AIMC Topic: Tomography, X-Ray Computed

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Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer.

JAMA network open May 1, 2025
IMPORTANCE: Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging characteristics of bot...
Female Aged Male Middle Aged Deep Learning Lymph Nodes Neck Lymphatic Metastasis Chemoradiotherapy Oropharyngeal Neoplasms Humans Prognosis Retrospective Studies Tomography, X-Ray Computed
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A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

International journal of surgery (London, England) May 1, 2025
BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting respo...
Immune Checkpoint Inhibitors Treatment Outcome Humans Male Tomography, X-Ray Computed Middle Aged Prognosis Female Deep Learning Aged Liver Neoplasms Radiomics Carcinoma, Hepatocellular Biomarkers, Tumor
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Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.

Journal of thoracic imaging May 1, 2025
PURPOSE: Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.
Lung Female Humans Radiographic Image Interpretation, Computer-Assisted Aged Lung Diseases, Interstitial Machine Learning Adult Photons Retrospective Studies Male Tomography, X-Ray Computed Middle Aged
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Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Journal of thoracic imaging May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Tumor Suppressor Protein p53 ErbB Receptors Adenocarcinoma of Lung Middle Aged Lung Humans Adult Deep Learning Retrospective Studies Sensitivity and Specificity Lung Neoplasms Tomography, X-Ray Computed Radiomics Mutation Female Male Reproducibility of Results Aged
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Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

Journal of thoracic imaging May 1, 2025
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).
Middle Aged Lung Deep Learning Lung Neoplasms Multiple Pulmonary Nodules Adult Sensitivity and Specificity Cross-Sectional Studies Male Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiography, Thoracic Humans Algorithms Prospective Studies Tomography, X-Ray Computed Female Aged Aged, 80 and over
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Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.

European journal of radiology Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Female Reproducibility of Results Aged Aged, 80 and over Adult Humans Machine Learning Tomography, X-Ray Computed Neoplasm Grading Radiographic Image Interpretation, Computer-Assisted Kidney Neoplasms Sensitivity and Specificity Carcinoma, Renal Cell Radiomics Boosting Machine Learning Algorithms Male Middle Aged Contrast Media
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Development and validation of a novel chronic pancreatitis pathological grade based on artificial intelligence.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] Apr 29, 2025
BACKGROUND: Effective chronic pancreatitis (CP) treatment requires accurate severity evaluation, but no histopathology grading system exists. This study aimed to develop and validate a novel CP pathological grade (Histopathology-derived CPpG) using q...
Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Female Aged Humans Deep Learning Adult Pancreas Male Pancreatitis, Chronic Middle Aged Severity of Illness Index
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Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

Journal of translational medicine Apr 29, 2025
BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma. However, not all patients with EGFR mutations respond to TKIs. This study...
Mutation Molecular Targeted Therapy ErbB Receptors Protein Kinase Inhibitors Humans Adenocarcinoma of Lung Aged Retrospective Studies Male Tomography, X-Ray Computed Middle Aged Female Deep Learning Lung Neoplasms
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Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.

Cancer imaging : the official publication of the International Cancer Imaging Society Apr 29, 2025
BACKGROUND: The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with metastasis in No. 253 lymph node (No. 253 LN) being a key determining factor. This study aimed to develo...
Male Middle Aged Lymph Nodes Lymphatic Metastasis Aged Humans Adult Colorectal Neoplasms Machine Learning Radiomics Retrospective Studies Tomography, X-Ray Computed Female
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Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.

BMC gastroenterology Apr 29, 2025
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...
Humans Esophageal Squamous Cell Carcinoma Radiomics Nomograms Machine Learning Male Treatment Outcome Retrospective Studies Middle Aged Esophageal Neoplasms ROC Curve Tomography, X-Ray Computed Chemoradiotherapy Logistic Models Female Aged
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