Oncology/Hematology

CPGPrompt: translating clinical guidelines into large language model-executable decision support.

OBJECTIVE: Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into artificial intelligence (AI) remains challenging. Previous approaches, such as rule-based systems or black-box AI models, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To addres...

Diagnosis of Gastric Intestinal Metaplasia: Histology, Endoscopy, and Artificial Intelligence.

Background Gastric intestinal metaplasia (GIM) is a well-established precancerous lesion and key biomarker for assessing the risk of gastric cancer. Histological classification into complete and incomplete types, along with the Operative Link on Gastric Intestinal Metaplasia (OLGIM), provides valuable prognostic information, with stage III-IV strongly associated with cancer development. Therefore,...

CMAF-Net: cross-modal attention fusion with information-theoretic regularization for imbalanced breast cancer histopathology.

Breast cancer diagnosis from histopathology images remains challenging due to two intertwined factors: severe class imbalance, where malignant cases r...

An Effective Breast Cancer Classification System Using Multiple Feature Extraction Techniques with Multi-scale Attention-Based Feature Fusion Model.

Breast cancer ranks as the second leading cause of death in women. Early identification of breast cancer can significantly reduce women's mortality ra...

A Review on Image Processing and Fractal Analysis in Oral Potentially Malignant Disorders.

AIM: The clinical evaluation of patients with oral potentially malignant disorders is primarily based on physical examination and observable clinical ...

Assessing the completeness of reporting in imaging studies using artificial neural network models for cancer diagnosis: Adherence to the TRIPOD-AI guideline.

BACKGROUND: Developments in artificial neural networks (ANNs) offer significant promise for cancer screening and risk prediction, with the potential t...

Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic health records and computed tomography scans.

BACKGROUND AND PURPOSE: Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer sur...

Targeting signaling pathways in lymphoma: From molecular mechanisms to clinical breakthroughs.

The treatment paradigm for lymphoma, a highly heterogeneous group of hematologic malignancies, has been revolutionized by the development of therapies...

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.

Cancer immunotherapy, which leverages the immune system to combat tumor cells, has made significant advancements in oncology treatment in recent years...

What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.

The integration of artificial intelligence into pathology is transforming the assessment of histological and immunohistochemical (IHC) slides, offerin...

Multi-Scale Mapping of Gene Expression from Whole-slide Images for Identifying Phenotype-Associated Subpopulations.

Discovery of phenotype-associated subpopulations is critical for targeted therapies and prognostic biomarker discovery, which requires multi-scale gen...

Comprehensive assessment of postoperative metachronous liver metastasis risk in colon cancer based on inflammatory indicators: a multicenter prospective study.

OBJECTIVE: Insidiousness is a hallmark of metachronous liver metastasis. Owing to the absence of a comprehensive machine-learning model integrating sy...

Comment on "Artificial Intelligence for predicting post-excision recurrence and malignant progression in oral potentially malignant disorders: a retrospective cohort study".

An artificial intelligence-assisted comprehensive model for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma: a diagnostic study.

BACKGROUND: Superficial esophageal squamous cell carcinoma (ESCC) is increasingly detected due to enhanced endoscopic surveillance, yet accurately pre...

Research trends of tumor-associated macrophages in colorectal cancer: a bibliometric analysis.

BACKGROUND: Colorectal cancer (CRC) represents the third most prevalent malignancy worldwide and accounts for the second-highest cancer-related mortal...

Phosphoenolpyruvate carboxykinase 2 as a prognostic biomarker: expression and clinical significance in Group 3 and Group 4 medulloblastoma.

PURPOSE: Despite Phosphoenolpyruvate carboxykinase 2 (PCK2) has attracted growing attention as a potential biomarker in cancer research, its role in m...

AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.

Isoform-selective inhibition of class I phosphoinositide 3-kinases (PI3Ks) remains a major challenge in oncology and immune-mediated diseases, where d...

Combination Immunotherapy as a Promising Strategy to Overcome Immunotherapy Resistance: From Emergence to Next-Generation Approaches.

Advances in immunotherapy, particularly with immune checkpoint inhibitors, have improved clinical outcomes in various cancer subtypes; however, resist...

Automated Delineation of Putative Non-Contrast-Enhancing Tumor in Glioblastoma: Prognostic Insights.

BACKGROUND: Precise delineation of non-contrast-enhancing tumor (nCET) in glioblastoma (GB) is critical for maximal safe resection, yet routine imagin...

Integrative machine learning and molecular docking reveal Narirutin targets ABCC1/ABCG2 for immune microenvironment remodeling in colorectal cancer.

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers worldwide, with late-stage diagnosis often associated with poor prognosis. The...