2020
Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudeček J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. Npj Breast Cancer 2020, 6: 16. PMID: 32411818, PMCID: PMC7217824, DOI: 10.1038/s41523-020-0154-2.Peer-Reviewed Original ResearchTumor-infiltrating lymphocytesComputer-aided diagnosticsPotential of machineAssessment of algorithmsInternational Immuno-Oncology Biomarker Working GroupHER2-positive breast cancerBiomarker Working GroupComputational workflowPrognostic workflowsVisual guidelinesTIL assessmentInfiltrating lymphocytesBreast cancerPredictive featuresSolid tumorsInter-reader variabilityWorkflowClinical validationComputational assessmentRipe opportunityComputational methodsReporting guidelinesLymphocytesVisual scoringClinical translation
2019
Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death
Kulkarni PM, Robinson EJ, Pradhan J, Gartrell-Corrado RD, Rohr BR, Trager MH, Geskin LJ, Kluger HM, Wong PF, Acs B, Rizk EM, Yang C, Mondal M, Moore MR, Osman I, Phelps R, Horst BA, Chen ZS, Ferringer T, Rimm DL, Wang J, Saenger YM. Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death. Clinical Cancer Research 2019, 26: 1126-1134. PMID: 31636101, PMCID: PMC8142811, DOI: 10.1158/1078-0432.ccr-19-1495.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAlgorithmsArea Under CurveBiopsyDeep LearningDisease ProgressionFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedMaleMelanomaMiddle AgedNeoplasm Recurrence, LocalNeural Networks, ComputerRetrospective StudiesRisk FactorsStaining and LabelingSurvival RateYoung AdultConceptsDeep neural network architectureNeural network architectureDeep learningNetwork architectureComputational modelImage sequencesDigital imagesVote aggregationDisease-specific survivalDSS predictionPractical advancesComputational methodsIHC-based methodsImagesGeisinger Health SystemNovel methodGHS patientsArchitectureLearningKaplan-Meier analysisPrimary melanoma tumorsEarly-stage melanomaClinical trial designModelAdjuvant immunotherapy