Cargando…
Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification
The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the s...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535024/ https://www.ncbi.nlm.nih.gov/pubmed/34679484 http://dx.doi.org/10.3390/diagnostics11101785 |
_version_ | 1784587677948968960 |
---|---|
author | Liu, Yongkai Zheng, Haoxin Liang, Zhengrong Miao, Qi Brisbane, Wayne G. Marks, Leonard S. Raman, Steven S. Reiter, Robert E. Yang, Guang Sung, Kyunghyun |
author_facet | Liu, Yongkai Zheng, Haoxin Liang, Zhengrong Miao, Qi Brisbane, Wayne G. Marks, Leonard S. Raman, Steven S. Reiter, Robert E. Yang, Guang Sung, Kyunghyun |
author_sort | Liu, Yongkai |
collection | PubMed |
description | The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar’s test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment. |
format | Online Article Text |
id | pubmed-8535024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85350242021-10-23 Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification Liu, Yongkai Zheng, Haoxin Liang, Zhengrong Miao, Qi Brisbane, Wayne G. Marks, Leonard S. Raman, Steven S. Reiter, Robert E. Yang, Guang Sung, Kyunghyun Diagnostics (Basel) Article The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar’s test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment. MDPI 2021-09-28 /pmc/articles/PMC8535024/ /pubmed/34679484 http://dx.doi.org/10.3390/diagnostics11101785 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Yongkai Zheng, Haoxin Liang, Zhengrong Miao, Qi Brisbane, Wayne G. Marks, Leonard S. Raman, Steven S. Reiter, Robert E. Yang, Guang Sung, Kyunghyun Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification |
title | Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification |
title_full | Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification |
title_fullStr | Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification |
title_full_unstemmed | Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification |
title_short | Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification |
title_sort | textured-based deep learning in prostate cancer classification with 3t multiparametric mri: comparison with pi-rads-based classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535024/ https://www.ncbi.nlm.nih.gov/pubmed/34679484 http://dx.doi.org/10.3390/diagnostics11101785 |
work_keys_str_mv | AT liuyongkai texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT zhenghaoxin texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT liangzhengrong texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT miaoqi texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT brisbanewayneg texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT marksleonards texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT ramanstevens texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT reiterroberte texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT yangguang texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification AT sungkyunghyun texturedbaseddeeplearninginprostatecancerclassificationwith3tmultiparametricmricomparisonwithpiradsbasedclassification |