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
2015
Cytology as a screening tool for anal squamous intraepithelial lesion for HIV positive men: 10-year experience in an inner city hospital
Johnson G, Nguyen M, Krishnamurti U, Seydafkan S, Flowers L, Ehdaivand S, Mosunjac M. Cytology as a screening tool for anal squamous intraepithelial lesion for HIV positive men: 10-year experience in an inner city hospital. Journal Of The American Society Of Cytopathology 2015, 5: 145-153. PMID: 31042517, DOI: 10.1016/j.jasc.2015.08.003.Peer-Reviewed Original ResearchLow-grade squamous intraepithelial lesionsSquamous intraepithelial lesionsHigh-risk patientsAnal cytologyIntraepithelial lesionsInner-city hospitalHuman papillomavirusAnal intraepithelial neoplasia grade 2High-grade squamous intraepithelial lesionsAnal squamous intraepithelial lesionsAnal cytology specimensPercent of patientsAtypical squamous cellsHIV-positive menHuman immunodeficiency virusHigh-risk populationASC-US casesMethod of biopsyDegree of dysplasiaPositive predictive valueCytology-histology correlationRate of detectionClear guidelinesHPV testingAnal carcinoma