Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis

22 Dec 2024

Abstract excerpt

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Genomics and Diagnostics
Share to Colorectal Cancer Screening and Detection
Share to Genetic factors in colorectal cancer

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 39710952
DOI: 10.1002/path.6376

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysisPMID 39710952Read ↗

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.Semantic proximity Read ↗

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.Semantic proximity Read ↗

Aligning computational pathology with clinical practice for colorectal cancerSemantic proximity Read ↗

Optimized data representation and convolutional neural network model for predicting tumor puritySemantic proximity Read ↗

Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology imagesSemantic proximity Read ↗

Weakly-Supervised Tumor Purity Prediction From Frozen H&E Stained SlidesSemantic proximity Read ↗

Tumour purity as an underlying key factor in tumour mutation detection in colorectal cancerSemantic proximity Read ↗

A Pathology-Interpretable Deep Learning Model for Predicting Microsatellite Instability State in Colorectal Cancer: Validation across Diverse Platforms and Asian CohortsSemantic proximity Read ↗

Improved Tumor Purity Metrics in Next-generation Sequencing for Clinical Practice: The Integrated Interpretation of Neoplastic Cellularity and Sequencing Results (IINCaSe) Approach.Semantic proximity Read ↗

Deep Learning-Based Screening for <i>POLE</i> mutations on Histopathology Slides in Endometrial CancerSemantic proximity Read ↗

PUREE: accurate pan-cancer tumor purity estimation from gene expression dataSemantic proximity Read ↗

Deep learning detects genetic alterations in cancer histology generated by adversarial networksSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.