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Gaudiano C, Mottola M, Bianchi L, Corcioni B, Braccischi L, Tomassoni MT, Cattabriga A, Cocozza MA, Giunchi F, Schiavina R, Fanti S, Fiorentino M, Brunocilla E, Mosconi C, Bevilacqua A. An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience. Cancers (Basel) 2023; 15:3438. [PMID: 37444548 DOI: 10.3390/cancers15133438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.
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Affiliation(s)
- Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Margherita Mottola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Beniamino Corcioni
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lorenzo Braccischi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | | | - Arrigo Cattabriga
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Riccardo Schiavina
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Stefano Fanti
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Department of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Michelangelo Fiorentino
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Eugenio Brunocilla
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy
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Gaudiano C, Braccischi L, Taninokuchi Tomassoni M, Paccapelo A, Bianchi L, Corcioni B, Ciccarese F, Schiavina R, Droghetti M, Giunchi F, Fiorentino M, Brunocilla E, Golfieri R. Transverse prostate maximum sectional area can predict clinically significant prostate cancer in PI-RADS 3 lesions at multiparametric magnetic resonance imaging. Front Oncol 2023; 13:1082564. [PMID: 36890814 PMCID: PMC9986422 DOI: 10.3389/fonc.2023.1082564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/07/2023] [Indexed: 02/22/2023] Open
Abstract
Background To evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA (transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions. Methods Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa. Results Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm2, 9.1cm2, 5.5cm2 and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P<0.031). Conclusions In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.
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Affiliation(s)
- Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Lorenzo Braccischi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | | | - Alexandro Paccapelo
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,University of Bologna, Bologna, Italy
| | - Beniamino Corcioni
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Federica Ciccarese
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Riccardo Schiavina
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,University of Bologna, Bologna, Italy
| | - Matteo Droghetti
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,University of Bologna, Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Michelangelo Fiorentino
- Department of Specialty, Diagnostic and Experimental Medicine, University of Bologna, Bologna, Italy
| | - Eugenio Brunocilla
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,University of Bologna, Bologna, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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Huang C, Qiu F, Jin D, Wei X, Chen Z, Wang X, Zhao X, Guo L, Pu J, Hou J, Huang Y. New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3. Front Oncol 2022; 12:908956. [PMID: 35860546 PMCID: PMC9289138 DOI: 10.3389/fonc.2022.908956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa).Patients and MethodsThe demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves.ResultsThe AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold.ConclusionsThis study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.
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Affiliation(s)
- Chen Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Feng Qiu
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Di Jin
- Department of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zongxin Chen
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaojun Zhao
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Linchuan Guo
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinxian Pu
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jianquan Hou
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Yuhua Huang,
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