AIMC Topic: Rectal Neoplasms

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MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.

International journal of colorectal disease Aug 8, 2025
PUROPOSE: Preoperative T-staging in rectal cancer is essential for treatment planning, yet conventional MRI shows limited accuracy (~ 60-78). Our study investigates whether radiomic analysis of high-resolution T2-weighted MRI can non-invasively impro...
Humans Retrospective Studies Female Aged Magnetic Resonance Imaging Neoplasm Staging Rectal Neoplasms Preoperative Period Preoperative Care Radiomics Male Middle Aged ROC Curve
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Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.

BMC medical imaging Jul 31, 2025
This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer Vision models to accurately predict neoadjuvant rectal (NAR) score for locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation (N...
Rectal Neoplasms Chemoradiotherapy Large Language Models Middle Aged Deep Learning Neoadjuvant Therapy Magnetic Resonance Imaging Language Female Aged Male Humans Tomography, X-Ray Computed
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Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.

Radiation oncology (London, England) Jul 30, 2025
BACKGROUND: This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MRI)-based delta-radiomics model to accurately predict severe acute radiation enteritis risk in patients undergoing total neoadjuvant therapy (TNT) for ...
Middle Aged Neoadjuvant Therapy Support Vector Machine Magnetic Resonance Imaging Radiation Injuries Enteritis Rectal Neoplasms Female Aged Adult Humans Male Prognosis Retrospective Studies
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Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.

BMC medical imaging Jul 1, 2025
BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, ...
Middle Aged Deep Learning ROC Curve Image Processing, Computer-Assisted Rectal Neoplasms Diffusion Magnetic Resonance Imaging Aged Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Aged, 80 and over Magnetic Resonance Imaging Humans Adult Neoplasm Staging Retrospective Studies Male Female
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Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.

BMC medical informatics and decision making Jun 5, 2025
BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.
Magnetic Resonance Imaging Neoplasm Recurrence, Local Adult Rectal Neoplasms Male Humans Retrospective Studies Disease-Free Survival Middle Aged Prognosis Deep Learning Female Aged
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Prediction of Tumor Budding Grading in Rectal Cancer Using a Multiparametric MRI Radiomics Combined with a 3D Vision Transformer Deep Learning Approach.

Academic radiology Apr 16, 2025
RATIONALE AND OBJECTIVES: The objective is to assess the effectiveness of a multiparametric MRI radiomics strategy combined with a 3D Vision Transformer (ViT) deep learning (DL) model in predicting tumor budding (TB) grading in individuals diagnosed ...
Adult Radiomics Male Middle Aged Deep Learning Humans Retrospective Studies Female Aged Rectal Neoplasms Image Interpretation, Computer-Assisted Multiparametric Magnetic Resonance Imaging Imaging, Three-Dimensional Adenocarcinoma Magnetic Resonance Imaging Neoplasm Grading
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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...
Deep Learning Treatment Outcome Neoadjuvant Therapy Supervised Machine Learning Chemoradiotherapy Humans Rectal Neoplasms Tomography, X-Ray Computed Female Male
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Biophysical modeling and artificial intelligence for quantitative assessment of anastomotic blood supply in laparoscopic low anterior rectal resection.

Surgical endoscopy Apr 14, 2025
PURPOSE: Fluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains limited by subjective interpretation and lack of quantitative standards. This study introduces an in...
Rectum Rectal Neoplasms Anastomosis, Surgical Optical Imaging Humans Aged Artificial Intelligence Male Machine Learning Middle Aged Female Laparoscopy
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MRI radiomics combined with delta-radiomics model for predicting pathological complete response in locally advanced rectal cancer patients after neoadjuvant chemoradiotherapy: A multi-institutional study.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine Apr 12, 2025
PURPOSE: To construct and validate a magnetic resonance imaging (MRI) radiomics combined with delta-radiomics and clinical information (C) model for predicting pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC...
Magnetic Resonance Imaging Rectal Neoplasms Chemoradiotherapy Diffusion Magnetic Resonance Imaging Male Middle Aged Treatment Outcome Neoadjuvant Therapy Humans Aged Adult Radiomics Retrospective Studies Female
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A High-resolution T2WI-based Deep Learning Model for Preoperative Discrimination Between T2 and T3 Rectal Cancer: A Multicenter Study.

Academic radiology Apr 11, 2025
RATIONALE AND OBJECTIVES: To construct a deep learning model (DL) based on high-resolution T2-weighted images for preoperative differentiation between T2 and T3 stage rectal cancer (RC), and to compare its performance with experienced radiologists.
Retrospective Studies Magnetic Resonance Imaging Neoplasm Staging Humans Female Rectal Neoplasms Aged Male Image Interpretation, Computer-Assisted Adult Aged, 80 and over Middle Aged Sensitivity and Specificity Deep Learning
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