Objective: This study evaluates the AI-assisted diagnostic potential of computed tomography (CT) for bone cancer and its influence on patient care during the pre- and post-treatment phases. It compares patient management approaches based on CT severity levels and identifies distinct CT phenotypes linked to disease severity.
Methodology: We retrospectively examined 50 patients diagnosed with bone cancer between December 2022 and June 2023. The CT scans were analyzed according to the Radiological Society of North America (RSNA) guidelines. This study was performed using the deep convolutional neutral network (DCNN) model to assist doctors in diagnosing bone tumors through CT scanning. Patients' management approaches were compared based on the severity levels indicated by CT scans.
Results: Fifty patients participated in this study, with a median age of 67.2 years, ranging from 32 to 89 years. Of them, 38 % were female and 62 % were male. In 2022, 19 individuals (13 males and 6 females, ages 32 to 84) were assessed, with a mean age of 59.9 years. In 2023, 31 individuals, aged 54 to 89 with a mean age of 71.6 years, were assessed; among them were 18 men and 13 women. SPECT scans revealed the following key diagnostic features: 85.9 % of patients exhibited bone lesions with ground-glass opacities, 88 % had multipolar involvement, 92.8 % had bilateral involvement, and 92.8 % showed peripheral involvement. The severity scores based on CT scans were significantly higher in patients requiring intensive care, with scores above 14 being more common in this group.
Conclusion: Distinct CT findings during the AI-assisted diagnosis and treatment of bone cancer provided prompt and sensitive examination capabilities. Notably, two CT phenotypes emerged, associated with large consolidation patterns and high severity scores, offering crucial insights into disease severity and aiding in clinical decision-making for intensive care requirements. The study underscores the importance of CT in the effective monitoring and management of bone cancer pre- and post-treatment.
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http://dx.doi.org/10.1016/j.jbo.2024.100639 | DOI Listing |
Accurate survival prediction of patients with long-bone metastases is challenging, but important for optimizing treatment. The Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA) has been previously developed and internally validated to predict 90-day and 1-year survival. External validation showed promise in the United States and Taiwan.
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Özel Medicabil Hastanesi, Bursa.
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Department of Otolaryngology, Changhai Hospital, Naval Medical University, Shanghai, China.
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National Key Laboratory of Draggability Evaluation and Systematic Translational Medicine, Tianjin's Clinical Research Center for Cancer, Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
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View Article and Find Full Text PDFNasopharyngeal carcinoma (NPC) is an epithelial malignancy commonly associated with Epstein-Barr virus infection. While bone, liver, and lung metastases are well-documented, central nervous system (CNS) involvement, particularly spinal and meningeal metastases, is extremely rare. We present a 41-year-old male with nasal obstruction and diplopia, diagnosed with locally advanced NPC.
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