AIMC Topic: Rectal Neoplasms
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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
Jul 17, 2024
OBJECTIVES: To evaluate deep learning reconstruction (DLR)-based accelerated rectal magnetic resonance imaging (MRI) compared with standard MRI.
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
Jul 16, 2024
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.
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
Jul 13, 2024
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...
Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.
Radiation oncology (London, England)
Jul 9, 2024
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.
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
Jul 3, 2024
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...
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)
Jul 2, 2024
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...
Can artificial intelligence help a digestive surgeon in management of rectal cancer?
Journal of visceral surgery
Jun 12, 2024
Artificial Intelligence Imaging Diagnosis Using Super-Resolution and Three-Dimensional Shape for Lymph Node Metastasis of Low Rectal Cancer: A Pilot Study From a Single Center.
Diseases of the colon and rectum
Jun 11, 2024
BACKGROUND: Although accurate preoperative diagnosis of lymph node metastasis is essential for optimizing treatment strategies for low rectal cancer, the accuracy of present diagnostic modalities has room for improvement.
Rectal Neoplasms
Multidetector Computed Tomography
Proctectomy
Aged
Adult
Sensitivity and Specificity
Male
Humans
Artificial Intelligence
Retrospective Studies
Female
Adenocarcinoma
Lymphatic Metastasis
Lymph Node Excision
Predictive Value of Tests
Pelvis
Middle Aged
Pilot Projects
Imaging, Three-Dimensional
Lymph Nodes
Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.
European journal of nuclear medicine and molecular imaging
Jun 11, 2024
Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, secondary curative therapy. However, with more patients being diagnosed with early stage and premali...
Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.
International journal of colorectal disease
May 24, 2024
PURPOSE: This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning-based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images.
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