Oncology/Hematology

Robust Kidney Abnormality Segmentation: A Validation Study of an AI-Based Framework

Kidney abnormality segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an important biomarker for renal diseases, with changes in volume correlating directly with kidney function. Currently, clinical practice often relies on subjective visual assessment for evaluating kidney size and abnorm...

Preoperative prediction of malignant transformation in sinonasal inverted papilloma: a novel MRI-based deep learning approach.

OBJECTIVE: To develop a novel MRI-based deep learning (DL) diagnostic model, utilizing multicenter large-sample data, for the preoperative differentiation of sinonasal inverted papilloma (SIP) from SIP-transformed squamous cell carcinoma (SIP-SCC).

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.

The purpose of this study was to investigate the utility of deep learning image reconstruction at medium and high intensity levels (DLIR-M and DLIR-H,...

An artificial intelligence model for nuclear grading of clear cell renal cell carcinoma using whole slide images: a retrospective, multicentre, diagnostic study.

BACKGROUND: The pathological assessment of International Society of Urological Pathology (ISUP) nuclear grading is crucial for the management of clear...

OptLung: An online predictive model for optimizing therapeutic strategies for second primary lung cancer in cancer survivors.

BACKGROUND: Advances in cancer detection and treatment have significantly improved survival rates and led to an increasing prevalence of the second pr...

Breast cancer neoadjuvant therapy outcome prediction based on clinical patient and tumor features: A cross-sectional study.

INTRODUCTION: Breast cancer is the most common malignant disease in the female population and one of the most common diseases in developed countries. ...

Developing a Machine-Learning Prediction Model for Infliximab Response in Crohn's Disease: Integrating Clinical Characteristics and Longitudinal Laboratory Trends.

BACKGROUND: Achieving long-term clinical remission in Crohn's disease (CD) with antitumor necrosis factor α (anti-TNF-α) agents remains challenging.

A machine learning-based prediction model for colorectal liver metastasis.

Colorectal liver metastasis (CRLM) is a primary factor contributing to poor prognosis and metastasis in colorectal cancer (CRC) patients. This study a...

Research on the functions and potential mechanisms of STAT3 in chronic myelogenous leukemia.

OBJECTIVE: To explore the bioinformatics characteristics and potential mechanisms of signal transducer and activator of transcription (STAT3) in chron...

Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types.

The growing burden of cancer and recent surge in healthcare data availability call for new ways of analysing this multifactorial disease and improving...

Construction of a novel CD8T cell-related index for predicting clinical outcomes and immune landscape in ovarian cancer by combined single-cell and RNA-sequencing analysis.

BACKGROUND: CD8T cells, also known as cytotoxic T lymphocytes, play a key role in the tumor immune microenvironment (TME) and immune response. The aim...

A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.

Combination therapies are one potential approach to improve the outcomes of patients with refractory or relapsed disease. However, comprehensive testi...

Unraveling the molecular mechanisms of paclitaxel in high-grade serous ovarian cancer through network pharmacology.

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of epithelial ovarian cancer, often diagnosed at advanced stages wi...

Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning.

Pancreatic cystic neoplasms (PCNs) are a complex group of lesions with a spectrum of malignancy. Accurate differentiation of PCN types is crucial for ...

Machine learning predicts prognosis in patients with gastroenteropancreatic neuroendocrine tumors with liver metastases.

BACKGROUND: Patients with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) and liver metastases typically exhibit poor prognoses. However, a...

Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma.

BACKGROUND: One of the most common and prevalent cancers is laryngeal squamous cell carcinoma (LSCC), which poses a great threat to the life and healt...

A systematic review and meta-analysis of lung cancer risk prediction models.

BACKGROUND: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly...

The Potential Role of AI- and Machine Learning Models in the Early Detection of Oral Cancer and Oral Potentially Malignant Disorders.

INTRODUCTION: Artificial Intelligence (AI) is playing an increasing role in advancing diagnostic processes and decision-making in healthcare. In the e...

Fusogenic Lipid Nanovesicles as Multifunctional Immunomodulatory Platforms for Precision Solid Tumor Therapy.

Although immunotherapy demonstrates considerable prospect in overcoming solid tumors, its clinical efficacy is limited by several factors, such as poo...

Blood-brain barrier crossing biopolymer targeting c-Myc and anti-PD-1 activate primary brain lymphoma immunity: Artificial intelligence analysis.

Primary Central Nervous System Lymphoma is an aggressive central nervous system neoplasm with poor response to pharmacological treatment, partially du...