Novel Bone Scan Features for Predicting Prognosis in Men With Bone Metastatic Prostate Cancer: A Retrospective Study.

Abstract

Background: Bone metastasis frequently occurs in patients with prostate cancer, however, a consensus has not been reached regarding bone scan image analysis. We aimed to analyse various bone scan imaging features of metastatic prostate cancer and to assess their impact on prognosis.

Methods: One thousand five hundred sixty-three paired sets of bone scan images (anterior and posterior) were obtained from patients with metastatic prostate cancer at Seoul National University Hospital. U-Net architecture was used for the segmentation of metastatic bone lesions. Imaging features describing the overall metastatic burden (n = 18) and largest metastatic burden (n = 32) were extracted using computer vision techniques. Kaplan-Meier survival analysis and Cox proportional risk model were used to analyse the prognostic impact of each feature.

Results: The correlation coefficient between the actual number of lesions and that predicted by the deep learning model was 0.87, indicating a strong correlation. Multivariate Cox regression showed that metastasis intensity difference (hazard ratio [HR], 0.53; P = 0.002) and the largest metastasis percentage (HR, 0.62; P = 0.038) were independently associated with disease progression and were even more strongly associated with the number of metastases (current standard). The Kaplan-Meier curves revealed that a higher total metastasis ratio (P < 0.001), a lower total metastasis intensity difference (P = 0.030), a lower largest metastatic lesion percentage (P < 0.001), higher compactness (P = 0.028), and lower eccentricity (P = 0.070) were associated with shorter progression-free survival.

Conclusion: Although the number of bone metastases is a standardised prognostic factor, additional consideration of morphological or intensity-related novel features may be useful to more accurately predict the prognosis of patients with metastatic prostate cancer.