Chemotherapy

Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.

OBJECTIVE:  Ovarian cancer's complexity and heterogeneity pose significant challenges in treatment, ...

Enhanced hair regrowth with five monthly sessions of minoxidil-dutasteride-copper peptides tattooing for androgenetic alopecia assessed by artificial intelligence and blinded evaluators.

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model b...

Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is a severe malignant tumor with a significant threat to women's hea...

Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.

In this retrospective observational study, we aimed to investigate the potential of natural language...

From text to insight: A natural language processing-based analysis of burst and research trends in HER2-low breast cancer patients.

With the intensification of population aging, the proportion of elderly breast cancer patients is co...

Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.

The viability of cells and the integrity of the genome depend on the detection and repair of damaged...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred fo...

CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy.

RATIONALE AND OBJECTIVES: Immunotherapy combined with chemotherapy has improved outcomes for some es...

Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom ...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

PURPOSE: The aim of this work is to compare different machine learning models for predicting patholo...

An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study.

OBJECTIVES: To develop and validate an interpretable and generalized machine learning model using MR...

Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed sc...

Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 ...

Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.

PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultra...

Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency.

BACKGROUND: Adjuvant radiotherapy is essential for reducing local recurrence and improving survival ...

F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) aft...

Machine learning-based lactate-related genes signature predicts clinical outcomes and unveils novel therapeutic targets in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC), a predominant subtype of esophageal cancer, typically pre...

Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis.

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in ...

Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning.

BACKGROUND: There is a need to improve risk stratification of primary cutaneous melanomas to better ...

Quantitative assessment of the generalizability of a brain tumor Raman spectroscopy machine learning model to various tumor types including astrocytoma and oligodendroglioma.

SIGNIFICANCE: Maximal safe resection of brain tumors can be performed by neurosurgeons through the u...