Predicting bone metastasis and high-grade Gleason scores in prostate cancer: a retrospective study integrating clinical features and magnetic resonance imaging radiomics.

[BACKGROUND] Prostate cancer (PCa) is a common malignant tumor in older men, and bone metastasis is its most frequent form. Once bone metastasis occurs, survival drops sharply. The Gleason score is the standard tool for judging how aggressive the cancer is; men with high-risk disease face higher chances of treatment failure and death. Therefore, early detection and prediction of bone metastasis and high Gleason scores by magnetic resonance imaging (MRI) are clinically important. In this study, we analyzed clinical and MRI data from 168 PCa patients to evaluate the role of clinical features and MRI-based radiomics in predicting bone metastasis and high-grade Gleason scores.

[METHODS] This retrospective study included 168 patients with pathologically confirmed PCa from Zhongshan Hospital of Traditional Chinese Medicine. Clinical and pathological data, as well as MRI images, were collected. Radiomics and clinical features were extracted and divided into training and testing sets using a random ratio. Feature selection was performed using -tests and least absolute shrinkage and selection operator (LASSO) regression to reduce dimensionality and identify effective features. Machine learning algorithms were constructed based on two datasets: one combining clinical information with radiomics features, and the other using radiomics features alone. Model performance was assessed using metrics such as accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

[RESULTS] Patients with bone metastasis and high-grade Gleason Scores had significantly higher levels of total prostate-specific antigen (tPSA) and free prostate-specific antigen (fPSA) compared to those without bone metastasis and with low-grade Gleason scores (P<0.05). In the testing set, the best-performing model for predicting bone metastasis was the Extreme Gradient Boosting (XGBoost) model that used clinical features combined with radiomics features, with an AUC of 0.875, which was superior to the AUC of 0.732 for radiomics features alone. For predicting high-grade Gleason scores, the XGBoost model using clinical features combined with radiomics features also performed best, with an AUC of 0.830, outperforming the AUC of 0.778 for radiomics features alone. The most significant clinical feature identified was fPSA, while the most significant radiomics features were log-sigma-5-0-mm-3D_glszm_ZoneEntropy for bone metastasis and wavelet-HLH_gldm_HighGrayLevelEmphasis for high-grade Gleason scores respectively.

[CONCLUSIONS] We proposed a predictive model that integrated clinical features and radiomics features obtained from prostate MRI, offering a non-invasive and radiation-free approach to predict bone metastasis and high-grade Gleason scores in PCa.