AIMC Topic: Antineoplastic Combined Chemotherapy Protocols
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Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.
Journal for immunotherapy of cancer
Sep 25, 2024
Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment.
Computers in biology and medicine
Aug 23, 2024
By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on nor...
MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.
Biomolecules
Aug 22, 2024
Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships bet...
Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.
Anti-cancer drugs
Aug 9, 2024
Tumorigenesis and treatment are closely associated with various programmed cell death (PCD) patterns. However, the coregulatory role of multiple PCD patterns in colorectal cancer (CRC) remains unknown. In this study, we developed a multiple PCD index...
Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.
Journal for immunotherapy of cancer
Jul 18, 2024
BACKGROUND: Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be fur...
Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma.
Annals of surgical oncology
Jun 24, 2024
PURPOSE: This study was designed to investigate the prognostic significance of artificial intelligence (AI)-based quantification of myxoid stroma in patients undergoing esophageal squamous cell carcinoma (ESCC) surgery after neoadjuvant chemotherapy ...
Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.
Frontiers in immunology
May 22, 2024
BACKGROUND: Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progress...
Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.
Journal of surgical oncology
May 7, 2024
BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on pre...
Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.
Clinical lung cancer
Apr 12, 2024
BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete re...
SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.
Computational biology and chemistry
Mar 19, 2024
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predi...
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