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

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 i...

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Autores principales: Algohary, Ahmad, Shiradkar, Rakesh, Pahwa, Shivani, Purysko, Andrei, Verma, Sadhna, Moses, Daniel, Shnier, Ronald, Haynes, Anne-Maree, Delprado, Warick, Thompson, James, Tirumani, Sreeharsha, Mahran, Amr, Rastinehad, Ardeshir R, Ponsky, Lee, Stricker, Phillip D., Madabhushi, Anant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465024/
https://www.ncbi.nlm.nih.gov/pubmed/32781640
http://dx.doi.org/10.3390/cancers12082200
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author Algohary, Ahmad
Shiradkar, Rakesh
Pahwa, Shivani
Purysko, Andrei
Verma, Sadhna
Moses, Daniel
Shnier, Ronald
Haynes, Anne-Maree
Delprado, Warick
Thompson, James
Tirumani, Sreeharsha
Mahran, Amr
Rastinehad, Ardeshir R
Ponsky, Lee
Stricker, Phillip D.
Madabhushi, Anant
author_facet Algohary, Ahmad
Shiradkar, Rakesh
Pahwa, Shivani
Purysko, Andrei
Verma, Sadhna
Moses, Daniel
Shnier, Ronald
Haynes, Anne-Maree
Delprado, Warick
Thompson, James
Tirumani, Sreeharsha
Mahran, Amr
Rastinehad, Ardeshir R
Ponsky, Lee
Stricker, Phillip D.
Madabhushi, Anant
author_sort Algohary, Ahmad
collection PubMed
description 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.
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spelling pubmed-74650242020-09-04 Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study Algohary, Ahmad Shiradkar, Rakesh Pahwa, Shivani Purysko, Andrei Verma, Sadhna Moses, Daniel Shnier, Ronald Haynes, Anne-Maree Delprado, Warick Thompson, James Tirumani, Sreeharsha Mahran, Amr Rastinehad, Ardeshir R Ponsky, Lee Stricker, Phillip D. Madabhushi, Anant Cancers (Basel) Article 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. MDPI 2020-08-06 /pmc/articles/PMC7465024/ /pubmed/32781640 http://dx.doi.org/10.3390/cancers12082200 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Algohary, Ahmad
Shiradkar, Rakesh
Pahwa, Shivani
Purysko, Andrei
Verma, Sadhna
Moses, Daniel
Shnier, Ronald
Haynes, Anne-Maree
Delprado, Warick
Thompson, James
Tirumani, Sreeharsha
Mahran, Amr
Rastinehad, Ardeshir R
Ponsky, Lee
Stricker, Phillip D.
Madabhushi, Anant
Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
title Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
title_full Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
title_fullStr Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
title_full_unstemmed Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
title_short Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
title_sort combination of peri-tumoral and intra-tumoral radiomic features on bi-parametric mri accurately stratifies prostate cancer risk: a multi-site study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465024/
https://www.ncbi.nlm.nih.gov/pubmed/32781640
http://dx.doi.org/10.3390/cancers12082200
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