2019
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology 2019, 16: 703-715. PMID: 31399699, PMCID: PMC6880861, DOI: 10.1038/s41571-019-0252-y.Peer-Reviewed Original ResearchConceptsArtificial intelligenceMachine learning toolsDigital pathologyUse of AIDeep neural networksLearning toolsStained tissue specimensWhole slide imagesFeature-based methodologyNeural networkIntelligencePotential future opportunitiesMorphometric phenotypesNetworkValidation datasetComputational approachToolMiningEnormous divergenceDatasetImagesPrecision oncologyFrameworkComplex processFuture opportunities
2018
Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens
Rawat RR, Ruderman D, Macklin P, Rimm DL, Agus DB. Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens. Npj Breast Cancer 2018, 4: 32. PMID: 30211313, PMCID: PMC6123433, DOI: 10.1038/s41523-018-0084-4.Peer-Reviewed Original ResearchEstrogen receptor statusInvasive ductal carcinomaER statusEstrogen receptorReceptor statusPercent of cellsHormonal therapyDuctal carcinomaImmunohistochemistry stainingHistology patternPathway statusPatient samplesPathway activationPilot studyNuclear featuresDeep neural networksTissue coresNeural networkReceptorsFuture studiesStatusTissue morphologyBiological featuresDeep learning approachCarcinoma