1
|
Sun H, Liu M, Liu A, Deng M, Yang X, Kang H, Zhao L, Ren Y, Xie B, Zhang R, Dai H. Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:268-279. [PMID: 38343257 DOI: 10.1007/s10278-023-00909-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 03/02/2024]
Abstract
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.
Collapse
Affiliation(s)
- Haishuang Sun
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases;Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, 100029, China
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong Province, 510060, China
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, China.
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Anqi Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, China
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Mei Deng
- Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, China
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Xiaoyan Yang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases;Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China
| | - Ling Zhao
- Department of Clinical Pathology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Yanhong Ren
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases;Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Bingbing Xie
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases;Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, 100029, China
| | | | - Huaping Dai
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases;Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, 100029, China.
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| |
Collapse
|
2
|
Prediction of chronic thromboembolic pulmonary hypertension with standardised evaluation of initial computed tomography pulmonary angiography performed for suspected acute pulmonary embolism. Eur Radiol 2021; 32:2178-2187. [PMID: 34854928 PMCID: PMC8921171 DOI: 10.1007/s00330-021-08364-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/05/2021] [Accepted: 09/27/2021] [Indexed: 11/21/2022]
Abstract
Objectives Closer reading of computed tomography pulmonary angiography (CTPA) scans of patients presenting with acute pulmonary embolism (PE) may identify those at high risk of developing chronic thromboembolic pulmonary hypertension (CTEPH). We aimed to validate the predictive value of six radiological predictors that were previously proposed. Methods Three hundred forty-one patients with acute PE were prospectively followed for development of CTEPH in six European hospitals. Index CTPAs were analysed post hoc by expert chest radiologists blinded to the final diagnosis. The accuracy of the predictors using a predefined threshold for ‘high risk’ (≥ 3 predictors) and the expert overall judgment on the presence of CTEPH were assessed. Results CTEPH was confirmed in nine patients (2.6%) during 2-year follow-up. Any sign of chronic thrombi was already present in 74/341 patients (22%) on the index CTPA, which was associated with CTEPH (OR 7.8, 95%CI 1.9–32); 37 patients (11%) had ≥ 3 of 6 radiological predictors, of whom 4 (11%) were diagnosed with CTEPH (sensitivity 44%, 95%CI 14–79; specificity 90%, 95%CI 86–93). Expert judgment raised suspicion of CTEPH in 27 patients, which was confirmed in 8 (30%; sensitivity 89%, 95%CI 52–100; specificity 94%, 95%CI 91–97). Conclusions The presence of ≥ 3 of 6 predefined radiological predictors was highly specific for a future CTEPH diagnosis, comparable to overall expert judgment, while the latter was associated with higher sensitivity. Dedicated CTPA reading for signs of CTEPH may therefore help in early detection of CTEPH after PE, although in our cohort this strategy would not have detected all cases. Key Points • Three expert chest radiologists re-assessed CTPA scans performed at the moment of acute pulmonary embolism diagnosis and observed a high prevalence of chronic thrombi and signs of pulmonary hypertension. • On these index scans, the presence of ≥ 3 of 6 predefined radiological predictors was highly specific for a future diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH), comparable to overall expert judgment. • Dedicated CTPA reading for signs of CTEPH may help in early detection of CTEPH after acute pulmonary embolism. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08364-0.
Collapse
|
3
|
Boon GJAM, Jairam PM, Groot GMC, van Rooden CJ, Ende-Verhaar YM, Beenen LFM, Kroft LJM, Bogaard HJ, Huisman MV, Symersky P, Vonk Noordegraaf A, Meijboom LJ, Klok FA. Identification of chronic thromboembolic pulmonary hypertension on CTPAs performed for diagnosing acute pulmonary embolism depending on level of expertise. Eur J Intern Med 2021; 93:64-70. [PMID: 34294517 DOI: 10.1016/j.ejim.2021.07.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/30/2021] [Accepted: 07/08/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Expert reading often reveals radiological signs of chronic thromboembolic pulmonary hypertension (CTEPH) or chronic PE on computed tomography pulmonary angiography (CTPA) performed at the time of acute pulmonary embolism (PE) presentation preceding CTEPH. Little is known about the accuracy and reproducibility of CTPA reading by radiologists in training in this setting. OBJECTIVES To evaluate 1) whether signs of CTEPH or chronic PE are routinely reported on CTPA for suspected PE; and 2) whether CTEPH-non-expert readers achieve comparable predictive accuracy to CTEPH-expert radiologists after dedicated instruction. METHODS Original reports of CTPAs demonstrating acute PE in 50 patients whom ultimately developed CTEPH, and those of 50 PE who did not, were screened for documented signs of CTEPH. All scans were re-assessed by three CTEPH-expert readers and two CTEPH-non-expert readers (blinded and independently) for predefined signs and overall presence of CTEPH. RESULTS Signs of chronic PE were mentioned in the original reports of 14/50 cases (28%), while CTEPH-expert radiologists had recognized 44/50 (88%). Using a standardized definition (≥3 predefined radiological signs), moderate-to-good agreement was reached between CTEPH-non-expert readers and the experts' consensus (k-statistics 0.46; 0.61) at slightly lower sensitivities. The CTEPH-non-expert readers had moderate agreement on the presence of CTEPH (κ-statistic 0.38), but both correctly identified most cases (80% and 88%, respectively). CONCLUSIONS Concomitant signs of CTEPH were poorly documented in daily practice, while most CTEPH patients were identified by CTEPH-non-expert readers after dedicated instruction. These findings underline the feasibility of achieving earlier CTEPH diagnosis by assessing CTPAs more attentively.
Collapse
Affiliation(s)
- Gudula J A M Boon
- Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands.
| | - Pushpa M Jairam
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Gerie M C Groot
- Department of Radiology, Medical Center Gelderse Vallei, Ede, the Netherlands
| | | | - Yvonne M Ende-Verhaar
- Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| | - Ludo F M Beenen
- Department of Radiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Lucia J M Kroft
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Harm Jan Bogaard
- Department of Pulmonary Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Menno V Huisman
- Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| | - Petr Symersky
- Department of Cardiothoracic Surgery, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Anton Vonk Noordegraaf
- Department of Pulmonary Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Lilian J Meijboom
- Department of Radiology and Nuclear Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederikus A Klok
- Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| |
Collapse
|
4
|
Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics. ELECTRONICS 2021. [DOI: 10.3390/electronics10202475] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.
Collapse
|
5
|
Nazari M, Shiri I, Zaidi H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med 2020; 129:104135. [PMID: 33254045 DOI: 10.1016/j.compbiomed.2020.104135] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 10/21/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients. METHODS According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models. RESULTS The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively). CONCLUSIONS We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.
Collapse
Affiliation(s)
- Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|