Oncology/Hematology

Hierarchical two-stage ensemble of dilated U-net models for brain tumor segmentation.

Ensemble methods for image segmentation improve performance by combining predictions from multiple models, yielding more accurate and reliable results. This study presents a two-stage hierarchical framework to enhance the accuracy and stability of brain tumor delineation in magnetic resonance imaging data. The proposed approach integrates ensemble strategies at different stages of the processing p...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for lung cancer brain metastases.

BACKGROUND: Prescription dose selection for lung brain metastases treated with stereotactic radiosurgery (SRS) remains largely guided by generalized practice patterns rather than tumor-specific modeling of local failure dynamics. We developed a Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS), an artificial intelligence framework for personalized ...

Quality and reliability of YouTube videos on PET/CT radiation safety: a comparative analysis between human experts and large language models.

PURPOSE: To evaluate the quality and accuracy of YouTube videos regarding PET/CT radiation safety and to assess the feasibility of using a Large Langu...

AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.

BACKGROUND: AI is increasingly being integrated into cancer screening, treatment, and patient care. However, AI adoption across cancer centers varies,...

CT scan range planning in clinical practice: An international survey of scan range extension, quality assurance, and AI integration.

INTRODUCTION: Computed tomography (CT) scan range planning is a modifiable determinant of radiation exposure but remains highly variable in clinical p...

RNA biosensors in oncology: Mechanisms, Cancer-specific applications, and a Hallmark-aligned clinical roadmap.

Cancer diagnosis continues to rely on invasive tissue sampling and static molecular assessments that cannot reflect the real time RNA alterations driv...

Association between body composition and recurrence in stage II-III colon cancer: a retrospective cohort study.

INTRODUCTION: Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine management. Patients with advance...

Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment.

PURPOSE: To develop and validate a pediatric diffuse midline glioma (DMG) auto-segmentation tool optimized for longitudinal treatment response assessm...

Multiparametric MRI-Based Interpretable Machine Learning Radiomics Model for Predicting Neoadjuvant Chemotherapy Sensitivity and Recurrence-Free Survival in Breast Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate an interpretable model using pretreatment multiparametric magnetic resonance imagin...

Intratumoral Heterogeneity-aware Imaging Biomarkers for Bone Metastasis Risk Stratification in Breast Cancer.

RATIONALE AND OBJECTIVES: Breast cancer carries a substantial long-term risk of bone metastasis, which marks progression to incurable disease and seve...

Attention-Guided TransMorph for Real-Time Tumor Tracking in Cine-MRI.

Magnetic Resonance Imaging (MRI) is a key modality in cancer treatment, providing high soft tissue contrast for the visualization of tumors and intern...

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images.

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroi...

Multimodal AI Methods for Predicting Pathologic Complete Response in Breast Cancer.

Neoadjuvant chemotherapy (NAC) can eliminate all invasive cancer in some breast cancer patients, achieving a pathologic complete response (pCR) that i...

Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.

This study aimed to develop machine learning models for predicting tumor recurrence in breast cancer before neoadjuvant systemic therapy (NST) by inte...

Deep learning differentiates myeloproliferative neoplasms and predicts JAK2 and CALR mutations from bone marrow smears.

Accurate differentiation of BCR::ABL1-negative myeloproliferative neoplasms (MPNs) based on bone marrow smear morphology remains challenging. While pr...

[Multi-modal tumor boundary clustering recognition based on artificial intelligence virtual cells and near-infrared surgical field].

In order to accurately identify tumor boundaries and improve diagnostic efficiency, this study proposes a multi-modal tumor boundary identification me...

Tissue-Agnostic Cellular Morphometric Biomarkers for Risk-Adapted Management Across Gastrointestinal Precancerous Lesions and Cancers.

While precision oncology increasingly adopts tissue-agnostic paradigms, current strategies remain heavily reliant on molecular alterations, with limit...

Precision Oncology via Radiotherapy-Triggered Drug Delivery: Mechanisms, Molecular Engineering, and Clinical Translation.

Radiotherapy-triggered drug delivery systems (RDDS) promise to integrate the spatial precision of ionizing radiation with controllable pharmacological...

Integrated Multi-Omics Analysis of Gut Microbiota-Associated Metabolites and Related Host Molecular Signatures in Cervical Squamous Cell Carcinoma.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common female malignancy. Gut microbiota and metabolites are critical reg...

Epigenetic markers expand genetic risk estimation in acute myeloid leukemia.

Gene mutations and chromosome abnormalities are important components of prognostication in acute myeloid leukemia (AML). Here we assessed whether DNA ...