The Automated Bone Scan Index as a Predictor of Response to Prostate Radiotherapy in Men with Newly Diagnosed Metastatic Prostate Cancer: An Exploratory Analysis of STAMPEDE’s “M1|RT Comparison”

e17507 Background: Prostate cancer commonly metastasizes to the bone and is associated with reduced survival, pathologic fractures and bone pain. The assessment of bone lesions is made with the technetium Tc99m(99mTc) bone scan, which relies on the subjective interpretation of radiologists and has a wide interobserver variability. There is an unmet need for a more objective and quantifiable measurement tool. Progenics Pharmaceuticals has introduced an automated bone scan index (aBSI), which employs artificial intelligence to quantify skeletal tumor burden. The automated bone scan index has been prospectively validated and is reproducible in large Phase III studies. The aBSI was validated by our study in the Veteran population at the West LA VA Medical Center. Methods: The first positive technetium 99 Tc99m bone scans of veterans diagnosed with metastatic, castration-sensitive prostate cancer were evaluated. Since 2011, a total of 107 evaluable patient bone scans were studied (n = 107). Patients with visceral metastases were excluded to evaluate only those with skeletal metastases. An automated bone scan index (aBSI) was generated for each scan using the Progenics Pharmaceuticals’ artificial intelligence platform. Multivariate analysis of aBSI with overall survival, prostate cancer specific survival, time from diagnosis to first positive bone scan, age at diagnosis, ethnicity, and Gleason score was assessed. Results: The study demonstrated a wide range of aBSI values (Range 0-16.84). Values calculated above the Median aBSI value (1.0) were prognostic for Overall Survival (p = 0.0009) and Prostate Cancer-Specific Survival (p = 0.0011). Patients in the highest quartile of aBSI values (range 5.2-16.84) showed a statistically significant Prostate Cancer-Specific Mortality (p = 0.0300) when compared to the lowest two quartiles (Range 0-1.07). The time from diagnosis to the first positive Tc99m bone scan statistically correlated with aBSI values (p = 0.0016). Multivariate analysis using Cox regression was utilized in the final statistical analysis of prostate cancer-specific mortality and overall survival. Conclusions: The automated Bone Scan Index provides a quantifiable and validated artificial intelligence biomarker to address an unmet need among metastatic prostate cancer patients. This tool was validated among Veterans, a pertinent population that is commonly affected by metastatic prostate cancer.

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Bone Scan Index: A Quantitative Treatment Response Biomarker for Castration-Resistant Metastatic Prostate Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2011.36.5791 ◽

2012 ◽

Vol 30 (5) ◽

pp. 519-524 ◽

Cited By ~ 113

Author(s):

Elizabeth R. Dennis ◽

Xiaoyu Jia ◽

Irina S. Mezheritskiy ◽

Ryan D. Stephenson ◽

Heiko Schoder ◽

...

Keyword(s):

Prostate Cancer ◽

Bone Scintigraphy ◽

Bone Scan ◽

Metastatic Prostate Cancer ◽

Univariate Analysis ◽

Imaging Biomarker ◽

Bone Scan Index ◽

Risk Of Death ◽

Percent Change ◽

Castration Resistant

Purpose There is currently no imaging biomarker for metastatic prostate cancer. The bone scan index (BSI) is a promising candidate, being a reproducible, quantitative expression of tumor burden seen on bone scintigraphy. Prior studies have shown the prognostic value of a baseline BSI. This study tested whether treatment-related changes in BSI are prognostic for survival and compared BSI to prostate-specific antigen (PSA) as an outcome measure. Patients and Methods We retrospectively examined serial bone scans from patients with castration-resistant metastatic prostate cancer (CRMPC) enrolled in four clinical trials. We calculated BSI at baseline and at 3 and 6 months on treatment and performed univariate and bivariate analyses of PSA, BSI, and survival. Results Eighty-eight patients were scanned, 81 of whom have died. In the univariate analysis, the log percent change in BSI from baseline to 3 and 6 months on treatment prognosticated for survival (hazard ratio [HR], 2.44; P = .0089 and HR, 2.54; P < .001, respectively). A doubling in BSI resulted in a 1.9-fold increase in risk of death. Log percent change in PSA at 6 months on treatment was also associated with survival (HR, 1.298; P = .013). In the bivariate analysis, change in BSI while adjusting for PSA was prognostic at 3 and 6 months on treatment (HR, 2.368; P = .012 and HR, 2.226; P = .002, respectively), but while adjusting for BSI, PSA was not prognostic. Conclusion These data furnish early evidence that on-treatment changes in BSI are a response indicator and support further exploration of bone scintigraphy as an imaging biomarker in CRMPC.

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