Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations

Diagnostic Approach to Interstitial Lung Diseases Associated with Connective Tissue Diseases

Interstitial lung disorders are a group of respiratory diseases characterized by interstitial compartment infiltration, varying degrees of infiltration, and fibrosis, with or without small airway involvement. Although some are idiopathic (e.g., idiopathic pulmonary fibrosis, idiopathic interstitial pneumonias, and sarcoidosis), the great majority have an underlying etiology, such as systemic autoimmune rheumatic disease (SARD, also called Connective Tissue Diseases or CTD), inhalational exposure to organic matter, medications, and rarely, genetic disorders. This review focuses on diagnostic approaches in interstitial lung diseases associated with SARDs. To make an accurate diagnosis, a multidisciplinary, personalized approach is required, with input from various specialties, including pulmonary, rheumatology, radiology, and pathology, to reach a consensus. In a minority of patients, a definitive diagnosis cannot be established. Their clinical presentations and prognosis can be variable even within subsets of SARDs.

Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists

PURPOSE

Several reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia.

METHODS

For this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar's test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar's test.

RESULTS

A comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009).

CONCLUSION

This multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

Quantitative Analysis for Lung Disease on Thin-Section CT

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.

Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism

INTRODUCTION

The objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning( ML).

METHODS

In this prospective observational study the 2D placental ultrasound (US) images from 11-14 weeks, 20-24 weeks, and 28-32 weeks were taken. The image data was divided into training, validating, and testing subsets in the ratio of 80%, 10%, and 10%. Three different ML techniques, deep learning, transfer learning, and vision transformer were used for UPIA.

RESULTS

Out of 1008 cases included in the study, 59.5% (600/1008) had a normal outcome. The image texture classification was compared between T1&T2, T2 &T3 and T1&T3 pairs. Using Inception v3 model, to classify T1& T2 images, gave the accuracy, Cohen Kappa score of 83.3%, 0.662 respectively. The image classification between T1&T3 achieved best results using EfficientNetB0 model, having the accuracy, Cohen Kappa score, sensitivity and specificity of 87.5%, 0.749, 83.4%, and 88.9% respectively. Comparison of placental image texture among cases with materno-fetal adverse outcome and controls was done using Efficient Net B0. The F1 score, was found to be 0.824 , 0.820, and 0.892 in T1, T2 and T3 respectively. The sensitivity and specificity of the model was 77.4% at 80.2% at T1 but increased to 81.0% and 93.9% at T2 &T3 respectively.

DISCUSSION

The study presents a novel technique to classify placental ultrasound image texture using ML models and could differentiate first and third-trimester normal placenta and normal and adverse pregnancy outcome images with good accuracy.

Area-Detector Computed Tomography for Pulmonary Functional Imaging

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect

Purpose

Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting.

Materials and methods

From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan–Meier method followed by Wilcoxon’s signed-rank test.

Results

% GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008).

Conclusion

ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.