Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: a 3D analysis of SPECT/CT images
Francesco Fiz, Helmut Dittmann, Cristina Campi, Matthias Weissinger,, Samine Sahbai, Matthias Reimold, Arnulf Stenzl, Michele Piana, Gianmario, Sambuceti, Christian la Foug\`ere

TL;DR
This study introduces a novel adaptive bone segmentation method for quantifying skeletal tumor burden in metastatic prostate cancer using SPECT/CT images, correlating well with existing estimates and clinical outcomes.
Contribution
The paper presents a new automated 3D analysis approach for skeletal disease burden estimation in prostate cancer, improving accuracy and clinical relevance over existing methods.
Findings
Segmentation-based tumor load correlates with radiological and laboratory indices.
Lower radionuclide uptake in progressive disease suggests reduced therapy benefit.
Automated software shows high correlation with commercial tools and manual assessments.
Abstract
To meet the current need for skeletal tumor-load estimation in prostate cancer (mCRPC), we developed a novel approach, based on adaptive bone segmentation. In this study, we compared the program output with existing estimates and with the radiological outcome. Seventy-six whole-body 99mTc-DPD-SPECT/CT from mCRPC patients were analyzed. The software identified the whole skeletal volume (SVol) and classified it voxels metastases (MVol) or normal bone (BVol). SVol was compared with the estimation of a commercial software. MVol was compared with manual assessment and with PSA-level. Counts/voxel were extracted from MVol and BVol. After six cycles of 223RaCl2-therapy every patient was re-evaluated as progressing (PD), stabilized (SD) or responsive (PR). SVol correlated with the one of the commercial software (R=0,99, p<0,001). MVol correlated with manually-counted lesions (R=0,61, p<0,001)…
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