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Open Access Highly Accessed Research article

Analysis of regional bone scan index measurements for the survival of patients with prostate cancer

Jonas Kalderstam1*, May Sadik2, Lars Edenbrandt23 and Mattias Ohlsson1

Author Affiliations

1 Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden

2 Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden

3 Department of Clinical Sciences, Lund University, Malmö, Sweden

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BMC Medical Imaging 2014, 14:24  doi:10.1186/1471-2342-14-24

Published: 10 July 2014

Abstract

Background

A bone scan is a common method for monitoring bone metastases in patients with advanced prostate cancer. The Bone Scan Index (BSI) measures the tumor burden on the skeleton, expressed as a percentage of the total skeletal mass. Previous studies have shown that BSI is associated with survival of prostate cancer patients. The objective in this study was to investigate to what extent regional BSI measurements, as obtained by an automated method, can improve the survival analysis for advanced prostate cancer.

Methods

The automated method for analyzing bone scan images computed BSI values for twelve skeletal regions, in a study population consisting of 1013 patients diagnosed with prostate cancer. In the survival analysis we used the standard Cox proportional hazards model and a more advanced non-linear method based on artificial neural networks. The concordance index (C-index) was used to measure the performance of the models.

Results

A Cox model with age and total BSI obtained a C-index of 70.4%. The best Cox model with regional measurements from Costae, Pelvis, Scapula and the Spine, together with age, got a similar C-index (70.5%). The overall best single skeletal localisation, as measured by the C-index, was Costae. The non-linear model performed equally well as the Cox model, ruling out any significant non-linear interactions among the regional BSI measurements.

Conclusion

The present study showed that the localisation of bone metastases obtained from the bone scans in prostate cancer patients does not improve the performance of the survival models compared to models using the total BSI. However a ranking procedure indicated that some regions are more important than others.

Keywords:
Artificial neural networks; Machine learning; Bone scan index; Survival analysis; Prostate cancer