AIMC Topic: Rectal Neoplasms
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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, ...
Aged, 80 and over
Adult
Male
Middle Aged
Humans
Retrospective Studies
Female
Aged
Neoplasm Staging
Rectal Neoplasms
Image Interpretation, Computer-Assisted
Signal-To-Noise Ratio
Diffusion Magnetic Resonance Imaging
Deep Learning
ROC Curve
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
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.
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...
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...
Habitat Radiomics Based on MRI for Predicting Metachronous Liver Metastasis in Locally Advanced Rectal Cancer: a Two‑center Study.
Academic radiology
Apr 9, 2025
RATIONALE AND OBJECTIVES: This study aimed to explore the feasibility of using habitat radiomics based on magnetic resonance imaging (MRI) to predict metachronous liver metastasis (MLM) in locally advanced rectal cancer (LARC) patients. A nomogram wa...
Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.
Scientific reports
Apr 9, 2025
To explore the value of applying the MRI-based radiomic nomogram for predicting lymph node metastasis (LNM) in rectal cancer (RC). This retrospective analysis used data from 430 patients with RC from two medical centers. The patients were categorized...
A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer.
PloS one
Mar 25, 2025
BACKGROUND: The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (...
AI-ready rectal cancer MR imaging: a workflow for tumor detection and segmentation.
BMC medical imaging
Mar 14, 2025
BACKGROUND: Magnetic Resonance (MR) imaging is the preferred modality for staging in rectal cancer; however, despite its exceptional soft tissue contrast, segmenting rectal tumors on MR images remains challenging due to the overlapping appearance of ...
Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning.
Scientific reports
Mar 7, 2025
No studies have examined the prognostic value of the log odds of negative lymph nodes/T stage (LONT) in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). We aimed to assess the prognostic value of LONT and devel...
A Preoperative CT-based Multiparameter Deep Learning and Radiomic Model with Extracellular Volume Parameter Images Can Predict the Tumor Budding Grade in Rectal Cancer Patients.
Academic radiology
Mar 6, 2025
RATIONALE AND OBJECTIVES: To investigate a computed tomography (CT)-based multiparameter deep learning-radiomic model (DLRM) for predicting the preoperative tumor budding (TB) grade in patients with rectal cancer.
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