Next-Generation Morphometry for pathomics-data mining in histopathology

Hölscher, David L.; Bouteldja, Nassim; Joodaki, Mehdi; Russo, María Luisa; Lan, Yu-Chia; Sadr, Alireza Vafaei; Cheng, Mingbo; Tesař, Vladimı́r; Stillfried, Saskia von; Klinkhammer, Barbara M.; Barratt, Jonathan; Floege, Jürgen; Roberts, Ian S.; Coppo, Rosanna; Costa, Ivan G.; Bülow, Roman David

doi:10.1038/s41467-023-36173-0

Cited by 49 publications

(51 citation statements)

References 54 publications

(43 reference statements)

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“…A blinded, deep learning–based digital analysis of segmented kidney tissue (pathomics) was used to minimize sources of analytical bias (Figure 2, A and B). 11,12 While renin-angiotensin-system inhibitor (RASi) or dual RAS/SGLT2 inhibition had only minor effects on the abnormalities in the tubulointerstitial compartment, triple therapy prevented these changes close to the level of wild-type controls (Figure 2, C and D, Supplemental Figure 8). Picro-Sirius red-stained and α stained kidney sections further demonstrated the significant amelioration of interstitial fibrosis with triple therapy compared with the other groups (Supplemental Figure 9).…”

Section: Resultsmentioning

confidence: 99%

Finerenone Added to RAS/SGLT2 Blockade for CKD in Alport Syndrome. Results of a Randomized Controlled Trial with Col4a3 −/− Mice

Zhu

Rosenkranz

Kusunoki

et al. 2023

JASN

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Background: Dual inhibition of the renin-angiotensin system (RAS) plus sodium-glucose transporter (SGLT)-2 or the mineralocorticoid receptor (MR) demonstrated additive renoprotective effects in large clinical trials. We hypothesized that triple therapy with RAS/SGLT2/MR inhibitors would be superior to dual RAS/SGLT2 blockade in attenuating CKD progression. Methods: We performed a preclinical randomized controlled trial (PCTE0000266) in Col4a3-deficient mice with established Alport nephropathy. Treatment was initiated late (6 weeks of age) in mice with elevated serum creatinine and albuminuria and with glomerulosclerosis, interstitial fibrosis, and tubular atrophy. We block-randomized 40 male and 40 female mice to either nil (vehicle) or late onset food admixes of ramipril monotherapy (10 mg/kg), ramipril plus empagliflozin (30 mg/kg), or ramipril plus empagliflozin plus finerenone (10 mg/kg). Primary endpoint was mean survival. Results: Mean survival was 63.7 ± 10.0 days (vehicle), 77.3 ± 5.3 days (ramipril), 80.3 ± 11.0 days (dual), and 103.1 ± 20.3 days (triple). Sex did not affect outcome. Histopathology, pathomics, and RNA sequencing revealed that finerenone mainly suppressed the residual interstitial inflammation and fibrosis despite dual RAS/SGLT2 inhibition. Conclusion: Experiments in mice suggest that triple RAS/SGLT2/MR blockade may substantially improve renal outcomes in Alport syndrome and possibly other progressive chronic kidney diseases due to synergistic effects on the glomerular and tubulointerstitial compartments.

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Section: Resultsmentioning

confidence: 99%

Finerenone Added to RAS/SGLT2 Blockade for CKD in Alport Syndrome. Results of a Randomized Controlled Trial with Col4a3 −/− Mice

Zhu

Rosenkranz

Kusunoki

et al. 2023

JASN

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“…For example, periodic acid-Schiff (PAS) staining is used routinely to study kidney disease. Recently, U-Net-based architectures have been reported for the segmentation of tissue compartments in PAS-stained kidney sections 16 , 19 , 20 . These tools use a large set of training data to produce remarkably accurate results.…”

Section: Discussionmentioning

confidence: 99%

“…In theory, these approaches can overcome the time and cost limitations of ROI-based strategies by performing analysis computationally over areas impractical to analyze using manual or semi-automated techniques. Despite their potential, these approaches have received only limited attention for studying kidney disease 8 , 9 , 15 – 20 , in part because deploying these techniques across large numbers of samples in a cost-effective fashion is challenging.…”

Section: Introductionmentioning

confidence: 99%

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury

McElliott¹,

Al-Suraimi²,

Telang³

et al. 2023

Sci Rep

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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.

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“…14 In the study of Hölscher et al, computational methods were also used to analyze renal WSIs and report morphometrics. 10 These measurements were reported from 17 samples from an internal biopsy cohort from the Institute of Pathology in Aachen that were classified as histologically normal. Glomeruli and tubules, along with other renal structures, were segmented through a DL pipeline and quantified from their associated segmentation masks.…”

Section: Discussionmentioning

confidence: 99%

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

Lucarelli,

Ginley,

Zee

et al. 2023

Kidney360

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Background: Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and estimated glomerular filtration rate (eGFR) in a multinational set of reference kidney tissue sections. Methods: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. Results: Our deep-learning model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased, as a function of increasing age. Conclusions: Using deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis.

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Next-Generation Morphometry for pathomics-data mining in histopathology

Cited by 49 publications

References 54 publications

Finerenone Added to RAS/SGLT2 Blockade for CKD in Alport Syndrome. Results of a Randomized Controlled Trial with Col4a3 −/− Mice

Finerenone Added to RAS/SGLT2 Blockade for CKD in Alport Syndrome. Results of a Randomized Controlled Trial with Col4a3 −/− Mice

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

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