Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT.

doi:10.1007/s12149-019-01414-0

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

Community

Back to search

Article

Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT.

Annals of nuclear medicine - 1 Jan 2020

Koyasu Sho, Nishio Mizuho, Isoda Hiroyoshi, Nakamoto Yuji, Togashi Kaori

Abstract excerpt

OBJECTIVE: To develop and evaluate a radiomics approach for classifying histological subtypes and epidermal growth factor receptor (EGFR) mutation status in lung cancer on PET/CT images. METHODS: PET/CT images of lung cancer patients were obtained from public databases and used to establish two datasets, respectively to classify histological subtypes (156 adenocarcinomas and 32 squamous cell carcinomas) and EGFR...

Read the complete abstract on PubMed

Topics

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

Share to Aged
Share to Carcinoma, Non-Small-Cell Lung
Share to ErbB Receptors
Share to Fluorodeoxyglucose F18

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Image Processing, Computer-Assisted

Share to Lung Neoplasms

Share to Positron Emission Tomography Computed Tomography

Share to ROC Curve

Source

Open in PubMed

Identifiers

PMID: 31659591
DOI: 10.1007/s12149-019-01414-0

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.