Breast Cancer

Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learning.

Breast fibrosis (BF) after radiotherapy remains one of the most dreaded late toxicities in breast cancer care, yet multiple additive predictors struggle to capture its underlying biological complexity. Radiation-induced lymphocyte apoptosis (RILA) has recently been associated with the risk of fibrosis more than 10 years post-RT. Here, we show that a combination of five independent factors, RILA, t...

Unimodal vs. multimodal deep learning for non-invasive MGMT promoter methylation prediction in glioblastoma: A systematic evaluation on the BraTS 2021 dataset.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with a median survival of 14.6 months under standard radiotherapy and temozolomide (TMZ) chemotherapy. The methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker predicting TMZ response; however, its determination currently requires invasive tissue sampling. Non-inv...

Predicting device-related complications among women undergoing breast implant surgeries.

BACKGROUND: Breast implant surgery is a high-volume procedure, yet predicting device-related complications that require revision surgery remains chall...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personali...

Machine Learning-Driven Prognostic Modeling in Head and Neck Adenoid Cystic Carcinoma: A Retrospective Cohort Study.

INTRODUCTION: Adenoid cystic carcinoma (ACC) of the head and neck is a rare malignancy characterized by perineural invasion, local recurrence, and dis...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

Prediction of early death in peripheral T-cell lymphoma-NOS patients based on machine learning.

Peripheral T-cell lymphoma-not otherwise specified (PTCL-NOS) is a highly aggressive and heterogeneous lymphoma subtype with a poor prognosis. This st...

Machine learning defines a histone deacetylase-associated transcriptional prognostic signature with single-cell resolution in breast cancer.

BACKGROUND: Breast cancer (BC) is a highly heterogeneous malignancy, and transcriptional programs associated with histone deacetylases (HDACs) may pro...

SiNuS: A comprehensive dataset for singular nuclei segmentation for HER2 grading of breast cancer.

Singular nuclei are those that are intact and do not overlap with neighboring nuclei. According to the American Society of Clinical Oncology and the C...

Challenges and opportunities of implementing open-source digital pathology in sub-Saharan Africa: Lessons learned from deploying QuPath for breast cancer biomarker assessment in Cameroon.

BACKGROUND: Digital pathology and artificial intelligence (AI) are transforming cancer diagnostics worldwide, yet their implementation in sub-Saharan ...

Simulation-Enhanced Learning in Nuclear Medicine: Theory, Modalities, and Applications Across the Training Continuum.

Simulation-based education has evolved into a foundational component of nuclear medicine technologist training, driven by increasing procedural comple...

Performance enhancement of THz antenna with dielectric resonator and prediction bandwidth using machine learning approach for 6G applications.

The need for ultra-low latency and ultra-wideband in 6G applications requires efficient solutions for dielectric resonator antenna design. This paper ...

Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality.

To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is n...

Machine learning-driven conditional survival prediction model for atypical teratoid/rhabdoid tumor.

PURPOSE: Atypical teratoid/rhabdoid tumor (AT/RT) is a rare and highly malignant pediatric brain tumor with dismal prognosis. We aimed to characterize...

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

Positron Emission Tomography (PET) is a critical modality in medical imaging for detecting abnormalities and diagnosing diseases. However, the radiati...

Breast Cancer Diagnosis and HER2+ Versus Triple Negative Discrimination by Infrared Spectral Histopathology.

Breast cancer is a heterogeneous disease comprising distinct molecular subtypes that require accurate diagnosis for effective treatment. Conventional ...

An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.

BACKGROUND: Oxaliplatin (OXA) is widely used in the treatment of gastrointestinal malignancies such as colorectal cancer. However, oxaliplatin-induced...

Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

OBJECTIVE: To critically evaluate machine learning (ML) models developed for predicting radiation-induced oral mucositis (OM) in head and neck cancer ...

Combining artificial intelligence analysis with expert mammogram reading: Determining the optimal AI positivity cut-off point for the French population-based breast cancer screening program.

PURPOSE: Artificial intelligence (AI) is increasingly explored as a complement to radiologists in population-based breast cancer screening, yet optima...

LAM-CATNet: lambda-aware multi-scale cross-attention swin transformer network for mammogram classification.

The fundamental requirement for the initial recognition of a mammogram is the accurate segmentation and classification of breast lesions in mammograms...