Breast Cancer

Personalized prediction of breast cancer candidates for Anti-HER2 therapy using F-FDG PET/CT parameters and machine learning: a dual-center study.

BACKGROUND: Accurately evaluating human epidermal growth factor receptor (HER2) expression status in...

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...

Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models.

BACKGROUND: Peritumoral characteristics demonstrate significant predictive value for neoadjuvant che...

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...

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...

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...

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...

Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.

Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and var...

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mo...

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We...

A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

OBJECTIVE: This study aimed to develop an effective model that identifies high-risk breast cancer (B...

Evaluation of machine learning algorithms and computational structural validation of CYP2D6 in predicting the therapeutic response to tamoxifen in breast cancer.

OBJECTIVE: CYP2D6 plays a critical role in metabolizing tamoxifen into its active metabolite, endoxi...

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...

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...

State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.

The implementation of deep neural networks has spurred the creation of deep learning reconstruction ...

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognosti...

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...

Utilizing patient data: A tutorial on predicting second cancer with machine learning models.

BACKGROUND: The article explores the potential risk of secondary cancer (SC) due to radiation therap...

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

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...

Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach.

To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging...