AIMC Topic: Tomography, X-Ray Computed

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Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.

Academic radiology
BACKGROUND: Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.

Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance.

Neurosurgical review
Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is critical for initiating timely interventions such as decompressive hemicraniectomy. Artificial intell...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

Critical reviews in oncology/hematology
The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma character...

Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.

BMC medical imaging
BACKGROUND: Accurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity of rib structures on CT images.

A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.

Scientific reports
This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using ...

SpineMamba: Enhancing 3D spinal segmentation in clinical imaging through residual visual Mamba layers and shape priors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of three-dimensional (3D) clinical medical images is critical for the diagnosis and treatment of spinal diseases. However, the complexity of spinal anatomy and the inherent uncertainties of current imaging technologies pose sign...

Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma.

EBioMedicine
BACKGROUND: We aim to predict outcomes of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC), a subtype of head and neck cancer characterized with improved clinical outcome and better response to therapy. Pathology an...

Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.

Abdominal radiology (New York)
Abdominopelvic trauma is a major cause of morbidity and mortality, typically resulting from high-energy mechanisms such as motor vehicle collisions and penetrating injuries. Admission abdominopelvic trauma CT, performed either selectively or as part ...