AIMC Topic: Rectal Neoplasms
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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.
Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer.
Academic radiology
Feb 26, 2025
RATIONALE AND OBJECTIVES: This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI...
Artificial intelligence-based model to predict recurrence after local excision in T1 rectal cancer.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Feb 20, 2025
BACKGROUND: According to current guideline, patients with resected specimens showing high-risk features are recommended additional surgery after local excision (LE) of T1 colorectal cancer, despite the low incidence of recurrence. However, surgical r...
T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.
Journal of applied clinical medical physics
Feb 20, 2025
PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).
Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.
Scientific reports
Feb 10, 2025
Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble learning models in predicting tumor deposits in rectal cancer (RC) and identify the optimal model for pr...
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