Oncology/Hematology

Augmentation of Infrared Microscopy of White Blood Cells and Medical Measures for Rapid and Accurate Diagnosis of Bacterial or Viral Infections in Febrile Pediatric Oncology Patients: An Expert System-Based Study.

Infectious diseases, a major contributor to high mortality rates, often exhibit similar symptoms, despite variations in immune responses to bacterial or viral infections. Rapidly differentiating bacterial infections from viral infections in febrile pediatric oncology patients is critical to reduce unnecessary antibiotic use and improve patient outcomes. Current diagnostic procedures require 2-4 da...

Medical management of pancreatic cancer: from personalization to broadening treatment strategies.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most heterogeneous and deadly cancers. This review examines recently implemented strategies to integrate predictive tools and targeted therapies to improve treatments personalization and patient outcomes. Predictive transcriptomic signatures based on machine learning should optimize first-line chemotherapy selection, while organoid-based chemo-...

Real-time brain tumor detection in intraoperative ultrasound: From model training to deployment in the operating room.

Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the su...

Computer-aided drug discovery of a dual-target inhibitor for ovarian cancer: therapeutic intervention targeting CDK1/TTK signaling pathway and structural insights in the NCI-60.

Ovarian cancer remains the third most prevalent and deadliest gynecologic malignancy worldwide, with most patients eventually developing resistance to...

Computer-aided unveiling molecular mechanisms of Xylocarpus granatum against colorectal cancer: therapeutic intervention targeting P13K-AKT signaling pathway.

Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality globally, with increasing incidence rates, particularly in early-...

Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study.

BACKGROUND: Artificial Intelligence (AI) models hold promise as useful tools in healthcare practice. We aimed to develop and assess AI models for auto...

Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival of under 10 % despite current therapies...

Deep Learning Image Reconstruction (DLIR) Algorithm to Maintain High Image Quality and Diagnostic Accuracy in Quadruple-low CT Angiography of Children with Pulmonary Sequestration: A Case Control Study.

RATIONALE AND OBJECTIVES: CT angiography (CTA) is a commonly used clinical examination to detect abnormal arteries and diagnose pulmonary sequestratio...

Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.

Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging-related pathologies. Accumulation of senescent ce...

Transforming the management of radiotherapy-induced hypothyroidism in nasopharyngeal carcinoma through an Innovative individualized radiation dosage model: A multicenter retrospective analysis.

PURPOSE: Current guidelines for thyroid radiation dose prescription lack uniformity and fail to consider the unique characteristics of individual pati...

Advances in photoactivated carbon-based nanostructured materials for targeted cancer therapy.

In this review, we explore key innovations in photoactivated therapeutic programming of carbon-based nanomaterials (CBNs), focusing on their diverse n...

Establishment and evaluation of an automatic multi?sequence MRI segmentation model of primary central nervous system lymphoma based on the nnU?Net deep learning network method.

Accurate quantitative assessment using gadolinium-contrast magnetic resonance imaging (MRI) is crucial in therapy planning, surveillance and prognosti...

Automated vertebrae identification and segmentation with structural uncertainty analysis in longitudinal CT scans of patients with multiple myeloma.

OBJECTIVES: Optimize deep learning-based vertebrae segmentation in longitudinal CT scans of multiple myeloma patients using structural uncertainty ana...

AI-driven simplification of surgical reports in gynecologic oncology: A potential tool for patient education.

INTRODUCTION: The emergence of large language models heralds a new chapter in natural language processing, with immense potential for improving medica...

Clinical advantages in providing artificial intelligence-assisted prostate cancer diagnosis: A pilot study.

Prostate cancer is a prevalent male malignancy, with increasing incidence rates placing significant diagnostic burdens on pathology services worldwide...

Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemot...

Trustworthy AI for stage IV non-small cell lung cancer: Automatic segmentation and uncertainty quantification.

Accurate segmentation of lung tumors is essential for advancing personalized medicine in non-small cell lung cancer (NSCLC). However, stage IV NSCLC p...

A machine learning framework for cross-institute standardized analysis of flow cytometry in differentiating acute myeloid leukemia from non-neoplastic conditions.

Flow cytometry (FC) remains a cornerstone diagnostic tool for acute myeloid leukemia (AML), yet standardizing panels across laboratories presents pers...

Integrating multi-omics data with artificial intelligence to decipher the role of tumor-infiltrating lymphocytes in tumor immunotherapy.

Tumor-infiltrating lymphocytes (TILs) are capable of recognizing tumor antigens, impacting tumor prognosis, predicting the efficacy of neoadjuvant the...

Multimodal AI framework for lung cancer diagnosis: Integrating CNN and ANN models for imaging and clinical data analysis.

Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate and early diagnostic solutions. ...