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Ji H, Kim S, Sunwoo L, Jang S, Lee HY, Yoo S. Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension. JMIR Med Inform 2024; 12:e59187. [PMID: 38996330 PMCID: PMC11282389 DOI: 10.2196/59187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/10/2024] [Accepted: 06/08/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. OBJECTIVE This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. METHODS Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. RESULTS This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.
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Affiliation(s)
- Hyerim Ji
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Seok Kim
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sowon Jang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Ho-Young Lee
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sooyoung Yoo
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
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Yang Q, Huang D, Wu J, Zhong H, Han Y, Jiang H, Chen Y, Chen G, Zhan X, Zhou P. Performance of [ 18F]FDG PET/CT versus FAPI PET/CT for lung cancer assessment: a systematic review and meta-analysis. Eur Radiol 2024; 34:1077-1085. [PMID: 37589901 DOI: 10.1007/s00330-023-10013-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/08/2023] [Accepted: 06/11/2023] [Indexed: 08/18/2023]
Abstract
PURPOSE This article aims to compare the diagnostic performance of 18-fluorodeoxyglucose ([18F]FDG) PET/CT and fibroblast activating protein inhibitor (FAPI) PET/CT in the assessment of primary tumors, lymph nodes, and distant metastases in lung cancer patients. METHODS A systematic search was conducted on the Cochrane Library, Embase, and PubMed/MEDLINE databases from inception until November 1, 2022. Included studies assessed the use of FAPI PET/CT and [18F]FDG PET/CT in patients with lung cancer. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to evaluate the risk of bias. A random variable model was used to analyze the diagnostic tests of the two imaging modalities. RESULTS The sensitivity of FAPI PET/CT in detecting primary lung cancer lesions was 0.98 (95% CI: 0.88-1.00), while the sensitivity of [18F]FDG PET/CT was 0.99 (95% CI: 0.74-1.00). For the detection of metastatic lesions (lymph node metastases and distant metastases), FAPI PET/CT had a sensitivity of 0.99 (95% CI: 0.90-1.00), while the sensitivity of [18F]FDG PET/CT was 0.77 (95% CI: 0.66-0.85). However, the specificity of the two imaging modalities could not be assessed due to the lack of sufficient information on pertinent true negatives. CONCLUSION In the diagnosis of metastatic lung cancer lesions, FAPI PET/CT demonstrated a higher sensitivity compared to [18F]FDG PET/CT. Therefore, FAPI PET/CT may be considered an alternative imaging modality for the assessment of primary lung cancer tumors, lymph node metastases, and distant metastases. CLINICAL RELEVANCE STATEMENT FAPI may be an alternative to [18F]FDG in the assessment of primary lung cancer tumors, lymph node metastases, and distant metastases, which plays a very important role in treatment. KEY POINTS • This article is to compare the performance of [18F]FDG PET/CT with FAPI PET/CT in the assessment of primary tumors, lymph nodes, and distant metastases in lung cancer. • However, FAPI PET/CT has a higher sensitivity for the diagnostic assessment of metastatic lung cancer lesions.
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Affiliation(s)
- Qiuya Yang
- Southwest Medical University, Luzhou, Sichuan, China
| | - Delong Huang
- Southwest Medical University, Luzhou, Sichuan, China
| | - Junhao Wu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Haoshu Zhong
- Department of Hematology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - YuanHang Han
- Southwest Medical University, Luzhou, Sichuan, China
| | - Haodong Jiang
- Southwest Medical University, Luzhou, Sichuan, China
| | - Yue Chen
- Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
- Institute of Nuclear Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Gong Chen
- Chengdu Sport University, Chengdu, Sichuan, China
| | - Xiang Zhan
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.25, Taiping St, Luzhou, 646000, Sichuan, China
| | - Ping Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.25, Taiping St, Luzhou, 646000, Sichuan, China.
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Vasiliuk A, Frolova D, Belyaev M, Shirokikh B. Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation. J Imaging 2023; 9:191. [PMID: 37754955 PMCID: PMC10532230 DOI: 10.3390/jimaging9090191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023] Open
Abstract
Deep learning models perform unreliably when the data come from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection methods help to identify such data samples, preventing erroneous predictions. In this paper, we further investigate OOD detection effectiveness when applied to 3D medical image segmentation. We designed several OOD challenges representing clinically occurring cases and found that none of the methods achieved acceptable performance. Methods not dedicated to segmentation severely failed to perform in the designed setups; the best mean false-positive rate at a 95% true-positive rate (FPR) was 0.59. Segmentation-dedicated methods still achieved suboptimal performance, with the best mean FPR being 0.31 (lower is better). To indicate this suboptimality, we developed a simple method called Intensity Histogram Features (IHF), which performed comparably or better in the same challenges, with a mean FPR of 0.25. Our findings highlight the limitations of the existing OOD detection methods with 3D medical images and present a promising avenue for improving them. To facilitate research in this area, we release the designed challenges as a publicly available benchmark and formulate practical criteria to test the generalization of OOD detection beyond the suggested benchmark. We also propose IHF as a solid baseline to contest emerging methods.
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Affiliation(s)
- Anton Vasiliuk
- Moscow Institute of Physics and Technology, Moscow 141701, Russia;
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia; (D.F.); (M.B.)
| | - Daria Frolova
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia; (D.F.); (M.B.)
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Mikhail Belyaev
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia; (D.F.); (M.B.)
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Boris Shirokikh
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia; (D.F.); (M.B.)
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
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Zhang R, Wang P, Bian Y, Fan Y, Li J, Liu X, Shen J, Hu Y, Liao X, Wang H, Song C, Li W, Wang X, Sun M, Zhang J, Wang M, Wang S, Shen Y, Zhang X, Jia Q, Tan J, Li N, Wang S, Xu L, Wu W, Zhang W, Meng Z. Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging. BMC Med Imaging 2023; 23:84. [PMID: 37328753 PMCID: PMC10273563 DOI: 10.1186/s12880-023-01037-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 05/29/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. METHODS We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named "YOLO" to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). RESULTS Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value < 0.05). CONCLUSION The AI system of our study showed excellent predictive accuracy in the diagnosis of MPI protocols, and therefore might be potentially helpful to aid radiologists in clinical practice and develop more sophisticated models.
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Affiliation(s)
- Ruyi Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Peng Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Yanzhu Bian
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Yan Fan
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Jianming Li
- Department of Nuclear Medicine, Teda International Cardiovascular Hospital, Tianjin, China
| | - Xuehui Liu
- Department of Nuclear Medicine, Tianjin Third Central Hospital, Tianjin, China
| | - Jie Shen
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, China
| | - Yujing Hu
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Xianghe Liao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - He Wang
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Chengyu Song
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Wangxiao Li
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Xiaojie Wang
- Department of Nuclear Medicine, Teda International Cardiovascular Hospital, Tianjin, China
| | - Momo Sun
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, China
| | - Jianping Zhang
- Department of Nuclear Medicine, Tianjin Third Central Hospital, Tianjin, China
| | - Miao Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Shen Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Yiming Shen
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Xuemei Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Qiang Jia
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Jian Tan
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Ning Li
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Sen Wang
- Department of Nuclear Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Lingyun Xu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Weiming Wu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Wei Zhang
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072.
| | - Zhaowei Meng
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052.
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Zakharov A, Pisov M, Bukharaev A, Petraikin A, Morozov S, Gombolevskiy V, Belyaev M. Interpretable vertebral fracture quantification via anchor-free landmarks localization. Med Image Anal 2023; 83:102646. [PMID: 36279768 DOI: 10.1016/j.media.2022.102646] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 07/20/2022] [Accepted: 09/27/2022] [Indexed: 12/12/2022]
Abstract
Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to the current clinical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an interpretable and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1mm), vertebrae 2D detection (precision and recall are 0.99), and fracture identification (ROC AUC at the patient level is up to 0.96). Our anchor-free vertebra detection network shows excellent generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85, specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.
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Affiliation(s)
- Alexey Zakharov
- IRA Labs Ltd, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Maxim Pisov
- IRA Labs Ltd, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia; Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | | | - Alexey Petraikin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Russia
| | | | | | - Mikhail Belyaev
- IRA Labs Ltd, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia.
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Shimovolos S, Shushko A, Belyaev M, Shirokikh B. Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency. J Imaging 2022; 8:jimaging8090234. [PMID: 36135401 PMCID: PMC9503667 DOI: 10.3390/jimaging8090234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/20/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Deep learning methods provide significant assistance in analyzing coronavirus disease (COVID-19) in chest computed tomography (CT) images, including identification, severity assessment, and segmentation. Although the earlier developed methods address the lack of data and specific annotations, the current goal is to build a robust algorithm for clinical use, having a larger pool of available data. With the larger datasets, the domain shift problem arises, affecting the performance of methods on the unseen data. One of the critical sources of domain shift in CT images is the difference in reconstruction kernels used to generate images from the raw data (sinograms). In this paper, we show a decrease in the COVID-19 segmentation quality of the model trained on the smooth and tested on the sharp reconstruction kernels. Furthermore, we compare several domain adaptation approaches to tackle the problem, such as task-specific augmentation and unsupervised adversarial learning. Finally, we propose the unsupervised adaptation method, called F-Consistency, that outperforms the previous approaches. Our method exploits a set of unlabeled CT image pairs which differ only in reconstruction kernels within every pair. It enforces the similarity of the network’s hidden representations (feature maps) by minimizing the mean squared error (MSE) between paired feature maps. We show our method achieving a 0.64 Dice Score on the test dataset with unseen sharp kernels, compared to the 0.56 Dice Score of the baseline model. Moreover, F-Consistency scores 0.80 Dice Score between predictions on the paired images, which almost doubles the baseline score of 0.46 and surpasses the other methods. We also show F-Consistency to better generalize on the unseen kernels and without the presence of the COVID-19 lesions than the other methods trained on unlabeled data.
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Affiliation(s)
| | - Andrey Shushko
- Moscow Institute of Physics and Technology, 141701 Moscow, Russia
| | - Mikhail Belyaev
- Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
- Artificial Intelligence Research Institute (AIRI), 105064 Moscow, Russia
| | - Boris Shirokikh
- Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
- Artificial Intelligence Research Institute (AIRI), 105064 Moscow, Russia
- Correspondence:
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Inter-Observer Agreement between Low-Dose and Standard-Dose CT with Soft and Sharp Convolution Kernels in COVID-19 Pneumonia. J Clin Med 2022; 11:jcm11030669. [PMID: 35160121 PMCID: PMC8836391 DOI: 10.3390/jcm11030669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 12/29/2022] Open
Abstract
Computed tomography (CT) has been an essential diagnostic tool during the COVID-19 pandemic. The study aimed to develop an optimal CT protocol in terms of safety and reliability. For this, we assessed the inter-observer agreement between CT and low-dose CT (LDCT) with soft and sharp kernels using a semi-quantitative severity scale in a prospective study (Moscow, Russia). Two consecutive scans with CT and LDCT were performed in a single visit. Reading was performed by ten radiologists with 3–25 years’ experience. The study included 230 patients, and statistical analysis showed LDCT with a sharp kernel as the most reliable protocol (percentage agreement 74.35 ± 43.77%), but its advantage was marginal. There was no significant correlation between radiologists’ experience and average percentage agreement for all four evaluated protocols. Regarding the radiation exposure, CTDIvol was 3.6 ± 0.64 times lower for LDCT. In conclusion, CT and LDCT with soft and sharp reconstructions are equally reliable for COVID-19 reporting using the “CT 0-4” scale. The LDCT protocol allows for a significant decrease in radiation exposure but may be restricted by body mass index.
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