AIMC Topic: Humans

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Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.

Japanese journal of radiology
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improv...

A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomas.

International journal of cancer
Isocitrate dehydrogenase-mutant gliomas are lethal brain cancers that impair quality of life in young adults. Although less aggressive than glioblastomas, IDH-mutant gliomas invariably progress to incurable disease with unpredictable recurrence. A be...

Radiomics for lung cancer diagnosis, management, and future prospects.

Clinical radiology
Lung cancer remains the leading cause of cancer-related mortality worldwide, with its early detection and effective treatment posing significant clinical challenges. Radiomics, the extraction of quantitative features from medical imaging, has emerged...

CASCADE-FSL: Few-shot learning for collateral evaluation in ischemic stroke.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Assessing collateral circulation is essential in determining the best treatment for ischemic stroke patients as good collaterals lead to different treatment options, i.e., thrombectomy, whereas poor collaterals can adversely affect the treatment by l...

A comparison of an integrated and image-only deep learning model for predicting the disappearance of indeterminate pulmonary nodules.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Indeterminate pulmonary nodules (IPNs) require follow-up CT to assess potential growth; however, benign nodules may disappear. Accurately predicting whether IPNs will resolve is a challenge for radiologists. Therefore, we aim to utilize d...

External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer Detection.

Academic radiology
PURPOSE: Prostate imaging reporting and data systems (PI-RADS) experiences considerable variability in inter-reader performance. Artificial Intelligence (AI) algorithms were suggested to provide comparable performance to PI-RADS for assessing prostat...

Using the counterfactual framework to estimate non-intention-to-treat estimands in randomised controlled trials: A methodological scoping review.

Contemporary clinical trials
BACKGROUND: Randomised controlled trials (RCTs) commonly estimate intention-to-treat (ITT) estimands. However, when nonadherence to assigned treatment occurs, ITT estimands reflect the effect of being offered treatment, rather than adhering to it and...

SwinCVS: a unified approach to classifying critical view of safety structures in laparoscopic cholecystectomy.

International journal of computer assisted radiology and surgery
PURPOSE: Laparoscopic cholecystectomy is one of the most commonly performed surgeries in the UK. Despite its safety, the volume of operations leads to a notable number of complications, with surgical errors often mitigated by the critical view of saf...

DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The expression levels of microRNAs (miRNAs) are strongly linked to cancer progression, making them promising biomarkers for cancer detection. Enzyme-free signal amplification DNA circuits have facilitated the detection of low-abundance miRNAs. Howeve...