Oncology/Hematology

Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based ligand screening. Additionally, toxicity prediction, molecular docking, and molecular dynamics (MD) simulations were conducted. NP study identified key g...

Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT.

AIM: To develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma.

Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.

RATIONALE AND OBJECTIVES: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperativ...

Attention-guided CenterNet deep learning approach for lung cancer detection.

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies...

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's pr...

Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model.

BACKGROUND: Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral squamous cell carcinoma (OSCC), in which occult met...

A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing.

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkp...

Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outc...

Tumor detection on bronchoscopic images by unsupervised learning.

The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians...

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.

Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory meth...

The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.

Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp...

Deep Learning for the Accurate Prediction of Triggered Drug Delivery.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in canc...

Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clini...

OTMorph: Unsupervised Multi-Domain Abdominal Medical Image Registration Using Neural Optimal Transport.

Deformable image registration is one of the essential processes in analyzing medical images. In particular, when diagnosing abdominal diseases such as...

Multi-Modal Federated Learning for Cancer Staging Over Non-IID Datasets With Unbalanced Modalities.

The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When ac...

A GPU-accelerated fuzzy method for real-time CT volume filtering.

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy imag...

Identification of key gene signatures for predicting chemo-immunotherapy efficacy in extensive-stage small-cell lung cancer using machine learning.

OBJECTIVES: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-S...

Iodine Density of Lymphoma, Metastatic SCCA, and Normal Cervical lymph nodes: A Comparative Analysis Based on DLSCT.

OBJECTIVE: To compare iodine density (ID) and contrast-enhanced attenuation value (CEAV) from dual-layer spectral computed tomography (DLSCT) scans of...

HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the cap...

Artificial intelligence-based multi-modal multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: a retrospective study.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. ...