Oncology/Hematology

The Impact of Data Consistency on Deep Learning Models for Nasopharyngeal Cancer Organ Auto-Segmentation.

PURPOSE: To investigate how annotation consistency influences deep learningbased auto-contouring performance for organs-at-risk (OARs) in nasopharyngeal cancer radiotherapy. METHODS: We evaluated CT scans from 1,301 nasopharyngeal carcinoma patients: 65 contoured by Physician A, 76 by Physician B, and 1,160 by heterogeneous multi-physician teams. Three cohorts (50 samples each for Physicians A/B; ...

Weakly and semi-supervised segmentation of uterus using coarse annotations in MRI.

Endometrial cancer seriously threatens women's lives via invasion and metastasis, potentially causing multi-system organ failure. Accurate segmentation of the uterus on MR images has important implications for determining the depth of myometrial infiltration in endometrial cancer, and for benign uterine diseases such as uterine fibroids, it enables efficient quantification of uterine volume and cl...

3D deep learning model to predict the recurrence of stage IA invasive lung adenocarcinoma after sub-lobar resection: a multicenter retrospective cohort study.

The purpose of this study was to investigate the efficacy of a three-dimensional (3D) deep learning (DL) model in predicting recurrence risk of stage ...

A Multitask Deep Learning Approach for Tumor Segmentation and Morphological Quantification in Acral Melanoma Whole-Slide Images.

This study presents an integrated multitask deep learning framework for the automated analysis of acral melanoma from whole-slide images (WSIs). We co...

Perspectives from machine learning and multi-omics to decoding the effects of VDAC2 malignant subsets on tumor evolution.

VDAC2's known role in cancer and immune regulation via enhancing the CD8+ T cell-mediated killing, and it is worth systematically digging out the role...

Rapid and noninvasive artificial intelligence-assisted diagnostic method for oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) remains the most common head and neck malignancy, for which early detection is critical yet challenging with curre...

[Machine learning and radiomics-based model for predicting response to lenvatinib combined with TACE in patients with unresectable hepatocellular carcinoma].

Objective: To evaluate the predictive value of machine learning combined with radiomics for treatment response to lenvatinib combined with transarteri...

Discovery of WRN helicase inhibitors by 3D-CNN docking and ML consensus from traditional Chinese medicine monomers.

The Werner syndrome (WRN) helicase is a validated synthetic-lethal vulnerability in cancers with microsatellite instability (MSI), making WRN inhibiti...

Inflammatory-nutritional biomarker scores and risk of malignant effusion in a pan-cancer cohort of 68,916 patients: Nonlinear associations and machine learning validation.

PURPOSE: Inflammatory-nutritional biomarker scores derived from routine blood tests have established prognostic value in cancer, yet their association...

Integrating multi-omics data and artificial intelligence for personalized medicine in glioblastoma.

Glioblastoma (GBM) is one of the most lethal primary brain tumors and is characterized by profound molecular heterogeneity, rapid progression, and lim...

High-accuracy instance segmentation of cellular structures in head and neck squamous cell carcinoma histopathology using a cyto R-CNN deep learning framework.

This study develops a deep learning-based model to automate the instance segmentation of nuclei and whole cells in hematoxylin and eosin-stained head ...

Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.

Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.

Extracellular vesicles (EVs) are nanoscale, membrane-bound particles that carry nucleic acids, proteins, metabolites, and lipids. Their omics profiles...

Animal Models of Allergen Immunotherapy for Allergic Airway Inflammation.

Allergen immunotherapy (AIT) is currently the only disease-modifying therapy for allergic airway inflammation. However, its underlying mechanisms rema...

A Comparative Study of IVIM-MRI Fitting Techniques in Glioma Grading: Conventional, Bayesian, and Voxel-Wise and Spatially-Aware Deep Learning Approaches.

BACKGROUND: Intravoxel incoherent motion (IVIM) analysis of diffusion-weighted MRI (DWI) provides microvascular perfusion and diffusion information. H...

Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.

OBJECTIVE: The primary aim of this study was to develop and internally validate ultrasound-based radiomics models to discriminate between all types of...

Treatment-Specific Prediction Models in Multiple Myeloma: A Critical Review of Current Evidence and Future Directions.

BACKGROUND AND OBJECTIVES: Multiple myeloma (MM) is characterized by substantial clinical heterogeneity, leading to wide variability in treatment resp...

Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.

Parkinson's disease (PD) affects 10 million globally, with accurate staging essential for personalized treatment planning. Current UPDRS assessments a...

Viewpoint on the Consequences and Mitigation of Cognitive Bias in the Radiological Interpretation of Breast Cancer Imaging Using Artificial Intelligence.

Artificial intelligence (AI) is increasingly integrated into breast imaging workflows, offering the potential to enhance diagnostic accuracy, efficien...

Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate-Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters.

INTRODUCTION: Prostate-specific antigen (PSA) alone is insufficient for the diagnosis of prostate cancer (PCa), particularly within the gray zone rang...