AIMC Topic: Tomography, X-Ray Computed
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Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach.
Medical & biological engineering & computing
Apr 15, 2025
The aim of this study was to develop a deep learning method for analyzing CT images with varying doses and qualities, aiming to categorize lung lesions into nodules and non-nodules. This study utilized the lung nodule analysis 2016 challenge dataset....
Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Apr 15, 2025
BACKGROUND: We developed and evaluated a skeletal muscle deep-learning (SMDL) model using skeletal muscle computed tomography (CT) imaging to predict the survival of patients with gastric cancer (GC).
Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.
European journal of medical research
Apr 15, 2025
BACKGROUND: The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the i...
Self-supervised network predicting neoadjuvant chemoradiotherapy response to locally advanced rectal cancer patients.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 14, 2025
Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data and the lack of labeled data make it challenging to accurately assess the response of locally advanced...
Explainable AI for lung cancer detection via a custom CNN on CT images.
Scientific reports
Apr 13, 2025
Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer frequently have a bad prognosis because of late-stage detection, which severely limits treatment opt...
Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.
Japanese journal of radiology
Apr 11, 2025
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...
Esophageal Squamous Cell Carcinoma
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Sensitivity and Specificity
Esophageal Neoplasms
Male
Middle Aged
Female
Lymphatic Metastasis
Aged, 80 and over
Humans
Aged
Deep Learning
Contrast Media
Adult
Lymph Nodes
Retrospective Studies
Tomography, X-Ray Computed
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
Apr 11, 2025
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...
Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.
Journal of applied clinical medical physics
Apr 11, 2025
PURPOSE: To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).
Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.
Scientific reports
Apr 11, 2025
Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims t...
Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification.
Scientific reports
Apr 11, 2025
To address the public health issue of renal failure and the global shortage of nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), and artificial...
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