Oncology/Hematology

Real-Time Characterization of Colonic Polyps: A Multicenter Prospective Study Evaluating the CAD-EYE System in Screening Colonoscopies.

BACKGROUND AND STUDY AIMS: Polypectomy-related costs could potentially be reduced through optical diagnosis strategies, such as 'diagnose-and-leave' and 'resect-and-discard.' Artificial intelligence, using computer-aided diagnosis (CAD), may provide a reproducible optical diagnosis of colorectal lesions. This study aimed to assess the performance of the CAD-EYE® system in the real-time characteriz...

Evolving strategies may facilitate oral pathology and cancer surveillance, monitoring, and detection for patients at risk.

BACKGROUND: Oral cancers account for considerable morbidity and mortality worldwide, and their incidence has increased in the past several years, despite growing awareness and screening and preventive efforts advocated in articles and guidelines. Visual examination remains the mainstay for oral cancer screening. In contradistinction, optical devices used for cutaneous screening in dermatologic pra...

Multi-omics integrative analysis of stemness-associated pathological signatures to guide prognosis and therapeutic strategies in uveal melanoma.

BACKGROUND: Uveal melanoma (UVM) is the most common intraocular malignancy in adults and exhibits poor prognosis upon metastasis. Stemness, a hallmark...

Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules.

PURPOSE: Clear visualization and diagnosis of lung nodules depend on the spatial resolution of CT images. Transformer-based generative neural networks...

Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.

Recent advances in digital pathology have enabled comprehensive analyses of Whole-Slide Images (WSIs) from tissue samples, leveraging high-resolution ...

iMCN: information compression-based multimodal confidence-guided fusion network for cancer survival prediction.

The rapid development of deep learning-based computational pathology and genomics has demonstrated the significant promise of effectively integrating ...

Deep radiomics for prognostic prediction in locally advanced non-small cell lung cancer by leveraging OmicsMap-based image representation.

Patients with locally advanced non-small cell lung cancer (LA-NSCLC) exhibit heterogeneous prognoses despite receiving standard treatments, highlighti...

Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer.

This study utilized a novel Proximity Barcoding Assay to perform high-resolution proteomic profiling of individual plasma extracellular vesicles from ...

An interpretable deep learning model for predicting endometrial cancer molecular subtypes from H&E-stained slides.

The molecular subtype of endometrial cancer is important for predicting prognosis and treatment effectiveness. This study aimed to develop an interpre...

CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

BACKGROUND: Accurate preoperative prediction of visceral pleural invasion (VPI) in lung adenocarcinoma is essential for guiding surgical decision-maki...

Deep learning-based multimodal fusion of MRI and whole slide image for predicting neoadjuvant therapy response in locally advanced head and neck squamous cell carcinoma.

Identification of endometrial cancer biomarkers using weighted gene coexpression network analysis and machine learning.

BACKGROUND: Endometrial carcinoma (UCEC) exhibits a rising incidence in China, imposing a substantial burden on both women and society. Identifying bi...

Digital horizons: Exploring the future of AI-enabled gastroenterology in the Kingdom of Saudi Arabia.

Artificial intelligence is rapidly reshaping gastroenterology through demonstrable gains in diagnostic precision, procedural quality, and operational ...

[Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning].

OBJECTIVES: To develop and validate a multimodal radiomics model based on machine learning for predicting central lymph node metastasis (CLNM) in pati...

A P4HA2 hypoxia signature derived from single cell atlas stratifies conserved subtypes with prognostic significance in cervical squamous cell carcinoma.

Machine learning using entropy-based texture features from MRI to differentiate histological subtypes of non-small cell lung cancer identified as metabolically active on PET/MRI.

Texture analysis is a foundational approach in imaging studies and demonstrates excellent diagnostic performance, with radiomic analysis being the mos...

Identification of hypoxia- and mitophagy-related diagnostic biomarkers for ulcerative colitis based on bioinformatic analysis and machine learning.

BACKGROUND: Ulcerative colitis (UC) is a chronic nonspecific inflammatory bowel disease of unknown etiology that is associated with a significant risk...

Atypical Histology in Pulmonary Biopsies Obtained Using Robotic-Assisted Bronchoscopy: Natural History and Clinical Practice.

BACKGROUND: A recent consensus statement recommends that atypical cells are considered nondiagnostic specimens in the calculation of diagnostic yield....

Assessing the performance of physics-informed neural networks for tumor growth prediction under noisy and sparse data conditions.

Cancer presents multiple challenges for its study, which is why mathematical models have become essential tools to understand its dynamics and reduce ...

Recent advances in metal-organic framework-based nanozymes for cancer theranostics driven by synthetic innovation and machine learning design.

Nanozymes are engineered nanomaterials designed at the atomic scale to fine-tune their structure, composition, and electronic properties, thereby crea...