AIMC Topic: Chemoradiotherapy
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Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jun 15, 2021
INTRODUCTION: To develop an image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance (MR) imaging.
An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.
Oral oncology
May 21, 2021
OBJECTIVES: We aimed to build a survival system by combining a highly-accurate machine learning (ML) model with explainable artificial intelligence (AI) techniques to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma (NPC...
Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Clinical radiology
May 1, 2021
AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.
Aged
Young Adult
Adult
Sensitivity and Specificity
Brain Neoplasms
Male
Middle Aged
Adolescent
Brain
Retrospective Studies
Female
Reproducibility of Results
Humans
Machine Learning
Chemoradiotherapy
Treatment Outcome
Contrast Media
Magnetic Resonance Imaging
Diagnosis, Differential
Predictive Value of Tests
Glioblastoma
Image Interpretation, Computer-Assisted
Image Enhancement
High-Precision Assessment of Chemoradiotherapy of Rectal Cancer with Near-Infrared Photoacoustic Microscopy and Deep Learning.
Radiology
Mar 23, 2021
Salvage Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) for T4b Esophageal Cancer After Definitive Chemoradiotherapy.
Annals of surgical oncology
Dec 19, 2020
BACKGROUND: Patients with esophageal cancer that invades adjacent structures (cT4b) are precluded from surgery and usually treated with definitive chemoradiotherapy (dCRT). dCRT might result in sufficient downstaging to enable a radical resection, ...
Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.
Clinical radiology
Oct 6, 2020
AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally a...
2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma.
Scientific reports
Sep 24, 2020
For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tu...
Chemoradiotherapy
Head and Neck Neoplasms
Follow-Up Studies
Neoplasm Recurrence, Local
Tumor Burden
Aged
Aged, 80 and over
Adult
Survival Rate
Male
Middle Aged
Image Processing, Computer-Assisted
Squamous Cell Carcinoma of Head and Neck
Prognosis
Retrospective Studies
Tomography, X-Ray Computed
Female
Humans
Neural Networks, Computer
Prospective Studies
Editorial for "MRI-Based Deep Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma".
Journal of magnetic resonance imaging : JMRI
Sep 17, 2020
Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sep 15, 2020
BACKGROUND: Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradi...
System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Sep 4, 2020
PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited...
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