Oncology/Hematology

From inhibition to regulation: serpins in health and disease.

This issue of the Biomedical Journal highlights regulatory mechanisms that shape aging, disease progression, and biological complexity across molecular, cellular, and systems levels. A central theme is plasminogen activator inhibitor-1 (PAI-1) as a multifunctional regulator linking fibrinolysis, cellular senescence, stem cell dynamics, fibrosis, and tumor biology. Contributions examine structure-g...

Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Qinduced non-obstructive azoospermia.

BACKGROUND: N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increasingly recognized as an emerging environmental contaminant with potential reproductive toxicity. Although growing evidence implicates 6PPD-Q exposure in male infertility, its role in specific pathological conditions such as sertoli cell-only syndrome...

Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology.

Breast cancer (BC) is a highly heterogeneous malignancy and remains a major cause of cancer-related mortality among women worldwide. Advances in multi...

Multicenter Prospective Cohort Study on Clinical Outcomes and Fibrosis Patterns in Biopsy-Proven Steatotic Liver Disease Subtypes.

BACKGROUND & AIMS: Clinical outcomes and histological characteristics of biopsy-proven steatotic liver disease (SLD) subtypes remain understudied. We ...

Deep Learning Model with Nodule Indexing Tailored to Early-Stage Lung Cancer Detection.

OBJECTIVE: To evaluate whether a deep learning-based AI system with suspected nodule indexing and malignancy risk stratification improves radiologist ...

Identification of Pyrrolo[2,3-d]pyrimidine-Based Dual MERTK and FLT3 Inhibitor: Hit-to-Lead, Machine Learning, Modeling, Synthesis, and Biological Evaluation.

Overexpression of MERTK and FLT3 plays a crucial role in activating signal transduction pathways in various human hematological malignancies. These si...

Organ preservation in rectal cancer: an endoscopist's perspective on current evidence and future directions.

Rectal cancer management has increasingly shifted toward organ-preserving strategies that aim to maintain oncologic control while preserving bowel, ur...

An explainable AI workflow integrating automated volumetric body composition analysis for predicting pathological grading of gastroenteropancreatic neuroendocrine neoplasms: a multicenter cohort study.

BACKGROUND: Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are heterogeneous tumors with rising incidence, necessitating precise preoperat...

Automated deep learning for detection and measurement of adrenal masses in contrast-enhanced abdominal CT.

OBJECTIVES: Adrenal tumors can be functional or malignant, yet they are often overlooked in abdominal CT. This study aimed to develop and validate a f...

Unraveling heterogeneity in LUAD via multi-omics integration: molecular classification and therapeutic implications.

Lung adenocarcinoma (LUAD) displays significant morphological and molecular heterogeneity both within and between tumors. This heterogeneity, coupled ...

Differentiation of focal liver lesions in contrast-enhanced ultrasound using a heuristic-guided hybrid machine-learning framework.

PURPOSE: Sonazoid contrast-enhanced ultrasound (CEUS) offers valuable diagnostic information on hepatic lesions, but it is time-consuming. In this stu...

Patient Perspectives on AI-Powered Medical Robots in Breast and Prostate Cancer Care: Qualitative Study.

BACKGROUND: Nearly 250,000 cancer cases are diagnosed annually in Canada, with breast and prostate cancer representing 25% and 22% of new cases, respe...

Machine learning based screening of potential paper mill publications in cancer research: methodological and cross sectional study.

OBJECTIVES: To train and validate a machine learning model to distinguish paper mill publications from genuine cancer research articles, and to screen...

APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.

Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research. Artificial Intelligence (AI...

Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.

[18F] fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is increasingly used for staging of breast cancer in the prim...

Benchmarking deep learning models for predicting anticancer drug potency (IC50) with insights for medicinal chemists.

Potency (IC50) prediction of small molecules is pivotal for anticancer drug development. This study benchmarked five deep learning (DL) models for IC5...

Construction of a risk model based on exosome-related genes predict clinical prognosis and therapeutic response and revealing TIMP1 as a promising target in colorectal cancer.

Various exosome-derived proteins have been reported to play essential roles in regulating colorectal cancer progression and affecting the prognosis of...

HRD-Informed Digital Histology Model for Predicting Platinum Chemo-Response and Prognosis in High-Grade Serous Ovarian Cancer.

Homologous recombination deficiency (HRD) is a critical biomarker in high-grade serous ovarian cancer for the clinical benefit from platinum-based che...

Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging.

Machine learning (ML), particularly deep learning (DL) and radiomics-based approaches, has emerged as a powerful tool for cancer outcome prediction us...

Interpretable machine learning model for predicting 5-Year postoperative recurrence risk in patients with stage III colon cancer using preoperative laboratory tests: a two-centre study.

BACKGROUND: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide and displays significant heterogeneity. The aim of this ...