Continuous Diagnostic Indices from BanffNET Automated Lesion Scores for Kidney Transplant Pathology: Development and Evaluation of a Diagnostic Prediction Model

BackgroundHistopathological diagnoses in kidney transplant biopsies, as described by the Banff classification, suffer from substantial inter-observer variability and misinterpretation, making them prone to diagnostic errors. Continuous indices based on BanffNET automated lesion scores are proposed to quantify the continuous phenotypic spectrum of kidney transplant biopsies, complementary to pathologist assessment of biopsies. MethodsThe BanffNET model was used to obtain continuous lesion scoring for a training cohort of 2544 biopsies, a large validation cohort of 3863 biopsies, and a multi-reader cohort of 36 biopsies scored by 67 pathologists. A penalized regression approach was used to condense 17 automated lesion scores into four indices, representing the most important dimensions of graft injury: a TCMR/TI Index, an AMR/MVI Index, an Activity Index and a Chronicity Index. The BanffNET Indices were evaluated against Banff diagnoses and histological indices in the training cohort and validation cohort. Since pathologist-assigned Banff lesions and diagnoses suffer from inter-observer variability, associations were also assessed with diagnoses in a multi-reader study, with time to kidney graft failure in the training and validation cohort and with biopsy-based molecular signatures in the validation cohort. ResultsThe BanffNET TCMR/TI Index, AMR/MVI Index and Activity Index showed excellent discrimination of Banff TCMR, Banff MVI and Banff Any Diagnosis respectively, with validation AUCs ranging from 0.80 to 0.92. In addition, the BanffNET Indices explained large amounts of variability in multi-observer reference standards, time to graft failure and molecular biopsy-based diagnostics. In the large majority of cases, variability explained by BanffNET Indices matched or exceeded the variability explained by pathologist-assigned diagnoses and histological indices. ConclusionsBanffNET Indices are reproducible descriptors of whole slide images that could complement and augment kidney transplant biopsy evaluation by pathologists. Associations of BanffNET Indices with multi-observer diagnoses and related outcomes suggest that they capture additional information on tissue injury type and severity on top of pathologist-assigned Banff diagnoses and histological indices.