Rectal Neoplasms

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

Current medical imaging 38874041

INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) imag...

Adult Aged Chemoradiotherapy Deep Learning Female Humans Magnetic Resonance Imaging Male Middle Aged Neoadjuvant Therapy Neural Networks, Computer Rectal Neoplasms Retrospective Studies Treatment Outcome

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[Application value of artificial intelligence in surgical precision diagnosis and treatment of rectal cancer].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery 38901989

Colorectal cancer is the most common malignant tumor of digestive tract, and the incidence of colorectal cancer in China is especially characterized by middle and low rectal cancer. In recent years, with the progress of computer science, artificial i...

Artificial Intelligence Humans Precision Medicine Prognosis Rectal Neoplasms

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Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach.

Cancer medicine 39046433

INTRODUCTION: In this study, we aimed to evaluate the predictive value of circulating lymphocyte subsets and inflammatory indexes in response to neoadjuvant chemoradiotherapy (NCRT) in patients with rectal mucinous adenocarcinomas (MACs).

Adenocarcinoma, Mucinous Adult Aged Chemoradiotherapy Chemoradiotherapy, Adjuvant Female Humans Inflammation Lymphocyte Subsets Machine Learning Male Middle Aged Neoadjuvant Therapy Nomograms Predictive Value of Tests Rectal Neoplasms Retrospective Studies Risk Factors Treatment Outcome

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Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time.

European radiology 39017934

OBJECTIVES: To evaluate deep learning reconstruction (DLR)-based accelerated rectal magnetic resonance imaging (MRI) compared with standard MRI.

Adenocarcinoma Adult Aged Aged, 80 and over Deep Learning Female Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Neoplasm Staging Prospective Studies Rectal Neoplasms Rectum

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Risk factors and development of machine learning diagnostic models for lateral lymph node metastasis in rectal cancer: multicentre study.

BJS open 39016280

BACKGROUND: The diagnostic criteria for lateral lymph node metastasis in rectal cancer have not been established. This research aimed to investigate the risk factors for lateral lymph node metastasis and develop machine learning models combining thes...

Adult Aged Female Humans Lymph Node Excision Lymph Nodes Lymphatic Metastasis Machine Learning Magnetic Resonance Imaging Male Middle Aged Prospective Studies Rectal Neoplasms Risk Factors ROC Curve

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Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors.

EBioMedicine 39013324

BACKGROUND: It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications.

Adult Aged Biomarkers, Tumor Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Male Middle Aged Prognosis Rectal Neoplasms Transcriptome Transforming Growth Factor beta Treatment Outcome

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Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.

Journal of cancer research and clinical oncology 39001926

PURPOSE: Neoadjuvant chemoradiotherapy has been the standard practice for patients with locally advanced rectal cancer. However, the treatment response varies greatly among individuals, how to select the optimal candidates for neoadjuvant chemoradiot...

Adult Aged Chemoradiotherapy Chemoradiotherapy, Adjuvant Deep Learning Female Humans Male Middle Aged Neoadjuvant Therapy Rectal Neoplasms Retrospective Studies Treatment Outcome

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Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.

Radiation oncology (London, England) 38982452

BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.

Adult Aged Deep Learning Feasibility Studies Female Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Organs at Risk Pelvis Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Rectal Neoplasms Tomography, X-Ray Computed

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Artificial intelligence-assisted decision making: Prediction of optimal level of distal mesorectal margin during transanal total mesorectal excision (taTME) using deep neural network modeling.

Journal of visceral surgery 38964939

BACKGROUND: With steep posterior anorectal angulation, transanal total mesorectal excision (taTME) may have a risk of dissection in the wrong plane or starting higher up, resulting in leaving distal mesorectum behind. Although the distal mesorectal m...

Aged Artificial Intelligence Clinical Decision-Making Decision Making, Computer-Assisted Female Humans Magnetic Resonance Imaging Male Margins of Excision Middle Aged Neural Networks, Computer Proctectomy Rectal Neoplasms Rectum Retrospective Studies Transanal Endoscopic Surgery

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Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.

Radiation oncology (London, England) 38956690

BACKGROUND AND PURPOSE: Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This...

Adult Aged Deep Learning Female Humans Male Middle Aged Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Rectal Neoplasms Retrospective Studies Tomography, X-Ray Computed

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Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

[Application value of artificial intelligence in surgical precision diagnosis and treatment of rectal cancer].

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach.

Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time.

Risk factors and development of machine learning diagnostic models for lateral lymph node metastasis in rectal cancer: multicentre study.

Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors.

Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.

Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.

Artificial intelligence-assisted decision making: Prediction of optimal level of distal mesorectal margin during transanal total mesorectal excision (taTME) using deep neural network modeling.

Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.

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