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
Distinctions 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