AIMC Topic: Rectal Neoplasms

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Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.

Annals of surgical oncology Dec 17, 2024
MAIN OBJECTIVES: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal cancer using endorectal ultrasound imaging.
Humans Prognosis Female Aged Radiomics Proto-Oncogene Proteins p21(ras) Male Middle Aged Deep Learning Follow-Up Studies ROC Curve Mutation Rectal Neoplasms Endosonography
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Incorporating patient-specific prior clinical knowledge to improve clinical target volume auto-segmentation generalisability for online adaptive radiotherapy of rectal cancer: A multicenter validation.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 13, 2024
BACKGROUND & PURPOSE: Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in th...
Humans Female Male Rectal Neoplasms Radiotherapy Planning, Computer-Assisted Deep Learning Magnetic Resonance Imaging
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Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study.

Academic radiology Dec 4, 2024
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
Deep Learning Lymphatic Metastasis Magnetic Resonance Imaging Rectal Neoplasms Image Interpretation, Computer-Assisted Adult Multiparametric Magnetic Resonance Imaging Humans Radiomics Prognosis Male Retrospective Studies Middle Aged Female Aged
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Artificial intelligence measured 3D lumbosacral body composition and clinical outcomes in rectal cancer patients.

ANZ journal of surgery Nov 27, 2024
INTRODUCTION: Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in ...
Body Composition Rectal Neoplasms Lumbosacral Region Middle Aged Imaging, Three-Dimensional Treatment Outcome Neoadjuvant Therapy Aged Adult Artificial Intelligence Cross-Sectional Studies Retrospective Studies Male Humans Tomography, X-Ray Computed Female
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Response prediction for neoadjuvant treatment in locally advanced rectal cancer patients-improvement in decision-making: A systematic review.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Nov 15, 2024
BACKGROUND: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of making clinical decisions and providing a more personalized treatment approach for better treatment outco...
Neoadjuvant Therapy Neoplasm Staging Rectal Neoplasms Chemoradiotherapy, Adjuvant Chemoradiotherapy Humans Clinical Decision-Making
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Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

European journal of radiology Oct 24, 2024
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).
Sensitivity and Specificity Male Middle Aged Deep Learning Retrospective Studies Female Reproducibility of Results Aged Diffusion Magnetic Resonance Imaging Neoadjuvant Therapy Rectum Rectal Neoplasms Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome.

Academic radiology Oct 22, 2024
RATIONALE AND OBJECTIVES: To investigate the predictive value of a deep learning model based on multiparametric MRI (mpMRI) for tumor deposit (TD) in rectal cancer (RC) patients and to analyze their prognosis.
Deep Learning Nomograms Predictive Value of Tests Rectal Neoplasms Aged Adult Male Middle Aged Preoperative Care Multiparametric Magnetic Resonance Imaging Humans Prognosis Retrospective Studies Female
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Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Oct 6, 2024
BACKGROUND: Precise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT) and can offer clues for the selec...
Humans Machine Learning Prognosis Neoplasm Staging Rectal Neoplasms Chemoradiotherapy Pathologic Complete Response Male Retrospective Studies Middle Aged Female ROC Curve Aged Neoadjuvant Therapy Adult
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Optimized machine learning model for predicting unplanned reoperation after rectal cancer anterior resection.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Sep 21, 2024
BACKGROUND: Unplanned reoperation (URO) after surgery adversely affects the quality of life and prognosis of patients undergoing anterior resection for rectal cancer. This study aims to meet the urgent need for reliable predictive tools by developing...
Middle Aged Retrospective Studies Humans Risk Assessment Rectal Neoplasms Female Machine Learning Postoperative Complications Aged Reoperation Algorithms Male Area Under Curve Prognosis
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Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study.

Academic radiology Sep 16, 2024
RATIONALE AND OBJECTIVES: To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status...
Middle Aged Deep Learning Microsatellite Instability Female Nomograms Humans Aged Rectal Neoplasms Magnetic Resonance Imaging Radiomics Retrospective Studies Image Interpretation, Computer-Assisted Male
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