Deep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia

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dc.creatorXinyi Zhang
dc.creatorFrederico O. Gleber Netto
dc.creatorShidan Wang
dc.creatorRoberta Rayra Martins Chaves
dc.creatorRicardo Santiago Gomez
dc.creatorNadarajah Vigneswaran
dc.creatorArunangshu Sarkar
dc.creatorWilliam N. William Jr.
dc.creatorVassiliki Papadimitrakopoulou
dc.creatorMichelle Williams
dc.creatorDiana Bell
dc.creatorDoreen Palsgrove
dc.creatorJustin Bishop
dc.creatorJohn V. Heymach
dc.creatorAnn M. Gillenwater
dc.creatorJeffrey N. Myers
dc.creatorRenata Ferrarotto
dc.creatorScott M. Lippman
dc.creatorCurtis Rg Pickering
dc.creatorGuanghua Xiao
dc.date.accessioned2025-02-27T17:29:44Z
dc.date.accessioned2025-09-09T01:06:07Z
dc.date.available2025-02-27T17:29:44Z
dc.date.issued2023
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1002/cam4.5478
dc.identifier.issn2045-7634
dc.identifier.urihttps://hdl.handle.net/1843/80498
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofCancer Medicine
dc.rightsAcesso Aberto
dc.subjectCarcinogenesis
dc.subjectDisease progression
dc.subjectLeukoplakia, oral
dc.subjectConvolutional neural network
dc.subjectMouth neoplasms
dc.subject.otherCarcinogenesis
dc.subject.otherConvolutional neural network
dc.subject.otherDisease progression
dc.subject.otherOral leukoplakia
dc.subject.otherPatient prognosis
dc.subject.otherPrecancer
dc.subject.otherWhole slide imaging
dc.titleDeep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia
dc.typeArtigo de periódico
local.citation.epage7518
local.citation.issue6
local.citation.spage7508
local.citation.volume12
local.description.resumoBackground: Oral leukoplakia (OL) is associated with an increased risk for oral cancer (OC) development. Prediction of OL cancer progression may contribute to decreased OC morbidity and mortality by favoring early intervention. Current OL progression risk assessment approaches face large interobserver variability and is weakly prognostic. We hypothesized that convolutional neural networks (CNN)-based histology image analyses could accelerate the discovery of better OC progression risk models. Methods: Our CNN-based oral mucosa risk stratification model (OMRS) was trained to classify a set of nondysplastic oral mucosa (OM) and a set of OC H&E slides. As a result, the OMRS model could identify abnormal morphological features of the oral epithelium. By applying this model to OL slides, we hypothesized that the extent of OC-like features identified in the OL epithelium would correlate with its progression risk. The OMRS model scored and categorized the OL cohort (n = 62) into high- and low-risk groups. Results: OL patients classified as high-risk (n = 31) were 3.98 (95% CI 1.36-11.7) times more likely to develop OC than low-risk ones (n = 31). Time-to-progression significantly differed between high- and low-risk groups (p = 0.003). The 5-year OC development probability was 21.3% for low-risk and 52.5% for high-risk patients. The predictive power of the OMRS model was sustained even after adjustment for age, OL site, and OL dysplasia grading (HR = 4.52, 1.5-13.7). Conclusion: The ORMS model successfully identified OL patients with a high risk of OC development and can potentially benefit OC early diagnosis and prevention policies.
local.identifier.orcidhttps://orcid.org/0000-0002-7175-426X
local.identifier.orcidhttps://orcid.org/0000-0001-7847-9902
local.identifier.orcidhttps://orcid.org/0000-0002-3561-215X
local.identifier.orcidhttps://orcid.org/0000-0001-9387-9883
local.identifier.orcidhttps://orcid.org/0000-0001-6182-9232
local.identifier.orcidhttps://orcid.org/0000-0001-8770-8009
local.identifier.orcidhttps://orcid.org/0000-0002-6995-1918
local.identifier.orcidhttps://orcid.org/0000-0003-3172-1399
local.identifier.orcidhttps://orcid.org/0000-0002-0935-0037
local.identifier.orcidhttps://orcid.org/0000-0002-7942-728X
local.publisher.countryBrasil
local.publisher.departmentFAO - DEPARTAMENTO DE CLÍNICA
local.publisher.initialsUFMG
local.url.externahttps://onlinelibrary.wiley.com/doi/10.1002/cam4.5478

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