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

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients.

World journal of surgical oncology - 16 Jun 2020

Peng Yanbin, Chu Yunfeng, Chen Zhong, Zhou Wen, Wan Shengxiang, Xiao Yingfeng, Zhang Youlong, Li Jialu

Abstract excerpt

BACKGROUND: Accurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis. METHODS: We enrolled in this study a total of 152 patients from TCGA-SKCM (The...

Read the complete abstract on PubMed

Topics

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

Share to Biomarkers, Tumor
Share to Histological Techniques

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Image Processing, Computer-Assisted

Share to Melanoma

Share to Melanoma, Cutaneous Malignant

Share to Skin Neoplasms

Share to Survival Rate

Share to Transcriptome

Source

Open in PubMed Open full text

Identifiers

PMID: 32546168
DOI: 10.1186/s12957-020-01909-5
PMCID: PMC7298832

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients.PMID 32546168Read ↗

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patientsSemantic proximity Read ↗

Predicting the clinical outcome of melanoma using an immune-related gene pairs signature.Semantic proximity Read ↗

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancerSemantic proximity Read ↗

TILseg: Automated Whole Slide-Level Spatial Scoring of Tumor-Infiltrating Lymphocytes Reveals Prognostic Patterns in Triple Negative Breast CancerSemantic proximity Read ↗

Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide imagesSemantic proximity Read ↗

Search and Retrieval in Dermatology Atlases of Histopathology Images for Risk Stratification of Cutaneous Squamous Cell CarcinomaSemantic proximity Read ↗

Integrated Histopathology–Transcriptomic Biomarker Enhances Survival Prediction in HNSCC Patients Treated with ImmunotherapySemantic proximity Read ↗

Multi-stain modelling of histopathology slides for breast cancer prognosis predictionSemantic proximity Read ↗

Image guidance improves freedom from recurrence in superficial radiation therapy for non-melanoma skin cancerSemantic proximity Read ↗

Identification and Validation of Prognostically Relevant Gene Signature in MelanomaSemantic proximity Read ↗

AI Generated Stromal Biomarkers for DCIS Reccurence PredictionSemantic proximity Read ↗

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.Semantic proximity Read ↗

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