2023
Deep learning‐based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia
Zhang X, Gleber‐Netto F, Wang S, Martins‐Chaves R, Gomez R, Vigneswaran N, Sarkar A, William W, Papadimitrakopoulou V, Williams M, Bell D, Palsgrove D, Bishop J, Heymach J, Gillenwater A, Myers J, Ferrarotto R, Lippman S, Pickering C, Xiao G. Deep learning‐based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia. Cancer Medicine 2023, 12: 7508-7518. PMID: 36721313, PMCID: PMC10067069, DOI: 10.1002/cam4.5478.Peer-Reviewed Original ResearchConceptsLow-risk groupOral leukoplakiaOL patientsProgression riskOral mucosaHigh-risk patientsOral cancer developmentRisk stratification modelCancer progression riskLarge interobserver variabilityEarly diagnosisHigh riskDysplasia gradingAbnormal morphological featuresOral epitheliumOC developmentEarly interventionLow-risk onesPatientsStratification modelCancer developmentCancer progressionInterobserver variabilityLeukoplakiaRisk
2013
High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma
Mroz EA, Tward AD, Pickering CR, Myers JN, Ferris RL, Rocco JW. High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer 2013, 119: 3034-3042. PMID: 23696076, PMCID: PMC3735618, DOI: 10.1002/cncr.28150.Peer-Reviewed Original ResearchConceptsMutant-allele tumor heterogeneityNeck squamous cell carcinomaSquamous cell carcinomaHigher mutant allele tumor heterogeneityClinical outcomesCell carcinomaWorse outcomesHigh-risk patientsWorse clinical outcomesOverall survival dataShorter overall survivalAdverse treatment outcomesTumor protein p53 (TP53) mutationsHigh genetic heterogeneityGenetic heterogeneityOverall survivalPrognostic valueAdverse outcomesHuman papillomavirusPatient cohortTreatment outcomesIndividual patientsHigh riskPatientsAdvanced stage