ArticleInternational journal of clinical oncology2022
Novel diagnostic model for bone metastases in renal cell carcinoma patients based on bone scintigraphy analyzed by computer-aided diagnosis software and bone turnover markers.
Article in International journal of clinical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 7 citations in OpenAlex.
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Diagnostic and prognostic significance of tartrate-resistant acid phosphatase type 5b in newly diagnosed prostate cancer with bone metastasis: A real-world multi-institutional study.International journal of urology : official journal of the Japanese Urological Association · 2023Article
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9 authors at 1 institution in 1 country.
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Abstract
backgroundComputer-assisted diagnosis (CAD) systems for bone scans have been introduced as clinical quality assurance tools, but few studies have reported on its utility for renal cell carcinoma (RCC) patients. The aim of this study was to assess the diagnostic validity of the CAD system for bone scans and to construct a novel diagnostic system for bone metastases in RCC patients.
methodsWe evaluated bone scan images of 300 RCC patients. Artificial neural network (ANN) values, which represent the probability of abnormality, were calculated by BONENAVI, the CAD software for bone scans. By analyzing ANN values, we assessed the diagnostic validity of BONENAVI. Next, we selected 108 patients who underwent measurements of bone turnover markers and assessed the combined diagnostic validity of BONENAVI and bone turnover markers.
resultsForty-three out of 300 RCC patients had bone metastases. The AUC of ANN values was 0.764 and the optimum sensitivity and specificity were 83.7 and 62.7%. By logistic analysis of 108 cases, we found that ICTP, a bone resorption marker, could be a diagnostic marker. The AUC of ICTP was 0.776 and the optimum sensitivity and specificity were 57.1 and 86.8%. Subsequently, we developed a novel diagnostic model based on ANN values and ICTP. Using this model, the AUC was 0.849 and the optimum sensitivity and specificity were 76.2 and 80.7%.
conclusionBy combining the high sensitivity provided by BONENAVI and the high specificity provided by ICTP, we constructed a novel, high-accuracy diagnostic model for bone metastases in RCC patients.
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