2019
A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk
Klimov S, Miligy I, Gertych A, Jiang Y, Toss M, Rida P, Ellis I, Green A, Krishnamurti U, Rakha E, Aneja R. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Research 2019, 21: 83. PMID: 31358020, PMCID: PMC6664779, DOI: 10.1186/s13058-019-1165-5.Peer-Reviewed Original ResearchConceptsDCIS patientsRecurrence riskDuctal carcinomaScreen-detected breast cancerLong-term outcome dataBreast-conserving surgeryUnmet clinical needNottingham University HospitalsPositive predictive valueAdjuvant radiationIpsilateral recurrenceAdditional therapyMethodsThe cohortHazard ratioLocal recurrenceClinicopathological markersClinicopathological variablesPrimary tumorUniversity HospitalLymphocyte regionBreast cancerOutcome dataBenign ductsPredictive valuePatients
2017
Tumor-infiltrating lymphocytes are significantly associated with better overall survival and disease-free survival in triple-negative but not estrogen receptor–positive breast cancers
Krishnamurti U, Wetherilt C, Yang J, Peng L, Li X. Tumor-infiltrating lymphocytes are significantly associated with better overall survival and disease-free survival in triple-negative but not estrogen receptor–positive breast cancers. Human Pathology 2017, 64: 7-12. PMID: 28153508, DOI: 10.1016/j.humpath.2017.01.004.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorBiopsyBreast NeoplasmsChemotherapy, AdjuvantDisease ProgressionDisease-Free SurvivalFemaleHumansLogistic ModelsLymphatic MetastasisLymphocytes, Tumor-InfiltratingMastectomyMultivariate AnalysisNeoplasm GradingNeoplasm Recurrence, LocalNeoplasm StagingOdds RatioPredictive Value of TestsProportional Hazards ModelsReceptors, EstrogenRisk FactorsTime FactorsTreatment OutcomeTriple Negative Breast NeoplasmsConceptsTriple-negative breast cancerTumor-infiltrating lymphocytesDisease-free survivalBetter overall survivalLymph node statusOverall survivalBreast cancerNeoadjuvant treatmentLymphovascular invasionEstrogen receptor-positive breast cancerReceptor-positive breast cancerOncotype DX recurrence scoreOncotype DX scorePossible prognostic valueDX recurrence scoreNottingham histologic gradeNeoadjuvant settingTILs correlateNegative associationNode statusPrognostic valueRecurrence scoreHistologic gradePrognostic parametersPathological responseDistinctions in Breast Tumor Recurrence Patterns Post-Therapy among Racially Distinct Populations
Wright N, Xia J, Cantuaria G, Klimov S, Jones M, Neema P, Il’yasova D, Krishnamurti U, Li X, Reid M, Gupta M, Rida P, Osan R, Aneja R. Distinctions in Breast Tumor Recurrence Patterns Post-Therapy among Racially Distinct Populations. PLOS ONE 2017, 12: e0170095. PMID: 28085947, PMCID: PMC5234824, DOI: 10.1371/journal.pone.0170095.Peer-Reviewed Original ResearchConceptsAdjuvant therapyNeoadjuvant chemotherapyRecurrence rateTumor recurrenceClinical studiesBreast cancer recurrence ratesBreast tumor recurrenceCohort of patientsBreast cancer patientsRate of recurrenceCancer recurrence rateAfrican American patientsFirst clinical studyHigh incidence rateEuropean American patientsForm of treatmentLocal recurrenceClinical outcomesPost therapyCancer patientsInvasive diseaseIncidence rateHigher overall rateRecurrence patternsHigh risk