Oncology/Hematology

Exploring graph-based models for predicting active compounds against triple-negative breast cancer.

Breast cancer is among the most dominant and rapidly rising cancers, both in India and around the world. Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer, distinguished by the absence of HER2, progesterone, and estrogen receptor expressions. This absence limits treatment options, emphasizing the urgent need to discover or design new drug candidates for T...

Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach.

BACKGROUND: The prognosis of ovarian cancer is closely related to the degree of immune cell infiltration within the tumor microenvironment. However, current methods for assessing immune infiltration have certain subjective limitations. This study aimed to establish an objective assessment model based on machine learning and single-cell RNA sequencing data to provide a basis for the individualized ...

Radiomics in Early Detection of Pancreatic Ductal Adenocarcinoma: A Close Look at Its Current Status and Challenges to Clinical Implementation.

Radiomics is a mathematical approach to medical images to extract quantitative features generating a "radiomics signature." The radiomics workflow inv...

Metastability and Ostwald step rule in the crystallisation of diamond and graphite from molten carbon.

Experimental challenges in determining the phase diagram of carbon at temperatures and pressures near the graphite-diamond-liquid triple point are oft...

Predicting time-to-first cancer diagnosis across multiple cancer types.

Cancer causes over 10 million deaths annually worldwide, with 40.5% of Americans expected to be diagnosed in their lifetime. Early detection is critic...

Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma.

Bone metastasis (BM) is common in high-grade lung neuroendocrine tumors (NETs). This study aimed to use multiple machine learning algorithms to explor...

Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis.

Lymph node micro-metastasis represents the initial stage of breast cancer spread or metastasis. However, the limited size of these hidden lesions rest...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models ...

Machine learning-based prediction model for post-ERCP cholangitis in patients with malignant biliary obstruction: a retrospective multicenter study.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is the preferred palliative treatment for patients with unresectable malignant bilia...

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic to...

Validation of the Toronto recurrence inference using machine-learning for post-transplant hepatocellular carcinoma model.

BACKGROUND: Organ shortages require prioritizing hepatocellular carcinoma (HCC) patients with the highest survival benefit for allografts. While tradi...

Differentiated thyroid cancer and positron emission computed tomography: when, how and why?

INTRODUCTION: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become an indispensable tool in oncology, offering ...

Current Bioinformatics Tools in Precision Oncology.

Integrating bioinformatics tools has profoundly transformed precision oncology by identifying essential molecular targets for personalized treatment. ...

OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.

Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our stu...

Machine learning-driven programmed cell death signature for prognosis and drug candidate discovery in diffuse large B-cell lymphoma: Multi-cohort study and experimental validation.

BACKGROUND: Relapse and drug resistance are major contributor to chemotherapy failure in diffuse large B-cell lymphoma (DLBCL). Programmed cell death ...

A Deep Learning Model for Comprehensive Automated Bone Lesion Detection and Classification on Staging Computed Tomography Scans.

RATIONALE AND OBJECTIVES: A common site of metastases for a variety of cancers is the bone, which is challenging and time consuming to review and impo...

Analysis of Single-Cell RNA-Seq Data to Investigate Tumor Cell Heterogeneity in Uroepithelial Bladder Cancer and Predict Immunotherapy Response.

BACKGROUND: Numerous studies have suggested a close association between cancer stem cells (CSCs) and the tumor microenvironment (TME), suggesting that...

Insights into the Diagnosis, Treatment, and Management of Prion Diseases.

The normal cellular prion protein (PrPC) can misfold into an infectious and pathogenic form (PrPSc) to produce prion diseases, also known as transmiss...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive...

Identification and predictive machine learning models construction of gut microbiota associated with lymph node metastasis in colorectal cancer.

This study focuses on the significant role between gut microbiota and lymph node metastasis (LNM) in colorectal cancer (CRC). By conducting 16S rRNA s...