LAUSR

Simple Summary Standardized reading schemes, the use of indicators derived from medical images and the use of deep learning-based algorithms become very popular in medical imaging. In this retrospective study, we evaluated the performance of an automatic deep-learning-based algorithm for computer-assisted diagnosis in the field of oncological whole-body bone imaging in nuclear medicine. In addition to prostate cancer, representing a tumor entity evaluated thoroughly using the examined methodology (Bone Scan Imaging (BSI) methodology), a modification of the BSI based standard rating scheme facilitate the use of the methodology for other tumor entities (e.g., breast cancer, lung cancer, hepatocellular carcinoma). Diagnostics in clinical routine can benefit from the examined methodology, mainly due to its sensitivity and the high negative predictive value. Non-pathological bone scans may be easily identified. This may lead to a reduced working load in nuclear medicine departments and may result in an improved and more standardized workflow. Abstract The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial neural network) in its development to provide automatic calculation, feature extraction, and diagnostic support. As its performance in tumor entities, not including prostate cancer, remains unclear, our aim was to obtain more data about this aspect. The results of BSI evaluation of bone scans from 951 consecutive patients with different tumors were retrospectively compared to clinical reports (bone metastases, yes/no). Statistical analysis included entity-specific receiver operating characteristics to determine optimized BSI cut-off values. In addition to prostate cancer (cut-off = 0.27%, sensitivity (SN) = 87%, specificity (SP) = 99%), the algorithm used provided comparable results for breast cancer (cut-off 0.18%, SN = 83%, SP = 87%) and colorectal cancer (cut-off = 0.10%, SN = 100%, SP = 90%). Worse performance was observed for lung cancer (cut-off = 0.06%, SN = 63%, SP = 70%) and renal cell carcinoma (cut-off = 0.30%, SN = 75%, SP = 84%). The algorithm did not perform satisfactorily in melanoma (SN = 60%). For most entities, a high negative predictive value (NPV ≥ 87.5%, melanoma 80%) was determined, whereas positive predictive value (PPV) was clinically not applicable. Automatically determined BSI showed good sensitivity and specificity in prostate cancer and various other entities. Particularly, the high NPV encourages applying BSI as a tool for computer-aided diagnostic in various tumor entities.