Breast Cancer

Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams

In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care teams. To address this, we introduce RadOnc-GPT, a specialized Large Language...

Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool

Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX (ODX) offer valuable genomic insights for HR+/HER2- patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-BCR-Auto, a deep learning-based computational pathology approach that ...

[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 and soft tissue account for 0.2% of all human malig...

Active learning for energy-based antibody optimization and enhanced screening

Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-...

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 baseline characteristics on treatment efficacy for e...

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 receptor 2 (HER2)-zero and HER2-low using MRI features p...

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

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 therapy (RT) and highlights the necessity for new modeli...

Interpretable breast cancer classification using CNNs on mammographic images

Deep learning models have achieved promising results in breast cancer classification, yet their 'black-box' nature raises interpretability concerns....

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 (DCE-MRI) radiomics and to explore their potentia...

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 patients with clear cell renal cell carcinoma (ccRCC...

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastri...

CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.

OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metasta...

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, e...

EDRAM-Net: Encoder-Decoder with Residual Attention Module Network for Low-dose Computed Tomography Reconstruction.

The medical application of Computed Tomography (CT) is to provide detailed anatomical structures of patients without the need for invasive procedures ...

Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model f...

Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma

Immunohistochemistry (IHC) analysis is a well-accepted and widely used method for molecular subtyping, a procedure for prognosis and targeted therap...

Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study.

To explore the value of machine learning (ML) models based on contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) radiomics features for...

Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers.

Objectives This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms...

Validating linalool as a potential drug for breast cancer treatment based on machine learning and molecular docking.

Breast cancer (BC) is a common cancer for women. This study aims to construct a prognostic risk model of BC and identify prognostic biomarkers through...