1
|
Yoo D, Divard G, Raynaud M, Cohen A, Mone TD, Rosenthal JT, Bentall AJ, Stegall MD, Naesens M, Zhang H, Wang C, Gueguen J, Kamar N, Bouquegneau A, Batal I, Coley SM, Gill JS, Oppenheimer F, De Sousa-Amorim E, Kuypers DRJ, Durrbach A, Seron D, Rabant M, Van Huyen JPD, Campbell P, Shojai S, Mengel M, Bestard O, Basic-Jukic N, Jurić I, Boor P, Cornell LD, Alexander MP, Toby Coates P, Legendre C, Reese PP, Lefaucheur C, Aubert O, Loupy A. A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. Nat Commun 2024; 15:554. [PMID: 38228634 DOI: 10.1038/s41467-023-44595-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/21/2023] [Indexed: 01/18/2024] Open
Abstract
In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.
Collapse
Affiliation(s)
- Daniel Yoo
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
| | - Gillian Divard
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
- Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Marc Raynaud
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
| | | | | | | | - Andrew J Bentall
- Division of Nephrology and Hypertension, Mayo Clinic Transplant Center, Rochester, MN, USA
| | | | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Huanxi Zhang
- Organ Transplant Center, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Changxi Wang
- Organ Transplant Center, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Juliette Gueguen
- Néphrologie-Immunologie Clinique, Hôpital Bretonneau, CHU Tours, Tours, France
| | - Nassim Kamar
- Department of Nephrology and Organ Transplantation, Paul Sabatier University, INSERM, Toulouse, France
| | - Antoine Bouquegneau
- Department of Nephrology-Dialysis-Transplantation, Centre hospitalier universitaire de Liège, Liège, Belgium
| | - Ibrahim Batal
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Shana M Coley
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - John S Gill
- Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Federico Oppenheimer
- Kidney Transplant Department, Hospital Clínic i Provincial de Barcelona, Barcelona, Spain
| | - Erika De Sousa-Amorim
- Kidney Transplant Department, Hospital Clínic i Provincial de Barcelona, Barcelona, Spain
| | - Dirk R J Kuypers
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Antoine Durrbach
- Department of Nephrology, AP-HP Hôpital Henri Mondor, Créteil, Île de France, France
| | - Daniel Seron
- Nephrology Department, Hospital Vall d'Hebrón, Autonomous University of Barcelona, Barcelona, Spain
| | - Marion Rabant
- Department of Pathology, Necker-Enfants Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Jean-Paul Duong Van Huyen
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
- Department of Pathology, Necker-Enfants Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Patricia Campbell
- Faculty of Medicine & Dentistry - Laboratory Medicine & Pathology Dept, University of Alberta, Edmonton, AB, Canada
| | - Soroush Shojai
- Faculty of Medicine & Dentistry - Laboratory Medicine & Pathology Dept, University of Alberta, Edmonton, AB, Canada
| | - Michael Mengel
- Faculty of Medicine & Dentistry - Laboratory Medicine & Pathology Dept, University of Alberta, Edmonton, AB, Canada
| | - Oriol Bestard
- Nephrology Department, Hospital Vall d'Hebrón, Autonomous University of Barcelona, Barcelona, Spain
| | - Nikolina Basic-Jukic
- Department of nephrology, arterial hypertension, dialysis and transplantation, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Ivana Jurić
- Department of nephrology, arterial hypertension, dialysis and transplantation, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Lynn D Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Mariam P Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - P Toby Coates
- Department of Renal and Transplantation, University of Adelaide, Royal Adelaide Hospital Campus, Adelaide, SA, Australia
| | - Christophe Legendre
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
- Department of Kidney Transplantation, Necker-Enfants Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Peter P Reese
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadephia, PA, USA
| | - Carmen Lefaucheur
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
- Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Olivier Aubert
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France
- Department of Kidney Transplantation, Necker-Enfants Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Alexandre Loupy
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015, Paris, France.
- Department of Kidney Transplantation, Necker-Enfants Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.
| |
Collapse
|
2
|
Besusparis J, Morkunas M, Laurinavicius A. A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images. J Imaging 2023; 9:220. [PMID: 37888327 PMCID: PMC10607091 DOI: 10.3390/jimaging9100220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/26/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. MATERIALS AND METHODS We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. RESULTS A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers. CONCLUSIONS We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.
Collapse
Affiliation(s)
- Justinas Besusparis
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| | - Mindaugas Morkunas
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| | - Arvydas Laurinavicius
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| |
Collapse
|
4
|
Denic A, Gaddam M, Moustafa A, Mullan AF, Luehrs AC, Sharma V, Thompson RH, Smith ML, Alexander MP, Lerman LO, Barisoni L, Rule AD. Tubular and Glomerular Size by Cortex Depth as Predictor of Progressive CKD after Radical Nephrectomy for Tumor. J Am Soc Nephrol 2023; 34:1535-1545. [PMID: 37430426 PMCID: PMC10482069 DOI: 10.1681/asn.0000000000000180] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/08/2023] [Indexed: 07/12/2023] Open
Abstract
SIGNIFICANCE STATEMENT Glomerular size differs by cortex depth. Larger nephrons are prognostic of progressive kidney disease, but it is unknown whether this risk differs by cortex depth or by glomeruli versus proximal or distal tubule size. We studied the average minor axis diameter in oval proximal and distal tubules separately and by cortex depth in patients who had radical nephrectomy to remove a tumor from 2019 to 2020. In adjusted analyses, larger glomerular volume in the middle and deep cortex predicted progressive kidney disease. Wider proximal tubular diameter did not predict progressive kidney disease independent of glomerular volume. Wider distal tubular diameter showed a gradient of strength of prediction of progressive kidney disease in the more superficial cortex than in the deep cortex. BACKGROUND Larger nephrons are prognostic of progressive kidney disease, but whether this risk differs by nephron segments or by depth in the cortex is unclear. METHODS We studied patients who underwent radical nephrectomy for a tumor between 2000 and 2019. Large wedge kidney sections were scanned into digital images. We estimated the diameters of proximal and distal tubules by the minor axis of oval tubular profiles and estimated glomerular volume with the Weibel-Gomez stereological model. Analyses were performed separately in the superficial, middle, and deep cortex. Cox proportional hazard models assessed the risk of progressive CKD (dialysis, kidney transplantation, sustained eGFR <10 ml/min per 1.73 m 2 , or a sustained 40% decline from the postnephrectomy baseline eGFR) with glomerular volume or tubule diameters. At each cortical depth, models were unadjusted, adjusted for glomerular volume or tubular diameter, and further adjusted for clinical characteristics (age, sex, body mass index, hypertension, diabetes, postnephrectomy baseline eGFR, and proteinuria). RESULTS Among 1367 patients were 62 progressive CKD events during a median follow-up of 4.5 years. Glomerular volume predicted CKD outcomes at all depths, but only in the middle and deep cortex after adjusted analyses. Proximal tubular diameter also predicted progressive CKD at any depth but not after adjusted analyses. Distal tubular diameter showed a gradient of more strongly predicting progressive CKD in the superficial than deep cortex, even in adjusted analysis. CONCLUSIONS Larger glomeruli are independent predictors of progressive CKD in the deeper cortex, whereas in the superficial cortex, wider distal tubular diameters are an independent predictor of progressive CKD.
Collapse
Affiliation(s)
- Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Mrunanjali Gaddam
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Amr Moustafa
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Aidan F. Mullan
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Anthony C. Luehrs
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Vidit Sharma
- Department of Urology, Mayo Clinic, Rochester, Minnesota
| | | | - Maxwell L. Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Lilach O. Lerman
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Laura Barisoni
- Department of Pathology and Medicine, Duke University, Durham, North Carolina
| | - Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| |
Collapse
|
5
|
van der Kamp A, de Bel T, van Alst L, Rutgers J, van den Heuvel-Eibrink MM, Mavinkurve-Groothuis AMC, van der Laak J, de Krijger RR. Automated Deep Learning-Based Classification of Wilms Tumor Histopathology. Cancers (Basel) 2023; 15:cancers15092656. [PMID: 37174121 PMCID: PMC10177041 DOI: 10.3390/cancers15092656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/05/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
(1) Background: Histopathological assessment of Wilms tumors (WT) is crucial for risk group classification to guide postoperative stratification in chemotherapy pre-treated WT cases. However, due to the heterogeneous nature of the tumor, significant interobserver variation between pathologists in WT diagnosis has been observed, potentially leading to misclassification and suboptimal treatment. We investigated whether artificial intelligence (AI) can contribute to accurate and reproducible histopathological assessment of WT through recognition of individual histopathological tumor components. (2) Methods: We assessed the performance of a deep learning-based AI system in quantifying WT components in hematoxylin and eosin-stained slides by calculating the Sørensen-Dice coefficient for fifteen predefined renal tissue components, including six tumor-related components. We trained the AI system using multiclass annotations from 72 whole-slide images of patients diagnosed with WT. (3) Results: The overall Dice coefficient for all fifteen tissue components was 0.85 and for the six tumor-related components was 0.79. Tumor segmentation worked best to reliably identify necrosis (Dice coefficient 0.98) and blastema (Dice coefficient 0.82). (4) Conclusions: Accurate histopathological classification of WT may be feasible using a digital pathology-based AI system in a national cohort of WT patients.
Collapse
Affiliation(s)
- Ananda van der Kamp
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 24, 3584 CS Utrecht, The Netherlands
| | - Thomas de Bel
- Department of Pathology, Radboud University Medical Center, Geert Grooteplein 1, 6500 HB Nijmegen, The Netherlands
| | - Ludo van Alst
- Department of Pathology, Radboud University Medical Center, Geert Grooteplein 1, 6500 HB Nijmegen, The Netherlands
| | - Jikke Rutgers
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 24, 3584 CS Utrecht, The Netherlands
| | | | | | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Geert Grooteplein 1, 6500 HB Nijmegen, The Netherlands
- Center for Medical Image Science and Visualization, Linköping University, 581 83 Linköping, Sweden
| | - Ronald R de Krijger
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 24, 3584 CS Utrecht, The Netherlands
- Department of Pathology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| |
Collapse
|
6
|
McElliott MC, Al-Suraimi A, Telang AC, Ference-Salo JT, Chowdhury M, Soofi A, Dressler GR, Beamish JA. High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury. Sci Rep 2023; 13:6361. [PMID: 37076596 PMCID: PMC10115810 DOI: 10.1038/s41598-023-33433-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/12/2023] [Indexed: 04/21/2023] Open
Abstract
Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.
Collapse
Affiliation(s)
- Madison C McElliott
- Division of Nephrology, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, SPC 5364, Ann Arbor, MI, 48109, USA
| | - Anas Al-Suraimi
- Division of Nephrology, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, SPC 5364, Ann Arbor, MI, 48109, USA
| | - Asha C Telang
- Division of Nephrology, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, SPC 5364, Ann Arbor, MI, 48109, USA
| | - Jenna T Ference-Salo
- Division of Nephrology, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, SPC 5364, Ann Arbor, MI, 48109, USA
| | - Mahboob Chowdhury
- Division of Nephrology, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, SPC 5364, Ann Arbor, MI, 48109, USA
| | - Abdul Soofi
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Jeffrey A Beamish
- Division of Nephrology, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, SPC 5364, Ann Arbor, MI, 48109, USA.
| |
Collapse
|