Annali italiani di chirurgia · 2026
Development and Validation of a Machine Learning-Based Radiomics Model Using Ultrasound Image Features for Prostate Cancer Risk Stratification.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zhao Anli, Du Shunlan, Du Yanhong, Zhang Maoliang, Wu Jiangfeng, Wang Zhengping, Hu Liyan
Аннотация
AIM: This study aimed to construct a risk stratification model for prostate cancer (PCa) ultrasound imaging data and machine learning algorithms, with the goal of providing an effective tool for early diagnosis, personalized treatment, and clinical decision-making.
METHODS: A total of 211 histopathologically confirmed PCa patients were retrospectively enrolled and categorized into low-risk (n = 65), intermediate-risk (n = 55), and high-risk (n = 91) groups based on prostate-specific antigen levels, Gleason scores, and clinical T stage. From ultrasound images, 135 quantitative radiomic features-including morphological, texture, and edge descriptors-were extracted using the PyRadiomics toolkit. Feature dimensionality was reduced using the Pearson correlation coefficient (PCC), followed by recursive feature elimination (RFE) with 10-fold nested cross-validation to select the most informative features. Three machine learning algorithms-support vector machine (SVM), random forest (RF), and logistic regression (LR)-were trained and evaluated. Model performance was assessed using accuracy, sensitivity, specificity, and area under the curve (AUC).
RESULTS: The RF model achieved the best performance in both training and test cohorts, with AUCs of 0.87 and 0.86, and accuracies of 90% and 88%, respectively. DeLong's test confirmed that RF significantly outperformed SVM (p = 0.016) and LR (p = 0.004) in AUC comparison. The RF model also demonstrated robust predictive ability across risk subgroups: in the high-risk group, it achieved an AUC of 0.89, accuracy of 89%, sensitivity of 88%, and specificity of 90%; in the intermediate- and low-risk groups, AUCs were 0.86 and 0.81, respectively. Feature importance analysis revealed that wavelet-transformed Gray Level Dependence Matrix (GLDM) texture features, particularly DependenceEntropy and DependenceVariance, were the most predictive, highlighting the value of intratumoral textural heterogeneity in risk classification.
CONCLUSIONS: The RF-based ultrasound radiomics model enables accurate stratification of PCa risk, with remarkable performance in identifying high-risk patients.
METHODS: A total of 211 histopathologically confirmed PCa patients were retrospectively enrolled and categorized into low-risk (n = 65), intermediate-risk (n = 55), and high-risk (n = 91) groups based on prostate-specific antigen levels, Gleason scores, and clinical T stage. From ultrasound images, 135 quantitative radiomic features-including morphological, texture, and edge descriptors-were extracted using the PyRadiomics toolkit. Feature dimensionality was reduced using the Pearson correlation coefficient (PCC), followed by recursive feature elimination (RFE) with 10-fold nested cross-validation to select the most informative features. Three machine learning algorithms-support vector machine (SVM), random forest (RF), and logistic regression (LR)-were trained and evaluated. Model performance was assessed using accuracy, sensitivity, specificity, and area under the curve (AUC).
RESULTS: The RF model achieved the best performance in both training and test cohorts, with AUCs of 0.87 and 0.86, and accuracies of 90% and 88%, respectively. DeLong's test confirmed that RF significantly outperformed SVM (p = 0.016) and LR (p = 0.004) in AUC comparison. The RF model also demonstrated robust predictive ability across risk subgroups: in the high-risk group, it achieved an AUC of 0.89, accuracy of 89%, sensitivity of 88%, and specificity of 90%; in the intermediate- and low-risk groups, AUCs were 0.86 and 0.81, respectively. Feature importance analysis revealed that wavelet-transformed Gray Level Dependence Matrix (GLDM) texture features, particularly DependenceEntropy and DependenceVariance, were the most predictive, highlighting the value of intratumoral textural heterogeneity in risk classification.
CONCLUSIONS: The RF-based ultrasound radiomics model enables accurate stratification of PCa risk, with remarkable performance in identifying high-risk patients.