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Open AccessArticle

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

1
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
2
Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
3
Section of Abdominal Imaging and Nuclear Radiology Department, Cleveland Clinic, OH 44195, USA
4
Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
5
Department of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
6
Cancer Division, The Kinghorn Cancer Centre/Garvan Institute of Medical Research, NSW 2010, Australia
7
Douglass Hanly Moir Pathology, Sydney, NSW 2000, Australia
8
Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
9
Urology Institute, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH 44106, USA
10
Urology at Lenox Hill and Northwell Health, New York, NY 10075, USA
11
Department of Urology, St. Vincent’s Clinic, Sydney, NSW 2010, Australia
12
Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(8), 2200; https://doi.org/10.3390/cancers12082200
Received: 26 June 2020 / Revised: 28 July 2020 / Accepted: 3 August 2020 / Published: 6 August 2020
(This article belongs to the Special Issue Radiomics and Cancers)
Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment. View Full-Text
Keywords: radiomics; prostate cancer; MRI; artificial intelligence; PIRADS; machine learning; peritumoral region radiomics; prostate cancer; MRI; artificial intelligence; PIRADS; machine learning; peritumoral region
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Algohary, A.; Shiradkar, R.; Pahwa, S.; Purysko, A.; Verma, S.; Moses, D.; Shnier, R.; Haynes, A.-M.; Delprado, W.; Thompson, J.; Tirumani, S.; Mahran, A.; Rastinehad, A.R.; Ponsky, L.; Stricker, P.D.; Madabhushi, A. Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers 2020, 12, 2200.

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