Oncology/Hematology

Integration of Bulk RNA and Single-Cell Analyses Reveal Distinct Expression Patterns of Anoikis-Related Genes and the Immunosuppressive Role of NQO1 Macrophages in Hepatocellular Carcinoma.

Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes involved remain to be identified. This study aimed to investigate the prognostic value and impact of anoikis-related genes (ARGs) on the immunosuppressive microenvironment in HCC patients through the integration of bulk RNA and single-cell RNA sequencing ...

An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks.

Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus molecular subtype (CMS) is a transcriptome-based system classifying CRC tumors into four subtypes with different characteristics: CMS1 (immune), CMS2 (...

Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades,...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to c...

Intercept Cancer: Cancer Pre-Screening with Large Scale Healthcare Foundation Models

Cancer screening, leading to early detection, saves lives. Unfortunately, existing screening techniques require expensive and intrusive medical proc...

PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset

Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognosti...

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching

Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diver...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cance...

Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma.

BACKGROUND: The presence of occult lymph node metastasis (OLNM) has significant implications for the staging, treatment and prognosis of patients with...

Opportunities and challenges in lung cancer care in the era of large language models and vision language models.

Lung cancer remains the leading cause of cancer-related deaths globally. Over the past decade, the development of artificial intelligence (AI) has sig...

Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.

BACKGROUND: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCL...

Integrated analysis of uterine leiomyosarcoma and leiomyoma utilizing TCGA and GEO data: a WGCNA and machine learning approach.

BACKGROUND: Uterine sarcoma is a gynecological mesenchymal tumor with an elusive pathogenesis. The uterine leiomyosarcoma (LMS) is the most common sub...

Deep learning for fine-grained molecular-based colorectal cancer classification.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies globally and a major cause of cancer-related deaths. In the molecular diagn...

Mammogram mastery: Breast cancer image classification using an ensemble of deep learning with explainable artificial intelligence.

Breast cancer is a serious public health problem and is one of the leading causes of cancer-related deaths in women worldwide. Early detection of the ...

MLOmics: Cancer Multi-Omics Database for Machine Learning.

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer...

Intelligent deep learning model for targeted cancer drug delivery.

Nanotechnology and information communication technology (ICT) are being combined to develop innovative drug delivery systems for targeted sites, such ...

Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.

BACKGROUND: Macrophages are closely associated with the progression of Kidney renal clear cell carcinoma (KIRC) and can influence programmed cell deat...

Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center.

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handl...

Improved Accuracy in Pelvic Tumor Resections Using a Real-Time Vision-Guided Surgical System

Pelvic bone tumor resections remain significantly challenging due to complex three-dimensional anatomy and limited surgical visualization. Current n...

ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer

Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learni...