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Updated: Jan 17, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Prognostic Value of BONENAVI Computer-Aided Diagnosis System Bone Scans as an Imaging Biomarker for Bone-Metastatic
Yasuhide Miyoshi1,2,3, Hiroji Uemura2, Junichi Ohta4
1Department of Urology, Kanagawa Prefectural Ashigarakami Hospital, Japan.
Objective:
BONENAVI is a software designed to automatically calculate the automated bone scan index (aBSI) and quantify the number of bone metastases on bone scans. We conducted an investigator-initiated prospective study using BONENAVI to evaluate its prognostic utility for metastatic castration-sensitive prostate cancer (mCSPC).
Methods:
Eligible patients were assigned to one of two treatment groups: androgen deprivation therapy (ADT) combined with abiraterone acetate and prednisolone (AA group) or ADT alone (ADT group). The primary objective was to evaluate the prognostic value of the median aBSI in predicting progression-free survival (PFS) in the AA group. Secondary objectives included assessing the prognostic value of the median aBSI for PFS across all patients and the ADT group, as well as evaluating the prognostic utility of the aCHAARTED classifications based on BONENAVI.
Results:
The median aBSI was not significantly associated with PFS in the AA group, the ADT group, or the overall patient cohort. Patients with aCHAARTED high-volume disease (HVD) exhibited worse PFS than those with low-volume disease (LVD) in the overall cohort and AA group; however, these differences were not statistically significant. In contrast, patients with aCHAARTED HVD in the ADT group had significantly worse PFS than those with LVD.
Conclusion:
The median aBSI could not predict the prognosis of patients with mCSPC. However, the aCHAARTED classification derived from BONENAVI may provide valuable insights for predicting PFS in patients with mCSPC undergoing ADT.
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