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Yang C, Liu Z, Fang Y, Cao X, Xu G, Wang Z, Hu Z, Wang S, Wu X. Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte. J Transl Med 2023; 21:465. [PMID: 37438820 DOI: 10.1186/s12967-023-04318-w] [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: 03/28/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa. METHODS We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm. RESULTS 197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573-0.853), specificity of 0.869 (95% CI 0.764-0.974), F1 of 0.699 (95% CI 0.557-0.841), and AUC of 0.864 (95% CI 0.794-0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved. CONCLUSION Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.
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Affiliation(s)
- Chunguang Yang
- Department of Urology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, People's Republic of China
| | - Zhenghao Liu
- Department of Urology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, People's Republic of China
| | - Yin Fang
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xinyu Cao
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Zhihua Wang
- Department of Urology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, People's Republic of China
| | - Zhiquan Hu
- Department of Urology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, People's Republic of China
| | - Shaogang Wang
- Department of Urology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, People's Republic of China
| | - Xinglong Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China.
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Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040799. [PMID: 35453847 PMCID: PMC9027206 DOI: 10.3390/diagnostics12040799] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 02/04/2023] Open
Abstract
Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
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Pan JF, Su R, Cao JZ, Zhao ZY, Ren DW, Ye SZ, Huang RD, Tao ZL, Yu CL, Jiang JH, Ma Q. Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making. Front Oncol 2021; 11:740868. [PMID: 34589437 PMCID: PMC8473816 DOI: 10.3389/fonc.2021.740868] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/20/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose The purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making. Methods We retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group. Results In the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators. Conclusion The prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.
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Affiliation(s)
- Jin-Feng Pan
- Medical School, Ningbo University, Ningbo, China.,Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Rui Su
- Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China.,Department of Urology, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China.,Ningbo Clinical Research Center for Urological Disease, Ningbo, China
| | - Jian-Zhou Cao
- Medical School, Ningbo University, Ningbo, China.,Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Zhen-Ya Zhao
- Department of Radiology, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Da-Wei Ren
- Department of Radiology, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Sha-Zhou Ye
- Ningbo Clinical Research Center for Urological Disease, Ningbo, China.,Translational Research Laboratory for Urology, the Key Laboratory of Ningbo City, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Rui-da Huang
- Medical School, Ningbo University, Ningbo, China.,Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Zhu-Lei Tao
- Medical School, Ningbo University, Ningbo, China.,Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Cheng-Ling Yu
- Ningbo Clinical Research Center for Urological Disease, Ningbo, China.,Translational Research Laboratory for Urology, the Key Laboratory of Ningbo City, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
| | - Jun-Hui Jiang
- Department of Urology, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China.,Ningbo Clinical Research Center for Urological Disease, Ningbo, China
| | - Qi Ma
- Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China.,Department of Urology, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China.,Ningbo Clinical Research Center for Urological Disease, Ningbo, China.,Translational Research Laboratory for Urology, the Key Laboratory of Ningbo City, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China
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