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

[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; = 0.002) and the largest metastasis percentage (HR, 0.62; = 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 ( < 0.001), a lower total metastasis intensity difference ( = 0.030), a lower largest metastatic lesion percentage ( < 0.001), higher compactness ( = 0.028), and lower eccentricity ( = 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.