AIMC Topic: Chemotherapy, Adjuvant
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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.
Female
Aged
Adult
Breast Neoplasms
Radiomics
Humans
Artificial Intelligence
Machine Learning
Retrospective Studies
Predictive Value of Tests
Positron Emission Tomography Computed Tomography
Radiopharmaceuticals
Fluorodeoxyglucose F18
Chemotherapy, Adjuvant
Middle Aged
Treatment Outcome
Neoadjuvant Therapy
Lymphatic Metastasis
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...
Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.
Ultrasonic imaging
Sep 10, 2024
We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enro...
Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy.
Academic radiology
Aug 24, 2024
RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast can...
Machine learning to predict completion of treatment for pancreatic cancer.
Journal of surgical oncology
Aug 19, 2024
BACKGROUND: Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would c...
Optimizing adjuvant treatment strategies for non-pancreatic periampullary cancers.
British journal of cancer
Jul 31, 2024
Non-pancreatic periampullary tumors have long been neglected, leading to blurred adjuvant treatment strategies. Recent research, like the ISGACA group's study, is uncovering nuances in chemotherapy efficacy for these diverse cancers. Tailored approac...
Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Jul 24, 2024
PURPOSE: Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with ...
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