Oncology/Hematology

CT-based radiomics for predicting the treatment response to PD-1/PD-L1 inhibitors combined with chemotherapy in unresectable gastric cancer.

OBJECTIVE: To develop and validate a CT-based radiomics model to predict immunotherapy response in unresectable gastric cancer and explore its underlying biological mechanisms. MATERIALS AND METHODS: This retrospective study included 368 unresectable gastric cancer patients receiving programmed death-1/programmed death ligand-1 (PD-1/PD-L1) inhibitors combined with chemotherapy from two centers. P...

Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.

BACKGROUND: Early diagnosis of oral squamous cell carcinoma (OSCC) remains challenging, with survival largely stage-dependent at presentation. Artificial intelligence (AI) promises to enhance detection and clinical decision-making across clinical photographs, radiology, optical imaging, and digital pathology. METHODS: This narrative review synthesizes peer-reviewed PubMed-indexed English-language ...

Initial experience of deep learning reconstruction algorithm in lung kernel: clinical usefulness for lung nodules at ultra-low-dose protocol.

OBJECTIVES: TrueFidelity (TF), a deep learning image reconstruction algorithm that was originally available only in standard kernel, has recently beco...

A lactylation-related gene signature predicts prognosis and immunotherapy response in clear cell renal cell carcinoma based on machine learning and multi-omics analysis.

Impurities in Oncology Pharmaceuticals: A Review of Classification, Detection Methods, Regulatory Frameworks and Emerging Trends.

Pharmaceutical impurities pose a significant challenge in the development and manufacturing of anti-cancer drugs due to their high potency, narrow the...

HyperSynergyX: Synergistic Drug Combination Prediction via Hypergraph Modeling and Knowledge Graph-Enhanced Retrieval-Augmented Generation.

Drug combination therapy is pivotal for complex diseases, but identifying synergistic three-drug regimens remains challenging due to both combinatoria...

CT Radiomics-Based Machine Learning Model for Predicting Capsular and Neural Invasion in Thyroid Carcinoma: Diagnostic Accuracy Study.

BACKGROUND: Thyroid carcinoma is the most prevalent endocrine malignancy, with a worldwide increasing incidence. Capsular invasion and neural invasion...

Ageing phenotypes and behavioural determinants of health on age acceleration: the Cancer Survivors' Trajectories of Ageing Research (C*STAR) study protocol.

INTRODUCTION: Age acceleration in survivors of breast cancer is a critical issue because cancer and its treatment can increase structural and numerica...

24-hour physical activity, sedentary behaviour and sleep profiles in individuals with cancer: a UK Biobank cohort study.

BACKGROUND: Behaviours across a 24-hour day, including physical activity, sedentary time and sleep, are disrupted following cancer and contribute to c...

Evaluating the ability of generative AI to standardize TNM staging data in lung cancer.

This study evaluates the ability of generative artificial intelligence (AI) models to standardize tumor-node-metastasis (TNM) staging data in lung can...

Mathematical fidelity vs. perceptual realism: A multi-center study of super-resolution radiomics for lung adenocarcinoma invasiveness.

OBJECTIVE: To identify the optimal super-resolution (SR) architecture for radiomics by comparing three models (Residual Channel Attention Network (RCA...

Prostate Cancer Screening in the MR Imaging Era: Evidence, Strategies, and Future Directions.

Prostate cancer screening has entered a new era with the integration of MR imaging and artificial intelligence (AI) into diagnostic workflows. This pa...

TRIM36 and CAMK2N2 regulate ferroptosis and antigen presentation in small cell lung cancer.

Small cell lung cancer (SCLC) is a highly aggressive tumor with poor prognosis. Ferroptosis is closely linked to tumor antigen presentation: it affect...

Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study.

BACKGROUND: Genomic assays such as Oncotype DX have transformed adjuvant treatment selection for hormone receptor-positive, HER2-negative, early breas...

Accelerated discovery of cell migration regulators using label-free deep learning-based automated tracking.

Cell migration underlies immune surveillance, tissue repair, embryogenesis, and-when dysregulated-tumor metastasis. Yet unlike proliferation, which ca...

Integrative analysis of mRNA stability regulation uncovers a metastasis-suppressive program in breast cancer.

Heterogeneity in cancer gene expression is typically linked to genetic and epigenetic alterations, yet the extent of contribution from posttranscripti...

SMC-LUD:Large-Scale B-Mode Liver Ultrasound Dataset for Hepatocellular Carcinoma and Hemangioma Classification.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally, and accurate classification of liver lesions using ultrasound ...

Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients.

The intratumoral and peritumoral architectural heterogeneities of papillary thyroid carcinoma (PTC) are important in preoperative prediction of gross ...

Development of head-to-head and longitudinal CycleGAN algorithm for MRI harmonization: validation in follow-up MRI evaluation in patients with brain metastasis.

Various harmonization methods have been employed for obtaining MRI from different scanners. However, no study has yet focused on the clinical utility ...

Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer.

Ovarian cancer (OC) ranks among the most aggressive malignancies of the female reproductive system. The immunosuppressive tumor microenvironment (TME)...