Analysis of the Histologic Features Associated With Interobserver Variation in Idiopathic Pulmonary Fibrosis

Presence of focal usual interstitial pneumonia is a key prognostic factor in progressive pulmonary fibrosis

AIMS

Progressive pulmonary fibrosis (PPF) is a newly recognised clinical phenotype of interstitial lung diseases in the 2022 interstitial pulmonary fibrosis (IPF) guidelines. This category is based entirely on clinical and radiological factors, and the background histopathology is unknown. Our objective was to investigate the histopathological characteristics of PPF and to examine the correlation between usual interstitial pneumonia (UIP) and prognosis in this new disease type. We hypothesised that the presence of UIP-like fibrosis predicts patients' survival in PPF cases.

METHODS AND RESULTS

We selected 201 cases fulfilling the clinical criteria of PPF from case archives. Cases diagnosed as IPF by a multidisciplinary team were excluded. Whole slide images were evaluated by three pathologists who were blinded to clinical and radiological data. We measured areas of UIP-like fibrosis and calculated what percentage of the total lesion area they occupied. The presence of focal UIP-like fibrosis amounting to 10% or more of the lesion area was seen in 148 (73.6%), 168 (83.6%) and 165 (82.1%) cases for each pathologist, respectively. Agreement of the recognition of UIP-like fibrosis in PPF cases was above κ = 0.6 between all pairs. Survival analysis showed that the presence of focal UIP-like fibrosis correlated with worsened survival under all parameters tested (P < 0.001).

CONCLUSIONS

The presence of UIP-like fibrosis is a core pathological feature of clinical PPF, and its presence within diseased areas is associated with poorer prognosis. This study highlights the importance of considering the presence of focal UIP-like fibrosis in the evaluation and management of PPF.

Unsupervised machine learning identifies predictive progression markers of IPF

OBJECTIVES

To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome.

METHODS

We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center.

RESULTS

Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort.

CONCLUSIONS

Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types.

KEY POINTS

• Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort.

Reproducibility and Feasibility of Classification and National Guidelines for Histological Diagnosis of Canine Mammary Gland Tumours: A Multi-Institutional Ring Study

Simple Summary

Tumours of the mammary gland are common in humans, as in canine species. They are very heterogenous with numerous morphological variants and different biologic behaviours. In the last few decades, several efforts have been made to classify these tumours histologically and establish the level of malignancy by using histologic grading systems. However, reproducibility and diagnostic agreement of such classification and grading have been only rarely assessed. In this study, we tested the variability in diagnoses performed by 15 pathologists using the same classification and grading system. Prior to the study, pathologists agreed on guidelines regarding how to apply these systems. Pathologists worked blindly on 36 digital histologic slides of canine mammary tumours. The agreement was statistically analysed using Cohen’s kappa coefficient that, when equal to 1, indicates perfect agreement. The overall agreement in the identification of hyperplastic-dysplastic/benign/malignant lesions was substantial (kappa 0.76), while outcomes on morphological classification had only a moderate agreement (k = 0.54). Tumour grade assigned by pathologists was the least concordant and kappa could not be calculated. Although promising, the results underline that each diagnostic/grading system should be assessed and optimized for standardization and high diagnostic agreement.

Abstract

Histological diagnosis of Canine Mammary Tumours (CMTs) provides the basis for proper treatment and follow-up. Nowadays, its accuracy is poorly understood and variable interpretation of histological criteria leads to a lack of standardisation and impossibility to compare studies. This study aimed to quantify the reproducibility of histological diagnosis and grading in CMTs. A blinded ring test on 36 CMTs was performed by 15 veterinary pathologists with different levels of education, after discussion of critical points on the Davis-Thompson Foundation Classification and providing consensus guidelines. Kappa statistics were used to compare the interobserver variability. The overall concordance rate of diagnostic interpretations of WP on identification of hyperplasia-dysplasia/benign/malignant lesions showed a substantial agreement (average k ranging from 0.66 to 0.82, with a k-combined of 0.76). Instead, outcomes on ICD-O-3.2 morphological code /diagnosis of histotype had only a moderate agreement (average k ranging from 0.44 and 0.64, with a k-combined of 0.54). The results demonstrated that standardised classification and consensus guidelines can produce moderate to substantial agreement; however, further efforts are needed to increase this agreement in distinguishing benign versus malignant lesions and in histological grading.

Usual interstitial pneumonia: a clinically significant pattern, but not the final word

Usual interstitial pneumonia (UIP) is a concept that is deeply entrenched in clinical practice and the prognostic significance of UIP is well established, but the field continues to suffer from the lack of a true gold standard for diagnosing fibrotic interstitial lung disease (ILD). The meaning and usage of UIP have shifted over time and this term is prone to misinterpretation and poor diagnostic agreement. For pathologists, it is worth reflecting on the limitations of UIP and our true role in the care of patients with ILD, a controversial topic explored in two point-counterpoint editorials published simultaneously in this journal. Current diagnostic guidelines are ambiguous and difficult to apply in clinical practice. Further complicating matters for the pathologist is the paradigm shift that occurred with the advent of anti-fibrotic agents, necessitating increased focus on the most likely etiology of fibrosis rather than simply the pattern of fibrosis when pulmonologists select appropriate therapy. Despite the wealth of information locked in tissue samples that could provide novel insights into fibrotic ILDs, pulmonologists increasingly shy away from obtaining biopsies, likely because pathologists no longer provide sufficient value to offset the risks of a biopsy procedure, and pathologic assessment is insufficiently reliable to meaningfully inform therapeutic decisionmaking. To increase the value of biopsies, pathologists must first recognize the problems with UIP as a diagnostic term. Second, pathologists must realize that the primary goal of a biopsy is to determine the most likely etiology to target with therapy, requiring a shift in diagnostic focus. Third, pathologists must devise and validate new classifications and criteria that are evidence-based, biologically relevant, easy to use, and predictive of outcome and treatment response. Only after the limitations of UIP are understood will pathologists provide maximum diagnostic value from biopsies to clinicians today and advance the field forward.

Progress of Statin Therapy in the Treatment of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease (ILD) characterized by the proliferation of fibroblasts and aberrant accumulation of extracellular matrix. These changes are accompanied by structural destruction of the lung tissue and the progressive decline of pulmonary function. In the past few decades, researchers have investigated the pathogenesis of IPF and sought a therapeutic approach for its treatment. Some studies have shown that the occurrence of IPF is related to pulmonary inflammatory injury; however, its specific etiology and pathogenesis remain unknown, and no effective treatment, with the exception of lung transplantation, has been identified yet. Several basic science and clinical studies in recent years have shown that statins, the traditional lipid-lowering drugs, exert significant antifibrotic effects, which can delay the progression of IPF and impairment of pulmonary function. This article is aimed at summarizing the current understanding of the pathogenesis of IPF, the progress of research on the use of statins in IPF models and clinical trials, and its main molecular targets.

The histologic diagnosis of usual interstitial pneumonia of idiopathic pulmonary fibrosis. Where we are and where we need to go

In the 50 years since its inception by Dr. Liebow, the diagnosis of usual interstitial pneumonia (UIP) by pathologists has changed significantly. This manuscript reviews the progressive history of the histologic diagnosis of UIP and summarizes the current state of histologic UIP and its relationship to the clinical syndrome idiopathic pulmonary fibrosis (IPF). Fibrotic lung disease mimics of UIP/IPF are reviewed and pearls for distinguishing these diseases from UIP/IPF are provided. Strategies for increasing the value of histologic assessment of biopsies in the setting of pulmonary fibrosis are also discussed.

MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low. Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), an original method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist's perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications. Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90 in validation set and AUC 0.86 in test set). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had poorer prognosis (5-year overall survival [OS]: 55.4%) than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for explainable artificial intelligence that can collaborate with humans.

Usual Interstitial Pneumonia in Contemporary Surgical Pathology Practice: Impact of International Consensus Guidelines for Idiopathic Pulmonary Fibrosis on Pathologists

CONTEXT.—

Idiopathic pulmonary fibrosis is a clinical syndrome characterized by the presence of usual interstitial pneumonia (UIP) radiologically and pathologically. Per consensus criteria adopted in 2011, diagnosis of idiopathic pulmonary fibrosis no longer requires a biopsy in an appropriate context if UIP is seen on imaging. As a result, lung biopsies are now typically reserved for patients having indeterminate clinical or imaging findings or suspicion for alternative diagnoses, but the impact of updated guidelines on pathology practice remains unclear.

OBJECTIVE.—

To determine the frequency of histologic UIP before and after 2011.

DESIGN.—

Surgical lung biopsies from adults were studied within two 4-year periods: July 1, 2006 through June 30, 2010 and January 1, 2012 through December 31, 2015. Pathology slides were reviewed in a fashion blinded to clinical information and were classified using current guidelines.

RESULTS.—

Biopsies from 177 and 86 patients (mean [SD] age, 62 [12] and 59 [14] years; 50.3% [89 of 177] and 48.8% [42 of 86] men) before and after 2011, respectively, were reviewed. Probable UIP or UIP was less-frequently encountered after 2011 in all patients with fibrosis (9 of 54 [16.7%] versus 41 of 119 [34.5%] before 2011, P = .02) and also in patients 50 years old and older (8 of 46 [17.4%] versus 39 of 109 [35.8%] before 2011, P = .02), with a concomitant rise in cases indeterminate for UIP or showing alternative diagnoses.

CONCLUSIONS.—

Histology for UIP is less frequently encountered in our contemporary practice compared with the historic era. The pretest probability of a non-UIP diagnosis is now high, even in elderly patients, underscoring the need for pathologists to be familiar with the histologic features of alternative diagnoses.

Reliability of histopathologic diagnosis of fibrotic interstitial lung disease: an international collaborative standardization project

Background

Current interstitial lung disease (ILD) diagnostic guidelines assess criteria across clinical, radiologic and pathologic domains. Significant interobserver variation in histopathologic evaluation has previously been shown but the specific source of these discrepancies is poorly documented. We sought to document specific areas of difficulty and develop improved criteria that would reduce overall interobserver variation.

Methods

Using an internet-based approach, we reviewed selected images of specific diagnostic features of ILD histopathology and whole slide images of fibrotic ILD. After an initial round of review, we confirmed the presence of interobserver variation among our group. We then developed refined criteria and reviewed a second set of cases.

Results

The initial round reproduced the existing literature on interobserver variation in diagnosis of ILD. Cases which were pre-selected as inconsistent with usual interstitial pneumonia/idiopathic pulmonary fibrosis (UIP/IPF) were confirmed as such by multi-observer review. Cases which were thought to be in the spectrum of chronic fibrotic ILD for which UIP/IPF were in the differential showed marked variation in nearly all aspects of ILD evaluation including extent of inflammation and extent and pattern of fibrosis. A proposed set of more explicit criteria had only modest effects on this outcome. While we were only modestly successful in reducing interobserver variation, we did identify specific reasons that current histopathologic criteria of fibrotic ILD are not well defined in practice.

Conclusions

Any additional classification scheme must address interobserver variation in histopathologic diagnosis of fibrotic ILD order to remain clinically relevant. Improvements to tissue-based diagnostics may require substantial resources such as larger datasets or novel technologies to improve reproducibility. Benchmarks should be established for expected outcomes among clinically defined subgroups as a quality metric.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01522-6.

Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis

A large number of fibroblast foci (FF) predict mortality in idiopathic pulmonary fibrosis (IPF). Other prognostic histological markers have not been identified. Artificial intelligence (AI) offers a possibility to quantitate possible prognostic histological features in IPF. We aimed to test the use of AI in IPF lung tissue samples by quantitating FF, interstitial mononuclear inflammation, and intra-alveolar macrophages with a deep convolutional neural network (CNN). Lung tissue samples of 71 patients with IPF from the FinnishIPF registry were analyzed by an AI model developed in the Aiforia® platform. The model was trained to detect tissue, air spaces, FF, interstitial mononuclear inflammation, and intra-alveolar macrophages with 20 samples. For survival analysis, cut-point values for high and low values of histological parameters were determined with maximally selected rank statistics. Survival was analyzed using the Kaplan-Meier method. A large area of FF predicted poor prognosis in IPF (p = 0.01). High numbers of interstitial mononuclear inflammatory cells and intra-alveolar macrophages were associated with prolonged survival (p = 0.01 and p = 0.01, respectively). Of lung function values, low diffusing capacity for carbon monoxide was connected to a high density of FF (p = 0.03) and a high forced vital capacity of predicted was associated with a high intra-alveolar macrophage density (p = 0.03). The deep CNN detected histological features that are difficult to quantitate manually. Interstitial mononuclear inflammation and intra-alveolar macrophages were novel prognostic histological biomarkers in IPF. Evaluating histological features with AI provides novel information on the prognostic estimation of IPF.

Reinterpreting Evidence of Rheumatoid Arthritis-Associated Interstitial Lung Disease to Understand Etiology

Interstitial Lung Disease (ILD) is a well-known complication of rheumatoid arthritis (RA) which often results in significant morbidity and mortality. It is often diagnosed late in the disease process via descriptive criteria. Multiple subtypes of RA-ILD exist as defined by chest CT and histopathology. In the absence of formal natural history studies and definitive diagnostics, a conventional dogma has emerged that there are two major subtypes of RA-ILD (nonspecific interstitial pneumonia (NSIP) and Usual Interstitial Pneumonia (UIP)). These subtypes are based on clinical experience and correlation studies. However, recent animal model data are incongruous with established paradigms of RA-ILD and beg reassessment of the clinical evidence in order to better understand etiology, pathogenesis, prognosis, and response to therapy. To this end, here we: 1) review the literature on epidemiology, radiology, histopathology and clinical outcomes of the various RAILD subtypes, existing animal models, and current theories on RA-ILD pathogenesis; 2) highlight the major gaps in our knowledge; and 3) propose future research to test an emerging theory of RAILD that posits initial rheumatic lung inflammation in the form of NSIP-like pathology transforms mesenchymal cells to derive chimeric disease, and subsequently develops into frank UIP-like fibrosis in some RA patients. Elucidation of the pathogenesis of RA-ILD is critical for the development of effective interventions for RA-ILD.

Morphological and molecular motifs of fibrosing pulmonary injury patterns

Interstitial lung diseases encompass a large number of entities, which are characterised by a small number of partially overlapping fibrosing injury patterns, either alone or in combination. Thus, the presently applied morphological diagnostic criteria do not reliably discriminate different interstitial lung diseases. We therefore analysed critical regulatory pathways and signalling molecules involved in pulmonary remodelling with regard to their diagnostic suitability. Using laser‐microdissection and microarray techniques, we examined the expression patterns of 45 tissue‐remodelling associated target genes in remodelled and non‐remodelled tissue samples from patients with idiopathic pulmonary fibrosis/usual interstitial pneumonia (IPF/UIP), non‐specific interstitial pneumonia (NSIP), organising pneumonia (OP) and alveolar fibroelastosis (AFE), as well as controls (81 patients in total). We found a shared usage of pivotal pathways in AFE, NSIP, OP and UIP, but also individual molecular traits, which set the fibrosing injury patterns apart from each other and correlate well with their specific morphological aspects. Comparison of the aberrant gene expression patterns demonstrated that (1) molecular profiling in fibrosing lung diseases is feasible, (2) pulmonary injury patterns can be discriminated with very high confidence on a molecular level (86–100% specificity) using individual gene subsets and (3) these findings can be adapted as suitable diagnostic adjuncts.

The place of high-resolution computed tomography imaging in the investigation of interstitial lung disease

INTRODUCTION

High-resolution computed tomography (HRCT) has revolutionized the diagnosis, prognosis and in some cases the prediction of therapeutic response in interstitial lung disease (ILD). HRCT represents an essential second step to a patient's clinical history, before considering any other investigation, including lung biopsy. Areas covered: This review describes the current place of HRCT in the diagnosis, prognosis and monitoring of ILD. It also lists some perspectives for the near future. Expert commentary: Since the 1980s, HRCT and its interpretation have improved, the diagnosis value of patterns, and the integration of bio-clinical elements to HRCT have been better standardized. The interobserver agreement has been investigated, allowing a better use of some limits in the interpretation of various signs. It not only takes into account one particular predominant sign, but the combination of patterns and the distribution of findings. Thanks to HRCT, the range of diagnoses and their probability are more accurately identified. The contribution of HRCT has been optimized during the multidisciplinary discussion that a difficult diagnosis calls for. HRCT quantification of the extent of diffuse lung disease becomes possible and is linked to prognosis. In the future, artificial intelligence may significantly modify the practice of radiology.

Organ and tissue fibrosis: Molecular signals, cellular mechanisms and translational implications

Fibrosis denotes excessive scarring, which exceeds the normal wound healing response to injury in many tissues. Although the extracellular matrix deposition appears unstructured disrupting the normal tissue architecture and subsequently impairing proper organ function, fibrogenesis is a highly orchestrated process determined by defined sequences of molecular signals and cellular response mechanisms. Persistent injury and parenchymal cell death provokes tissue inflammation, macrophage activation and immune cell infiltration. The release of biologically highly active soluble mediators (alarmins, cytokines, chemokines) lead to the local activation of collagen producing mesenchymal cells such as pericytes, myofibroblasts or Gli1 positive mesenchymal stem cell-like cells, to a transition of various cell types into myofibroblasts as well as to the recruitment of fibroblast precursors. Clinical observations and experimental models highlighted that fibrosis is not a one-way road. Specific mechanistic principles of fibrosis regression involve the resolution of chronic tissue injury, the shift of inflammatory processes towards recovery, deactivation of myofibroblasts and finally fibrolysis of excess matrix scaffold. The thorough understanding of common principles of fibrogenic molecular signals and cellular mechanisms in various organs - such as liver, kidney, lung, heart or skin - is the basis for developing improved diagnostics including biomarkers or imaging techniques and novel antifibrotic therapeutics.