Background Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis.

Methods Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset.

Results N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA.

Conclusions Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: FF, JJ and AJ have received financial support from NIH funding list in the acknowledgement. JZ has received financial support from NIDDK and NCATS for the submitted work and received grants from Boehringer-Ingelheim, Travere Therapeutics, Reliant Glycosciences, HiBio, and Takeda Pharmaceuticals in the past 3 years. JZ has also received an honorarium for technical expert panel participation from Booz Allen Hamilton. LM has received financial support from NIDDK and NCATS for the submitted work and received grants from Boehringer-Ingelheim, Travere Therapeutics, Reliant Glycosciences, HiBio and Takeda Pharmaceuticals. LM has also received consulting fee from Novartis, Calliditas and Travere and payment for educational events from WebMD/Medscape and MedLive/PlatformQ. JR has received grants from National Science Foundation Graduate Research Fellowship. LBH has received grants from NIDDK CureGN-Penn PCC, NIDDK Nephrotic Syndrome Rare Disease Clinical Research Network III and NIDDK Computational Pathology for Proteinuric Glomerulopathies. Additionally, LBH holds a leadership role in the Scientific Advisory Board of NephCure Kidney International. JH has received grants from NIH and Department of Defense. AM is an equity holder in Picture Health, Elucid Bioimaging, and Inspirata Inc. Currently he serves on the advisory board of Picture Health, and SimBioSys. AM currently consults for Takeda Inc. AM also has sponsored research agreements with AstraZeneca and Bristol Myers-Squibb. His technology has been licensed to Picture Health and Elucid Bioimaging. AM is also involved in 2 different R01 grants with Inspirata Inc. AM also serves as a member for the Frederick National Laboratory Advisory Committee. LB has received grants from NIH fundings listed in Acknowledgment, Nephcure and Haller Foundation. LB has also participated on a Data Safety Monitoring Board or Advisory Board for Vertex and holds a leadership role in the International Society of Glomerular Diseases.

Research reported in this publication was supported by 1) the National Institute of Health (NIH) under the following awards: i) by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) under the award number 2R01DK118431-04; ii) the National Cancer Institute (NCI) under award numbers R01LM013864, R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01, and 1U01DK133090; iii) the National Heart, Lung and Blood Institute under award numbers 1R01HL15127701A1, R01HL15807101A1; iv) the National Institute of Biomedical Imaging and Bioengineering under award number 1R43EB028736-01; and v) the National Center for Research Resources under award number 1 C06 RR12463-01, 2) VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service. 3) the Office of the Assistant Secretary of Defense for Health Affairs, through i) the Breast Cancer Research Program (W81XWH-19-1-0668), ii) the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), iii) the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), iv) the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345). 4) the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, and Astrazeneca. 4) The Nephrotic Syndrome Study Network (NEPTUNE) is part of the Rare Diseases Clinical Research Network (RDCRN), which is funded by the National Institutes of Health (NIH) and led by the National Center for Advancing Translational Sciences (NCATS) through its Division of Rare Diseases Research Innovation (DRDRI). NEPTUNE is funded under grant number U54DK083912 as a collaboration between NCATS and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Additional funding and/or programmatic support is provided by the University of Michigan, NephCure Kidney International, Alport Syndrome Foundation, and the Halpin Foundation. RDCRN consortia are supported by the RDCRN Data Management and Coordinating Center (DMCC), funded by NCATS and the National Institute of Neurological Disorders and Stroke (NINDS) under U2CTR002818. 5) Additional support was also provided by NephCure and the Henry E. Haller, Jr. Foundation. 6) Funding for the CureGN consortium is provided by U24DK100845 (formerly UM1DK100845), U01DK100846 (formerly UM1DK100846), U01DK100876 (formerly UM1DK100876), U01DK100866 (formerly UM1DK100866), and U01DK100867 (formerly UM1DK100867) from the NIDDK/NIH. Patient recruitment is supported by NephCure. Dates of funding for first phase of CureGN was 9/16/2013-5/31/2019. 7) This work was supported by the National Science Foundation Graduate Research Fellowship [DGE-2236662 to JR]

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The DUHS IRB has determined that the following protocol meets the definition of research not involving human subjects as described in 45 CFR 46.102(f), 21 CFR 56.102(e) and 21 CFR 812.3(p) and satisfies the Privacy Rule as described in 45CFR164.514. Protocol ID: Pro00108417 Reference ID: Pro00108417-INIT-1.0 Protocol Title: Computational Pathology of Proteinuric Disease II Principal Investigator: Laura Barisoni 6810708, M.D. This IRB Declaration is in effect from May 31, 2021 and does not expire. However, please be advised that any changes to the proposed research will require re-review by the IRB.

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