Oncology/Hematology

Brain Tumor Classification and Severity Identification Using Deep Convolutional Spiking U-Net Lyrebird Neural Network and Alpha Piecewise Linear-Fuzzy.

Purpose: To develop a robust framework that accurately classifies brain tumors and provides an estimation of their severity using an artificial intelligence approach to solve issues related to multimodal MRIs (Magnetic Resonance Imaging), such as resolution variability, misalignment and heterogeneity.Methodology: An intelligent Deep Convolutional Spiking U-Net Lyrebird Neural Network combined with...

Artificial Intelligence-Assisted reflectance confocal microscopy for Real-Time intraoperative margin assessment in oral squamous cell carcinoma.

BACKGROUND: Oral cavity squamous cell carcinoma (OSCC) is a global health burden, where negative margins are essential for reducing recurrence and improving survival. Intraoperative frozen-section analysis is limited by time, sampling error, and interpretive variability, underscoring the need for more reliable margin assessment. Reflectance confocal microscopy (RCM) enables real-time, in vivo high...

Reconstructing tumor tissues in 3D: From organoids to bioengineered niches.

Tumor tissue engineering, integrating organoid, microfluidic, and biofabrication technologies, has opened new avenues for cancer research. Leveraging ...

Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer.

UNLABELLED: Metastasis is the leading cause of cancer deaths. To develop strategies for intercepting metastatic progression, a better understanding of...

Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.

UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise...

Malignant vs. Non-malignant Annotations on TCGA Breast Cancer Whole Slide Images for AI Analysis.

Identification of malignant and non-malignant regions in breast cancer whole slide images (WSIs) is essential for understanding tumor heterogeneity an...

Multimodal AI-driven analysis in breast and lung cancer: insights from the OPTIMA prototyping workshop.

This paper reports on insights from the OPTIMA (Optimal Treatment for Patients with Solid Tumours in Europe Through Artificial Intelligence) prototypi...

Photon-counting Detector CT Spectral Reconstructions for Radiomics-based Liver Lesion Classification: A Multicenter Study.

OBJECTIVES: Accurate noninvasive classification of hepatic lesions remains a diagnostic challenge, particularly on conventional CT. Photon Counting De...

Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge....

European Society of Urogenital Radiology (ESUR) perspectives on the role of prostate MRI in active surveillance.

MRI has a central role in the diagnosis and management of prostate cancer, including active surveillance (AS) of low- and favourable intermediate-risk...

Utilizing natural language processing (NLP) to identify breast cancer-associated lung metastases from pathology reports to delineate characteristics of this site of recurrence.

PURPOSE: Natural language processing (NLP, artificial intelligence) can enable automated identification of records in large datasets. The purpose of t...

ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.

Accurate prediction of peptide-protein interactions (PepPI) is crucial for advancing peptide-based anticancer drug design. In this study, we introduce...

Dosimetry Analysis of 177Lu-PSMA-I&T in Patients with Low-Volume Oligometastatic Hormone-Sensitive Prostate Cancer: A Secondary Analysis of the LUNAR Trial.

The phase 2 LUNAR trial randomized (1:1) patients with oligorecurrent hormone-sensitive prostate cancer to neoadjuvant [177Lu]Lu-PSMA-I&T (2 cycles, 6...

Multimodal AI model for early detection of hepatocellular carcinoma.

Detection of early hepatocellular carcinoma (eHCC) is important for timely treatment and improved prognosis. However, it is challenging to distinguish...

CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.

Sequence-based deep learning models have become the state of the art for analyzing the genomic regulatory code. Particularly for enhancers, these mode...

Polymer Drug Conjugate: A Revolution in Drug Delivery.

Polymer-drug conjugates (PDCs) represent a remarkable advancement in modern medicine, leveraging the physicochemical properties of polymers to enhance...

Generative Artificial Intelligence for Medical Summarization in Prostate Cancer: Comparative Evaluation by Physicians and Patient Advocates-A Pilot Study.

PURPOSE: The exponential growth of scientific publications presents increasing challenges for clinicians and patients seeking to access up-to-date med...

Explainable machine learning to predict muscle loss during radiotherapy for oral cavity cancer.

BACKGROUND AND PURPOSE: Muscle loss during adjuvant radiotherapy is associated with poor survival outcomes in patients with oral cavity cancer (OCC). ...

Towards generalizable pathology reports via a multimodal LLM with the multicenter in-context learning.

Pathology report generation has received increasing attention in recent years. However, existing pathology report generation methods still face two ma...

LCPBert: ProtBERT-based early-stage lung cancer prediction from T cell receptor beta sequences.

Early detection of lung cancer remains challenging due to limitations of current methods. We developed LCPBert, a deep learning framework leveraging p...