Performance and limitations of a supervised deep learning approach for the histopathological Oxford Classification of glomeruli with IgA nephropathy.

BACKGROUND AND OBJECTIVE: The Oxford Classification for IgA nephropathy is the most successful example of an evidence-based nephropathology classification system. The aim of our study was to replicate the glomerular components of Oxford scoring with an end-to-end deep learning pipeline that involves automatic glomerular segmentation followed by classification for mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S) and active crescents (C). METHODS: A total number of 1056 periodic acid-Schiff (PAS) whole slide images (WSIs), coming from 386 kidney biopsies, were annotated. Several detection models for glomeruli, based on the Mask R-CNN architecture, were trained on 587 WSIs, validated on 161 WSIs, and tested on 127 WSIs. For the development of segmentation models, 20,529 glomeruli were annotated, of which 16,571 as training and 3958 as validation set. The test set of the segmentation module comprised of 2948 glomeruli. For the Oxford classification, 6206 expert-annotated glomeruli from 308 PAS WSIs were labelled for M, E, S, C and split into a training set of 4298 glomeruli from 207 WSIs, and a test set of 1908 glomeruli. We chose the best-performing models to construct an end-to-end pipeline, which we named MESCnn (MESC classification by neural network), for the glomerular Oxford classification of WSIs. RESULTS: Instance segmentation yielded excellent results with an AP50 ranging between 78.2-80.1 % (79.4 ± 0.7 %) on the validation and 75.1-77.7 % (76.5 ± 0.9 %) on the test set. The aggregated Jaccard Index was between 73.4-75.9 % (75.0 ± 0.8 %) on the validation and 69.1-73.4 % (72.2 ± 1.4 %) on the test set. At granular glomerular level, Oxford Classification was best replicated for M with EfficientNetV2-L with a mean ROC-AUC of 90.2 % and a mean precision/recall area under the curve (PR-AUC) of 81.8 %, best for E with MobileNetV2 (ROC-AUC 94.7 %) and ResNet50 (PR-AUC 75.8 %), best for S with EfficientNetV2-M (mean ROC-AUC 92.7 %, mean PR-AUC 87.7 %), best for C with EfficientNetV2-L (ROC-AUC 92.3 %) and EfficientNetV2-S (PR-AUC 54.7 %). At biopsy-level, correlation between expert and deep learning labels fulfilled the demands of the Oxford Classification. CONCLUSION: We designed an end-to-end pipeline for glomerular Oxford Classification on both a granular glomerular and an entire biopsy level. Both the glomerular segmentation and the classification modules are freely available for further development to the renal medicine community.

Comprehensive aptamer-based screen of 1317 proteins uncovers improved stool protein markers of colorectal cancer.

BACKGROUND: To screen and validate novel stool protein biomarkers of colorectal cancer (CRC). METHODS: A novel aptamer-based screen of 1317 proteins was used to uncover elevated proteins in the stool of patients with CRC, as compared to healthy controls (HCs) in a discovery cohort. Selected biomarker candidates from the discovery cohort were ELISA validated in three independent cross-sectional cohorts comprises 76 CRC patients, 15 adenoma patients, and 63 healthy controls, from two different ethnicities. The expression of the potential stool biomarkers within CRC tissue was evaluated using single-cell RNA-seq datasets. RESULTS: A total of 92 proteins were significantly elevated in CRC samples as compared to HCs in the discovery cohort. Among Caucasians, the 5 most discriminatory proteins among the 16 selected proteins, ordered by their ability to distinguish CRC from adenoma and healthy controls, were MMP9, haptoglobin, myeloperoxidase, fibrinogen, and adiponectin. Except myeloperoxidase, the others were significantly associated with depth of tumor invasion. The 8 stool proteins with the highest AUC values were also discriminatory in a second cohort of Indian CRC patients. Several of the stool biomarkers elevated in CRC were also expressed within CRC tissue, based on the single-cell RNA-seq analysis. CONCLUSIONS: Stool MMP9, fibrinogen, myeloperoxidase, and haptoglobin emerged as promising CRC stool biomarkers, outperforming stool Hemoglobin. Longitudinal studies are warranted to assess the clinical utility of these novel biomarkers in early diagnosis of CRC.

Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification.

Computer-aided diagnosis (CAD) systems must constantly cope with the perpetual changes in data distribution caused by different sensing technologies, imaging protocols, and patient populations. Adapting these systems to new domains often requires significant amounts of labeled data for re-training. This process is labor-intensive and time-consuming. We propose a memory-augmented capsule network for the rapid adaptation of CAD models to new domains. It consists of a capsule network that is meant to extract feature embeddings from some high-dimensional input, and a memory-augmented task network meant to exploit its stored knowledge from the target domains. Our network is able to efficiently adapt to unseen domains using only a few annotated samples. We evaluate our method using a large-scale public lung nodule dataset (LUNA), coupled with our own collected lung nodules and incidental lung nodules datasets. When trained on the LUNA dataset, our network requires only 30 additional samples from our collected lung nodule and incidental lung nodule datasets to achieve clinically relevant performance (0.925 and 0.891 area under receiving operating characteristic curves (AUROC), respectively). This result is equivalent to using two orders of magnitude less labeled training data while achieving the same performance. We further evaluate our method by introducing heavy noise, artifacts, and adversarial attacks. Under these severe conditions, our network's AUROC remains above 0.7 while the performance of state-of-the-art approaches reduce to chance level.

Kidney Level Lupus Nephritis Classification Using Uncertainty Guided Bayesian Convolutional Neural Networks.

The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality. Although various Computer Aided Diagnosis (CAD) systems have been developed for other nephrohistopathological applications, little has been done to accurately classify kidneys based on their kidney level Lupus Glomerulonephritis (LGN) scores. The successful implementation of CAD systems has also been hindered by the diagnosing physician's perceived classifier strengths and weaknesses, which has been shown to have a negative effect on patient outcomes. We propose an Uncertainty-Guided Bayesian Classification (UGBC) scheme that is designed to accurately classify control, class I/II, and class III/IV LGN (3 class) at both the glomerular-level classification task (26,634 segmented glomerulus images) and the kidney-level classification task (87 MRL/lpr mouse kidney sections). Data annotation was performed using a high throughput, bulk labeling scheme that is designed to take advantage of Deep Neural Network's (or DNNs) resistance to label noise. Our augmented UGBC scheme achieved a 94.5% weighted glomerular-level accuracy while achieving a weighted kidney-level accuracy of 96.6%, improving upon the standard Convolutional Neural Network (CNN) architecture by 11.8% and 3.5% respectively.

Quantitative planar array screen of 1000 proteins uncovers novel urinary protein biomarkers of lupus nephritis.

OBJECTIVE: The goal of these studies is to discover novel urinary biomarkers of lupus nephritis (LN). METHODS: Urine from systemic lupus erythematosus (SLE) patients was interrogated for 1000 proteins using a novel, quantitative planar protein microarray. Hits were validated in an independent SLE cohort with inactive, active non-renal (ANR) and active renal (AR) patients, in a cohort with concurrent renal biopsies, and in a longitudinal cohort. Single-cell renal RNA sequencing data from LN kidneys were examined to deduce the cellular origin of each biomarker. RESULTS: Screening of 1000 proteins revealed 64 proteins to be significantly elevated in SLE urine, of which 17 were ELISA validated in independent cohorts. Urine Angptl4 (area under the curve (AUC)=0.96), L-selectin (AUC=0.86), TPP1 (AUC=0.84), transforming growth factor-ß1 (TGFß1) (AUC=0.78), thrombospondin-1 (AUC=0.73), FOLR2 (AUC=0.72), platelet-derived growth factor receptor-ß (AUC=0.67) and PRX2 (AUC=0.65) distinguished AR from ANR SLE, outperforming anti-dsDNA, C3 and C4, in terms of specificity, sensitivity and positive predictive value. In multivariate regression analysis, urine Angptl4, L-selectin, TPP1 and TGFß1 were highly associated with disease activity, even after correction for demographic variables. In SLE patients with serial follow-up, urine L-selectin (followed by urine Angptl4 and TGFß1) were best at tracking concurrent or pending disease flares. Importantly, several proteins elevated in LN urine were also expressed within the kidneys in LN, either within resident renal cells or infiltrating immune cells, based on single-cell RNA sequencing analysis. CONCLUSION: Unbiased planar array screening of 1000 proteins has led to the discovery of urine Angptl4, L-selectin and TGFß1 as potential biomarker candidates for tracking disease activity in LN.

Artificial intelligence and machine learning in nephropathology.

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications. NEW METHODS: Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance. RESULTS: The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD. COMPARISON WITH EXISTING METHODS: Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population. CONCLUSIONS: These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.