Oncology/Hematology

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

UNLABELLED: Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this ...

Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears.

The detection of genetic aberrations is crucial for early therapy decisions in acute myeloid leukemia (AML) and recommended for all patients. Because genetic testing is expensive and time consuming, a need remains for cost-effective, fast, and broadly accessible tests to predict these aberrations in this aggressive malignancy. Here, we developed a novel fully automated end-to-end deep learning pip...

The Reactome Pathway Knowledgebase 2024.

The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a ...

Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings.

BACKGROUND: Cancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and nationa...

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the ...

Investigations on druggable gene mutations related to AML/ALL lineage genes in Advanced Phases of CML: Implications in patient-tailored therapy of blast crisis CML in TKI era

Chronic Myeloid Leukemia (CML) is a myeloproliferative stem cell malignancy. Chronic Phase CML (CP-CML) is treatable with overall survival equivalent ...

Attention-based whole-slide image compression achieves pathologist-level pre-screening of multi-organ routine histopathology biopsies

Screening programs for early detection of cancer such as colorectal and cervical cancer have led to an increased demand for histopathological analysis...

Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models

Real-world evidence (RWE) is increasingly recognized as a valuable type of oncology research but extracting fit-for-purpose real-world data (RWD) from...

Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.

The delivery of effective targeted therapies requires comprehensive analyses of the molecular profiling of tumors and matching with clinical phenotype...

PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability.

Building on the success of the Segment Anything Model (SAM) in image segmentation, "PathSAM: SAM for Pathological Images in Oral Cancer Detection" add...

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure.

Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart ...

Unlocking Early Cancer Detection: Leveraging Machine Learning in Cell-Free DNA Analysis for Precision Oncology.

This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning al...

Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy.

This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 33...

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.

BACKGROUND/AIM: Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) mo...

Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.

BACKGROUND/AIM: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing ste...

Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning.

INTRODUCTION: Histological grading of cutaneous squamous cell carcinoma (cSCC) is crucial for prognosis and treatment decisions, but manual grading is...

Robot-assisted retroperitoneal lymph node dissection as primary treatment for stage II seminoma germ cell tumor.

INTRODUCTION: Historically, therapeutic avenues for patients with clinical stage II seminoma germ cell tumors (SGCT) were confined to radiotherapy and...

Evaluation of the efficacy and safety of robot-assisted and video assisted thoracic surgery for early non-small cell lung cancer: A meta-analysis.

BACKGROUND: Radical resection of lung cancer and chemotherapy are the main methods for the treatment of early lung cancer, but surgical treatment is s...

Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still ne...

Machine Learning-derived Multi-omics Prognostic Signature of Pyroptosis-related lncRNA with Regard to ZKSCAN2-DT and Tumor Immune Infiltration in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) has become the most prevalent gastrointestinal malignant tumor, ranking third (10.2%) in incidence and second (9.2...