AIMC Topic: Tomography, X-Ray Computed
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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...
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...
Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.
Biomedical physics & engineering express
Apr 11, 2025
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...
Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...
Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).
Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.
BMC medical imaging
Apr 10, 2025
OBJECTIVE: Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
Signal-To-Noise Ratio
Female
Computed Tomography Angiography
Aged
Radiography, Dual-Energy Scanned Projection
Aged, 80 and over
Adult
Male
Radiographic Image Interpretation, Computer-Assisted
Humans
Middle Aged
Quality Improvement
Algorithms
Deep Learning
Artifacts
Prospective Studies
Head and Neck Neoplasms
Head
Tomography, X-Ray Computed
Neck
Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.
BMC cancer
Apr 10, 2025
OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment ...
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