Comparison of CNNs and Transformer Models in Diagnosing Bone Metastases in Bone Scans Using Grad-CAM.

Purpose: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and transformer models has not yet been explored. This study aims to evaluate the performance of various deep learning models, including the ConvNeXt and transformer models, in diagnosing metastatic lesions from bone scans.

Materials And Methods: We retrospectively analyzed bone scans from patients with cancer obtained at 2 institutions: the training and validation sets (n=4626) were from Hospital 1 and the test set (n=1428) was from Hospital 2.

Conformation-sensitive targeting of CD18 depletes M2-like tumor-associated macrophages resulting in inhibition of solid tumor progression.

Background: Tumor-associated macrophages (TAMs) primarily exist in the M2-like phenotype in the tumor microenvironment (TME). M2-TAMs contribute to tumor progression by establishing an immunosuppressive environment. However, TAM targeting is hindered, mainly owing to a lack of specific biomarkers for M2-TAMs.

ChatGPT-assisted deep learning for diagnosing bone metastasis in bone scans: Bridging the AI Gap for Clinicians.

Background: Bone scans are often used to identify bone metastases, but their low specificity may necessitate further studies. Deep learning models may improve diagnostic accuracy but require both medical and programming expertise. Therefore, we investigated the feasibility of constructing a deep learning model employing ChatGPT for the diagnosis of bone metastasis in bone scans and to evaluate its diagnostic performance.