Chemotherapy

Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer.

BACKGROUND: The study aimed at developing and validating a deep learning (DL) model based on the ultrasound imaging for predicting the platinum resistance of patients with epithelial ovarian cancer (EOC).

Effect of Cell-Cell Interaction on Single-Cell Behavior Revealed by a Deep Learning-Aided High-Throughput Addressable Single-Cell Coculture System.

Cell-cell interactions are crucial for understanding various physiological and pathological processes, yet conventional population-level methods fail to disclose the heterogeneity at a single-cell resolution. Single-cell coculture systems that isolate and cultivate single-cell pairs can help reveal heterogeneous interactions between different types of individual cells. However, precise and high-th...

Breast cancer neoadjuvant therapy outcome prediction based on clinical patient and tumor features: A cross-sectional study.

INTRODUCTION: Breast cancer is the most common malignant disease in the female population and one of the most common diseases in developed countries. ...

Construction of a novel CD8T cell-related index for predicting clinical outcomes and immune landscape in ovarian cancer by combined single-cell and RNA-sequencing analysis.

BACKGROUND: CD8T cells, also known as cytotoxic T lymphocytes, play a key role in the tumor immune microenvironment (TME) and immune response. The aim...

Hair Transplantation: State of the Art.

BACKGROUND: Hair transplantation is a minimally invasive procedure for treating hair loss in men and women, based on the principle of donor dominance,...

Unraveling the molecular mechanisms of paclitaxel in high-grade serous ovarian cancer through network pharmacology.

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of epithelial ovarian cancer, often diagnosed at advanced stages wi...

[Pulmonary vascular interventions: innovating through adaptation and advancing through differentiation].

Pulmonary vascular intervention technology, with its minimally invasive and precise advantages, has been a groundbreaking advancement in the treatment...

Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, includin...

Application of in-silico approaches in subunit vaccines: Overcoming the challenges of antigen and adjuvant development.

Subunit vaccines are crucial in preventing modern diseases due to their safety, stability, and ability to elicit targeted immune responses. However, c...

CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma

Causal machine learning (CML) enables individualized estimation of treatment effects, offering critical advantages over traditional correlation-base...

Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy.

While neoadjuvant chemotherapy combined with surgical resection has improved the prognosis for patients with osteosarcoma, its impact on metastatic an...

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide.

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemother...

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

PURPOSE: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast ...

Machine learning algorithms integrating positron emission tomography/computed tomography features to predict pathological complete response after neoadjuvant chemoimmunotherapy in lung cancer.

OBJECTIVES: Reliable methods for predicting pathological complete response (pCR) in non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant...

Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer

This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pat...

Automated segmenta-on of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023

Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magneti...

Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI.

Purpose To develop and validate a deep multitask network, MultiRecNet, for fully automatic prediction of disease-free survival (DFS) in patients with ...

Optimizing chemoradiotherapy for malignant gliomas: a validated mathematical approach

Malignant gliomas (MGs), particularly glioblastoma, are among the most aggressive brain tumors, with limited treatment options and a poor prognosis....

Optimizing Post-Cancer Treatment Prognosis: A Study of Machine Learning and Ensemble Techniques

The aim is to create a method for accurately estimating the duration of post-cancer treatment, particularly focused on chemotherapy, to optimize pat...

A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication re...