AIMC Topic: Chemoradiotherapy, Adjuvant

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Integration of Deep Learning and Sub-regional Radiomics Improves the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.

Academic radiology Jan 13, 2025
RATIONALE AND OBJECTIVES: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop ...
Middle Aged Deep Learning Treatment Outcome Neoadjuvant Therapy Magnetic Resonance Imaging Female Aged Adult Radiomics Male Humans Retrospective Studies Rectal Neoplasms Chemoradiotherapy, Adjuvant Chemoradiotherapy
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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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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...
Male Middle Aged Deep Learning Treatment Outcome Neoadjuvant Therapy Rectal Neoplasms Chemoradiotherapy, Adjuvant Chemoradiotherapy Humans Retrospective Studies Female Aged Adult
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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.
Humans Male Middle Aged ROC Curve Treatment Outcome Neoadjuvant Therapy Machine Learning Female Aged Adult Radiomics Magnetic Resonance Imaging Neoplasm Grading Rectal Neoplasms Chemoradiotherapy, Adjuvant Chemoradiotherapy
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Predicting treatment response from longitudinal images using multi-task deep learning.

Nature communications Mar 25, 2021
Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simul...
Magnetic Resonance Imaging Rectum Rectal Neoplasms Chemoradiotherapy, Adjuvant Middle Aged Deep Learning Image Processing, Computer-Assisted Treatment Outcome Neoadjuvant Therapy Humans Female Biomarkers, Tumor Male
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Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.

BMC medical imaging Feb 16, 2021
BACKGROUND: To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of rectal cancer.
Models, Statistical Rectum Radiographic Image Interpretation, Computer-Assisted Rectal Neoplasms Image Interpretation, Computer-Assisted Chemoradiotherapy, Adjuvant Male Middle Aged ROC Curve Neoadjuvant Therapy Magnetic Resonance Imaging Humans Machine Learning Algorithms Female
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Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.

JAMA network open Nov 2, 2020
IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.
Humans Neural Networks, Computer Machine Learning Retrospective Studies Mouth Neoplasms Patient Selection Tumor Burden Logistic Models Chemoradiotherapy, Adjuvant Proportional Hazards Models Lymph Nodes Radiotherapy, Adjuvant Female Squamous Cell Carcinoma of Head and Neck Laryngeal Neoplasms Aged Neoplasm Staging Otorhinolaryngologic Surgical Procedures Male Neoplasm Grading Oropharyngeal Neoplasms Deep Learning Hypopharyngeal Neoplasms Cohort Studies
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Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer.

JAMA network open Aug 3, 2020
IMPORTANCE: A tool for precisely stratifying postoperative patients with advanced oral cancer is crucial for the treatment plan, such as intensifying or deintensifying the regimen to improve their quality of life and prognosis.
Middle Aged Risk Assessment Mouth Neoplasms Disease-Free Survival Chemoradiotherapy, Adjuvant Kaplan-Meier Estimate Prognosis Retrospective Studies Female Adult Male Humans Machine Learning
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

Cancer reports (Hoboken, N.J.) Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
DNA Repair Enzymes Cancer Vaccines Genetic Heterogeneity Drug Resistance, Neoplasm Humans Models, Genetic Prognosis Chemoradiotherapy, Adjuvant Microtubules Machine Learning Antineoplastic Combined Chemotherapy Protocols Tumor Microenvironment Tumor Suppressor Protein p53 Brain Glioblastoma Clonal Evolution Brain Neoplasms Tumor Suppressor Proteins Mutation DNA Modification Methylases Immune Checkpoint Inhibitors Precision Medicine DNA Mutational Analysis Computational Biology
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MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

European journal of radiology Jun 22, 2019
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with ...
Humans Rectum Artificial Intelligence Rectal Neoplasms Prospective Studies Chemoradiotherapy, Adjuvant Retrospective Studies Female Cohort Studies Reproducibility of Results Aged ROC Curve Aged, 80 and over Treatment Outcome Male Neoadjuvant Therapy Middle Aged Magnetic Resonance Imaging
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