AIMC Topic: Neoadjuvant Therapy
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Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI.
Radiology. Imaging cancer
May 1, 2025
Purpose To develop and validate a deep multitask network, MultiRecNet, for fully automatic prediction of disease-free survival (DFS) in patients with neoadjuvant chemoradiotherapy (nCRT)-treated locally advanced rectal cancer (LARC). Materials and Me...
A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma.
Pancreas
May 1, 2025
BACKGROUND AND OBJECTIVES: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin-stained whole s...
Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings.
Clinical cancer research : an official journal of the American Association for Cancer Research
Jan 17, 2025
PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predic...
[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].
Zhonghua bing li xue za zhi = Chinese journal of pathology
Jan 8, 2025
To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Specimens were collected from 209 breast cancer patients who receive...
Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.
Cancer medicine
Dec 1, 2024
BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture informatio...
Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.
The British journal of radiology
Sep 1, 2024
OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT).
Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach.
Cancer medicine
Jul 1, 2024
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).
Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis.
The British journal of radiology
Jun 18, 2024
OBJECTIVES: To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.
Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.
Cancer immunology research
May 2, 2024
Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multipl...
Artificial Intelligence Enhances NSCLC Care by Predicting Treatment Outcomes, Validating Neoadjuvant Therapies, and Improving Precision.
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
May 1, 2024
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