AIMC Topic: Neoadjuvant Therapy
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Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.
JCO clinical cancer informatics
Nov 22, 2024
PURPOSE: Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete response (pCR) to NAC. We test machine learning (ML) models to predict pCR in breast cancer and explore met...
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...
Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.
Academic radiology
Nov 13, 2024
RATIONALE AND OBJECTIVES: The early prediction of response to neoadjuvant chemotherapy (NAC) will aid in the development of personalized treatments for patients with breast cancer. This study investigated the value of longitudinal multimodal deep lea...
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.
Nature communications
Nov 7, 2024
Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consider...
Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.
European journal of radiology
Oct 24, 2024
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).
TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.
Medical image analysis
Oct 16, 2024
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning ...
A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients.
Academic radiology
Oct 15, 2024
RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting axillary lymph node (ALN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients and ...
Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Oct 6, 2024
BACKGROUND: Precise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT) and can offer clues for the selec...
Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.
Medical physics
Oct 6, 2024
BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indic...
Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.
Annals of surgical oncology
Oct 5, 2024
OBJECTIVES: This study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreatment scans in estimating pathologic treatment response to neoadjuvant therapy, and to compare treatm...
Aged
Adult
Colorectal Neoplasms
Radiomics
Prognosis
Retrospective Studies
Tomography, X-Ray Computed
Female
Neoadjuvant Therapy
Follow-Up Studies
Radiologists
Response Evaluation Criteria in Solid Tumors
Male
Middle Aged
Liver Neoplasms
Antineoplastic Combined Chemotherapy Protocols
Humans
Machine Learning
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