AI Article Synopsis

  • This study explores the effectiveness of AI-assisted CT scans in diagnosing bone cancer and its impact on patient care before and after treatment, highlighting different patient management strategies based on CT severity levels.
  • 50 patients diagnosed with bone cancer were analyzed using RSNA guidelines and a deep convolutional neural network to detect specific diagnostic features from their CT scans.
  • The results identified two significant CT phenotypes linked to higher disease severity, revealing that patients with more severe conditions often required intensive care, thus informing better clinical decision-making.

Article Abstract

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490708PMC
http://dx.doi.org/10.1016/j.jbo.2024.100639DOI Listing

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