: 10.1016/j.ebiom.2020.103163. View