Oncology/Hematology

Deep Learning for Differentiating Pulmonary Metastasis from Primary Lung Cancer Constructed on Frozen Sections for Intraoperative Diagnosis.

The surgical procedures for primary lung cancer and metastatic lung tumors differ. An intraoperative diagnosis is important for surgical procedure decisions. This study digitized frozen sections for intraoperative diagnosis, developing and evaluating deep learning models to differentiate lung cancer from metastatic tumors. A total of 1,668 slides from 1,458 patients with lung cancer or metastatic ...

Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.

OBJECTIVE: This study developed and validated monolingual and bilingual sentence-bidirectional encoder representations from transformers (SBERT) models for detecting cancer recurrence within Thai-English electronic medical records (EMRs) from Thai cancer hospitals. METHOD: A multicentre dataset of 32 436 documents from 1250 patients was used for model development. External validation involved an i...

Multiview Laryngoscopic Image Fusion for Patient-Level Stratification of Vocal Fold Lesions.

Malignant or high-grade vocal fold disease is usually judged across repeated laryngoscopic views at different observation distances and fields of view...

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of a longitudinal ultrasound (US)-based stack-model for early prediction of pathologi...

Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study.

Metastatic renal cell carcinoma remains clinically challenging because of heterogeneous outcomes and limited predictive biomarkers for immunotherapy. ...

Integrating machine learning and structure-based simulations to prioritise Z4P as a mutation-resilient IRE1α inhibitor for breast cancer.

Breast cancer is one of the most prevalent and lethal malignancies affecting women globally. The increasing resistance to current therapeutic strategi...

Geometric-structural multi-label learning for non-invasive prediction of breast cancer biomarkers.

Accurate profiling of molecular biomarkers, including ER, PR, HER2, and Ki-67, is pivotal for tailoring therapeutic strategies in breast cancer manage...

BoneCoT: multicentre validation of a whole-body skeleton foundation model for bone metastases guided by clinician-derived chain of thought.

Given the rising incidence of bone metastases, computed tomography is widely used worldwide as the initial imaging modality for their detection. Accur...

Key molecular mechanisms of mitochondrial metabolic pathways in specific cell subpopulations of pancreatic cancer based on scRNA-seq and bulk RNA-seq.

BACKGROUND: Pancreatic cancer (PC) is a highly malignant tumor with increasing incidence, mortality, and a low five-year survival rate. Mitochondrial ...

Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.

BACKGROUND: Early detection of epithelial ovarian cancer (EOC) remains a major clinical challenge. Although serum tumor markers are widely used for de...

From heterogeneity to mechanism: a systems-level analysis pinpoints the S100A8/A9 axis in colorectal cancer.

BACKGROUND: The functional roles of the S100 protein family in colorectal cancer (CRC) are enigmatic, marked by widespread dysregulation but limited p...

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-gener...

Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.

PURPOSE: The purpose of this study was to evaluate whether explicitly modeling diabetes mellitus (DM) without diabetic retinopathy (DR) as its own sta...

Integrative multi-omics and machine learning reveal PLAUR as a Pan-Cancer prognostic biomarker and potential therapeutic target.

Tumor heterogeneity remains a major barrier to effective cancer therapy, driving the need for novel biomarkers and targeted strategies. A bibliometric...

Unraveling the adverse outcome pathways of metabolic dysfunction-associated steatotic liver disease triggered by environmental mixtures via biological knowledge-driven machine learning.

Environmental pollutant mixtures are potential risk factors for metabolic dysfunction-associated steatotic liver disease (MASLD), yet their joint effe...

SAP18 drives vasculogenic mimicry in esophageal squamous cell carcinoma: a machine learning and multi-omics investigation.

Vasculogenic mimicry (VM), a novel endothelial-independent blood perfusion pathway, is linked to advanced stage and poor prognosis in esophageal squam...

Fair by chance? On the use of algorithms in therapeutic decisions.

Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocat...

MRI for Prostate Cancer Local Staging: AJR Expert Panel Narrative Review.

The incremental value of multiparametric MRI (mpMRI) in prostate cancer staging has been increasingly recognized, with the accumulated literature indi...

ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.

PURPOSE: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell ...

AI-Powered Deep Visual Proteomics Reveals Critical Molecular Transitions in Pancreatic Cancer Precursors.

UNLABELLED: Pancreatic ductal adenocarcinoma (PDAC) evolves through precursors, yet the protein programs governing early progression remain poorly def...