1
|
Zhang M, Liu Y, Yao J, Wang K, Tu J, Hu Z, Jin Y, Du Y, Sun X, Chen L, Wang Z. Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer. Front Endocrinol (Lausanne) 2023; 14:1137322. [PMID: 36967794 PMCID: PMC10031096 DOI: 10.3389/fendo.2023.1137322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
OBJECTIVE To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer. METHODS Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC). RESULTS Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855. CONCLUSION Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.
Collapse
Affiliation(s)
- Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yuanzhen Liu
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jincao Yao
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jing Tu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yun Jin
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yue Du
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xingbo Sun
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Liyu Chen
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Liyu Chen, ; Zhengping Wang,
| | - Zhengping Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
- *Correspondence: Liyu Chen, ; Zhengping Wang,
| |
Collapse
|
2
|
An P, Lin Y, Hu Y, Qin P, Ye Y, Gu W, Li X, Song P, Feng G. Predicting Model of Biochemical Recurrence of Prostate Carcinoma (PCa-BCR) Using MR Perfusion-Weighted Imaging-Based Radiomics. Technol Cancer Res Treat 2023; 22:15330338231166766. [PMID: 37016971 PMCID: PMC10084547 DOI: 10.1177/15330338231166766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Abstract
OBJECTIVE To build a combined model that integrates clinical data, contrast-enhanced ultrasound, and magnetic resonance perfusion-weighted imaging-based radiomics for predicting the possibility of biochemical recurrence of prostate carcinoma and develop a nomogram tool. METHOD We retrospectively analyzed the clinical, ultrasound, and magnetic resonance imaging data of 206 patients pathologically confirmed with prostate carcinoma and receiving radical prostatectomy at Xiangyang No. 1 People's Hospital from February 2015 to August 2021. Based on one to 7 years of follow-up (prostate specific antigen [PSA] level≥0.2 ng/mL, indicative of prostate carcinoma-biochemical recurrence), the patients were divided into biochemical recurrence group (n = 77) and normal group (n = 129). The training and testing sets were formed by dividing the patients at a 7:3 ratio. In training set, The magnetic resonance perfusion-weighted imaging-based radiomics radscore was generated using lasso regression. Several predictive models were built based on the patients' clinical imaging data. The predictive efficacy (area under the curve) of these models was compared using the MedCalc software. The decision curve analysis was conducted using the R to compare the net benefit. Finally, an external validation was carried out on the testing set, and the nomogram tool was developed for predicting prostate carcinoma-biochemical recurrence. RESULT The univariate analysis confirmed that Tumor diameter, tumor node metastasis classification stage of tumor, lymph node metastasis or distance metastasis, Gleason grade, preoperative PSA, ultrasound (peak intensity, arrival time, and elastography grade), and magnetic resonance imaging-radscore1/2 were predictors of prostate carcinoma-biochemical recurrence. On the training set, the combined model based on the above factors had the highest predictive efficacy for prostate carcinoma-biochemical recurrence (area under the curve: 0.91; odds ratio 0.02, 95% confidence interval: 0.85-0.95). The predictive performance of the combined model was significantly higher than that of the model based on general clinical data (area under the curve: 0.74; odds ratio 0.04, 95% confidence interval: 0.67-0.81, P < .05), contrast-enhanced ultrasound (area under the curve: 0.61; odds ratio 0.05 95% confidence interval: 0.53-0.69, P < .05), and the magnetic resonance imaging-based radiomics model (area under the curve: 0.85; odds ratio 0.03, 95% confidence interval: 0.78-0.91, P = .01). The decision curve analysis also indicated the maximum net benefit derived from the combined model, which agreed with the validation results on the testing set. The nomogram tool developed based on the combined model achieved a good performance in clinical applications. CONCLUSION The magnetic resonance imaging texture parameters extracted by magnetic resonance perfusion-weighted imaging Lasso regression could help increase the accuracy of the predictive model. The combined model and the nomogram tool provide support for the clinical screening of the populations at a risk for biochemical recurrence.
Collapse
Affiliation(s)
- Peng An
- Department of Radiology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Urology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yong Lin
- Department of Gynaecology and Reproductive medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yan Hu
- Department of Urology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Gynaecology and Reproductive medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pharmacy and Laboratory, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ping Qin
- Department of Gynaecology and Reproductive medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - YingJian Ye
- Department of Urology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Weiping Gu
- Department of Urology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Gynaecology and Reproductive medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiumei Li
- Department of Radiology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ping Song
- Department of Radiology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pharmacy and Laboratory, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Guoyan Feng
- Department of Radiology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Urology, 584878Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| |
Collapse
|