AIMC Topic: Chemotherapy, Adjuvant
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Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.
Esophagus : official journal of the Japan Esophageal Society
Apr 28, 2025
BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We previously developed a deep neural network-based endoscopic evaluation to determine pCR preoperativ...
Female
Aged
Sensitivity and Specificity
Male
Middle Aged
Humans
Neural Networks, Computer
Retrospective Studies
Japan
Esophageal Squamous Cell Carcinoma
Chemotherapy, Adjuvant
Esophagoscopy
Pathologic Complete Response
Deep Learning
Treatment Outcome
Neoadjuvant Therapy
Predictive Value of Tests
Esophageal Neoplasms
Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.
International journal of clinical oncology
Mar 17, 2025
BACKGROUND AND PURPOSE: Accurate recurrence risk evaluation in patients with stage II and III colorectal cancer (CRC) remains difficult. Traditional histopathological methods frequently fall short in predicting outcomes after adjuvant chemotherapy. T...
Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.
Tomography (Ann Arbor, Mich.)
Mar 13, 2025
RATIONALE: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure t...
Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.
Nature communications
Mar 8, 2025
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The C...
Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.
Breast cancer research : BCR
Feb 27, 2025
OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical features to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breas...
F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.
La Radiologia medica
Jan 28, 2025
PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Fluorodeoxyglucose F18
Chemotherapy, Adjuvant
Machine Learning
Retrospective Studies
Lymphatic Metastasis
Humans
Female
Predictive Value of Tests
Breast Neoplasms
Artificial Intelligence
Aged
Positron Emission Tomography Computed Tomography
Radiomics
Adult
Radiopharmaceuticals
Middle Aged
Treatment Outcome
Neoadjuvant Therapy
Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Dec 21, 2024
PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This ...
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...
The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma.
Frontiers in immunology
Nov 12, 2024
BACKGROUND: Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced...
Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Sep 14, 2024
BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machi...
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