Oncology/Hematology

Development of a risk prediction model for second primary thyroid cancer in female breast cancer patients based on machine learning algorithms.

Female breast cancer patients remain at risk of developing additional primary cancers even following surgical treatment. Second primary thyroid cancer (SPTC) is the most common type of multiple primary cancer (MPC) in this demographic during their survivorship period, significantly burdening patients' quality of life. This study aimed to develop a risk prediction model for the occurrence of SPTC i...

A machine learning predictive model for staging chronic hepatitis B inflammation based on non-invasive metrics.

Early screening and antiviral therapy for significant liver inflammation in chronic hepatitis B (CHB) remain significant barriers as the key to disease reversal. The aim of this study was to develop a machine learning predictive model based on clinically available hematological indicators to assess liver inflammation in patients with CHB. This multicentre retrospective study comprised patients wit...

Counterfactual Reasoning for Mammogram Classification via Semantic Texture Masking.

Artificial intelligence-based computer-aided diagnosis (CADx) systems have seen growing adoption in mammography, yet the limited interpretability of t...

BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.

The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearanc...

Accurate profiling of single-cell alternative transcript start sites by correcting RNA degradation.

The generation of transcript variants via alternative utilisation of transcription start sites (TSSs) is a pivotal regulatory mechanism in physiologic...

Cigarette smoking and long-term incidence of colorectal cancer subclassified by tumor-infiltrating myeloid cells: A prospective cohort study.

Evidence indicates that cigarette smoking affects anti-tumor immunity. We tested a hypothesis that the association of smoking with long-term colorecta...

Beyond HLA-Ia: A multidimensional framework for Neoantigen immunotherapy.

Glioblastoma (GBM) and other malignant gliomas are associated with aggressive progression, high recurrence rates, and poor long-term outcomes, while c...

Anoctamin 5 as a protective factor in prostate cancer: Insights from WGCNA, machine learning, and experimental analysis, with a focus on the anoctamin family.

OBJECTIVES: Prostate cancer (PCa) is a prevalent malignancy in males, triggered by multiple factors. This study aimed to identify PCa-specific key gen...

Harnessing Artificial Intelligence for the Management of Patients With GI Cancers.

Artificial intelligence (AI) has the potential of reshaping GI oncology by enabling more nuanced interpretation of complex clinical, imaging, and mole...

Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.

This study aims to assess the predictive value of dietary antioxidants in diabetes-cancer comorbidity using interpretable machine learning (ML) models...

AI-Powered Image-Based Assessment of Pressure Injuries Using You Only Look Once Version 8 (YOLOv8) Models.

OBJECTIVE: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for pr...

MS-ConTab: Multi-Scale Contrastive Learning of Mutation Signatures for Pan-Cancer Representation and Stratification.

MOTIVATION: Understanding pan-cancer level mutational landscape offers critical insights into the molecular mechanisms underlying tumorigenesis. While...

Multi-Omics and Machine Learning Analyses Reveal PIK3CG, PRKCD, and TRIM22 as Potential Markers of Poor Prognosis and Immune Activation in Glioblastoma.

BACKGROUND: Glioblastoma (GBM) is one of the most aggressive brain tumors with a poor prognosis despite current treatment modalities. This study aimed...

Assessing large language models in radiation risk communication: susceptibility, cultural-linguistic effects, and ethical reasoning.

PURPOSE: To assess the extent to which large language models (LLMs) amplify or attenuate inaccurate or contested narratives in radiation contexts and ...

Identification of key genes as diagnostic biomarkers for lung adenocarcinoma using bioinformatics and machine learning.

Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.

BACKGROUND: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis. Autom...

Biomarker identification of triple negative breast cancer subtypes using machine learning.

Triple Negative Breast Cancer is a clinically aggressive and molecularly heterogeneous subtype of breast cancer that currently lacks effective targete...

Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study.

Accurate preoperative prediction of occult lymph node metastasis (OLNM) in early-stage non-small cell lung cancer (NSCLC) is crucial for treatment pla...

Virus-immune signatures reveal distinct clinical phenotypes and predict prognosis in patients with Kaposi's sarcoma.

Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification.

One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the ...