Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study

BACKGROUND

Diagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical planning. This indicates the need for a more efficient and patient-centred approach, suggesting a gap in the existing diagnostic methods and the potential for artificial intelligence technologies to address this gap. This study aimed to create a multimodal hybrid transformer model using the Vision Transformer that leverages LDCT features and clinical data to improve surgical decision-making for patients with incidentally detected mediastinal tumours.

METHODS

This retrospective study analysed patients with mediastinal tumours between 2010 and 2021. Patients eligible for surgery (n=30) were considered 'positive,' whereas those without tumour enlargement (n=32) were considered 'negative.' We developed a hybrid model combining a convolutional neural network with a transformer to integrate imaging and clinical data. The dataset was split in a 5:3:2 ratio for training, validation and testing. The model's efficacy was evaluated using a receiver operating characteristic (ROC) analysis across 25 iterations of random assignments and compared against conventional radiomics models and models excluding clinical data.

RESULTS

The multimodal hybrid model demonstrated a mean area under the curve (AUC) of 0.90, significantly outperforming the non-clinical data model (AUC=0.86, p=0.04) and radiomics models (random forest AUC=0.81, p=0.008; logistic regression AUC=0.77, p=0.004).

CONCLUSION

Integrating clinical and LDCT data using a hybrid transformer model can improve surgical decision-making for mediastinal tumours, showing superiority over models lacking clinical data integration.

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

PURPOSE

Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images.

MATERIALS AND METHODS

A novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient.

RESULTS

The accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm's categorisation was a significant predictor of IPF progression.

CONCLUSIONS

The deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.

Three-dimensional analysis of hard and soft tissue changes in skeletal class II patients with high mandibular plane angle undergoing surgery

This study aimed to study 3-dimensional (3D) changes of hard and soft tissues of skeletal class II patients after 2-jaw surgery and genioplasty. 32 adult patients diagnosed with mandibular hypoplasia who underwent 2-jaw surgery of maxillary impaction, mandibular advancement and genioplasty were enrolled. Cone-beam computed tomography and 3D stereophotogrammetry was conducted 1 week before and 6 months after surgery. Dolphin imaging software was used to establish a 3D digitizing model and 3D measurement system. Paired t-test was performed to compare the values before and after surgery. Pearson's correlation test assessed the degree of correlations between hard and soft tissue change. The mean impaction of the maxilla was 2.600 ± 3.088 mm at A. The mean advancement of the mandible was 7.806 ± 2.647 mm at B. There was a significant upward and forward movement for most landmarks of the nose and lip, while a significant decrease in nasal tip height (lateral view), upper lip height, and upper and lower vermilion height. The nose's width was significantly increased. For maxillary, Sn, Ac-r, Ac-l, and Ls demonstrated a significant correlation with A and U1 in the anteroposterior axis. However, there were no significant correlations among them in the vertical axis. For mandibular, Li demonstrated a significant correlation with L1 in the anteroposterior axis specifically for the mandible. Notably, correlations between the landmarks of the chin's hard and soft tissues were observed across all axes. The utilization of 3-D analysis facilitated a quantitative comprehension of both hard and soft tissues, thereby furnishing valuable insights for the strategic formulation of orthognathic treatment plans targeting patients with skeletal class II conditions.

The Impact of Telemedicine during Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic and Future Perspectives: A Systematic Review

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide since December 2019, causing the COVID-19 pandemic. Several measures have taken place in many countries to avoid further spread of the virus and to manage infected people according to disease severity. Notably, telemedicine (TM) was successfully used to manage less severe patients. Our aim was to assess the impact and the edges of using TM in home-isolated or hospitalized patients affected by SARS-CoV-2 infection and its further application.

METHODS

We performed a systematic review according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, focusing on randomized controlled trials (RCTs) published in English and available on PubMed database. Full texts were blindly reviewed and then assessed according to PICO model.

RESULTS

Our research identified a total of 1,959 records, of which 24 were potentially eligible through the articles full-text review. Six papers were included for data extraction and 18 articles were excluded: 10 articles were not RCTs and 8 articles did not involve SARS-CoV-2 patients. The TM application showed an improvement in psychological stress, mental disorders, and a significant reduction of general stress in patients affected by SARS-CoV-2 infection. The effectiveness of using TM in rehabilitative respiratory programs has been also reported. Furthermore, the benefits of TM application in tailored monitoring of vital parameters in home-isolated patients helped clinicians to early identify a deterioration of clinical conditions.

CONCLUSION

The use of TM during COVID-19 pandemic represented a novel, intriguing, versatile, and useful tool to support clinical practice. This evidence suggests considering TM in a wider range of clinical applications.

Precision medicine advances in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a highly heterogeneous, unpredictable and ultimately lethal chronic lung disease. Over the last decade, two anti-fibrotic agents have been shown to slow disease progression, however, both drugs are administered uniformly with minimal consideration of disease severity and inter-individual molecular, genetic, and genomic differences. Advances in biological understanding of disease endotyping and the emergence of precision medicine have shown that "a one-size-fits-all approach" to the management of chronic lung diseases is no longer appropriate. While precision medicine approaches have revolutionized the management of other diseases such as lung cancer and asthma, the implementation of precision medicine in IPF clinical practice remains an unmet need despite several reports demonstrating a large number of diagnostic, prognostic and theragnostic biomarker candidates in IPF. This review article aims to summarize our current knowledge of precision medicine in IPF and highlight barriers to translate these research findings into clinical practice.

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

Introduction

Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).

Methods

In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.

Results

Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.

Conclusion

Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Rituximab for the treatment of connective tissue disease-associated interstitial lung disease: A systematic review and meta-analysis

Background: Interstitial lung disease (ILD) is a common pulmonary disease often associated with significant morbidity and mortality in patients with connective tissue diseases (CTD). Currently, no gold-standard therapies are available for CTD-ILD. Recently, several studies have proposed that rituximab (RTX) may be effective for the treatment of CTD-ILD. Objectives: This study aimed to systematically evaluate the efficacy and safety of RTX for the treatment of CTD-ILD. Methods: Studies were selected from PubMed, Embase, and Cochrane Library, up to 20 July 2022. Improvement and stable rates were extracted as the main outcomes and pooled using the weighted mean proportion with fixed or random-effects models, in case of significant heterogeneity (I 2 > 50%). Safety analysis was performed based on the adverse events reported in all of the studies. Results: Thirteen studies (312 patients) were included in the meta-analysis. The follow-up durations ranged from 6 to 36 months. The pooled improvement rate was 35.0% (95% CI: 0.277-0.442), while the pooled stable rate was 59.2% (95% CI: 0.534-0.656). Anti-synthetase syndrome associated with ILD [ASS-ILD, 48.1% (95% CI, 0.373-0.620)] and idiopathic inflammatory myopathies associated with ILD [IIM-ILD, non-ASS, 47.4% (95% CI, 0.266-0.846)] had higher improvement rates than the other types. A total of 106 adverse events associated with RTX or progressive ILD were reported among the 318 patients, 55.7% of which were mild. Among 19 deaths, 17 were due to ILD progression, one to severe pulmonary arterial hypertension, and one to Pneumocystis jirovecii infection. Conclusion: RTX, which exhibits a satisfactory safety profile, is an effective treatment option for CTD-ILD, even in patients who fail to respond to other therapies. Further randomized trials are needed to assess the efficacy of rituximab compared to other treatments for CTD-ILD. Systematic review registration: PROSPERO, identifier (CRD42022363403).