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Baniasadi A, Das JP, Prendergast CM, Beizavi Z, Ma HY, Jaber MY, Capaccione KM. Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis. J Transl Med 2024; 22:567. [PMID: 38872212 DOI: 10.1186/s12967-024-05379-1] [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: 02/11/2024] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.
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Affiliation(s)
- Alireza Baniasadi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
| | - Jeeban P Das
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Conor M Prendergast
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Zahra Beizavi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Hong Y Ma
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | | | - Kathleen M Capaccione
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
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Gui Y, Chen F, Ren J, Wang L, Chen K, Zhang J. MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1054-1066. [PMID: 38351221 PMCID: PMC11169408 DOI: 10.1007/s10278-024-01024-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 06/13/2024]
Abstract
The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.
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Affiliation(s)
- Yuan Gui
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Fen Chen
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Limei Wang
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Kuntao Chen
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Jing Zhang
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China.
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Leng Y, Wang X, Zheng T, Peng F, Xiong L, Wang Y, Gong L. Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer. Sci Rep 2024; 14:12456. [PMID: 38816463 PMCID: PMC11139946 DOI: 10.1038/s41598-024-63369-1] [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: 05/30/2023] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Xiwen Wang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Tian Zheng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Fei Peng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Liangxia Xiong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, 200072, Shanghai, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China.
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Zhang C, Shu Z, Chen S, Peng J, Zhao Y, Dai X, Li J, Zou X, Hu J, Huang H. A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients. Sci Rep 2024; 14:12081. [PMID: 38802526 PMCID: PMC11130122 DOI: 10.1038/s41598-024-63095-8] [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: 12/17/2023] [Accepted: 05/24/2024] [Indexed: 05/29/2024] Open
Abstract
Early assessment and accurate staging of liver fibrosis may be of great help for clinical diagnosis and treatment in patients with chronic hepatitis B (CHB). We aimed to identify serum markers and construct a machine learning (ML) model to reliably predict the stage of fibrosis in CHB patients. The clinical data of 618 CHB patients between February 2017 and September 2021 from Zhejiang Provincial People's Hospital were retrospectively analyzed, and these data as a training cohort to build the model. Six ML models were constructed based on logistic regression, support vector machine, Bayes, K-nearest neighbor, decision tree (DT) and random forest by using the maximum relevance minimum redundancy (mRMR) and gradient boosting decision tree (GBDT) dimensionality reduction selected features on the training cohort. Then, the resampling method was used to select the optimal ML model. In addition, a total of 571 patients from another hospital were used as an external validation cohort to verify the performance of the model. The DT model constructed based on five serological biomarkers included HBV-DNA, platelet, thrombin time, international normalized ratio and albumin, with the area under curve (AUC) values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the training cohort were 0.898, 0.891, 0.907 and 0.944, respectively. The AUC values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the external validation cohort were 0.906, 0.876, 0.931 and 0.933, respectively. The simulated risk classification based on the cutoff value showed that the classification performance of the DT model in distinguishing hepatic fibrosis stages can be accurately matched with pathological diagnosis results. ML model of five serum markers allows for accurate diagnosis of hepatic fibrosis stages, and beneficial for the clinical monitoring and treatment of CHB patients.
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Affiliation(s)
- Congjie Zhang
- Center for Plastic & Reconstructive Surgery, Department of Dermatology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Zhenyu Shu
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Shanshan Chen
- Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Yueyue Zhao
- Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Xuan Dai
- Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Jie Li
- Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Xuehan Zou
- Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Jianhua Hu
- Department of Infectious Diseases, The First Affiliated Hospital of Zhejiang University of Medicine, Hangzhou, Zhejiang, China
| | - Haijun Huang
- Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang, China.
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Mai XF, Zhang H, Wang Y, Zhong WX, Zou LQ. Multiparametric MRI-based whole-liver radiomics for predicting early-stage liver fibrosis in rabbits. Br J Radiol 2024; 97:964-970. [PMID: 38552321 DOI: 10.1093/bjr/tqae063] [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: 01/13/2024] [Revised: 02/26/2024] [Accepted: 03/18/2024] [Indexed: 05/09/2024] Open
Abstract
OBJECTIVES To develop and validate a whole-liver radiomic model using multiparametric MRI for predicting early-stage liver fibrosis (LF) in rabbits. METHODS A total of 134 rabbits (early-stage LF, n = 91; advanced-stage LF, n = 43) who underwent liver magnetic resonance elastography (MRE), hepatobiliary phase, dynamic contrast enhanced (DCE), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging, and T2* scanning were enrolled and randomly allocated to either the training or validation cohort. Whole-liver radiomic features were extracted and selected to develop a radiomic model and generate quantitative Rad-scores. Then, multivariable logistic regression was utilized to determine the Rad-scores associated with early-stage LF, and effective features were integrated to establish a combined model. The predictive performance was assessed by the area under the curve (AUC). RESULTS The MRE model achieved superior AUCs of 0.95 in the training cohort and 0.86 in the validation cohort, followed by the DCE-MRI model (0.93 and 0.82), while the IVIM model had lower AUC values of 0.91 and 0.82, respectively. The Rad-scores of MRE, DCE-MRI and IVIM were identified as independent predictors associated with early-stage LF. The combined model demonstrated AUC values of 0.96 and 0.88 for predicting early-stage LF in the training and validation cohorts, respectively. CONCLUSIONS Our study highlights the remarkable performance of a multiparametric MRI-based radiomic model for the individualized diagnosis of early-stage LF. ADVANCES IN KNOWLEDGE This is the first study to develop a combined model by integrating multiparametric radiomic features to improve the accuracy of LF staging.
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Affiliation(s)
- Xiao-Fei Mai
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen 518052, China
| | - Hao Zhang
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen 518052, China
| | - Yang Wang
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen 518052, China
| | - Wen-Xin Zhong
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen 518052, China
| | - Li-Qiu Zou
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen 518052, China
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Wang XM, Zhang XJ. Role of radiomics in staging liver fibrosis: a meta-analysis. BMC Med Imaging 2024; 24:87. [PMID: 38609843 PMCID: PMC11010385 DOI: 10.1186/s12880-024-01272-x] [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: 06/13/2023] [Accepted: 04/10/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis. METHOD After literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were performed to achieve pooled estimates of area under receiver-operator curve (AUROC), accuracy, sensitivity, and specificity of radiomics in staging liver fibrosis compared to histopathology. RESULTS Fifteen studies (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% [95% CI: 65, 73] males) were included. AUROC values of radiomics for detecting significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4) were 0.91 [95%CI: 0.89, 0.94], 0.92 [95%CI: 0.90, 0.95], and 0.94 [95%CI: 0.93, 0.96] in training cohorts and 0.89 [95%CI: 0.83, 0.91], 0.89 [95%CI: 0.83, 0.94], and 0.93 [95%CI: 0.91, 0.95] in validation cohorts, respectively. For diagnosing significant fibrosis, advanced fibrosis, and cirrhosis the sensitivity of radiomics was 84.0% [95%CI: 76.1, 91.9], 86.9% [95%CI: 76.8, 97.0], and 92.7% [95%CI: 89.7, 95.7] in training cohorts, and 75.6% [95%CI: 67.7, 83.5], 80.0% [95%CI: 70.7, 89.3], and 92.0% [95%CI: 87.8, 96.1] in validation cohorts, respectively. Respective specificity was 88.6% [95% CI: 83.0, 94.2], 88.4% [95% CI: 81.9, 94.8], and 91.1% [95% CI: 86.8, 95.5] in training cohorts, and 86.8% [95% CI: 83.3, 90.3], 94.0% [95% CI: 89.5, 98.4], and 88.3% [95% CI: 84.4, 92.2] in validation cohorts. Limitations included use of several methods for feature selection and classification, less availability of studies evaluating a particular radiological modality, lack of a direct comparison between radiology and radiomics, and lack of external validation. CONCLUSION Although radiomics offers good diagnostic accuracy in detecting liver fibrosis, its role in clinical practice is not as clear at present due to comparability and validation constraints.
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Affiliation(s)
- Xiao-Min Wang
- School of Medical Imaging, Tianjin Medical University, No.1, Guangdong Road, Hexi District, Tianjin, 300203, China.
| | - Xiao-Jing Zhang
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
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Xiao L, Zhao H, Liu S, Dong W, Gao Y, Wang L, Huang B, Li Z. Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease. Abdom Radiol (NY) 2024; 49:1165-1174. [PMID: 38219254 DOI: 10.1007/s00261-023-04142-2] [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: 09/25/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVES To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease. MATERIALS AND METHODS Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures. RESULTS In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042). CONCLUSIONS Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4.
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Affiliation(s)
- Longyang Xiao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Haichen Zhao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Wenlu Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Lili Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Baoxiang Huang
- College of Computer Science and Technology of Qingdao University, No.308 Ningxia Road, Qingdao, 266071, China.
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China.
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [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: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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Meng H, Sun YF, Zhang Y, Yu YN, Wang J, Wang JN, Xue LY, Yin XP. Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:81-91. [PMID: 38343262 DOI: 10.1007/s10278-023-00936-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 03/02/2024]
Abstract
Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.
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Affiliation(s)
- Huan Meng
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Quality and Technical Supervision, Hebei University, Lianchi District, No. 180, Wusi East Road, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Ya-Nan Yu
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Jing Wang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Jia-Ning Wang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, Lianchi District, No. 180, Wusi East Road, Baoding, 071000, China.
| | - Xiao-Ping Yin
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China.
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Gao Y, Yu Q, Li X, Xia C, Zhou J, Xia T, Zhao B, Qiu Y, Zha JH, Wang Y, Tang T, Lv Y, Ye J, Xu C, Ju S. An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding. Eur Radiol 2023; 33:8965-8973. [PMID: 37452878 DOI: 10.1007/s00330-023-09938-w] [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: 12/12/2022] [Revised: 05/08/2023] [Accepted: 05/15/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVES To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.
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Affiliation(s)
- Yin Gao
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Qian Yu
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Xiaohuan Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Cong Xia
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Jiaying Zhou
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Tianyi Xia
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Ben Zhao
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Yue Qiu
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Jun-Hao Zha
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Yuancheng Wang
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Tianyu Tang
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China
| | - Yan Lv
- Department of Medical Imaging, Subei People's Hospital, Medical School of Yangzhou University, Yangzhou, China
| | - Jing Ye
- Department of Medical Imaging, Subei People's Hospital, Medical School of Yangzhou University, Yangzhou, China
| | - Chuanjun Xu
- Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Shenghong Ju
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China.
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Cao X, Yang H, Luo X, Zou L, Zhang Q, Li Q, Zhang J, Li X, Shi Y, Jin C. A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics. J Magn Reson Imaging 2023; 58:1930-1941. [PMID: 37177868 DOI: 10.1002/jmri.28725] [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: 12/12/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. PURPOSE To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings. STUDY TYPE Retrospective. SUBJECTS 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. FIELD STRENGTH/SEQUENCE Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T. ASSESSMENT Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC. STATISTICAL TESTS The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant. RESULTS The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. CONCLUSION The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xinshan Cao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Haoran Yang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Xin Luo
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Linxuan Zou
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qiang Zhang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qilin Li
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Juntao Zhang
- GE Healthcare Precision Health Institution, Shanghai, China
| | - Xiangfeng Li
- Department of Radiology, The Fourth People Hospital of Zibo, Zibo, China
| | - Yan Shi
- Department of Medical Ultrasonics, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Zheng S, He K, Zhang L, Li M, Zhang H, Gao P. Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging. Eur J Radiol 2023; 165:110912. [PMID: 37290363 DOI: 10.1016/j.ejrad.2023.110912] [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: 03/13/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023]
Abstract
Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.
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Affiliation(s)
- Shuang Zheng
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Kan He
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Lei Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Mingyang Li
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Huimao Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Pujun Gao
- Department of Hepatology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
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Ge XY, Lan ZK, Lan QQ, Lin HS, Wang GD, Chen J. Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease. Eur Radiol 2023; 33:2386-2398. [PMID: 36454259 PMCID: PMC10017610 DOI: 10.1007/s00330-022-09268-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/15/2022] [Accepted: 10/24/2022] [Indexed: 12/02/2022]
Abstract
OBJECTIVES To predict kidney fibrosis in patients with chronic kidney disease using radiomics of two-dimensional ultrasound (B-mode) and Sound Touch Elastography (STE) images in combination with clinical features. METHODS The Mindray Resona 7 ultrasonic diagnostic apparatus with SC5-1U convex array probe (bandwidth frequency of 1-5 MHz) was used to perform two-dimensional ultrasound and STE software. The severity of cortical tubulointerstitial fibrosis was divided into three grades: mild interstitial fibrosis and tubular atrophy (IFTA), fibrotic area < 25%; moderate IFTA, fibrotic area 26-50%; and severe IFTA, fibrotic area > 50%. After extracting radiomics from B-mode and STE images in these patients, we analyzed two classification schemes: mild versus moderate-to-severe IFTA, and mild-to-moderate versus severe IFTA. A nomogram was constructed based on multiple logistic regression analyses, combining clinical and radiomics. The performance of the nomogram for differentiation was evaluated using receiver operating characteristic (ROC), calibration, and decision curves. RESULTS A total of 150 patients undergoing kidney biopsy were enrolled (mild IFTA: n = 74; moderate IFTA: n = 33; severe IFTA: n = 43) and randomized into training (n = 105) and validation cohorts (n = 45). To differentiate between mild and moderate-to-severe IFTA, a nomogram incorporating STE radiomics, albumin, and estimated glomerular filtration (eGFR) rate achieved an area under the ROC curve (AUC) of 0.91 (95% confidence interval [CI]: 0.85-0.97) and 0.85 (95% CI: 0.77-0.98) in the training and validation cohorts, respectively. Between mild-to-moderate and severe IFTA, the nomogram incorporating B-mode and STE radiomics features, age, and eGFR achieved an AUC of 0.93 (95% CI: 0.89-0.98) and 0.83 (95% CI: 0.70-0.95) in the training and validation cohorts, respectively. Finally, we performed a decision curve analysis and found that the nomogram using both radiomics and clinical features exhibited better predictability than any other model (DeLong test, p < 0.05 for the training and validation cohorts). CONCLUSION A nomogram based on two-dimensional ultrasound and STE radiomics and clinical features served as a non-invasive tool capable of differentiating kidney fibrosis of different severities. KEY POINTS • Radiomics calculated based on the ultrasound imaging may be used to predict the severities of kidney fibrosis. • Radiomics may be used to identify clinical features associated with the progression of tubulointerstitial fibrosis in patients with CKD. • Non-invasive ultrasound imaging-based radiomics method with accuracy aids in detecting renal fibrosis with different IFTA severities.
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Affiliation(s)
- Xin-Yue Ge
- Department of Medical Ultrasound, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Zhong-Kai Lan
- Department of Medical Ultrasound, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Qiao-Qing Lan
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hua-Shan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, 410005, China
| | - Guo-Dong Wang
- Department of Oncology, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
| | - Jing Chen
- Department of Medical Ultrasound, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
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Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 2023; 33:1653-1667. [PMID: 36149481 DOI: 10.1007/s00330-022-09137-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/05/2022] [Accepted: 09/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B. METHODS Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis. CONCLUSIONS Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis. KEY POINTS • Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Zhang WY, Sun HY, Zhang WL, Feng R. Effect of type 2 diabetes on liver images of GD-EOB-DTPA-enhanced MRI during the hepatobiliary phase. Sci Rep 2023; 13:543. [PMID: 36631556 PMCID: PMC9834214 DOI: 10.1038/s41598-023-27730-0] [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: 10/31/2022] [Accepted: 01/06/2023] [Indexed: 01/12/2023] Open
Abstract
To analyze alterations of the liver appearance during the hepatobiliary phase of individuals with type 2 diabetes who are receiving gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI). Fifty-seven individuals who received Gd-EOB-DTPA-enhanced MRI and had normal liver and renal function but did not have (control group) or have type 2 diabetes (observation group) were retrospectively included in this study. The liver enhancement ratio (LER) and contrast between liver parenchyma and portal vein (LPC) were calculated from hepatobiliary phase images. Utilizing liver to kidney signal intensity, signs of the biliary system, and signs of the portal vein, a functional liver imaging score (FLIS) was calculated. Wilcoxon rank-sum test was used to assess the between-group differences in LER, LPC, and FLIS. FLIS constituent ratios between the two groups were tested using the χ2 test. The effectiveness of LER, LPC, and FLIS for identifying type 2 diabetes was assessed by receiver operating characteristic curves (ROCs). The interobserver consistency of FLIS was evaluated using the intraclass correlation coefficients. The observation group's LER and LPC were lower than the control group. The constituent ratio of the FLIS score (liver to kidney signal intensity, p = 0.011) showed a significant between-group difference. According to ROCs, LER and LPC were associated with the identification of type 2 diabetes. LER = 0.54 and LPC = 1.46 were the optimal cutoff for identifying type 2 diabetes, respectively. FLIS demonstrated excellent inter-reader agreement. The relative signal intensity of the liver during the hepatobiliary phase is decreased in patients with type 2 diabetes. This should be considered when individuals with type 2 diabetes undergo Gd-EOB-DTPA-enhanced MRI to avoid misdiagnoses, such as small hepatocellular carcinoma or abnormal liver function.
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Affiliation(s)
- Wen-Yu Zhang
- grid.410587.fDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People’s Republic of China
| | - Hao-Yang Sun
- Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No.16369 Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.
| | - Wen-Long Zhang
- grid.410587.fDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People’s Republic of China
| | - Rui Feng
- grid.410587.fDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People’s Republic of China
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Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
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Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
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Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
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Tang S, Wu J, Xu S, Li Q, He J. Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study. Front Genet 2023; 14:1071085. [PMID: 37021007 PMCID: PMC10069650 DOI: 10.3389/fgene.2023.1071085] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 03/09/2023] [Indexed: 04/07/2023] Open
Abstract
Purpose: Our aim is to build and validate a clinical-radiomic model for non-invasive liver steatosis prediction based on non-contrast computed tomography (CT). Methods: We retrospectively reviewed 342 patients with suspected NAFLD diagnoses between January 2019 and July 2020 who underwent non-contrast CT and liver biopsy. Radiomics features from hepatic and splenic regions-of-interests (ROIs) were extracted based on abdominal non-contrast CT imaging. The radiomics signature was constructed based on reproducible features by adopting the least absolute shrinkage and selection operator (LASSO) regression. Then, multivariate logistic regression analysis was applied to develop a combined clinical-radiomic nomogram integrating radiomics signature with several independent clinical predictors in a training cohort of 124 patients between January 2019 and December 2019. The performance of models was determined by the area under the receiver operating characteristic curves and calibration curves. We conducted an internal validation during 103 consecutive patients between January 2020 and July 2020. Results: The radiomics signature was composed of four steatosis-related features and positively correlated with pathologic liver steatosis grade (p < 0.01). In both subgroups (Group One, none vs. steatosis; Group Two, none/mild vs. moderate/severe steatosis), the clinical-radiomic model performed best within the validation cohort with an AUC of 0.734 and 0.930, respectively. The calibration curve confirmed the concordance of excellent models. Conclusion: We developed a robust clinical-radiomic model for accurate liver steatosis stage prediction in a non-invasive way, which may improve the clinical decision-making ability.
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Affiliation(s)
- Shengnan Tang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jin Wu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Shanshan Xu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Qi Li
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jian He
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- *Correspondence: Jian He,
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Yang Y, Zhang X, Zhao L, Mao H, Cai TN, Guo WL. Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children. J Magn Reson Imaging 2022. [PMID: 36583731 DOI: 10.1002/jmri.28586] [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: 09/16/2022] [Revised: 12/09/2022] [Accepted: 12/10/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis. PURPOSE To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM. STUDY TYPE Retrospective. POPULATION A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination. FIELD STRENGTH/SEQUENCE A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging. ASSESSMENT Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis. STATISTICAL TESTS Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models. RESULTS Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05). DATA CONCLUSION Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yang Yang
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Xinxian Zhang
- Department of Radiology, Xuzhou Children's Hospital, Xuzhou, China
| | - Lian Zhao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Huimin Mao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Tian-Na Cai
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Wan-Liang Guo
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
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Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1147111. [PMID: 36619303 PMCID: PMC9812615 DOI: 10.1155/2022/1147111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 12/29/2022]
Abstract
Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease.
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22
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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23
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Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han NY, Han YE, Kim TH, Lee YJ. Radiomics Analysis of Magnetic Resonance Proton Density Fat Fraction for the Diagnosis of Hepatic Steatosis in Patients With Suspected Non-Alcoholic Fatty Liver Disease. J Korean Med Sci 2022; 37:e339. [PMID: 36536543 PMCID: PMC9763710 DOI: 10.3346/jkms.2022.37.e339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/23/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND This study aimed to assess the diagnostic feasibility of radiomics analysis based on magnetic resonance (MR)-proton density fat fraction (PDFF) for grading hepatic steatosis in patients with suspected non-alcoholic fatty liver disease (NAFLD). METHODS This retrospective study included 106 patients with suspected NAFLD who underwent a hepatic parenchymal biopsy. MR-PDFF and MR spectroscopy were performed on all patients using a 3.0-T scanner. Following whole-volume segmentation of the MR-PDFF images, 833 radiomic features were analyzed using a commercial program. Radiologic features were analyzed, including median and mean values of the multiple regions of interest and variable clinical features. A random forest regressor was used to extract the important radiomic, radiologic, and clinical features. The model was trained using 20 repeated 10-fold cross-validations to classify the NAFLD steatosis grade. The area under the receiver operating characteristic curve (AUROC) was evaluated using a classifier to diagnose steatosis grades. RESULTS The levels of pathological hepatic steatosis were classified as low-grade steatosis (grade, 0-1; n = 82) and high-grade steatosis (grade, 2-3; n = 24). Fifteen important features were extracted from the radiomic analysis, with the three most important being wavelet-LLL neighboring gray tone difference matrix coarseness, original first-order mean, and 90th percentile. The MR spectroscopy mean value was extracted as a more important feature than the MR-PDFF mean or median in radiologic measures. Alanine aminotransferase has been identified as the most important clinical feature. The AUROC of the classifier using radiomics was comparable to that of radiologic measures (0.94 ± 0.09 and 0.96 ± 0.08, respectively). CONCLUSION MR-PDFF-derived radiomics may provide a comparable alternative for grading hepatic steatosis in patients with suspected NAFLD.
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Affiliation(s)
- Ki Choon Sim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Min Ju Kim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Hyun Jin Kim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Beom Jin Park
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Deuk Jae Sung
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Na Yeon Han
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Yeo Eun Han
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Tae Hyung Kim
- Department of Gastroenterology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea
| | - Yoo Jin Lee
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
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Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen. J Imaging 2022; 8:jimaging8100277. [PMID: 36286371 PMCID: PMC9605113 DOI: 10.3390/jimaging8100277] [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: 07/10/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 12/03/2022] Open
Abstract
Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. Methods: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). Results: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. Conclusions: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity.
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25
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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Zou L, Zhang H, Wang Q, Zhong W, Du Y, Liu H, Xing W. Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model. Magn Reson Imaging 2022; 94:36-42. [PMID: 35988836 DOI: 10.1016/j.mri.2022.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/08/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features. METHODS One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models. RESULTS The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05). CONCLUSIONS It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.
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Affiliation(s)
- LiQiu Zou
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - Hao Zhang
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - WenXin Zhong
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - YaNan Du
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - HaiFeng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China.
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Wang L, Zhang L, Jiang B, Zhao K, Zhang Y, Xie X. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review. Br J Radiol 2022; 95:20211136. [PMID: 35816550 PMCID: PMC10162062 DOI: 10.1259/bjr.20211136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/26/2022] [Accepted: 07/05/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging. METHODS We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability. RESULTS The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions. CONCLUSIONS The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future. ADVANCES IN KNOWLEDGE Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keke Zhao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022; 16:707-719. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. AREAS COVERED This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. EXPERT OPINION For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.
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Affiliation(s)
- Jiaying Bao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xiao Feng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Yan Ma
- Department of Ultrasound, Zibo Central Hospital, Zibo, P.R. China
| | - Yanyan Wang
- Departments of Emergency Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Jianni Qi
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Chengyong Qin
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xu Tan
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Yongmei Tian
- Department of Geriatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
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Meng Y, Yu J, Zhu M, Zhou J, Li N, Liu F, Zhang H, Fang X, Li J, Feng X, Wang L, Jiang H, Lu J, Shao C, Bian Y. CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2022; 47:2822-2834. [PMID: 35451626 DOI: 10.1007/s00261-022-03512-6] [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: 12/13/2021] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To develop and validate a radiomics model to predict fibroblast activation protein (FAP) expression in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS This retrospective study included consecutive 152 patients with PDAC who underwent MDCT scan and surgical resection from January 2017 to December 2017 (training set) and from January 2018 to April 2018 (validation set). In the training set, 1409 portal radiomic features were extracted from each patient's preoperative imaging. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm, whereupon the extreme gradient boosting (XGBoost) was developed using the radiomics features. The performance of the XGBoost classifier performance was assessed by its calibration, discrimination, and clinical usefulness. RESULTS The patients were divided into FAP-low (n = 91; 59.87%) and FAP-high (n = 61; 40.13%) groups according to the optimal FAP cutoff (45.71%). Patients in the FAP-low group showed longer survival. The XGBoost classifier comprised 13 selected radiomics features and showed good discrimination in the training set [area under the curve (AUC), 0.97] and the validation set (AUC, 0.75). It also performed well in the calibration test and decision-curve analysis, demonstrating its potential clinical value. CONCLUSIONS The XGBoost classifier based on CT radiomics in the portal venous phase can non-invasively predict FAP expression and may help to improve clinical decision-making in patients with PDAC.
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Affiliation(s)
- Yinghao Meng
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
- Department of Radiology, No. 971 Hospital of Navy, Qingdao, Shandong, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jian Zhou
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xiaocheng Feng
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
- Department of Radiology, Changhai Hospital, 168 Changhai Road, Shanghai, 200433, China.
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
- Department of Radiology, Changhai Hospital, 168 Changhai Road, Shanghai, 200433, China.
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Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis. Eur Radiol 2022; 33:633-644. [DOI: 10.1007/s00330-022-08992-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 06/17/2022] [Accepted: 06/29/2022] [Indexed: 11/28/2022]
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Duan YY, Qin J, Qiu WQ, Li SY, Li C, Liu AS, Chen X, Zhang CX. Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram. Clin Radiol 2022; 77:e723-e731. [PMID: 35811157 DOI: 10.1016/j.crad.2022.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 12/18/2022]
Abstract
AIM To investigate the performance of a generative adversarial network (GAN) model for staging liver fibrosis and its radiomics-based nomogram for predicting cirrhosis. MATERIALS AND METHODS This two-centre retrospective study included 434 patients for whom input data of ultrasound images and histopathological data (obtained within 1 month of ultrasound examinations) were assigned to the training cohort (249 patients), the internal cohort (92 patients), and the external (93 patients) cohort. A data augmentation method based on a GAN model was used. The discriminative performance was evaluated for classifying fibrosis of S4 and ≥S3. Deep-learning radiomics features were extracted for the prediction of cirrhosis (S4). To perform feature reduction and selection, the least absolute shrinkage and selection operator (LASSO) algorithm was applied. Radiomics scores, along with clinical factors, were incorporated into a nomogram using multivariable logistic regression analysis. The performance of the models was estimated with respect to discrimination power, calibration, and clinical benefits. RESULTS The areas under the receiver operating characteristic curve (AUCs) values of the GAN were 0.832/0.762 (≥S3), and 0.867/0.835 (S4) for internal/external test sets, respectively. The radiomics nomogram that intergrated radiomics scores and clinical factors showed good calibration and discrimination ability of 0.922 (AUC) in the training dataset, 0.896 in the internal dataset, and 0.861 in the external dataset. Decision curve analysis (DCA) demonstrated that the nomogram outperformed radiologist and haematological indices in terms of the most clinical benefits. CONCLUSIONS The GAN model could be applied to discriminate fibrosis stages, and a favourable predictive accuracy for diagnosing cirrhosis was achieved using a deep-learning radiomics nomogram.
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Affiliation(s)
- Y-Y Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
| | - J Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
| | - W-Q Qiu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
| | - S-Y Li
- Department of Ultrasound, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, No. 20 Yuhuangdingdong Road, Zhifu District, Yantai 264099, Shandong Province, China
| | - C Li
- Department of Biomedical Engineering, Hefei University of Technology, No. 193 Tunxi Road, Baohe District, Hefei 230009, Anhui Province, China
| | - A-S Liu
- Department of Ultrasound, The First Affiliated Hospital of Anhui University of Chinese Medicine, No. 117 Meishan Road, Shushan District, Hefei 230022, Anhui Province, China
| | - X Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, No. 93 Jinzhai Road, Baohe District, Hefei 230026, Anhui Province, China
| | - C-X Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China.
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Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer. Eur Radiol 2022; 33:1-10. [PMID: 35726100 DOI: 10.1007/s00330-022-08952-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/30/2022] [Accepted: 06/08/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC). METHODS Seven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named modelHRT2 and modelSRT2 were respectively constructed with high-dimensional quantitative features extracted from manually segmented volume of interests of HRT2WI and SRT2WI through the Least Absolute Shrinkage and Selection Operator method. The performances of the models were evaluated by ROC, calibration, and decision curves. RESULTS ModelSRT2 outperformed modelHRT2 (AUC 0.869, sensitivity 71.1%, specificity 93.1%, and accuracy 83.3% vs. AUC 0.810, sensitivity 89.5%, specificity 70.1%, and accuracy 77.3%) in distinguishing T1/2 and T3/4 RC with significant difference (p < 0.05). Both radiomics models achieved higher AUCs than the expert radiologists (0.685, 95% confidence interval 0.595-0.775, p < 0.05). The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. CONCLUSIONS ModelSRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with modelHRT2 and expert radiologists' visual assessments. KEY POINTS • For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility. • Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively. • This is the largest radiomics study for T-staging prediction in RC.
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Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis. Can J Gastroenterol Hepatol 2022; 2022:2249447. [PMID: 35775068 PMCID: PMC9239804 DOI: 10.1155/2022/2249447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis. MATERIALS AND METHODS Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve. RESULTS ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone. CONCLUSIONS The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.
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Wang J, Tang S, Mao Y, Wu J, Xu S, Yue Q, Chen J, He J, Yin Y. Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker. Hepatol Int 2022; 16:627-639. [PMID: 35347597 PMCID: PMC9174317 DOI: 10.1007/s12072-022-10326-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/03/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images. MATERIALS AND METHODS This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men). RESULTS In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis. CONCLUSION Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.
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Affiliation(s)
- Jincheng Wang
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Preparatory School for Chinese Students To Japan, The Training Center of Ministry of Education for Studying Overseas, Changchun, China
| | - Shengnan Tang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Yingfan Mao
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Jin Wu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Shanshan Xu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Qi Yue
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Jun Chen
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jian He
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
| | - Yin Yin
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
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Ren Q, Zhu P, Li C, Yan M, Liu S, Zheng C, Xia X. Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor. Front Bioeng Biotechnol 2022; 10:872044. [PMID: 35677305 PMCID: PMC9168370 DOI: 10.3389/fbioe.2022.872044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/22/2022] [Indexed: 11/15/2022] Open
Abstract
Aim: Trans-arterial chemoembolization (TACE) in combination with tyrosine kinase inhibitor (TKI) has been evidenced to improve outcomes in a portion of patients with hepatocellular carcinoma (HCC). Developing biomarkers to identify patients who might benefit from the combined treatment is needed. This study aims to investigate the efficacy of radiomics/deep learning features-based models in predicting short-term disease control and overall survival (OS) in HCC patients who received the combined treatment. Materials and Methods: A total of 103 HCC patients who received the combined treatment from Sep. 2015 to Dec. 2019 were enrolled in the study. We exacted radiomics features and deep learning features of six pre-trained convolutional neural networks (CNNs) from pretreatment computed tomography (CT) images. The robustness of features was evaluated, and those with excellent stability were used to construct predictive models by combining each of the seven feature exactors, 13 feature selection methods and 12 classifiers. The models were evaluated for predicting short-term disease by using the area under the receiver operating characteristics curve (AUC) and relative standard deviation (RSD). The optimal models were further analyzed for predictive performance on overall survival. Results: A total of the 1,092 models (156 with radiomics features and 936 with deep learning features) were constructed. Radiomics_GINI_Nearest Neighbors (RGNN) and Resnet50_MIM_Nearest Neighbors (RMNN) were identified as optimal models, with the AUC of 0.87 and 0.94, accuracy of 0.89 and 0.92, sensitivity of 0.88 and 0.97, specificity of 0.90 and 0.90, precision of 0.87 and 0.83, F1 score of 0.89 and 0.92, and RSD of 1.30 and 0.26, respectively. Kaplan-Meier survival analysis showed that RGNN and RMNN were associated with better OS (p = 0.006 for RGNN and p = 0.033 for RMNN). Conclusion: Pretreatment CT-based radiomics/deep learning models could non-invasively and efficiently predict outcomes in HCC patients who received combined therapy of TACE and TKI.
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Affiliation(s)
- Qianqian Ren
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhu
- Department of Hepatobiliary Surgery, Wuhan No.1 Hospital, Wuhan, China
| | - Changde Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Meijun Yan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Song Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiangwen Xia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- *Correspondence: Xiangwen Xia,
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Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. J Comput Assist Tomogr 2022; 46:505-513. [PMID: 35483092 DOI: 10.1097/rct.0000000000001308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVE The aim of the study was to investigate the diagnostic feasibility of radiomics analysis using magnetic resonance elastography (MRE) to assess hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). METHODS One hundred patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. Magnetic resonance elastography was performed using a 3.0-T scanner. After multislice segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of the regions of interest and variable clinical features, were analyzed. A random forest regressor was used to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve was evaluated using a classifier for fibrosis stage diagnosis. RESULTS The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0-F1, n = 82) or clinically significant fibrosis (stages F2-F4, n = 18). Eight important features were extracted from radiomics analysis, with the 2 most important being wavelet-high high low gray level dependence matrix dependence nonuniformity-normalized and wavelet-high high low gray level dependence matrix dependence entropy. The median value of the multiple small regions of interest was identified as the most important radiologic feature. Platelet count has been identified as an important clinical feature. The area under the receiver operating characteristic curve of the classifier using radiomics was comparable with that of radiologic measures (0.97 ± 0.07 and 0.96 ± 0.06, respectively). CONCLUSIONS Magnetic resonance elastography radiomics analysis provides diagnostic performance comparable with conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.
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Cheng S, Yu X, Chen X, Jin Z, Xue H, Wang Z, Xie P. CT-based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. Br J Radiol 2022; 95:20210792. [PMID: 35019776 PMCID: PMC9153699 DOI: 10.1259/bjr.20210792] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To develop and evaluate a machine learning-based CT radiomics model for the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). METHODS A total of 106 patients who underwent TIPS placement were consecutively enrolled in this retrospective study. Regions of interest (ROIs) were drawn on unenhanced, arterial phase, and portal venous phase CT images, and radiomics features were extracted, respectively. A radiomics model was established to predict the occurrence of HE after TIPS by using random forest algorithm and 10-fold cross-validation. Receiver operating characteristic (ROC) curves were performed to validate the capability of the radiomics model and clinical model on the training, test and original data sets, respectively. RESULTS The radiomics model showed favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.899 (95% CI, 0.848 to 0.951), while in the test cohort, it was confirmed with an AUC of 0.887 (95% CI, 0.760 to 1.00). After applying this model to original data set, it had an AUC of 0.955 (95% CI, 0.896 to 1.00). A clinical model was also built with an AUC of 0.649 (95% CI, 0.530 to 0.767) in the original data set, and a Delong test demonstrated its relative lower efficiency when compared with the radiomics model (p < 0.05). CONCLUSION Machine learning-based CT radiomics model performed better than traditional clinical parameter-based models in the prediction of post-TIPS HE. ADVANCES IN KNOWLEDGE Radiomics model for the prediction of post-TIPS HE was built based on feature extraction from routine acquired pre-operative CT images and feature selection by random forest algorithm, which showed satisfied performance and proved the advantages of machine learning in this field.
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Affiliation(s)
- Sihang Cheng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.,Department of Radiology, Sichuan Provincial People's Hospital, Sichuan Academy of Medical Sciences, Chengdu, China
| | - Xiang Yu
- Department of Radiology, Sichuan Provincial People's Hospital, Sichuan Academy of Medical Sciences, Chengdu, China
| | - Xinyue Chen
- CT Collaboration, Siemens-Healthineers, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Huadan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhiwei Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Ping Xie
- Department of Radiology, Sichuan Provincial People's Hospital, Sichuan Academy of Medical Sciences, Chengdu, China
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Shen PC, Huang WY, Dai YH, Lo CH, Yang JF, Su YF, Wang YF, Lu CF, Lin CS. Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy. Biomedicines 2022; 10:biomedicines10030597. [PMID: 35327398 PMCID: PMC8945465 DOI: 10.3390/biomedicines10030597] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/27/2022] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: The application of stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) limited the risk of the radiation-induced liver disease (RILD) and we aimed to predict the occurrence of RILD more accurately. (2) Methods: 86 HCC patients were enrolled. We identified key predictive factors from clinical, radiomic, and dose-volumetric parameters using a multivariate analysis, sequential forward selection (SFS), and a K-nearest neighbor (KNN) algorithm. We developed a predictive model for RILD based on these factors, using the random forest or logistic regression algorithms. (3) Results: Five key predictive factors in the training set were identified, including the albumin–bilirubin grade, difference average, strength, V5, and V30. After model training, the F1 score, sensitivity, specificity, and accuracy of the final random forest model were 0.857, 100, 93.3, and 94.4% in the test set, respectively. Meanwhile, the logistic regression model yielded an F1 score, sensitivity, specificity, and accuracy of 0.8, 66.7, 100, and 94.4% in the test set, respectively. (4) Conclusions: Based on clinical, radiomic, and dose-volumetric factors, our models achieved satisfactory performance on the prediction of the occurrence of SBRT-related RILD in HCC patients. Before undergoing SBRT, the proposed models may detect patients at high risk of RILD, allowing to assist in treatment strategies accordingly.
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Affiliation(s)
- Po-Chien Shen
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Wen-Yen Huang
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 114, Taiwan
| | - Yang-Hong Dai
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
| | - Cheng-Hsiang Lo
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
| | - Jen-Fu Yang
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
- National Defense Medical Center, Institute of Medical Science, Taipei 114, Taiwan
| | - Yu-Fu Su
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
- National Defense Medical Center, Institute of Medical Science, Taipei 114, Taiwan
| | - Ying-Fu Wang
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (C.-F.L.); (C.-S.L.); Tel.: +886-2-2826-7308 (C.-F.L.); +886-2-8792-7122 (C.-S.L.)
| | - Chun-Shu Lin
- National Defense Medical Center, Department of Radiation Oncology, Tri-Service General Hospital, Taipei 114, Taiwan; (P.-C.S.); (W.-Y.H.); (Y.-H.D.); (C.-H.L.); (J.-F.Y.); (Y.-F.S.); (Y.-F.W.)
- Correspondence: (C.-F.L.); (C.-S.L.); Tel.: +886-2-2826-7308 (C.-F.L.); +886-2-8792-7122 (C.-S.L.)
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Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. Eur J Radiol 2022; 150:110250. [DOI: 10.1016/j.ejrad.2022.110250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 02/25/2022] [Accepted: 03/07/2022] [Indexed: 11/22/2022]
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Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging. Diagnostics (Basel) 2022; 12:diagnostics12020550. [PMID: 35204639 PMCID: PMC8870954 DOI: 10.3390/diagnostics12020550] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/09/2022] [Accepted: 02/17/2022] [Indexed: 02/05/2023] Open
Abstract
Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.
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Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2491023. [PMID: 35103236 PMCID: PMC8800621 DOI: 10.1155/2022/2491023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/09/2021] [Accepted: 12/31/2021] [Indexed: 11/30/2022]
Abstract
This study is aimed at determining the ability of computed tomography- (CT-) based radiomic analysis to distinguish between grade 0/1 and grade 2/3 macrovesicular steatosis (MaS) in cadaveric donor liver transplantation cases. Preoperative noncontrast-enhanced CT images of 150 patients with biopsy-confirmed MaS were analyzed retrospectively; these patients were classified into the low-grade MaS (n = 100, grade 0 or 1) and high-grade MaS (n = 50, grade 2 or 3) groups. Three-dimensional spherical regions of interest of 40 pixel (2.5 cm) in diameter were placed in the right anterior and left lateral segments of the liver. Thereafter, 300 regions of interest (ROIs) were segmented and randomly assigned to the training and testing groups at a ratio of 7 : 3. A total of 402 radiomic features were extracted from each ROI. For MaS classification, a radiomic model was established using multivariate logistic regression analysis. Clinical data, including age, sex, and liver function, were collected to establish the clinical model at the patient level. The performances of the radiomic and clinical models, i.e., the diagnostic discrimination, calibration, and clinical utilities, were evaluated. The radiomic model, with seven selected features, depicted a good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.907 (95% confidence interval (CI): 0.869–0.940) in the training cohort and 0.906 (95% CI: 0.843–0.959) in the testing cohort. The calibration curve revealed good agreement between the predicted and observed probabilities in the training and testing cohorts (both P > 0.05 in the H-L test). Decision curve analysis revealed that the radiomic model was more beneficial than the treat-all or treat-none schemes for predicting the MaS grade. Alanine transaminase and gamma-glutamyl transferase were used for building the clinical model, and the AUC was 0.784 in the total cohort. The CT-based radiomic model outperforming the conventional clinical model could provide an important reference for MaS grading in cadaveric liver donors.
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Vernuccio F, Cannella R, Bartolotta TV, Galia M, Tang A, Brancatelli G. Advances in liver US, CT, and MRI: moving toward the future. Eur Radiol Exp 2021; 5:52. [PMID: 34873633 PMCID: PMC8648935 DOI: 10.1186/s41747-021-00250-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 10/18/2021] [Indexed: 02/06/2023] Open
Abstract
Over the past two decades, the epidemiology of chronic liver disease has changed with an increase in the prevalence of nonalcoholic fatty liver disease in parallel to the advent of curative treatments for hepatitis C. Recent developments provided new tools for diagnosis and monitoring of liver diseases based on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), as applied for assessing steatosis, fibrosis, and focal lesions. This narrative review aims to discuss the emerging approaches for qualitative and quantitative liver imaging, focusing on those expected to become adopted in clinical practice in the next 5 to 10 years. While radiomics is an emerging tool for many of these applications, dedicated techniques have been investigated for US (controlled attenuation parameter, backscatter coefficient, elastography methods such as point shear wave elastography [pSWE] and transient elastography [TE], novel Doppler techniques, and three-dimensional contrast-enhanced ultrasound [3D-CEUS]), CT (dual-energy, spectral photon counting, extracellular volume fraction, perfusion, and surface nodularity), and MRI (proton density fat fraction [PDFF], elastography [MRE], contrast enhancement index, relative enhancement, T1 mapping on the hepatobiliary phase, perfusion). Concurrently, the advent of abbreviated MRI protocols will help fulfill an increasing number of examination requests in an era of healthcare resource constraints.
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Affiliation(s)
- Federica Vernuccio
- Section of Radiology- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy.
| | - Roberto Cannella
- Section of Radiology- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy.,Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University Hospital of Palermo, Via del Vespro 129, 90127, Palermo, Italy.,Service de radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy.,Department of Radiology, Fondazione Istituto Giuseppe Giglio Ct.da Pietrapollastra, Via Pisciotto, 90015, Cefalù (Palermo), Italy
| | - Massimo Galia
- Section of Radiology- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy
| | - An Tang
- Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Quebec, Canada.,Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada.,Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada
| | - Giuseppe Brancatelli
- Section of Radiology- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy
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A Combination Model of Radiomics Features and Clinical Biomarkers as a Nomogram to Differentiate Nonadvanced From Advanced Liver Fibrosis: A Retrospective Study. Acad Radiol 2021; 28 Suppl 1:S45-S54. [PMID: 34023199 DOI: 10.1016/j.acra.2020.08.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/20/2020] [Accepted: 08/20/2020] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a combination model of radiomics features and clinical biomarkers to differentiate nonadvanced from advanced liver fibrosis. MATERIALS AND METHODS One hundred and eight consecutive patients with pathologically diagnosed liver fibrosis were randomly placed in a training or a test cohort at a ratio of 2:1. For each patient, 1674 radiomics features extracted from portal venous phase CT images were reduced by using minimum redundancy and maximum relevant. The optimal features identified were incorporated into the radiomics model. Eight clinical markers were evaluated. Integrated with clinical independent risk factors, a combination model was built. A nomogram was also established from the model. The performance of the models was assessed. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomogram. RESULTS The radiomics model established using five features achieved a promising level of discrimination between nonadvanced and advanced liver fibrosis. The combination model incorporated the radiomics signature with two clinical biomarkers and showed good calibration and discrimination. The training and testing cohort results of the radiomics model were area under curve values 0.864 and 0.772, accuracy 77.8% and 77.8%, sensitivity 86.7% and 73.1%, and specificity 71.4% and 90.0%, respectively. For the combination model, the training and testing cohort results were area under curve values 0.915 and 0.897, accuracy 83.3% and 86.1%, sensitivity 86% and 80.6%, and specificity 82.6% and 92.3%, respectively. The decision curve indicated the nomogram has potential in clinical application. CONCLUSION This combination model provides a promising approach for differentiating non-advanced from advanced liver fibrosis.
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Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27:6825-6843. [PMID: 34790009 PMCID: PMC8567471 DOI: 10.3748/wjg.v27.i40.6825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.
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Affiliation(s)
- Charles E Hill
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Luca Biasiolli
- Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | | | - Vicente Grau
- Department of Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Michael Pavlides
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
- Translational Gastroenterology Unit, University of Oxford, Oxford OX3 9DU, United Kingdom
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, United Kingdom
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Bian Y, Liu YF, Jiang H, Meng Y, Liu F, Cao K, Zhang H, Fang X, Li J, Yu J, Feng X, Li Q, Wang L, Lu J, Shao C. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2021; 46:4800-4816. [PMID: 34189612 DOI: 10.1007/s00261-021-03159-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use. RESULTS The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79-0.93) and validation sets (AUC 0.79; 95% CI 0.64-0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier. CONCLUSION The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Yan Fang Liu
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China.
- Department of Radiology, Changhai Hospital, 168 Changhai Road, Shanghai, 200433, China.
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Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021; 29:451-463. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [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] [Indexed: 11/17/2022]
Abstract
Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation.
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Affiliation(s)
- Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell, P.O. Box 245067, Tucson, AZ 85724-5067, USA.
| | - Bradley M Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA 70112, USA
| | - Ahmed W Moawad
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA
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Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. Abdom Radiol (NY) 2021; 46:3866-3876. [PMID: 33751193 DOI: 10.1007/s00261-021-03051-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/05/2021] [Accepted: 03/09/2021] [Indexed: 12/14/2022]
Abstract
PURPOSES To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices. MATERIALS AND METHODS The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images. CatBoost was utilized for ML model investigation by using the features with good reproducibility. The diagnostic performance of ML models based on each single- and three-phase CT image was compared with that of radiologists' interpretations, the aminotransferase-to-platelet ratio index, and the fibrosis index based on four factors (FIB-4) by using the receiver operating characteristic curve with the area under the curve (AUC) value. RESULTS Although the ML model based on three-phase CT image (AUC = 0.65-0.80) achieved higher AUC value than that based on PCP (AUC = 0.56-0.69) and PVP (AUC = 0.51-0.74) in predicting various stage of LF, significant difference was not found. The best CT-based ML model (AUC = 0.65-0.80) outperformed the FIB-4 in differentiating advanced LF and cirrhosis and radiologists' interpretation (AUC = 0.50-0.76) in the diagnosis of significant and advanced LF. CONCLUSION All PCP, PVP, and three-phase CT-based ML models can be an acceptable in assessing LF, and the performance of the PCP-based ML model is comparable to that of the enhanced CT image-based ML model.
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Abstract
ABSTRACT Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.
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Costa G, Cavinato L, Masci C, Fiz F, Sollini M, Politi LS, Chiti A, Balzarini L, Aghemo A, di Tommaso L, Ieva F, Torzilli G, Viganò L. Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases. Cancers (Basel) 2021; 13:3077. [PMID: 34203103 PMCID: PMC8234168 DOI: 10.3390/cancers13123077] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/16/2021] [Accepted: 06/16/2021] [Indexed: 12/12/2022] Open
Abstract
Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2-3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.
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Affiliation(s)
- Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
| | - Lara Cavinato
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (L.C.); (C.M.)
| | - Chiara Masci
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (L.C.); (C.M.)
| | - Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20189 Milan, Italy;
| | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20189 Milan, Italy;
| | - Letterio Salvatore Politi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
- Department of Radiology, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy;
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20189 Milan, Italy;
| | - Luca Balzarini
- Department of Radiology, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy;
| | - Alessio Aghemo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
- Division of Internal Medicine and Hepatology, Department of Internal Medicine, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy
| | - Luca di Tommaso
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
- Pathology Unit, IRCCS Humanitas Research Hospital, 20189 Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (L.C.); (C.M.)
- CADS—Center for Analysis, Decisions and Society, Human Technopole, 20157 Milan, Italy
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
| | - Luca Viganò
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (M.S.); (L.S.P.); (A.C.); (A.A.); (L.d.T.)
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Zhou Z, Yang J, Wang S, Li W, Xie L, Li Y, Zhang C. The diagnostic value of a non-contrast computed tomography scan-based radiomics model for acute aortic dissection. Medicine (Baltimore) 2021; 100:e26212. [PMID: 34087897 PMCID: PMC8183783 DOI: 10.1097/md.0000000000026212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 05/14/2021] [Indexed: 01/04/2023] Open
Abstract
To investigate the diagnostic value of a computed tomography (CT) scan-based radiomics model for acute aortic dissection.For the dissection group, we retrospectively selected 50 patients clinically diagnosed with acute aortic dissection between October 2018 and November 2019, for whom non-contrast CT and CT angiography images were available. Fifty individuals with available non-contrast CT and CT angiography images for other causes were selected for inclusion in the non-dissection group. Based on the aortic dissection locations on the CT angiography images, we marked the corresponding regions-of-interest on the non-contrast CT images of both groups. We collected 1203 characteristic parameters from these regions by extracting radiomics features. Subsequently, we used a random number table to include 70 individuals in the training group and 30 in the validation group. Finally, we used the Lasso regression for dimension reduction and predictive model construction. The diagnostic performance of the model was evaluated by a receiver operating characteristic (ROC) curve.Fourteen characteristic parameters with non-zero coefficients were selected after dimension reduction. The accuracy, sensitivity, specificity, and area under the ROC curve of the prediction model for the training group were 94.3% (66/70), 91.2% (31/34), 97.2% (35/36), and 0.988 (95% confidence interval [CI]: 0.970-0.998), respectively. The respective values for the validation group were 90.0% (27/30), 94.1% (16/17), 84.6% (11/13), and 0.952 (95% CI: 0.883-0.986).Our non-contrast CT scan-based radiomics model accurately facilitated acute aortic dissection diagnosis.
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Affiliation(s)
- Zewang Zhou
- Department of Radiology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Jinquan Yang
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shuntao Wang
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Weihao Li
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lei Xie
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yifan Li
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Changzheng Zhang
- Department of Radiology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
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