Investigation on Automatic Classification of Bone, Lymph Node, and Local Lesions in CT Images of Prostate Cancer Patients

Abstract Accurate assessment of different types of prostate cancer (PCa) lesions is a crucial part of treatment planning. Following a computed tomography (CT) or a positron emission tomography (PET)/CT imaging scan, a significant amount of time is required from the clinicians to detect lesions and characterize them by their anatomical locations. To facilitate the development of new image analysis applications, we investigated the potential of automated classification of PCa lesions between bone metastases, lymph node metastases, local lesions, and other PCa lesions in CT images via machine learning (ML) and deep learning models. We annotated a total of 4949 PCa lesions by their type in a private dataset of $$^{18}$$ 18 F-prostate-specific membrane antigen (PSMA) PET/CT images of 532 PCa patients and an external test set of publicly available PSMA PET/CT images from 30 patients. Using information of coordinates of the detected lesions, we automatically extracted 15 different features from the CT and the results of the automated 3D anatomical segmentation tool TotalSegmentator for each lesion, and trained several traditional ML models to classify lesions by their anatomical type. Additionally, we experimented with convolutional neural networks (CNNs) of different dimensionalities with CT and TotalSegmentator inputs. According to our results, the most accurate model was XGBoost with an overall accuracy of 96.0%. It identified bone metastases, lymph node metastases, and local lesions with sensitivities of 97.9±1.2%, 95.2±1.3%, and 93.1±2.8% in the five-fold cross-validation experiments of the primary data and 96.3%, 94.0%, and 80.0% on the external test set, respectively. Conversely, the CNNs underperformed the best ML models in distinguishing local lesions. To conclude, a simple ML model can accurately classify PCa lesions between bone, lymph node, and local lesions based on CT images, lesion locations, and automated anatomical segmentation data, but more research is required for distinguishing PCa lesions outside these categories.