Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

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

doi:10.1016/j.ejro.2023.100496

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

Eur J Radiol Open. 2023 Jun 13:10:100496. doi: 10.1016/j.ejro.2023.100496. eCollection 2023.

Authors

Lin Li¹, Rakesh Shiradkar^{1

2}, Sree Harsha Tirumani³, Leonardo Kayat Bittencourt³, Pingfu Fu⁴, Amr Mahran⁵, Christina Buzzy¹, Phillip D Stricker⁶, Ardeshir R Rastinehad⁷, Cristina Magi-Galluzzi⁸, Lee Ponsky^{5

9}, Eric Klein¹⁰, Andrei S Purysko^{10

11}, Anant Madabhushi^{2

12}

Affiliations

¹ Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
² Department of Biomedical Engineering, Emory University and Georgia Institute of Technology.
³ Department of Radiology, University Hospitals, Cleveland, OH, USA.
⁴ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
⁵ Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
⁶ Department of Urology, St. Vincent's Clinic, Sydney, NSW 2010, Australia.
⁷ Department of Urology, Lenox Hill Hospital, Northwell Health, New York, NY, USA.
⁸ Department of Pathology, University of Alabama at Birmingham, AL, USA.
⁹ Case Western Reserve University School of Medicine, Cleveland, OH, USA.
¹⁰ Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA.
¹¹ Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
¹² Atlanta Veterans Affairs Medical Center, Atlanta, GA, United States.

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_v) comprised 52 patients from a single institution, the remaining 112 patients were used for training (D_t). 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_t to identify radiomic features associated with MRI^─ and MRI⁺ CsPCa to generate corresponding risk scores R^MRI─ and R^MRI+. R_bpMRI was further generated by integrating R^MRI─ and R^MRI+. 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). R_bpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of R^MRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on D_v. R_bpMRI correctly reclassified 10 out of 14 MRI^─ CsPCa on D_v.

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.

Keywords: Clinically significant prostate cancer; MR non-visible; Radiomic.