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Chen Z, Zhang J, Jin D, Wei X, Qiu F, Wang X, Zhao X, Pu J, Hou J, Huang Y, Huang C. A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score. BMC Cancer 2023; 23:1138. [PMID: 37996859 PMCID: PMC10668430 DOI: 10.1186/s12885-023-11306-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 08/16/2023] [Indexed: 11/25/2023] Open
Abstract
PURPOSE This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa). METHODS The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed. RESULTS The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model. CONCLUSION P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.
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Affiliation(s)
- Zongxin Chen
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
| | - Jun Zhang
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
| | - Di Jin
- Department of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
| | - Feng Qiu
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Xiaojun Zhao
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
| | - Jinxian Pu
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
- Department of Urology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215000, China
| | - Jianquan Hou
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China
- Department of Urology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215000, China
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China.
| | - Chen Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China.
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Singh D, Kumar V, Das CJ, Singh A, Mehndiratta A. Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer. Front Oncol 2022; 12:961985. [PMID: 36505875 PMCID: PMC9730331 DOI: 10.3389/fonc.2022.961985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 10/27/2022] [Indexed: 11/27/2022] Open
Abstract
Background Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods. Methods The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores. Results The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation. Conclusion The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring.
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Affiliation(s)
- Dharmesh Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of Nuclear Magnetic Resonance (NMR), All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J. Das
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India,*Correspondence: Amit Mehndiratta,
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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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Garmer M, Karpienski J, Groenemeyer DH, Wagener B, Kamper L, Haage P. Structured reporting in radiologic education - Potential of different PI-RADS versions in prostate MRI controlled by in-bore MR-guided biopsies. Br J Radiol 2021; 95:20210458. [PMID: 34914538 PMCID: PMC8978241 DOI: 10.1259/bjr.20210458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Objectives: To evaluate the efficiency of structured reporting in radiologic education – based on the example of different PI-RADS score versions for multiparametric MRI (mpMRI) of the prostate. Methods: MpMRI of 688 prostate lesions in 180 patients were retrospectively reviewed by an experienced radiologist and by a student using PI-RADS V1 and V2. Data sets were reviewed for changes according to PI-RADS V2.1. The results were correlated with results obtained by MR-guided biopsy. Diagnostic potency was evaluated by ROC analysis. Sensitivity, specificity and correct-graded samples were evaluated for different cutpoints. The agreement between radiologist and student was determined for the aggregation of the PI-RADS score in three categories. The student’s time needed for evaluation was measured. Results: The area under curve of the ROC analysis was 0.782/0.788 (V1/V2) for the student and 0.841/0.833 (V1/V2) for the radiologist. The agreement between student and radiologist showed a Cohen‘s weighted κ coefficient of 0.495 for V1 and 0.518 for V2. Median student’s time needed for score assessment was 4:34 min for PI-RADSv1 and 2:00 min for PI-RADSv2 (p < 0.001). Re-evaluation for V2.1 changed the category in 1.4% of all ratings. Conclusion: The capacity of prostate cancer detection using PI-RADS V1 and V2 is dependent on the reader‘s experience. The results from the two observers indicate that structured reporting using PI-RADS and, controlled by histopathology, can be a valuable and quantifiable tool in students‘ or residents’ education. Herein, V2 was superior to V1 in terms of inter-observer agreement and time efficacy. Advances in knowledge: Structured reporting can be a valuable and quantifiable tool in radiologic education. Structured reporting using PI-RADS can be used by a student with good performance. PI-RADS V2 is superior to V1 in terms of inter-observer agreement and time efficacy.
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Affiliation(s)
- Marietta Garmer
- Witten/Herdecke University, Witten, Germany.,Clinical Radiology, Helios University Hospital Wuppertal, Wuppertal, Germany
| | | | - Dietrich Hw Groenemeyer
- Witten/Herdecke University, Witten, Germany.,Grönemeyer Institute of Microtherapy, Bochum, Germany
| | | | - Lars Kamper
- Witten/Herdecke University, Witten, Germany.,Clinical Radiology, Helios University Hospital Wuppertal, Wuppertal, Germany
| | - Patrick Haage
- Witten/Herdecke University, Witten, Germany.,Clinical Radiology, Helios University Hospital Wuppertal, Wuppertal, Germany
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