Other Cancers

Multimodal deep learning for laryngeal squamous cell carcinoma staging using CT and laryngoscopy.

OBJECTIVES: To develop and validate a multimodal deep learning model integrating clinical data, contrast-enhanced CT, and laryngoscopic images for differentiating early-stage (I-II) from advanced-stage (III-IV) laryngeal squamous cell carcinoma (LSCC). MATERIALS AND METHODS: This retrospective multicenter study included 450 patients with pathologically confirmed LSCC from two Chinese medical cente...

A clinically applicable and generalizable deep learning model for anterior mediastinal tumors in CT images across multiple institutions.

Rare diseases are often difficult to diagnose, and their scarcity also makes it challenging to develop deep learning models for them due to limited large-scale datasets. Anterior mediastinal tumors-including thymoma and thymic carcinoma-represent such rare entities. A few diagnostic support systems for these tumors have been proposed; however, no prior studies have tested them across multiple inst...

The METTL3-IGF2BP3 axis drives osteosarcoma progression by enhancing ID1 mRNA stability.

OBJECTIVE: Osteosarcoma (OS) is a highly aggressive malignant bone tumor. While ID1 plays a critical role in OS progression, the underlying mechanisms...

Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.

BACKGROUND: Hymenoptera venom immunotherapy is an established treatment for severe allergic reactions, aiming to modulate the immune response and redu...

Multi-cohort and single-cell profiling of aging genes reveals prognostic and therapeutic targets in breast cancer.

Aging-related transcriptional programs shape breast cancer progression, immune regulation, and therapeutic response. We integrated curated aging-assoc...

Multi-omics analysis reveals features improving outcomes and immunotherapy resistance in MSS colorectal cancer.

In contrast to the MSI-H subtype, most microsatellite-stable (MSS) colorectal cancer (CRC) patients exhibit poor prognosis and limited responsiveness ...

Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach.

UNLABELLED: Pulmonary nodules are a common radiological finding that can be classified as either benign or Malignant, with significant clinical implic...

Pathology of peripheral neuroblastic tumors.

Neuroblastoma is the most prevalent extracranial solid tumor in pediatric age populations worldwide and the most common neoplastic disease diagnosed i...

Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of lymph node metastases in esophageal squamous cell carcinoma.

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 molecula...

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 ...

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...

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...

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...

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

BACKGROUND: Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessm...

Development and Validation of a Machine Learning Model for Dementia Staging in a Heterogeneous Cognitive Impairment Cohort Using Multimodal Features.

RATIONALE AND OBJECTIVES: With the emergence of disease-modifying therapies, precise staging of dementia is urgent. This study aimed to develop a mach...