Chemotherapy

Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.

INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonl...

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.

RATIONALE: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast ...

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology feature...

Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning.

No studies have examined the prognostic value of the log odds of negative lymph nodes/T stage (LONT)...

Continuous nursing symptom management in cancer chemotherapy patients using deep learning.

To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, a...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining th...

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...

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...

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...