Oncology/Hematology

PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation

In medical image segmentation, limited external validity remains a critical obstacle when models are deployed across unseen datasets, an issue particularly pronounced in the ultrasound image domain. Existing solutions-such as domain adaptation and GAN-based style transfer-while promising, often fall short in the medical domain where datasets are typically small and diverse. This paper presents a...

A Network-Guided Penalized Regression with Application to Proteomics Data

Network theory has proven invaluable in unraveling complex protein interactions. Previous studies have employed statistical methods rooted in network theory, including the Gaussian graphical model, to infer networks among proteins, identifying hub proteins based on key structural properties of networks such as degree centrality. However, there has been limited research examining a prognostic rol...

Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.

To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinica...

Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer.

The increased risk of recurrence and metastasis are obstacles to treating TNBC. Propionate-related genes play an important role in tumor development a...

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for i...

Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy.

Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its ability to stimulate antitumor immune responses whi...

Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.

AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion ...

Robotic radiation shielding system reduces radiation-induced DNA damage in operators performing electrophysiological procedures.

Fluoroscopically guided electrophysiology (EP) procedures expose operators to low doses of ionizing radiation, which can induce DNA double-strand brea...

Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology.

BACKGROUND: Cervical cancer remains a significant global health issue, with accurate differentiation between low-grade (LSIL) and high-grade squamous ...

MedOrchestra: A Hybrid Cloud-Local LLM Approach for Clinical Data Interpretation

Deploying large language models (LLMs) in clinical settings faces critical trade-offs: cloud LLMs, with their extensive parameters and superior perf...

Knowledge Distillation Approach for SOS Fusion Staging: Towards Fully Automated Skeletal Maturity Assessment

We introduce a novel deep learning framework for the automated staging of spheno-occipital synchondrosis (SOS) fusion, a critical diagnostic marker ...

Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamics

Hematotoxicity, drug-induced damage to the blood-forming system, is a frequent side effect of cytotoxic chemotherapy and poses a significant challen...

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG

This is a Machine Learning guided study towards zone-specific ray therapy. Combining Machine Learning (Extreme Gradient Boosting) with continuum mod...

Progress in the application of fludeoxyglucose positron emission tomography computed tomography in biliary tract cancer.

Biliary tract cancer (BTC) is a group of heterogeneous sporadic diseases, including intrahepatic, hilar, and distal cholangiocarcinoma, as well as gal...

Machine Learning Empowered a Graphical User Interface on Native Fluorescence to Predict Breast Cancer.

Breast cancer poses a significant global health challenge, requiring improved diagnostic solutions for its timely intervention and treatment. Real-tim...

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation.

BACKGROUND: Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, ...

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT...

Differentiation of canine and feline neoplasms using multi-modal imaging and machine learning.

Canine/feline (sub-)cutaneous tumors, which include lipomas, mastocytomas and soft tissue sarcomas, introduce diagnostic challenges due to inherent ti...

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.

Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential o...

PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology

The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide imag...