A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis

Refaee, Turkey; Bondue, Benjamin; Simaeys, Gaëtan Van; Wu, Guangyao; Yan, Changqing; Woodruff, Henry C.; Goldman, Serge; Lambin, Philippe

doi:10.3390/jpm12030373

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…Recent research has shown promising results in the use of radiomics at various stages of colorectal cancer diagnosis and treatment, including preoperative, intraoperative, and postoperative phases [19]. In the field of pulmonary fibrosis, radiomics have been used with many benefits, such as differentiating between different types of pulmonary fibrosis [20], predicting the functional decline and long-term survival of pulmonary fibrosis patients [21], and enhancing the diagnosis and severity staging of idiopathic pulmonary fibrosis [22]. The added utility of radiomic characteristics in the differential diagnosis of ulcerative colitis and CD has been assessed.…”

Section: Introductionmentioning

confidence: 99%

The Role of Radiomics in Fibrosis Crohn’s Disease: A Review

Alyami

2023

Diagnostics

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Inflammatory bowel disease (IBD) is a global health concern that has been on the rise in recent years. In addition, imaging is the established method of care for detecting, diagnosing, planning treatment, and monitoring the progression of IBD. While conventional imaging techniques are limited in their ability to provide comprehensive information, cross-sectional imaging plays a crucial role in the clinical management of IBD. However, accurately characterizing, detecting, and monitoring fibrosis in Crohn’s disease remains a challenging task for clinicians. Recent advances in artificial intelligence technology, machine learning, computational power, and radiomic emergence have enabled the automated evaluation of medical images to generate prognostic biomarkers and quantitative diagnostics. Radiomics analysis can be achieved via deep learning algorithms or by extracting handcrafted radiomics features. As radiomic features capture pathophysiological and biological data, these quantitative radiomic features have been shown to offer accurate and rapid non-invasive tools for IBD diagnostics, treatment response monitoring, and prognosis. For these reasons, the present review aims to provide a comprehensive review of the emerging radiomics methods in intestinal fibrosis research that are highlighted and discussed in terms of challenges and advantages.

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Section: Introductionmentioning

confidence: 99%

The Role of Radiomics in Fibrosis Crohn’s Disease: A Review

Alyami

2023

Diagnostics

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“…Artificial intelligence has been widely applied in ILD. [16][17][18][19][20] Hwang et al 19 and Choe et al 20 discovered that the content-based image retrieval system based on chest CT images could improve the diagnostic precision of ILD and interreader agreement in readers with varying degrees of expertise. Computer-Aided Lung Informatics for Pathological Evaluation and Ratings (CALIPER) achieved good results in predicting the mortalities of idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonia, and CTD-ILD.…”

mentioning

confidence: 99%

“…Artificial intelligence has been widely applied in ILD 16–20 . Hwang et al 19 and Choe et al 20 discovered that the content-based image retrieval system based on chest CT images could improve the diagnostic precision of ILD and interreader agreement in readers with varying degrees of expertise.…”

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confidence: 99%

Computed Tomography–Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease–Associated Interstitial Lung Disease

Hou

Zheng

et al. 2023

J Comput Assist Tomogr

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Objectives This study aimed to develop a computed tomography (CT)–based deep learning model for assessing the severity of patients with connective tissue disease (CTD)–associated interstitial lung disease (ILD). Methods The retrospective study included 298 CTD-ILD patients between January 2018 and May 2022. A deep learning–based RDNet model was established (1610 fully annotated CT images for training and 402 images for validation). The model was used to automatically classify and quantify 3 radiologic features (ground glass opacities [GGOs], reticulation, and honeycombing), along with a volumetric sum of 3 areas (ILD%). As a control, we used 4 previously defined CT threshold methods to calculate the ILD assessment index. The Spearman rank correlation coefficient (r) evaluated the correlation between various indicators and the lung function index in the remaining 184 CTD-ILD patients who were staged according to the gender-age-physiology (GAP) system. Results The RDNet model accurately identified GGOs, reticulation, and honeycombing, with corresponding Dice indexes of 0.784, 0.782, and 0.747, respectively. A total of 137 patients were at GAP1 (73.9%), 36 patients at GAP2 (19.6%), and 11 patients at GAP3 (6.0%). The percentages of reticulation and honeycombing at GAP2 and GAP3 were markedly elevated compared with those at GAP1 (P < 0.001). The percentage of GGOs was not significantly different among the GAP stages (P = 0.62). As the GAP stage increased, all lung function indicators tended to decrease, and the composite physiologic index (CPI) indicated an upward tendency. The percentage of honeycombs moderately correlated with the percentage of diffusing capacity of the lung for carbon monoxide (DLco%) (r = −0.58, P < 0.001) and CPI (r = 0.63, P < 0.001). The ILD assessment index calculated by the CT threshold method (−260 to −600 Hounsfield units) had a low correlation with DLco% and CPI (DLco%: r = −0.42, P < 0.001; CPI: r = 0.45, P < 0.001). Conclusions The RDNet model can quantify GGOs, reticulation, and honeycombing of chest CT images in CTD-ILD patients, among which honeycombing had the most significant effect on lung function indicators. In addition, this model provided good clinical utility for evaluating the severity of CTD-ILD.

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Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases

Sun,

Liu,

Liu

et al. 2024

J Digit Imaging. Inform. med.

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Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively. Graphical Abstract Given a sequence of HRCT slices from a patient, the lung field is first automatically extracted. Next, this lung region is divided into 36 sub-regions using geometric rules, obtaining a lung atlas. And then, the lung graph is built based on 3D radiomics features of each sub-region of the lung atlas. Finally, the model’s predictions were compared to the physicians’ assessment results.

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A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis

Cited by 6 publications

References 51 publications

The Role of Radiomics in Fibrosis Crohn’s Disease: A Review

The Role of Radiomics in Fibrosis Crohn’s Disease: A Review

Computed Tomography–Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease–Associated Interstitial Lung Disease

Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases

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