Other Cancers

Differentiating sub-centimeter lung metastases in colorectal cancer by deep learning: a multicenter retrospective study.

BACKGROUND: Early diagnosis of sub-centimeter lung metastases is critical for timely decision-making and improved prognosis in patients with colorectal cancer. The diagnostic evaluation of indeterminate sub-centimeter lung nodules in colorectal cancer patients remains a crucial challenge. We aim to develop and validate a deep learning model for differentiating sub-centimeter lung metastases noninv...

Risk stratification for reoperation outcomes in recurrent or persistent papillary thyroid carcinoma: development and validation of a machine learning model.

BACKGROUNDS: Reoperation is a key therapeutic strategy for recurrent or persistent papillary thyroid carcinoma (PTC), but its outcomes remain highly heterogeneous. Effective early risk stratification is essential to guide clinical decision-making and improve patient prognosis. This study aims to develop and validate interpretable machine learning (ML) models for the early prediction of reoperation...

Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ligands, is essential for immune surveillance but ...

Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.

BACKGROUND: Prognostic models for hepatocellular carcinoma (HCC) may have limited accuracy. We aimed to construct and validate a novel prognostic mode...

Comparing clinical decision-making between colposcopists and large language models in cervical dysplasia management: a pilot prospective multicenter study.

PURPOSE: This prospective multicenter study aimed to compare the decision-making abilities of board-certified colposcopists and two commercially avail...

Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.

OBJECTIVES: To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detect...

Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges.

Hematologic malignancies remain among the most challenging cancers to treat due to genetic heterogeneity, clonal evolution, and therapy resistance. Ex...

Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): a mixture-of-experts AI model for vestibular schwannoma.

BACKGROUND: Outcome prediction after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma remains largely guided by tumor size, Koos grade, basel...

Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.

BACKGROUND: Breast cancer is the most common malignant tumor affecting women, and pathology serves as the primary method for its diagnosis. In recent ...

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell-Cell Interaction Inference.

Spatial transcriptomics (ST) enables the study of tissue architecture by resolving gene expression in space, but current ST platforms are constrained ...

A selective deep learning framework for pressure injury staging with calibrated confidence and automated clinical documentation.

OBJECTIVES: Pressure injuries are common chronic wounds that require accurate staging to guide management. Deep learning has shown promise for automat...

Deep Learning of CT Imaging Predicts PD-L1 Expression and Immunotherapy Response in Metastatic NSCLC: A Multi-Center Study.

Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies...

Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.

T cell receptors (TCRs) are central to adaptive immunity, yet their vast sequence and structural diversity present a significant challenge to fully un...

End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.

Although deep learning models have improved individual PET analysis, image processing, and quantification tasks, end-to-end automation from raw DICOM ...

[Reirradiation: A new therapeutic paradigm in oncology].

Reirradiation (reRT) has become an essential therapeutic option for selected patients with locoregional recurrences, when surgery or systemic therapie...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...

Deep Learning on Tumor Habitat Sub-regions vs. Whole-Tumor ROI: A Comparative Study of Fusion Strategies for Breast Cancer Molecular Subtype Prediction.

This study aimed to compare deep learning models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) whole-tumor and habitat regio...

Multifaceted role of histopathology in liver transplantation for hepatocellular carcinoma: A narrative review towards precision prognosis.

Hepatocellular carcinoma is a leading cause of cancer mortality globally. Liver transplantation is considered the best curative treatment for selected...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

ULK1's role in cancer progression and its emerging therapeutic potential.

Macroautophagy (autophagy) enables cellular stress adaptation by degrading damaged components; ULK1, a serine/threonine kinase, initiates this process...