AIMC Topic: Antineoplastic Combined Chemotherapy Protocols
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Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.
Breast cancer research and treatment
Jul 1, 2025
PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application...
Predicting the risk of ibrutinib in combination with R-ICE in patients with relapsed or refractory DLBCL using explainable machine learning algorithms.
Clinical and experimental medicine
May 26, 2025
Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) poses significant therapeutic challenges due to heterogeneous patient outcomes. This study aimed to evaluate the efficacy of the ibrutinib plus R-ICE regimen and to leverage explainable mac...
Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics.
BMC cancer
May 19, 2025
OBJECTIVES: This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combin...
Humans
Machine Learning
Prognosis
Retrospective Studies
Male
Middle Aged
Liver Neoplasms
Carcinoma, Hepatocellular
Immune Checkpoint Inhibitors
Female
Aged
Adult
Radiomics
Progression-Free Survival
Nomograms
Angiogenesis Inhibitors
Antineoplastic Combined Chemotherapy Protocols
Magnetic Resonance Imaging
Artificial intelligence generated 3D body composition predicts dose modifications in patients undergoing neoadjuvant chemotherapy for rectal cancer.
Journal of cancer research and clinical oncology
May 16, 2025
PURPOSE: Chemotherapy administration is a balancing act between giving enough to achieve the desired tumour response while limiting adverse effects. Chemotherapy dosing is based on body surface area (BSA). Emerging evidence suggests body composition ...
Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia.
Clinical cancer research : an official journal of the American Association for Cancer Research
May 15, 2025
PURPOSE: The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a suboptimal response. To optimally select the best therapy, predictive biomarkers are necessary. In t...
Aged
Aged, 80 and over
Biomarkers, Tumor
Leukemia, Lymphocytic, Chronic, B-Cell
Male
Humans
Female
Phosphatidylinositol 3-Kinases
Protein Kinase Inhibitors
Middle Aged
Agammaglobulinaemia Tyrosine Kinase
Antineoplastic Combined Chemotherapy Protocols
Benzamides
Treatment Outcome
Phosphoinositide-3 Kinase Inhibitors
Pyrazines
Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy.
Investigative and clinical urology
Jan 1, 2025
PURPOSE: To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-therapy contrast-enhanced computed tomography (CECT).
Humans
Artificial Intelligence
Precision Medicine
Retrospective Studies
Tomography, X-Ray Computed
Female
Male
Infant
Dactinomycin
Adolescent
Kidney Neoplasms
Child, Preschool
Nephrectomy
Child
Preoperative Care
Antineoplastic Combined Chemotherapy Protocols
Vincristine
Treatment Outcome
Wilms Tumor
Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.
The clinical respiratory journal
Dec 1, 2024
BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict...
DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.
Briefings in bioinformatics
Nov 22, 2024
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex i...
Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.
Cancer research communications
May 23, 2024
UNLABELLED: Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (M...
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