Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study

Objective: Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated. Methods: We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning. Results: In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions. Conclusions: Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer. Advances In knowledge: Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.