Application of geostatistical functions and deep features to kidney biopsy images to differentiate focal segmental glomerulosclerosis from minimal change disease

Santos, Justino; Silva, Romuere; Veras, Rodrigo

doi:10.5753/sibgrapi.est.2020.12984

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Each individual tree in the Random Forest outputs a class prediction and the class with the most votes becomes the model's prediction [64]. The Random Forest model introduces extra randomness when growing trees allowing it to be more resistant to overfitting [51,64]. Logistic regression is a model that predicts classes using linear combinations of a feature set [76].…”

Section: Glomerular Classificationmentioning

confidence: 99%

Computational Classification Methods in Renal Pathology

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2024

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When assessing kidney biopsies, pathologists use Light microscopy (LM), immunofluorescence, and electron microscopy to describe and diagnose glomerular lesions and diseases. These are laborious, costly, fraught with inter-observer variability, and can have delays in turn-around time. Thus, computational approaches can be designed as screening and/or diagnostic tools, potentially relieving pathologist time, healthcare resources, while also having the ability to identify novel biomarkers, including subvisual features. This thesis compares a novel Biomarker feature extraction (BFE) model and test several pre- trained deep learning models to diagnose three glomerular diseases using Periodic acid-Schiff (PAS) stained images. The BFE model extracted a panel of 233 explainable biomarkers related to colour, morphology, and microstructural texture that describe underlying pathology. These biomarkers were subsequently narrowed to ten morphological and microstructural texture fea- tures for classification. The proposed pre-trained deep learning models used data augmentation and Gradient-weighted Class Activation Mapping (Grad-CAM) for better performance and inter- pretability. All classification models were then used to classify Minimal change disease (MCD), Membranous nephropathy (MN), and Thin-basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis. The BFE model resulted in a glomerular validation accuracy of 67.6% and testing accuracy of 76.8%. All deep learning approaches had higher validation accuracies (most for VGG16 at 78.5%) but lower testing accuracies. The highest testing accuracy at the glomerular level was VGG16 at 71.9%, while that at the patient-level was InceptionV3 at 73.3%. Patient-level classification results showed higher accuracy for the BFE model (86.76%) compared to all deep learning methods. The BFE model was superior to three established deep learning models. The small dataset was likely contributory to the lower performance of the deep learning models, while model in- terpretability was clearly superior for the BFE. The results highlight the technical performance of both the traditional and deep learning approaches and describe how they may translate to clinical utility. Furthermore, these algorithms can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.

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Section: Glomerular Classificationmentioning

confidence: 99%

Computational Classification Methods in Renal Pathology

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2024

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“…It is utilized in fields such as petroleum geology, earth and atmospheric sciences, agriculture, soil science, and environmental exposure assessment [35][36][37][38][39][40][41][42] . In recent years, geostatistical methods have also been employed in digital pathology images 43,44 . For instance, in a study on breast carcinoma, researchers introduced a novel measurement of heterogeneity using geostatistical methods for histopathological grading tasks 45 .…”

Section: Geostatistical Datamentioning

confidence: 99%

SpatialQPFs: An R package for deciphering cell-cell spatial relationship

2024

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Understanding the spatial dynamics within tissue microenvironments is crucial for deciphering cellular interactions and molecular signaling in living systems. These spatial characteristics govern cell distribution, extracellular matrix components, and signaling molecules, influencing local biochemical and biophysical conditions. Decoding these features offers insights into physiological processes, disease progression, and clinical outcomes. By elucidating spatial relationships between cell types, researchers uncover tissue architecture, cell communication networks, and microenvironment dynamics, aiding in the identification of biomarkers and therapeutic targets. Digital pathology imaging, including Hematoxylin and Eosin (H&E) staining, provides high-resolution histological information that offer intricate insights into cell-cell spatial relationships with greater details. However, current methods for capturing cell-cell spatial interactions are constrained by either methodological scopes or implementations restricted to script-level access. This limitation undermines generalizability and standardization, crucial for ensuring reproducibility. To address these limitations, we introduceSpatialQPFs, an extendable R package designed for extraction of interpretable spatial features from digital pathology images. By leveraging segmented cell information, our package provides researchers with a comprehensive toolkit for applying a range of spatial statistical methods within a stochastic process framework which includes analysis of point pattern data, areal data, and geostatistical data. This allows for a thorough analysis of cell spatial relationships, enhancing the depth and accuracy of spatial insights derived from the tissue, thereby empowering researchers to conduct comprehensive spatial analyses efficiently and reproducibly.

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Application of geostatistical functions and deep features to kidney biopsy images to differentiate focal segmental glomerulosclerosis from minimal change disease

Cited by 2 publications

References 26 publications

Computational Classification Methods in Renal Pathology

Computational Classification Methods in Renal Pathology

SpatialQPFs: An R package for deciphering cell-cell spatial relationship

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