Deep learning-based detection and segmentation of osseous metastatic prostate cancer lesions on computed tomography
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Purpose
Prostate adenocarcinoma frequently metastasizes to bone and is detected via computed tomography (CT) scans. Accurate detection and segmentation of these lesions are critical for diagnosis, prognosis, and monitoring. This study aims to automate lesion detection and segmentation using deep learning models.
Methods and Materials
We evaluated several deep learning models for lesion detection (EfficientNet, ResNet34, DenseNet) and segmentation (nnUNetv2, UNet, ResUNet, ResAttUNet). Performance metrics included F1 score, precision, recall, Area Under the Curve (AUC), and Dice Similarity Coefficient (DSC). Pairwise t-tests compared segmentation accuracy. Additionally, we conducted radiomic analyses to compare lesions segmented by deep learning to manual segmentations
Results
EfficientNet achieved the highest detection performance, with an F1 score of 0.82, precision of 0.88, recall of 0.79, and AUC of 0.71. Among segmentation models, nnUNetv2 performed best, achieving a DSC of 0.74, with precision and recall values of 0.73 and 0.83, respectively. Pairwise t-tests showed that nnUNetv2 outperformed ResAttUNet, ResUNet, and UNet in segmentation accuracy (p < 0.01). Clinically, nnUNetv2 also demonstrated superior specificity for lesion detection (0.9) compared to the other models. All models performed similarly in distinguishing diffuse and focal lesions, predicting weight-bearing lesions, and identifying lesion locations, although nnUNetv2 had higher specificity for these tasks. Sensitivity was highest for rib lesions and lowest for spine lesions across all models.
Conclusions
EfficientNet and nnUNetv2 were the top-performing models for detection and segmentation, respectively. The radiomic features derived from deep learning-based segmentations were comparable to those from manual segmentations, supporting the clinical applicability of these methods. Further analysis of lesion detection and spatial distribution, as well as lesion quality differentiation, underscores the models’ potential for improving diagnostic workflows and patient outcomes in clinical settings.
