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Yoon H, Kim J, Lim HJ, Lee MJ. Quantitative Liver Imaging in Children. Invest Radiol 2024:00004424-990000000-00238. [PMID: 39047265 DOI: 10.1097/rli.0000000000001101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
ABSTRACT In children and adults, quantitative imaging examinations determine the effectiveness of treatment for liver disease. However, pediatric liver disease differs in presentation from liver disease in adults. Children also needed to be followed for a longer period from onset and have less control of their bodies, showing more movement than adults during imaging examinations, which leads to a greater need for sedation. Thus, it is essential to appropriately tailor and accurately perform noninvasive imaging tests in these younger patients. This article is an overview of updated imaging techniques used to assess liver disease quantitatively in children. The common initial imaging study for diffuse liver disease in pediatric patients is ultrasound. In addition to preexisting echo analysis, newly developed attenuation imaging techniques have been introduced to evaluate fatty liver. Ultrasound elastography is also now actively used to evaluate liver conditions, and the broad age spectrum of the pediatric population requires caution to be taken even in the selection of probes. Magnetic resonance imaging (MRI) is another important imaging tool used to evaluate liver disease despite requiring sedation or anesthesia in young children because it allows quantitative analysis with sequences such as fat analysis and MR elastography. In addition to ultrasound and MRI, we review quantitative imaging methods specifically for fatty liver, Wilson disease, biliary atresia, hepatic fibrosis, Fontan-associated liver disease, autoimmune hepatitis, sinusoidal obstruction syndrome, and the transplanted liver. Lastly, concerns such as growth and motion that need to be addressed specifically for children are summarized.
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Affiliation(s)
- Haesung Yoon
- From the Department of Radiology, Gangnam Severance Hospital, Seoul, South Korea (H.Y.); Department of Radiology and Research Institute of Radiological Science, Yonsei University, College of Medicine, Seoul, South Korea (H.Y., J.K., H.J.L., M.-J.L.); and Department of Pediatric Radiology, Severance Children's Hospital, Seoul, South Korea (J.K., H.J.L., M.-J.L.)
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Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Liu J, Wang H, Shan X, Zhang L, Cui S, Shi Z, Liu Y, Zhang Y, Wang L. Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT. BMC Med Imaging 2024; 24:62. [PMID: 38486185 PMCID: PMC10938662 DOI: 10.1186/s12880-024-01240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
OBJECTIVE Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based radiomics model for osteoporosis screening in routine CT. METHODS To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore, we included 283 vertebral bodies and randomly divided them into the training cohort (n = 204) and test cohort (n = 79) for radiomics analysis. Area receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were applied to compare the performance and clinical value between radiomics models and Hounsfield Unit (HU) values to detect dual-energy X-ray absorptiometry (DXA) based osteoporosis. RESULTS HTCNN algorithm revealed high precision for the segmentation of the vertebral body and trabecular compartment. In test sets, the mean dice scores reach 0.968 and 0.961. 12 features from the trabecular compartment and 15 features from the entire vertebral body were used to calculate the radiomics score (rad score). Compared with HU values and trabecular rad-score, the vertebrae rad-score suggested the best efficacy for osteoporosis and non-osteoporosis discrimination (training group: AUC = 0.95, 95%CI 0.91-0.99; test group: AUC = 0.97, 95%CI 0.93-1.00) and the differences were significant in test group according to the DeLong test (p < 0.05). CONCLUSIONS This retrospective study demonstrated the superiority of the HTCNN-based vertebrae radiomics model for osteoporosis discrimination in routine CT.
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Affiliation(s)
- Jiachen Liu
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Huan Wang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Xiuqi Shan
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Lei Zhang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Shaoqian Cui
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Zelin Shi
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yunpeng Liu
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yingdi Zhang
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China.
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Lee H, Chang W, Kim HY, Sung P, Cho J, Lee YJ, Kim YH. Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients. Eur Radiol 2024; 34:2036-2047. [PMID: 37656175 DOI: 10.1007/s00330-023-10135-y] [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: 11/07/2022] [Revised: 07/06/2023] [Accepted: 07/14/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluate the effect of deep learning-based image conversion on CT reconstruction algorithms. METHODS This study included 78 hepatocellular carcinoma (HCC) patients who underwent four-phase liver CTs comprising non-contrast, late arterial (LAP), portal venous (PVP), and delayed phase (DP), reconstructed using both filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). PVP images were used to train a convolutional neural network (CNN) model to convert images from FBP to ADMIRE and vice versa. LAP, PVP, and DP images were used for validation and testing. Radiomic features were extracted for each patient with a semi-automatic segmentation tool. We used concordance correlation coefficients (CCCs) to evaluate the radiomics reproducibility for original FBP (oFBP) vs. original ADMIRE (oADMIRE), oFBP vs. converted FBP (cFBP), and oADMIRE vs. converted ADMIRE (cADMIRE). RESULTS In the test group including 30 patients, the CCC and proportion of reproducible features (CCC ≥ 0.85) for oFBP vs. oADMIRE were 0.65 and 32.9% (524/1595) for LAP, 0.65 and 35.9% (573/1595) for PVP, and 0.69 and 43.8% (699/1595) for DP. For oFBP vs. cFBP, the values increased to 0.92 and 83.9% (1339/1595) for LAP, 0.89 and 71.0% (1133/1595) for PVP, and 0.90 and 79.7% (1271/1595) for DP. Similarly, for oADMIRE vs. cADMIRE, the values increased to 0.87 and 68.1% (1086/1595) for LAP, 0.91 and 82.1% (1309/1595) for PVP, and 0.89 and 76.2% (1216/1595) for DP. CONCLUSIONS CNN-based image conversion between CT reconstruction algorithms improved the radiomics reproducibility of HCCs. CLINICAL RELEVANCE STATEMENT This study demonstrates that using a CNN-based image conversion technique significantly improves the reproducibility of radiomic features in HCCs, highlighting its potential for enhancing radiomics research in HCC patients. KEY POINTS Radiomics reproducibility of HCC was improved via CNN-based image conversion between two different CT reconstruction algorithms. This is the first clinical study to demonstrate improvements across a range of radiomic features in HCC patients. This study promotes the reproducibility and generalizability of different CT reconstruction algorithms in radiomics research.
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Affiliation(s)
- Heejin Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Hae Young Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Pamela Sung
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Zhong J, Wu Z, Wang L, Chen Y, Xia Y, Wang L, Li J, Lu W, Shi X, Feng J, Dong H, Zhang H, Yao W. Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:123-133. [PMID: 38343265 DOI: 10.1007/s10278-023-00901-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 03/02/2024]
Abstract
This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Zhiyuan Wu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Haipeng Dong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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Fu C, Zhang B, Guo T, Li J. Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques. Korean J Radiol 2024; 25:86-102. [PMID: 38184772 PMCID: PMC10788608 DOI: 10.3348/kjr.2023.0840] [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: 06/22/2023] [Revised: 09/28/2023] [Accepted: 10/08/2023] [Indexed: 01/08/2024] Open
Abstract
Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.
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Affiliation(s)
- Chen Fu
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
| | - Bangxing Zhang
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Tiankang Guo
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Junliang Li
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China.
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Goller SS, Foreman SC, Rischewski JF, Weißinger J, Dietrich AS, Schinz D, Stahl R, Luitjens J, Siller S, Schmidt VF, Erber B, Ricke J, Liebig T, Kirschke JS, Dieckmeyer M, Gersing AS. Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:4314-4320. [PMID: 37401945 DOI: 10.1007/s00586-023-07838-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/25/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jon F Rischewski
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jürgen Weißinger
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Anna-Sophia Dietrich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Robert Stahl
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Bernd Erber
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Thomas Liebig
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
| | - Alexandra S Gersing
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
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Li S, Yuan L, Lu T, Yang X, Ren W, Wang L, Zhao J, Deng J, Liu X, Xue C, Sun Q, Zhang W, Zhou J. Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases. Eur J Radiol 2023; 168:111128. [PMID: 37816301 DOI: 10.1016/j.ejrad.2023.111128] [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: 04/25/2023] [Revised: 08/07/2023] [Accepted: 09/28/2023] [Indexed: 10/12/2023]
Abstract
OBJECTIVE To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM). METHODS Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images. RESULTS All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P<0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P>0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01). CONCLUSION Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Ting Lu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xinmei Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Wei Ren
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.
| | - Jun Zhao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China.
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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9
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Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol 2023; 95:75-87. [PMID: 37499847 DOI: 10.1016/j.semcancer.2023.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/29/2023]
Abstract
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
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Affiliation(s)
- Yichen Meng
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Yue Yang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Miao Hu
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Zheng Zhang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
| | - Xuhui Zhou
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
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10
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Yu Q, Ning Y, Wang A, Li S, Gu J, Li Q, Chen X, Lv F, Zhang X, Yue Q, Peng J. Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study. Eur Radiol 2023; 33:6054-6065. [PMID: 37067576 DOI: 10.1007/s00330-023-09568-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/31/2023] [Accepted: 02/26/2023] [Indexed: 04/18/2023]
Abstract
OBJECTIVES To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors. METHODS Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model. RESULTS MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001). CONCLUSIONS The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors. KEY POINTS • The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.
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Affiliation(s)
- Qiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Youquan Ning
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Anran Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Shuang Li
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Jinming Gu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Quanjiang Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xinwei Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | | | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China.
| | - Juan Peng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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11
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Hwang HJ, Kim H, Seo JB, Ye JC, Oh G, Lee SM, Jang R, Yun J, Kim N, Park HJ, Lee HY, Yoon SH, Shin KE, Lee JW, Kwon W, Sun JS, You S, Chung MH, Gil BM, Lim JK, Lee Y, Hong SJ, Choi YW. Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease. Korean J Radiol 2023; 24:807-820. [PMID: 37500581 PMCID: PMC10400368 DOI: 10.3348/kjr.2023.0088] [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: 01/31/2023] [Revised: 06/12/2023] [Accepted: 06/18/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. MATERIALS AND METHODS This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. RESULTS Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. CONCLUSION CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.
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Affiliation(s)
- Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyunjong Kim
- Robotics Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ryoungwoo Jang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee Jun Park
- Coreline Soft, Co., Ltd, Seoul, Republic of Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyung Eun Shin
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
| | - Jae Wook Lee
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
| | - Woocheol Kwon
- Department of Radiology, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea
- Department of Radiology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Joo Sung Sun
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seulgi You
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Myung Hee Chung
- Department of Radiology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Bo Mi Gil
- Department of Radiology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Kwang Lim
- Department of Radiology, Kyungpook National University School of Medicine, Daegu, Republic of Korea
| | - Youkyung Lee
- Department of Radiology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Republic of Korea
| | - Su Jin Hong
- Department of Radiology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Republic of Korea
| | - Yo Won Choi
- Department of Radiology, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
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Lee SB, Hong Y, Cho YJ, Jeong D, Lee J, Yoon SH, Lee S, Choi YH, Cheon JE. Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation. Korean J Radiol 2023; 24:294-304. [PMID: 36907592 PMCID: PMC10067697 DOI: 10.3348/kjr.2022.0588] [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: 08/17/2022] [Revised: 12/19/2022] [Accepted: 01/24/2023] [Indexed: 03/14/2023] Open
Abstract
OBJECTIVE We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. MATERIALS AND METHODS We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. RESULTS The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). CONCLUSION Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.
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Affiliation(s)
- Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Youngtaek Hong
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul, Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
| | - Dawun Jeong
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul, Korea.,Brain Korea, 21 PLUS Project for Medical Science, Yonsei University, Seoul, Korea
| | - Jina Lee
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul, Korea.,Brain Korea, 21 PLUS Project for Medical Science, Yonsei University, Seoul, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,MEDICALIP Co. Ltd., Seoul, Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
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13
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Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2023; 58:199-208. [PMID: 36070524 DOI: 10.1097/rli.0000000000000921] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. MATERIALS AND METHODS Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. RESULTS Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. CONCLUSION Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.
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Affiliation(s)
- Barbara D Wichtmann
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Felix N Harder
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Stefan O Schönberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Germany
| | - Ulrike I Attenberger
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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Chun SH, Suh YJ, Han K, Kwon Y, Kim AY, Choi BW. Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions. Sci Rep 2022; 12:15171. [PMID: 36071138 PMCID: PMC9452656 DOI: 10.1038/s41598-022-19546-1] [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: 03/14/2022] [Accepted: 08/30/2022] [Indexed: 11/09/2022] Open
Abstract
We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 consecutive patients with 285 cardiac CT scans that were reconstructed with DLR, IR, and FBP, were retrospectively enrolled. Radiomic features were extracted from the left ventricular (LV) myocardium, and from the periprosthetic mass if patients had cardiac valve replacement. Radiomic features of LV myocardium from each reconstruction were compared using a fitting linear mixed model. Radiomics models were developed to diagnose periprosthetic abnormality, and the performance was evaluated using the area under the receiver characteristics curve (AUC). Most radiomic features of LV myocardium (73 of 88) were significantly different in pairwise comparisons between all three reconstruction methods (P < 0.05). The radiomics model on IR exhibited the best diagnostic performance (AUC 0.948, 95% CI 0.880-1), relative to DLR (AUC 0.873, 95% CI 0.735-1) and FBP (AUC 0.875, 95% CI 0.731-1), but these differences did not reach significance (P > 0.05). In conclusion, applying DLR to cardiac CT scans yields radiomic features distinct from those obtained with IR and FBP, implying that feature robustness is not guaranteed when applying DLR.
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Affiliation(s)
- Sei Hyun Chun
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Young Joo Suh
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yonghan Kwon
- Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea
| | | | - Byoung Wook Choi
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study. Eur Radiol 2022; 32:4587-4595. [PMID: 35174400 PMCID: PMC9213380 DOI: 10.1007/s00330-022-08592-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 01/05/2022] [Accepted: 01/24/2022] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). METHODS Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient ≥ 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient ≥ 0.75). RESULTS The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses ≥ 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. CONCLUSIONS AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. KEY POINTS • Image quality of CT images reconstructed with filtered back projection and iterative methods is inadequate for the majority of radiomics features due to inconsistent tissue characterization, low discriminative power, or low repeatability. • Deep learning reconstruction enhances image quality for radiomics and more than doubled the feature yield at doses that are typically used in clinical CT imaging. • Image reconstruction algorithms can optimize image quality for more reliable quantification of tissues in CT images.
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