Establishment of models for the intelligent detection and risk prediction of prostate cancer based on the combination of multi-modality magnetic resonance imaging radiomics and clinical indicators(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第2期
- Page:
- 251-260
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Establishment of models for the intelligent detection and risk prediction of prostate cancer based on the combination of multi-modality magnetic resonance imaging radiomics and clinical indicators
- Author(s):
- WANG Yi; LI Yuanzhe; LI Shuting; LAI Qingquan
- Department of CT/MRI, the Second Affiliated Hospital, Fujian Medical University, Quanzhou 362000, China
- Keywords:
- Keywords: prostate cancer radiomics magnetic resonance imaging multi-modality machine learning
- PACS:
- R318；R737.5
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.02.021
- Abstract:
- Abstract: Objective To develop an automatic detection model of prostate cancer using multi-modality magnetic resonance imaging (MRI) radiomics, and to predict the risk of prostate cancer by a multifactor regression model constructed by nomogram based on the combination of prostate MRI radiomics and clinical indicators. Methods A retrospective analysis was conducted on 133 patients with prostate cancer and other benign prostatic lesions confirmed by pathology from February 2019 to October 2021. All patients underwent directeral rectum examination (DRE), and were tested for prostate specific antigen (PSA), free-prostate specific antigen (F-PSA) and F-PSA/PSA. After extracting radiological features from multi-modality prostate MRI images (DWI+DCE+T2WI) before treatment, the minimal redundancy maximal relevance (mRMR) algorithm was used for eliminating hybrid variables, and the least absolute shrinkage and selection operator (LASSO) for radiological feature selection. The diagnostic performances of radiological features were evaluated by area under ROC curve (AUC), accuracy, specificity and sensitivity. Multiple logistic regression analysis was used to select clinical indicators which were then combined with radiomics feature model to formulate radiomics nomogram. The model reliability was verified by calibration curve and Hosmer-lemeshow test. Results The ICC of all data measured by two observers was above 0.80. All MRI images of the prostate were randomly divided into training group and verification group at a ratio of 7:3. The AUC of DWI, DCE and T2WI were 0.882, 0.821, 0.848 in training group, and 0.861, 0.810, 0.838 in verification group, while the combination model of triple-modality MRI achieved AUC of 0.912 and 0.898 in training group and validation group, respectively. The Delong test results show that DWI model outperformed DCE and T2WI models (the latter two had similar performances), and that the performance of combination model of triple-modality MRI was superior to that of any other model. ROC curve was used to evaluate the predictive performance of nomogram, radicomics and clinical indicators, and the results revealed that the AUC, accuracy, sensitivity and specificity of nomogram were 0.941, 0.929, 0.891 and 0.893, respectively. Nomogram had the best predictive performance for prostate cancer, and the predictive performance of clinical factors was poor. Both calibration curve and Hosmer-lemeshow test results verified the above findings. Conclusion Multi-modality prostate MRI radiomics model can accurately identify benign and malignant prostate tumors. Radiomics nomogram shows a satisfactory performance in the prediction of prostate cancer risk.
Last Update: 2023-03-03