A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia

Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis

BACKGROUND

The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the impact of interobserver variability. Several studies have endeavored to utilize image-based machine learning to diagnose IPF and its subtype of usual interstitial pneumonia (UIP). However, the diagnostic accuracy of this approach lacks evidence-based support.

OBJECTIVE

We conducted a systematic review and meta-analysis to explore the diagnostic efficiency of image-based machine learning (ML) for IPF.

DATA SOURCES AND METHODS

We comprehensively searched PubMed, Cochrane, Embase, and Web of Science databases up to August 24, 2024. During the meta-analysis, we carried out subgroup analyses by imaging source (computed radiography/computed tomography) and modeling type (deep learning/other) to evaluate its diagnostic performance for IPF.

RESULTS

The meta-analysis findings indicated that in the diagnosis of IPF, the C-index, sensitivity, and specificity of ML were 0.93 (95% CI 0.89-0.97), 0.79 (95% CI 0.73-0.83), and 0.84 (95% CI 0.79-0.88), respectively. The sensitivity of radiologists/clinicians in diagnosing IPF was 0.69 (95% CI 0.56-0.79), with a specificity of 0.93 (95% CI 0.74-0.98). For UIP diagnosis, the C-index of ML was 0.91 (95% CI 0.87-0.94), with a sensitivity of 0.92 (95% CI 0.80-0.97) and a specificity of 0.92 (95%CI 0.82-0.97). In contrast, the sensitivity of radiologists/clinicians in diagnosing UIP was 0.69 (95% CI 0.50-0.84), with a specificity of 0.90 (95% CI 0.82-0.94).

CONCLUSIONS

Image-based machine learning techniques demonstrate robust data processing and recognition capabilities, providing strong support for accurate diagnosis of idiopathic pulmonary fibrosis and usual interstitial pneumonia. Future multicenter large-scale studies are warranted to develop more intelligent evaluation tools to further enhance clinical diagnostic efficiency. Trial registration This study protocol was registered with PROSPERO (CRD42022383162).

Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis

Interstitial lung diseases (ILDs) are a heterogeneous group of pulmonary disorders characterised by variable degrees of inflammation, interstitial thickening, and fibrosis leading to distortion of the pulmonary architecture and gas exchange impairment. There are approximately 200 different entities in this category. ILDs are commonly classified based on several criteria, including causes, clinical features, and radiological patterns. Chest HRCT is the gold standard for the recognition of lung alteration patterns underlying interstitial lung diseases (ILDs), diagnosing specific patterns, and evaluating radiologic progression. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. The evolving field of radiomics offers a unique and non-invasive approach to extracting quantitative information from medical images, particularly high-resolution computed tomography (HRCT) scans. This comprehensive review explores the burgeoning role of radiomics in unravelling the intricacies of interstitial lung disease. It focuses on its potential applications in diagnosis, prognostication, and treatment response evaluation.

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating the initial (baseline) LDCT examination, which provides a wealth of information relevant to the screening participant's health. This includes the detection of large-size prevalent LC and small-size malignant nodules that are typically diagnosed as LCs upon growth in subsequent annual LDCT scans. Additionally, the baseline LDCT examination provides valuable information about smoking-related comorbidities, including cardiovascular disease, chronic obstructive pulmonary disease, and interstitial lung disease (ILD), by identifying relevant markers. Notably, these comorbidities, despite the slow progression of their markers, collectively exceed LC as ultimate causes of death at follow-up in LC screening participants. Computer-assisted diagnosis tools currently improve the reproducibility of radiologic readings and reduce the false negative rate of LDCT. Deep learning (DL) tools that analyze the radiomic features of lung nodules are being developed to distinguish between benign and malignant nodules. Furthermore, AI tools can predict the risk of LC in the years following a baseline LDCT. AI tools that analyze baseline LDCT examinations can also compute the risk of cardiovascular disease or death, paving the way for personalized screening interventions. Additionally, DL tools are available for assessing osteoporosis and ILD, which helps refine the individual's current and future health profile. The primary obstacles to AI integration into the LDCT screening pathway are the generalizability of performance and the explainability.

Progress in the radiologic diagnosis of idiopathic pulmonary fibrosis

PURPOSE OF REVIEW

To discuss the most recent applications of radiological imaging, from conventional to quantitative, in the setting of idiopathic pulmonary fibrosis (IPF) diagnosis.

RECENT FINDINGS

In this article, current concepts on radiological diagnosis of IPF, from high-resolution computed tomography (CT) to other imaging modalities, are reviewed. In a separate section, advances in quantitative CT and development of novel imaging biomarkers, as well as current limitations and future research trends, are described.

SUMMARY

Radiological imaging in IPF, particularly quantitative CT, is an evolving field which holds promise in the future to allow for an increasingly accurate disease assessment and prognostication of IPF patients. However, further standardization and validation studies of alternative imaging applications and quantitative biomarkers are needed.