Genomic Classifier and Its Role in Supporting MDD: History
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Contributor:
Stefano Sanduzzi Zamparelli
,
Alessandro Sanduzzi Zamparelli
,
Marialuisa Bocchino

Interstitial lung diseases (ILDs) are a group of heterogeneous diseases characterized by inflammation and/or fibrosis of the lung interstitium, leading to a wide range of clinical manifestations and outcomes. Over the years, the literature has demonstrated the increased diagnostic accuracy and confidence associated with a multidisciplinary approach (MDA) in assessing diseases involving lung parenchyma. This approach was recently emphasized by the latest guidelines from the American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Latin American Thoracic Association for the diagnosis of ILDs.

  • interstitial lung diseases
  • idiopathic pulmonary fibrosis
  • multidisciplinary diagnosis
  • Genomic classifier

1. Introduction

Interstitial lung diseases (ILDs) represent a huge and heterogenous group of pulmonary disorders, characterized by varying degrees of inflammation and fibrosis of the lung with different prognoses and management options. Despite extensive analysis, the diagnosis of ILDs remains challenging, as the specific etiology cannot be identified in 10–20% of cases, due to the common respiratory clinic and the frequent overlap of radiologic and/or histopathologic patterns among various diseases [1]. Although idiopathic pulmonary fibrosis (IPF) is the prototype of a progressive fibrotic disease associated with a poor prognosis and premature mortality, it is associated with over 40% of non-IPF ILDs [2]. Based on this, the paramount significance of achieving an accurate and early diagnosis, along with the timely initiation of disease-specific therapy, becomes evident to alter the outcome [3][4].
Since 2001, the American Thoracic Society (ATS) and the European Respiratory Society (ERS) have introduced a critical change in the diagnostic process of ILDs, highlighting the importance of a multidisciplinary diagnosis (MDD) based on the integration of clinical, radiologic, and pathologic data [5]. A face-to-face discussion between different health professionals, traditionally represented by an ILD-expert respiratory physician, and other figures, such as radiologists and histopathologists, aims to use available clinical data to generate a consensus diagnosis with the highest possible level of accuracy.
Despite the literature demonstrating an exceedingly low inter-observer agreement between expert thoracic pathologists, histopathological evaluation was considered the gold standard for ILD diagnosis [6][7]. In addition to the low level of consensus on the histological sample between different doctors, the main limitation of this kind of diagnosis was linked to the extreme variability of the histological data, concerning the representativity of the sample taken strongly depending on the operator’s experience. In this context, out of 133 lung biopsies taken from 83 ILD patients, the Nicholson et al. study [8] showed a poor inter-observer agreement among 10 thoracic pathologists, with a 0.38 kappa coefficient of agreement (κ), making a 100% confidence diagnosis in only 39% of cases. Notably, more than 50% of the inter-observer variation was related to the differential diagnosis between nonspecific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP). The uncertainty linked to the histopathologic data is at a maximum in NSIP patterns because of the presence of different pathological patterns in different lobes in the same patient. Moreover, divergent pathological diagnoses can be often caused by surgical lung biopsy (SLB) sampling error [9]. In this context, Flaherty et al. [10] demonstrated that a histopathological diagnosis based on a single lung biopsy site can be inaccurate, in particular about NSIP/UIP differentiation, with around 26% of patients with discordant diagnoses. Furthermore, histopathological diagnosis is not fixed, but it can be modified in 20% of cases, particularly when an intermediate pattern is present [11]. It is important to bear in mind that even a histopathologic UIP pattern is not surely related to IPF, as demonstrated in Tominaga’s study [12]. In this study, the integration of clinical and radiological data from 95 IPF patients confirmed by a histological pattern compatible with UIP led to a progressive reduction in post-MDD IPF diagnosis.
More recently, MDA was first emphasized by the 2013 ATS/ERS update for interstitial idiopathic pneumonia’s (IIP’s) [13] management and further reinforced by the 2018 Fleischner statement [14]. These guidelines proposed updated IPF diagnostic criteria, endorsing the formulation of an MDD of IPF, mainly in the absence of definitive radiological or histopathological findings. Based on the literature, which has shown a variable frequency of IPF ranging from 60 to 90% for patients with probable UIP patterns, MDD is mandatory in all ILD patients who lack a definite UIP pattern on high-resolution chest tomography (HRCT). The impact of this approach is supported by Kondoh’s study [15], where out of 179 patients with probable HRCT UIP patterns, an IPF diagnosis was established in 50% of cases following MDD.
The growing confidence in MDD as a highly accurate assessment of ILDs was emphasized in the 2021 guidelines from the ATS, ERS, Japanese Respiratory Society (JRS), and Latin American Thoracic Association (ALAT) [16]. In addition to its role in determining the need for biopsy investigations and avoiding invasive procedures—UIP or probable UIP patterns on imaging in the appropriate clinical setting is enough to make a diagnosis of IPF—MDD can also aid in selecting the most appropriate sampling tool for lung parenchyma, such as transbronchial cryo-biopsy (TBLC) or SLB. According to this guideline [16], a biopsy is generally considered when an indeterminate or inconsistent HRCT UIP pattern conflicts with clinical data. Data from the literature suggest restricting the use of MDD to more complex diagnostic cases, with a focus on clinician consensus when the diagnostic IPF criteria are more evident—UIP on HRCT, a rapidly progressive disease course, and no identifiable triggers. This approach, as emphasized by the retrospective evaluation of 318 ILD patients made by Chaudhuri et al. [17], is associated with the potential to change the diagnosis.
In contemporary practice, multidisciplinary assessment (MDA) has become the standard approach for evaluating ILDs due to its high level of diagnostic confidence and inter-observer agreement. Although initially developed for IIP’s diagnosis, an MDA is now widely used as the standard in the evaluation of ILDs, not only those that are idiopathic but also those related to an underlying connective tissue disease (CTD) or linked with autoimmune features such as idiopathic pneumonia with autoimmune features (IPAF) [11]. In contrast to an IPF diagnosis, many ILDs are not covered by evidence-based diagnostic guidelines; therefore, a level of disagreement in MDD is predictable [13]. Furthermore, apart from assessing new cases, MDT discussions are key for reconsidering a previous diagnosis according to disease behavior and response to therapy.

2. Genomic Classifier and Its Role in Supporting MDD

Another topic covered by the latest IPF guidelines [16] was the recent introduction of genomic classifier testing (GCT) to clinical use. This technique utilizes machine learning to analyze the whole-transcriptome RNA sequencing and the gene expression patterns in lung tissue samples obtained by transbronchial biopsy, thereby improving the diagnostic yield of ILD [18][19]. By categorizing the presence or absence of diagnostic features, GCT enables distinguishing UIP from non-UIP histopathology. This supports the MDT in making a more confident diagnosis of IPF in patients without a definite UIP pattern on chest imaging.
A recent systematic literature review [20] estimated that GCT can differentiate UIP and non-UIP histopathology with a sensitivity of 68% (95% Confidence Interval (CI), 55–73%) and a specificity of 92% (95% CI, 81–95%). This provides valuable support to physicians and MDTs in confirming the diagnosis of UIP in patients without a definitive radiologic pattern for UIP. Opposite to the diagnostic confidence improvement of a UIP histopathology-predicted pattern using GCT, non-UIP histopathology prediction may require further confirmation through SLB or TBLC due to the frequent occurrence of false-negative results. Higher confidence in the diagnostic evaluation of ILD can be implemented via the integration of GCT results with clinical and radiologic information in an MDD context. Two different studies [19][21] have shown that the diagnostic accuracy of GCT increases from 56% to 89% (agreement, k = 0.64) and from 43% to 93% (agreement, k = 0.75) after MDT evaluation.
The integration of GCT into the MDD process increases diagnosis confidence levels, particularly in cases where chest imaging shows an indeterminate UIP pattern or probable UIP with confounding clinical factors—such as autoantibodies, ILD-associated medication, and a history of environmental or work exposure. This integration increases the proportion of IPF cases diagnosed from 31% to 92%. In a study by Kheir et al. [21], the addition of GCT to the clinical and radiological data resulted in a mild increase ranging from 17% to 29% in the proportion of high-confidence diagnoses, despite a high overall agreement of 92% between the GCT and the final MDD of UIP or non-UIP. The combination of GCT with TBLC can further improve the diagnostic yield in patients with a probable UIP pattern on HRCT imaging, while TBLC alone may be used in cases with a confident non-UIP diagnosis. However, GCT implementation appears to be less beneficial when the HRCT scan shows patterns inconsistent with UIP.
While additional studies are needed to define its precise accuracy due to the high frequency of false-negative results, GCT provides important diagnostic information. When integrated with clinical and radiological elements, it may reduce the need for additional and more invasive sampling as SLB or TBLC. A review conducted by Richeldi et al. [22], which analyzed the impact of radiological data integration in reducing false-negative results, reported that GCT sensitivity increased from 60.3% to 79.2% when in conjunction with the HRCT pattern of UIP.

This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13142437

References

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