AIMC Topic: Chemoradiotherapy
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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...
Humans
Neural Networks, Computer
Prospective Studies
Prognosis
Squamous Cell Carcinoma of Head and Neck
Survival Rate
Retrospective Studies
Head and Neck Neoplasms
Male
Tomography, X-Ray Computed
Follow-Up Studies
Middle Aged
Neoplasm Recurrence, Local
Image Processing, Computer-Assisted
Tumor Burden
Female
Chemoradiotherapy
Aged
Aged, 80 and over
Adult
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...
MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.
Journal of magnetic resonance imaging : JMRI
Aug 9, 2020
BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC).
Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.
Scientific reports
Jul 28, 2020
For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal prognosis. However, no reliable prediction model is presently available...
CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study.
Journal of medical imaging and radiation oncology
May 9, 2020
INTRODUCTION: Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT-based radiomic imaging biomarker to predict path...
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