Chemotherapy

Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics.

OBJECTIVE: This study aims to predict the early efficacy of induction chemotherapy (ICT) in patients...

Microbiota as diagnostic biomarkers: advancing early cancer detection and personalized therapeutic approaches through microbiome profiling.

The important function of microbiota as therapeutic modulators and diagnostic biomarkers in cancer h...

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

We consider the problem of renal mass risk classification to support doctors in adjuvant treatment d...

Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy.

PURPOSE: To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsivene...

Single and Multi-Objective Optimization of the Red Pine Mushroom Lactarius deliciosus (Agaricomycetes) Extraction Conditions Using Artificial Intelligence Methods and Biological Activities of Optimized Extracts.

In this study, the biological activities of Lactarius deliciosus were determined. Experimental studi...

Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER) database.

The chemotherapy benefit for high-grade chondrosarcoma remains controversial. Ensemble learning has...

Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers.

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for man...

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvan...

Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains th...

NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patien...

[Applications of artificial intelligence for imaging-driven diagnosis and treatment of bone and soft tissue tumors].

Bone and soft tissue tumors occur in the musculoskeletal system, and malignant bone tumors of bone a...

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of ba...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor recept...

Tailored chemotherapy: Innovative deep-learning model customizing chemotherapy for high-grade serous ovarian carcinoma.

Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning.

This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to ...

Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer Metastasis Using Real-World Clinical Data with AUC up to 0.9

Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,00...

Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.

PURPOSE: Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with n...

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of pa...

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach.

INTRODUCTION: In this study, we aimed to evaluate the predictive value of circulating lymphocyte sub...

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the performance of machine learning models in predicting pathological comple...

Avelumab first-line maintenance in advanced urothelial carcinoma: Complete screening for prognostic and predictive factors using machine learning in the JAVELIN Bladder 100 phase 3 trial.

BACKGROUND: Avelumab first-line (1 L) maintenance is a standard of care for advanced urothelial carc...