Oncology/Hematology

Predicting the stage of gastric cancer after gastrectomy based on machine learning algorithms

Gastric cancer (GC) is the fourth most common cause of cancer death worldwide, with a 5-year survival rate of less than 40%. One of the most important methods for diagnosing stomach cancer is endoscopy, which is quite costly and invasive. The aim of this study was to develop machine learning-based diagnostic prediction models for the stage of GC. To create a highly accurate predictive model for th...

Circulating extracellular vesicles in serum carry Trop2 marker for prostate cancer liquid biopsy and clinical care

Extracellular vesicles (EVs) are lipid nano-to-micro-sized vesicles increasingly identified as valuable liquid biopsy tools for medical applications. However, the heterogeneity of cargo and the lack of convenient quantification methods to characterize EVs pose challenges in identifying vesicles with specific markers. In this study, we show the isolation, characterization, detection, and quantifica...

Discovery of Dynamic Models for AML Disease Progression from Longitudinal Multi-Modal Clinical Data Using Explainable Machine Learning

Acute Myeloid Leukemia (AML) is a complex and heterogeneous disease identified by severe clinical progression, fast cellular proliferation, and often ...

Deep learning clarifies association of osteoporosis risk with bone metastasis in premenopausal women after surgery for early-stage breast cancer: a multicenter retrospective cohort study

Adjuvant use of bone-modifying agents (BMAs) to early-stage breast cancer (eBC) aims to maintain bone density, leading to prevention of bone metastasi...

A multivariate cell-based assay for blood-based diagnostics enhances lung cancer risk stratification

The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that addresses the low signal-to-noise ratio of blood biomarkers by usi...

Optimized BERT-based NLP outperforms Zero-Shot Methods for Automated Symptom Detection in Clinical Practice

Large Language Nodels (LLMs) have raised broad expectations for clinical use, particularly in the processing of complex medical narratives. However, i...

AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH, and AIH

Annotation of liver biopsies, for disease staging is increasingly aided by digital pathology, however existing systems do not quantify inflammation an...

iMDPath: Interpretable Multi-task Digital Pathology Model for Clinical Pathological Image Prediction and Interpretation

Deep learning (DL)-based pathological image modelling and analysis approaches offer transformative potential for early cancer diagnostics, yet limited...

Artificial Intelligence-based Diagnosis of Kaposi Sarcoma using Photographs in Dark-skinned Patients

Advanced-stage disease at the time of diagnosis, with resultant high mortality, is among the most urgent issues for HIV-related Kaposi sarcoma (KS) in...

A Novel Swarm Intelligence-Driven Feature Selection for Interpretable Machine Learning in GBM Overall Survival Analysis

In this study, we develop and validate an interpretable machine learning (ML) model that integrates a hybrid Swarm Intelligence (SI)–based feature sel...

Bridging Data Gaps in Oncology: Large Language Models and Collaborative Filtering for Cancer Treatment Recommendations

Patients with rare cancers face substantial challenges due to limited evidence-based treatment options, resulting from sparse clinical trials. Advance...

Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records

Accurate data resources are essential for impactful medical research, but available structured datasets are often incomplete or inaccurate. Recent adv...

Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets

Recent work on computer vision and image processing has relied substantially on open datasets, which allow for an objective comparison of techniques a...

Peritoneal Metastasis Prediction in Gastric Cancer: A Machine Learning Approach

Evaluate the predictive efficacy of six machine learning (ML) algorithms in identifying peritoneal metastasis in gastric cancer (GC) patients. Data fr...

Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images

Human epidermal growth factor receptor 2 (HER2) expression is a critical biomarker for assessing breast cancer (BC) severity and guiding targeted anti...

Reliable Radiologic Skeletal Muscle Area Assessment – A Biomarker for Cancer Cachexia Diagnosis

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Mon...

Clinicodemographic Prediction of Overall Survival in Patients with Head and Neck Merkel Cell Carcinoma: A Machine Learning Approach

Merkel cell carcinoma (MCC) is a rare cutaneous neuroendocrine malignancy with a higher case-fatality rate than melanoma. The prognosis of MCC is comp...

Histopathology-based Protein Multiplex Generation using Deep Learning

Multiplexed protein imaging offers valuable insights into interactions between tumors and their surrounding tumor microenvironment (TME), but its wide...

Machine learning-Based Classification of Papillary Thyroid Carcinoma Versus Multinodular Goiter Using Preoperative Laboratory and Cytology Data

Thyroid nodules are frequently encountered in clinical practice, with their detection increasing due to advancements in imaging modalities. While most...

AI-HOPE-TGFbeta: A Conversational AI Agent for Integrative Clinical and Genomic Analysis of TGF-β Pathway Alterations in Colorectal Cancer to Advance Precision Medicine

Early-onset colorectal cancer (EOCRC) is rising rapidly, particularly among Hispanic/Latino (H/L) populations, who face disproportionately poor outcom...