1
|
Yu H, Yin H, Zhang H, Zhang J, Yue Y, Lu Y. Placental T2WI MRI-based radiomics-clinical nomogram predicts suspicious placenta accreta spectrum in patients with placenta previa. BMC Med Imaging 2024; 24:146. [PMID: 38872133 PMCID: PMC11177524 DOI: 10.1186/s12880-024-01328-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 06/07/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND The incidence of placenta accreta spectrum (PAS) increases in women with placenta previa (PP). Many radiologists sometimes cannot completely and accurately diagnose PAS through the simple visual feature analysis of images, which can affect later treatment decisions. The study is to develop a T2WI MRI-based radiomics-clinical nomogram and evaluate its performance for non-invasive prediction of suspicious PAS in patients with PP. METHODS The preoperative MR images and related clinical data of 371 patients with PP were retrospectively collected from our hospital, and the intraoperative examination results were used as the reference standard of the PAS. Radiomics features were extracted from sagittal T2WI MR images and further selected by LASSO regression analysis. The radiomics score (Radscore) was calculated with logistic regression (LR) classifier. A nomogram integrating Radscore and selected clinical factors was also developed. The model performance was assessed with respect to discrimination, calibration and clinical usefulness. RESULTS A total of 6 radiomics features and 1 clinical factor were selected for model construction. The Radscore was significantly associated with suspicious PAS in both the training (p < 0.001) and validation (p < 0.001) datasets. The AUC of the nomogram was also higher than that of the Radscore in the training dataset (0.891 vs. 0.803, p < 0.001) and validation dataset (0.897 vs. 0.780, p < 0.001), respectively. The calibration was good, and the decision curve analysis demonstrated the nomogram had higher net benefit than the Radscore. CONCLUSIONS The T2WI MRI-based radiomics-clinical nomogram showed favorable diagnostic performance for predicting PAS in patients with PP, which could potentially facilitate the obstetricians for making clinical decisions.
Collapse
Affiliation(s)
- Hongchang Yu
- Department of Radiology, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Gusu District, Suzhou, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Jibin Zhang
- Department of Radiology, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Gusu District, Suzhou, China
| | - Yongfei Yue
- Department of Obstetrics and Gynecology, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Gusu District, Suzhou, China.
| | - Yanli Lu
- Department of Radiology, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Gusu District, Suzhou, China.
| |
Collapse
|
2
|
Xia J, Hu Y, Huang Z, Chen S, Huang L, Ruan Q, Zhao C, Deng S, Wang M, Zhang Y. A novel MRI-based diagnostic model for predicting placenta accreta spectrum. Magn Reson Imaging 2024; 109:34-41. [PMID: 38408691 DOI: 10.1016/j.mri.2024.02.014] [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: 01/14/2024] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
Objective To develop and evaluate a diagnostic model based on MRI signs for predicting placenta accreta spectrum. Materials and Methods A total of 155 pregnant women were included in this study, randomly divided into 104 cases in the training set and 51 cases in the validation set. There were 93 Non-PAS cases, and 62 cases in the PAS group. The training set included 62 Non-PAS cases and 42 PAS cases. Clinical factors and MRI signs were collected for univariate analysis. Then, binary logistic regression analysis was used to develop independent diagnostic models with clinical relevant risk factors or MRI signs, as well as those combining clinical risk factors and MRI signs. The ROC curve analysis was used to evaluate the diagnostic performance of each diagnostic model. Finally, the validation was performed with the validation set. Results In the training set, four clinical factors (gestity, parity, uterine surgery history, placental position) and 11 MRI features (T2-dark bands, placental bulge, T2 hypointense interface loss, myometrial thinning, bladder wall interruption, focal exophytic mass, abnormal placental bed vascularization, placental heterogeneity, asymmetric placental thickening/shape, placental ischemic infarction, abnormal intraplacental vascularity) were considered as risk factors for PAS. The AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.779, 0.854, and 0.874, respectively. In the validation set, the AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.655, 0.728, and 0.735, respectively. Conclusion Diagnosis model based on MRI features in this study can well predict placenta accreta spectrum.
Collapse
Affiliation(s)
- Jianfeng Xia
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China
| | - Yongren Hu
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China
| | - Zehe Huang
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China
| | - Song Chen
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China;.
| | - Lanbin Huang
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China
| | - Qizeng Ruan
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China
| | - Chen Zhao
- MR Research Collaboration, Siemens Healthineers, Guangzhou 510620, China
| | - Shicai Deng
- Department of Radiology, The First People's Hospital of Qinzhou, 53500, China
| | - Mengzhu Wang
- MR Research Collaboration, Siemens Healthineers, Beijing 100102, China
| | - Yu Zhang
- Department of Research Administration, The First People's Hospital of Qinzhou, 53500, China
| |
Collapse
|
3
|
Zheng C, Zhong J, Wang Y, Cao K, Zhang C, Yue P, Xu X, Yang Y, Liu Q, Zou Y, Huang B. Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes. J Magn Reson Imaging 2024. [PMID: 38390981 DOI: 10.1002/jmri.29317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder. PURPOSE To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI. STUDY TYPE Retrospective. POPULATION 361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122). FIELD STRENGTH/SEQUENCE Coronal T2-weighted sequence at 1.5 T and 3.0 T. ASSESSMENT Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes). STATISTICAL TESTS AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference. RESULTS In PAS diagnosis, the DRC-1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC-2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively). DATA CONCLUSION The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Changye Zheng
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Jian Zhong
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Ya Wang
- Dongguan Maternal and Child Health Care Hospital, Dongguan, China
| | - Kangyang Cao
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
| | - Chang Zhang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Peiyan Yue
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Xiaoyang Xu
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Qinghua Liu
- Dongguan Maternal and Child Health Care Hospital, Dongguan, China
| | - Yujian Zou
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| |
Collapse
|
4
|
Song Z, Wang P, Zou L, Zhou Y, Wang X, Liu T, Zhang D. Enhancing postpartum hemorrhage prediction in pernicious placenta previa: a comparative study of magnetic resonance imaging and ultrasound nomogram. Front Physiol 2023; 14:1177795. [PMID: 37614762 PMCID: PMC10443221 DOI: 10.3389/fphys.2023.1177795] [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: 03/02/2023] [Accepted: 07/25/2023] [Indexed: 08/25/2023] Open
Abstract
Objective: To explore the risk factors of postpartum hemorrhage (PPH) in patients with pernicious placenta previa (PPP) and to develop and validate a clinical and imaging-based predictive model. Methods: A retrospective analysis was conducted on patients diagnosed surgically and pathologically with PPP between January 2018 and June 2022. All patients underwent PPP magnetic resonance imaging (MRI) and ultrasound scoring in the second trimester and before delivery, and were categorized into two groups according to PPH occurrence. The total imaging score and sub-item prediction models of the MRI risk score/ultrasound score were used to construct Models A and B/Models C and D. Models E and F were the total scores of the MRI combined with the ultrasound risk and sub-item prediction model scores. Model G was based on the subscores of MRI and ultrasound with the introduction of clinical data. Univariate logistic regression analysis and the logical least absolute shrinkage and selection operator (LASSO) model were used to construct models. The receiver operating characteristic curve andision curve analysis (DCA) were drawn, and the model with the strongest predictive ability and the best clinical effect was selected to construct a nomogram. Internal sampling was used to verify the prediction model's consistency. Results: 158 patients were included and the predictive power and clinical benefit of Models B and D were better than those of Models A and C. The results of the area under the curve of Models B, D, E, F, and G showed that Model G was the best, which could reach 0.93. Compared with Model F, age, vaginal hemorrhage during pregnancy, and amniotic fluid volume were independent risk factors for PPH in patients with PPP (p < 0.05). We plotted the DCA of Models B, D, E, F, and G, which showed that Model G had better clinical benefits and that the slope of the calibration curve of Model G was approximately 45°. Conclusion: LASSO regression nomogram based on clinical risk factors and multiple conventional ultrasound plus MRI signs has a certain guiding significance for the personalized prediction of PPH in patients with PPP before delivery.
Collapse
Affiliation(s)
- Zixuan Song
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Pengyuan Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lue Zou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yangzi Zhou
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoxue Wang
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tong Liu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
5
|
Hu Y, Chen W, Kong C, Lin G, Li X, Zhou Z, Shen S, Chen L, Zhou J, Zhao H, Yu Z, Wang Z, Lu C, Ji J. Prediction of placenta accreta spectrum with nomogram combining radiomic and clinical factors: A novel developed and validated integrative model. Int J Gynaecol Obstet 2023; 162:639-650. [PMID: 36728539 DOI: 10.1002/ijgo.14710] [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: 08/08/2022] [Revised: 01/20/2023] [Accepted: 02/01/2023] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To develop and validate a clinicoradiomic nomogram based on sagittal T2WI images to predict placenta accreta spectrum (PAS). METHODS Between October 2016 and April 2022, women suspected of PAS by ultrasound were enrolled. After taking into account exclusion criteria, 132 women were retrospectively included in the study. The variance threshold SelectKBest and the least absolute shrinkage and selection operator were applied to select radiomic features, which was further used to calculate the Rad-score. Multivariable logistic regression was used to screen clinical factor. RESULTS Based on 13 radiomic features, five radiomic models were constructed. A clinical factor of intraplacental T2-hypointense bands was obtained by multivariate logistic regression. The area under the curve (AUC) value of the stochastic gradient descent (SGD) radiomic model was 0.82 in the training cohort and 0.78 in the test cohort. After adding clinical factors to the SGD radiomic model, the AUC value of the clinicoradiomic model was significantly increased from 0.82 and 0.78 to 0.84 in both the training and test cohorts. The nomogram of the clinicoradiomic model was constructed, which had good performance verified by calibration and a decision curve. CONCLUSION The presented nomogram could be useful for predicting PAS.
Collapse
Affiliation(s)
- Yumin Hu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Xia Li
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Zhangwei Zhou
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Shaobo Shen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Ling Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Jiahui Zhou
- Department of Pathology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Hongyan Zhao
- Department of Obstetrics, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Zhuo Yu
- Huiying Medical Technology (Beijing) Co., Beijing, China
| | - Zufei Wang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| |
Collapse
|
6
|
Saeed H, Lu YC, Andescavage N, Kapse K, Andersen NR, Lopez C, Quistorff J, Barnett S, Henderson D, Bulas D, Limperopoulos C. Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture. Sci Rep 2023; 13:7374. [PMID: 37164993 PMCID: PMC10172401 DOI: 10.1038/s41598-023-33343-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 04/12/2023] [Indexed: 05/12/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.
Collapse
Affiliation(s)
- Haleema Saeed
- Department of Obstetrics & Gynecology, MedStar Washington Hospital Center, Washington, DC, 20010, USA
| | - Yuan-Chiao Lu
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Nickie Andescavage
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
- Division of Neonatology, Children's National Hospital, Washington, DC, 20010, USA
| | - Kushal Kapse
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Nicole R Andersen
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Catherine Lopez
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Jessica Quistorff
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Scott Barnett
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Diedtra Henderson
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Dorothy Bulas
- Division of Radiology, Children's National Hospital, Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA.
- Division of Radiology, Children's National Hospital, Washington, DC, 20010, USA.
| |
Collapse
|
7
|
Song F, Li R, Lin J, Lv M, Qian Z, Wang L, Wu W. Predicting the risk of fetal growth restriction by radiomics analysis of the placenta on T2WI: A retrospective case-control study. Placenta 2023; 134:15-22. [PMID: 36863127 DOI: 10.1016/j.placenta.2023.02.007] [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: 11/07/2022] [Revised: 02/18/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
INTRODUCTION Fetal growth restriction (FGR) is associated with placental abnormalities, and its precise diagnosis is challenging. This study aimed to explore the role of radiomics based on placental MRI in predicting FGR. METHODS A retrospective study using T2-weighted placental MRI data were conducted. A total of 960 radiomic features were automatically extracted. Features were selected using three-step machine learning methods. A combined model was constructed by combining MRI-based radiomic features and ultrasound-based fetal measurements. The receiver operating characteristic curves (ROC) were conducted to assess model performance. Additionally, decision curves and calibration curves were performed to evaluate prediction consistency of different models. RESULTS Among the study participants, pregnant women who delivered from January 2015 to June 2021 were randomly divided into training (n = 119) and test (n = 40) sets. Forty-three other pregnant women who delivered from July 2021 to December 2021 were used as the time-independent validation set. After training and testing, three radiomic features that were strongly correlated with FGR were selected. The area under the ROC curves (AUCs) of the MRI-based radiomics model reached 0.87 (95% confidence interval [CI]: 0.74-0.96) and 0.87 (95% CI: 0.76-0.97) in the test and validation sets, respectively. Moreover, the AUCs for the model comprising MRI-based radiomic features and ultrasound-based measurements were 0.91 (95% CI: 0.83-0.97) and 0.94 (95% CI: 0.86-0.99) in the test and validation sets, respectively. DISCUSSION MRI-based placental radiomics could accurately predict FGR. Moreover, combining placental MRI-based radiomic features with ultrasound indicators of the fetus could improve the diagnostic accuracy of FGR.
Collapse
Affiliation(s)
- Fuzhen Song
- The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Institute of Birth Defects and Rare Diseases, Shanghai Jiao Tong University, Shanghai, China
| | - Ruikun Li
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Lin
- The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Institute of Birth Defects and Rare Diseases, Shanghai Jiao Tong University, Shanghai, China
| | - Mingli Lv
- The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Institute of Birth Defects and Rare Diseases, Shanghai Jiao Tong University, Shanghai, China
| | - Zhaoxia Qian
- The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Institute of Birth Defects and Rare Diseases, Shanghai Jiao Tong University, Shanghai, China.
| | - Lisheng Wang
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.
| | - Weibin Wu
- The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Institute of Birth Defects and Rare Diseases, Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|
8
|
Zhang Y, Hu M, Wen X, Huang Y, Luo R, Chen J. MRI-based radiomics nomogram in patients with high-risk placenta accreta spectrum: can it aid in the prenatal diagnosis of intraoperative blood loss? Abdom Radiol (NY) 2023; 48:1107-1118. [PMID: 36604318 DOI: 10.1007/s00261-022-03784-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE To develop and validate the nomogram by combining MRI-derived radiomics and clinical features for preoperatively predicting massive intraoperative blood loss (IBL) in high-risk placenta accreta spectrum (PAS) patients. METHODS A total of 152 high-risk PAS patients from Hospital A were enrolled and constituted the training cohort, and 64 patients from Hospital B constituted the validation cohort. Clinical features were analyzed retrospectively. Placental regions of interest were manually positioned on sagittal T2-weighted HASTE images for each patient to extract quantitative radiomics features. Clinical model, radiomics model, and nomogram were built to predict the risk of massive IBL. The diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC) and the DeLong test. Decision curve analysis (DCA) was performed to determine the performance of the best predictive model. RESULTS The nomogram (AUC = 0.866 and 0.876, respectively) and radiomics model (AUC = 0.821 and 0.855, respectively) outperformed the clinical model (AUC = 0.685 and 0.619, respectively) both in the training and validation cohorts (Delong test, P < 0.05). Furthermore, the nomogram performed best with an accuracy of 0.844, sensitivity of 0.882, and specificity of 0.830 for differentiating massive IBL in the validation cohort. DCA confirmed the clinical utility of the nomogram. CONCLUSION The nomogram can be used to noninvasively predict massive IBL patients and guide obstetricians to make reasonable preoperative treatment plans.
Collapse
Affiliation(s)
- Yang Zhang
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Meidong Hu
- Department of Medical Imaging and Interventional Radiology, First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, China
| | - Xuehua Wen
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Yaqing Huang
- Center for Reproductive Medicine, Department of Obstetrics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Rongguang Luo
- Department of Medical Imaging and Interventional Radiology, First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
- Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, China.
| | - Junfa Chen
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
| |
Collapse
|
9
|
Minamiguchi K, Nishiofuku H, Saito N, Sato T, Taiji R, Matsumoto T, Maeda S, Chanoki Y, Tachiiri T, Kunichika H, Inoue T, Marugami N, Tanaka T. Quantitative Analysis of Signal Heterogeneity in the Hepatobiliary Phase of Pretreatment Gadoxetic Acid-Enhanced MRI as a Prognostic Imaging Biomarker in Transarterial Chemoembolization for Intermediate-Stage Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15041238. [PMID: 36831582 PMCID: PMC9954181 DOI: 10.3390/cancers15041238] [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: 12/17/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND In the era of local and systemic therapies for intermediate-stage hepatocellular carcinoma (HCC), personalized therapy has become available. The aim of our study was to evaluate the usefulness of quantitative analysis of pretreatment gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) to predict prognosis following transarterial chemoembolization (TACE). METHODS This retrospective study included patients with treatment-naïve intermediate-stage HCC who underwent EOB-MRI before the initial TACE and were treated by initial TACE between February 2007 and January 2016. Signal heterogeneity in the hepatobiliary phase (HBP) of EOB-MRI was quantitatively evaluated by the coefficient of variation (CV). The cutoff CV value was determined using the Classification and Regression Tree algorithm. RESULTS A total of 64 patients were enrolled. In multivariate analysis, High CV (≥0.16) was significantly associated with poor prognosis (p = 0.038). In a subgroup analysis of patients within up-to-7 criteria, MST was significantly shorter in the High CV group than in the Low CV group (37.7 vs. 82.9 months, p = 0.024). In patients beyond up-to-7 criteria, MST was 18.0 and 38.3 months in the High CV and Low CV groups, respectively (p = 0.182). In both groups scanned at 1.5 T or 3.0 T, High CV was significantly associated with poor prognosis (p = 0.001 and 0.003, respectively). CONCLUSION CV of the tumor in the HBP of EOB-MRI is a valuable prognostic factor of TACE.
Collapse
Affiliation(s)
- Kiyoyuki Minamiguchi
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
- Correspondence: ; Tel.:+81-744-22-3051
| | - Hideyuki Nishiofuku
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Natsuhiko Saito
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Takeshi Sato
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Ryosuke Taiji
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Takeshi Matsumoto
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Shinsaku Maeda
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Yuto Chanoki
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Tetsuya Tachiiri
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Hideki Kunichika
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Takashi Inoue
- Department of Evidence-Based Medicine, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Nagaaki Marugami
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| | - Toshihiro Tanaka
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara City 634-8522, Japan
| |
Collapse
|
10
|
Abonyi OE, Idigo UF, Anakwue CAM, Agbo AJ, Ohagwu CC. Texture analysis of sonographic image of placenta in pregnancies with normal and adverse outcomes, a pilot study. Radiography (Lond) 2023; 29:14-18. [PMID: 36198242 DOI: 10.1016/j.radi.2022.09.008] [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: 07/21/2022] [Revised: 09/12/2022] [Accepted: 09/17/2022] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Many maternal and fetal morbidity and mortality from complications of pregnancy have been attributed to placenta abnormality. Placenta assessment in developing countries is mainly through ultrasonography which is subjective and prone to error. Objective assessment of placental abnormalities through texture analysis has been frequently done using magnetic resonance images with little done on ultrasound generated images, thus, the need for this study. The study is aimed at using statistical texture analysis in characterizing placenta tissue into normal and abnormal placenta as well as testing the accuracy of different texture analysis algorithms in differentiating placenta into normal and abnormal placental tissues. METHODS This longitudinal study involved 500 ultrasound-generated placenta images from patients screened for adverse pregnancy outcomes in a private hospital in Enugu. These images were loaded onto an HP laptop for viewing. Two regions of interest were selected from the placenta tissue where texture features were extracted and were classified into normal and abnormal placentas using MaZda® software version 47 while the accuracy of the classification descriptors was assessed using WEKA classification algorithms. RESULTS Co-occurrence matrix, run length matrix and histogram parameters differentiated normal placenta tissue from abnormal placental tissues (p-value <0.05) while variance is the only absolute gradient parameter that can differentiate normal placenta tissue from abnormal placenta tissues. All feature descriptors show high classification accuracy using KNN and ANN algorithms. CONCLUSION Texture analysis can differentiate normal placenta tissues from abnormal placenta tissue which will reduce the errors associated with subjective assessment of the placenta echogenicity. IMPLICATIONS FOR PRACTICE Integrating these computer-aided algorithms into our ultrasound machines will lead to early detection of abnormal placenta tissues as early management results in better pregnancy outcomes.
Collapse
Affiliation(s)
- Obinna Everistus Abonyi
- Department of Medical Radiography and Radiological Sciences, Faculty of Health Sciences and Technology. College of Medicine, University of Nigeria, Enugu Campus, Enugu State, Nigeria
| | - Ugochinyere Felicitas Idigo
- Department of Medical Radiography and Radiological Sciences, Faculty of Health Sciences and Technology. College of Medicine, University of Nigeria, Enugu Campus, Enugu State, Nigeria
| | - Chukwunyelu Angel-Mary Anakwue
- Department of Medical Radiography and Radiological Sciences, Faculty of Health Sciences and Technology. College of Medicine, University of Nigeria, Enugu Campus, Enugu State, Nigeria
| | - Amechi Julius Agbo
- Department of Medical Radiography and Radiological Sciences, Faculty of Health Sciences and Technology. College of Medicine, University of Nigeria, Enugu Campus, Enugu State, Nigeria.
| | - Chukwuemeka Christopher Ohagwu
- Department of Radiography and Radiological Sciences, Faculty of Health Sciences and Technology, College of Health Sciences, Nnamdi Azikiwe University, Nnewi Campus, Anambra state, Nigeria
| |
Collapse
|
11
|
MRI-radiomics-clinical-based nomogram for prenatal prediction of the placenta accreta spectrum disorders. Eur Radiol 2022; 32:7532-7543. [PMID: 35587828 DOI: 10.1007/s00330-022-08821-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To investigate whether an MRI-radiomics-clinical-based nomogram can be used to prenatal predict the placenta accreta spectrum (PAS) disorders. METHODS The pelvic MR images and clinical data of 156 pregnant women with pathologic-proved PAS (PAS group) and 115 pregnant women with no PAS (non-PAS group) identified by clinical and prenatal ultrasonic examination were retrospectively collected from two centers. These pregnancies were divided into a training (n = 133), an independent validation (n = 57), and an external validation (n = 81) cohort. Radiomic features were extracted from images of transverse oblique T2-weighted imaging. A radiomics signature was constructed. A nomogram, composed of MRI morphological findings, radiomic features, and prenatal clinical characteristics, was developed. The discrimination and calibration of the nomogram were conducted to assess its performance. RESULTS A radiomics signature, including three PAS-related features, was associated with the presence of PAS in the three cohorts (p < 0.001 to p = 0.001). An MRI-radiomics-clinical nomogram incorporating radiomics signature, two prenatal clinical features, and two MRI morphological findings was developed, yielding a higher area under the curve (AUC) than that of the MRI morphological-determined PAS in the training cohort (0.89 vs. 0.78; p < 0.001) and external validation cohort (0.87 vs. 0.75; p = 0.003), while a comparable AUC value in the validation cohort (0.91 vs. 0.81; p = 0.09). The calibration was good. CONCLUSIONS An MRI-radiomics-clinical nomogram had a robust performance in antenatal predicting the PAS in pregnancies. KEY POINTS • An MRI-radiomics-clinical-based nomogram might serve as an adjunctive approach for the treatment decision-making in pregnancies suspicious of PAS. • The radiomic score provides a mathematical formula that predicts the possibility of PAS by using the MRI data, and pregnant women with PAS had higher radiomic scores than those without PAS.
Collapse
|
12
|
Lu T, Zhang T, Wang Y, Guo A, Deng Y, Song B, Liu S. Radiomics analysis of T 2 -weighted images for differentiating invasive placentas in women at high risks. Magn Reson Med 2022; 88:2621-2632. [PMID: 36045635 DOI: 10.1002/mrm.29396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To develop and validate an MRI-based radiomics model for differentiating invasive placentas in patients with high risks. METHODS A total of 181 pregnant women suspected of placenta accreta spectrum (PAS) disorders and who underwent MRI for placenta evaluation were retrospectively enrolled. The data set was randomly divided into the training (n = 125; invasive = 63, noninvasive = 62) and test (n = 56; invasive = 28, noninvasive = 28) groups. Radiomics features were extracted from half-Fourier acquisition single-shot turbo spin echo (HASTE) and sagittal true fast imaging in steady-state precession (TRUFISP) sequences independently and mainly selected based on their correlations with invasive placentas to construct two radiomics signatures including HASTE-Radscore and TRUFISP-Radscore. Then, the predictive performance of radiomic signatures, clinical features, radiographic features, and their combination were evaluated. The model with the best predictive performance was validated with its discrimination ability, calibration, and clinical usefulness. RESULTS Five radiomics features from HASTE and three radiomics features from TRUFISP were retained, respectively, for predicting invasive placentas. The combination of radiomics signatures and clinical features including prior cesarean delivery, placenta previa, and radiographic feature, the placental thickness resulted in the best discrimination ability, with area under the curve of 0.898 (95% confidence interval [CI] 0.844-0.9522) and 0.858 (95% confidence interval 0.7514-0.9655) in the training and test cohort, respectively. The combined model showed a significantly better area under the curve performance and clinical usefulness than independent clinical or radiographic model according to DeLong test (p < .05), net reclassification improvement and integrated discrimination improvement analysis (positive improvement) and decision curve analysis (higher net benefit). CONCLUSIONS The T2 -weighted imaging MRI radiomics model could serve as a potential prenatal diagnosis tool for identifying invasive placentas in patients with high risks.
Collapse
Affiliation(s)
- Tao Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Tianyue Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, and Sichuan Key Laboratory of Medical Imaging, Nanchong, China
| | - Yishuang Wang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Aiwen Guo
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Deng
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | | |
Collapse
|
13
|
Stanzione A, Verde F, Cuocolo R, Romeo V, Paolo Mainenti P, Brunetti A, Maurea S. Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal. Eur J Radiol 2022; 155:110497. [PMID: 36030661 DOI: 10.1016/j.ejrad.2022.110497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/13/2022] [Accepted: 08/18/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Ultrasound and magnetic resonance imaging are the imaging modalities of choice for placenta accrete spectrum (PAS) disorders assessment. Radiomics could further increase the value of medical images and allow to overcome the limitations linked to their visual assessment. Aim of this systematic review was to identify and appraise the methodological quality of radiomics studies focused PAS disorders applications. METHOD Three online databases (PubMed, Scopus and Web of Science) were searched to identify original research articles on human subjects published in English. For the qualitative synthesis of results, data regarding study design (e.g., retrospective or prospective), purpose, patient population (e.g., sample size), imaging modalities and radiomics pipelines (e.g., segmentation and feature extraction strategy) were collected. The appraisal of methodological quality was performed using the Radiomics Quality Score (RQS). RESULTS 10 articles were finally included and analyzed. All were retrospective and MRI-powered. The majority included more than 100 patients (6/10). Four were prognostic (focused on either the prediction of bleeding volume or the prediction of needed management) while six diagnostic (PAS vs not PAS classification) studies. The median RQS was 8, with maximum and minimum respectively equal to 17/36 and - 6/36. Major methodological concerns were the lack of feature stability to multiple segmentation testing and poor data openness. CONCLUSIONS Radiomics studies focused on PAS disorders showed a heterogeneous methodological quality, overall lower than desirable. Furthermore, many relevant research questions remain unexplored. More robust investigations are needed to foster advancements in the field and possibly clinical translation.
Collapse
Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy; Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Research Council, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| |
Collapse
|
14
|
Texture analysis of myometrium-derived T2WI in the evaluation of placenta increta: An observational retrospective study. Placenta 2022; 126:32-39. [PMID: 35738112 DOI: 10.1016/j.placenta.2022.06.002] [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: 04/04/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 11/20/2022]
Abstract
INTRODUCTION MRI has demonstrated its potential in the diagnosis of placenta percreta. Texture analysis is a novel technique to quantify tissue heterogeneity. The study aimed to evaluate the feasibility of using texture analysis based on myometrium-derived T2WI to differentiate placenta accreta from increta. METHODS Participants with MRI and clinical or histopathological diagnosis of placenta increta were retrospectively enrolled. Texture analysis of T2WI was implemented on normal myometrium and placenta increta by MaZda software. With the Fisher discriminant method, parameter selection and reduction were done automatically. Multivariate analysis was used for the comparison of response variables between two groups. The contours of multivariable average vectors were compared using profile analysis. Two-step clustering was performed to assess the importance of parameters. RESULTS There were a total of 23 participants (median age 29 years, range 22-43 years). The pixel intensity distribution was narrow and wide in two first-order histograms taken from normal myometrium and placenta increta, respectively. Multivariate analysis showed nine second-order parameters derived from the histogram were statistically significant (P < 0.05). The results of two-step clustering indicated that three second-order parameters (Mean, Percentile 90%, and Percentile 99%) were important (predictor importance > 0.8). Multivariate analysis of three second-order parameters further showed they were different between normal myometrium and placenta increta. DISCUSSION Texture analysis based on myometrium-derived T2WI may be a useful add-on to MRI in diagnosing placenta increta. TRIAL REGISTRATION Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).
Collapse
|
15
|
Chu C, Liu M, Zhang Y, Zhao S, Ge Y, Li W, Gao C. MRI-Based Radiomics Analysis for Intraoperative Risk Assessment in Gravid Patients at High Risk with Placenta Accreta Spectrum. Diagnostics (Basel) 2022; 12:diagnostics12020485. [PMID: 35204575 PMCID: PMC8870740 DOI: 10.3390/diagnostics12020485] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 12/20/2021] [Accepted: 12/25/2021] [Indexed: 02/06/2023] Open
Abstract
Background: Gravid patients at high risk with placenta accreta spectrum (PAS) face life-threatening risk at delivery. Intraoperative risk assessment for patients is currently insufficient. We aimed to develop an assessment system of intraoperative risks through MRI-based radiomics. Methods: A total of 131 patients enrolled were randomly grouped according to a ratio of 7:3. Clinical data were analyzed retrospectively. Radiomic features were extracted from sagittal Fast Imaging Employing State-sate Acquisition images. Univariate and multivariate regression analyses were performed to build models using R software. A receiver operating characteristic curve and decision curve analysis (DCA) were performed to determine the predictive performance of models. Results: Six radiomic features and two clinical variables were used to construct the combined model for selection of removal protocols of the placenta, with an area under the curve (AUC) of 0.90 and 0.91 in the training and test cohorts, respectively. Nine radiomic features and two clinical variables were obtained to establish the combined model for prediction of intraoperative blood loss, with an AUC of 0.90 and 0.88 in the both cohorts, respectively. The DCA confirmed the clinical utility of the combined model. Conclusion: The analysis of combined MRI-based radiomics with clinics could be clinically beneficial for patients.
Collapse
Affiliation(s)
- Caiting Chu
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; (C.C.); (M.L.); (Y.Z.); (S.Z.)
| | - Ming Liu
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; (C.C.); (M.L.); (Y.Z.); (S.Z.)
| | - Yuzhen Zhang
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; (C.C.); (M.L.); (Y.Z.); (S.Z.)
| | - Shuhui Zhao
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; (C.C.); (M.L.); (Y.Z.); (S.Z.)
| | - Yaqiong Ge
- GE Healthcare, Pudong New Town, No. 1, Huatuo Road, Shanghai 201203, China;
| | - Wenhua Li
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; (C.C.); (M.L.); (Y.Z.); (S.Z.)
- Correspondence: (W.L.); (C.G.)
| | - Chengjin Gao
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; (C.C.); (M.L.); (Y.Z.); (S.Z.)
- Correspondence: (W.L.); (C.G.)
| |
Collapse
|
16
|
Maurea S, Verde F, Mainenti PP, Barbuto L, Iacobellis F, Romeo V, Liuzzi R, Raia G, De Dominicis G, Santangelo C, Romano L, Brunetti A. Qualitative evaluation of MR images for assessing placenta accreta spectrum disorders in patients with placenta previa: A pilot validation study. Eur J Radiol 2021; 146:110078. [PMID: 34871935 DOI: 10.1016/j.ejrad.2021.110078] [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: 10/10/2021] [Revised: 11/22/2021] [Accepted: 11/25/2021] [Indexed: 11/03/2022]
Abstract
PURPOSE To validate a qualitative imaging method using magnetic resonance (MR) for predicting placental accreta spectrum (PAS) in patients with placenta previa (PP). METHOD Two MR imaging methods built in our previous experience was tested in an external comparable group of sixty-five patients with PP; these methods consisted of presence of at least one (Method 1) or two (Method 2) of the following abnormal MR imaging signs: intraplacental dark bands, focal interruption of myometrial border and abnormal placental vascularity. Three groups of radiologists with different level of expertise evaluated MR images: at least 5 years of experience in body imaging (Group 1); at least 10 (Group 2) or 20 (Group 3) years of experience in genito-urinary MR. While radiologists of Group 1 routinely evaluated MR images, those of Groups 2 and 3 used both Methods 1 and 2. RESULTS A significant (p < 0.005) difference was found between the diagnostic accuracy values of imaging evaluation performed by Group 3 using Method 1 (63%) and Method 2 (89%); of note, the accuracy of Method 2 by Group 3 was also significantly (p < 0.005) higher compared to that of both Methods 1 (46%) and 2 (63%) by Group 2 as well as to that of the routine evaluation by Group 1 (60%). CONCLUSIONS The qualitative identification of at least two abnormal MR signs (Method 2) represents an accurate method for predicting PAS in patients with PP particularly when this method was used by more experienced radiologists; thus, imaging expertise and methodology is required for this purpose.
Collapse
Affiliation(s)
- Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), Naples, Italy
| | - Luigi Barbuto
- Department of General and Emergency Radiology, "Antonio Cardarelli" Hospital, Antonio Cardarelli st 9, 80131 Naples, Italy
| | - Francesca Iacobellis
- Department of General and Emergency Radiology, "Antonio Cardarelli" Hospital, Antonio Cardarelli st 9, 80131 Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Raffaele Liuzzi
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), Naples, Italy
| | - Giorgio Raia
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Gianfranco De Dominicis
- Department of Anatomical Pathology, "Antonio Cardarelli" Hospital, Antonio Cardarelli st 9, 80131 Naples, Italy
| | - Claudio Santangelo
- Department of Obstetrics and Gynecology, "Antonio Cardarelli" Hospital, Antonio Cardarelli st 9, 80131 Naples, Italy
| | - Luigia Romano
- Department of General and Emergency Radiology, "Antonio Cardarelli" Hospital, Antonio Cardarelli st 9, 80131 Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| |
Collapse
|
17
|
Ren H, Mori N, Mugikura S, Shimizu H, Kageyama S, Saito M, Takase K. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. Abdom Radiol (NY) 2021; 46:5344-5352. [PMID: 34331104 DOI: 10.1007/s00261-021-03226-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/20/2021] [Accepted: 07/20/2021] [Indexed: 01/01/2023]
Abstract
PURPOSE To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS). METHODS Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0-2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance. RESULTS The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001). CONCLUSION The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.
Collapse
|
18
|
Andescavage N, Limperopoulos C. Emerging placental biomarkers of health and disease through advanced magnetic resonance imaging (MRI). Exp Neurol 2021; 347:113868. [PMID: 34562472 DOI: 10.1016/j.expneurol.2021.113868] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/09/2021] [Accepted: 09/19/2021] [Indexed: 12/12/2022]
Abstract
Placental dysfunction is a major cause of fetal demise, fetal growth restriction, and preterm birth, as well as significant maternal morbidity and mortality. Infant survivors of placental dysfunction are at elevatedrisk for lifelong neuropsychiatric morbidity. However, despite the significant consequences of placental disease, there are no clinical tools to directly and non-invasively assess and measure placental function in pregnancy. In this work, we will review advanced MRI techniques applied to the study of the in vivo human placenta in order to better detail placental structure, architecture, and function. We will discuss the potential of these measures to serve as optimal biomarkers of placental dysfunction and review the evidence of these tools in the discrimination of health and disease in pregnancy. Efforts to advance our understanding of in vivo placental development are necessary if we are to optimize healthy pregnancy outcomes and prevent brain injury in successive generations. Current management of many high-risk pregnancies cannot address placental maldevelopment or injury, given the standard tools available to clinicians. Once accurate biomarkers of placental development and function are constructed, the subsequent steps will be to introduce maternal and fetal therapeutics targeting at optimizing placental function. Applying these biomarkers in future studies will allow for real-time assessments of safety and efficacy of novel interventions aimed at improving maternal-fetal well-being.
Collapse
Affiliation(s)
- Nickie Andescavage
- Developing Brain Institute, Department of Radiology, Children's National, Washington DC, USA; Department of Neonatology, Children's National, Washington DC, USA
| | | |
Collapse
|
19
|
Xuan R, Li T, Wang Y, Xu J, Jin W. Prenatal prediction and typing of placental invasion using MRI deep and radiomic features. Biomed Eng Online 2021; 20:56. [PMID: 34090428 PMCID: PMC8180077 DOI: 10.1186/s12938-021-00893-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/25/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND To predict placental invasion (PI) and determine the subtype according to the degree of implantation, and to help physicians develop appropriate therapeutic measures, a prenatal prediction and typing of placental invasion method using MRI deep and radiomic features were proposed. METHODS The placental tissue of abdominal magnetic resonance (MR) image was segmented to form the regions of interest (ROI) using U-net. The radiomic features were subsequently extracted from ROI. Simultaneously, a deep dynamic convolution neural network (DDCNN) with codec structure was established, which was trained by an autoencoder model to extract the deep features from ROI. Finally, combining the radiomic features and deep features, a classifier based on the multi-layer perceptron model was designed. The classifier was trained to predict prenatal placental invasion as well as determine the invasion subtype. RESULTS The experimental results show that the average accuracy, sensitivity, and specificity of the proposed method are 0.877, 0.857, and 0.954 respectively, and the area under the ROC curve (AUC) is 0.904, which outperforms the traditional radiomic based auxiliary diagnostic methods. CONCLUSIONS This work not only labeled the placental tissue of MR image in pregnant women automatically but also realized the objective evaluation of placental invasion, thus providing a new approach for the prenatal diagnosis of placental invasion.
Collapse
Affiliation(s)
- Rongrong Xuan
- Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, Zhejiang, China
| | - Tao Li
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China
| | - Yutao Wang
- Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, Zhejiang, China
| | - Jian Xu
- Ningbo Women's and Children's Hospital, Ningbo, 315012, Zhejiang, China
| | - Wei Jin
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
| |
Collapse
|
20
|
Lu T, Song B, Pu H, Li KD, Huang MW, Mei J, Wang SY. Prognosticators of intravoxel incoherent motion (IVIM) MRI for adverse maternal and neonatal clinical outcomes in patients with placenta accreta spectrum disorders. Transl Androl Urol 2020; 9:258-266. [PMID: 32420131 PMCID: PMC7214992 DOI: 10.21037/tau.2019.12.27] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The incidence of placenta accreta spectrum (PAS) disorders has increased rapidly in recent years and is associated with several maternal and neonatal complications. Intravoxel incoherent motion (IVIM) imaging is a method which can assess placental perfusion quantitatively. Therefore, the first aim of this study was to investigate whether patients with adverse maternal and neonatal outcomes of PAS disorders differed in the parameters from IVIM. A second aim was to identify these parameters for adverse peripartum outcome in gravid patients at risk for PAS. METHODS The subject group consisted of patients with placenta previa, in which 75 patients had PAS disorders and 24 patients did not have PAS disorders between 28+0 and 39+6 weeks, respectively. All women underwent magnetic resonance imaging (MRI) examination including an IVIM sequence with 8 b values on a 1.5T scanner. The perfusion fraction (f), pseudodiffusion coefficient (D*), and standard diffusion coefficient (D) were calculated. All medical records were received postpartum. The final degree of placental invasion was established either by placental villi alterations from a placental sample or from maternity records of the women's general practitioners. RESULTS Women with PAS disorders had a higher perfusion fraction (34.12%) than women without the disease (29.39%) (P<0.05). The perfusion fraction was 36.86% in women with massive blood loss and was 35.15% in women requiring transfusion, which was higher than women without massive blood loss and not requiring transfusion (P<0.05). The D value was 1.65×10-3 mm2/s in women with low birth weight, which was lower than that in women with appropriate birth weight (1.70×10-3 mm2/s) (P<0.05). CONCLUSIONS Patients with PAS disorders differed in placental perfusion fraction from women without PAS disorders. The f and D value may be used to recognize patients with certain adverse clinical outcomes.
Collapse
Affiliation(s)
- Tao Lu
- Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610037, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, China
| | - Kui-De Li
- Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, China
| | - Meng-Wei Huang
- Department of Obstetrics, Sichuan Academy of Medical Sciences & Sichuan Provincial Peopleal Sciences sChengdu 610072, China
| | - Jie Mei
- Department of Obstetrics, Sichuan Academy of Medical Sciences & Sichuan Provincial Peopleal Sciences sChengdu 610072, China
| | - Shao-Yu Wang
- MR Scientific Marketing Specialist, Siemens Healthineer, Shanghai 201318, China
| |
Collapse
|
21
|
The new era of advanced placental tissue characterization using MRI texture analysis: Clinical implications. EBioMedicine 2019; 51:102588. [PMID: 31901570 PMCID: PMC6940719 DOI: 10.1016/j.ebiom.2019.11.049] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 11/28/2019] [Indexed: 01/24/2023] Open
|
22
|
Can introvoxel incoherent motion MRI be used to differentiate patients with placenta accreta spectrum disorders? BMC Pregnancy Childbirth 2019; 19:531. [PMID: 31888572 PMCID: PMC6937691 DOI: 10.1186/s12884-019-2676-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/12/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The incidence of PAS disorders increased rapidly in recent years, and introvoxel incoherent motion (IVIM) MRI has been applied in the assessment of placenta. The study aims to investigate whether the parameters from IVIM can be used to differentiate patients with PAS disorders complicating placenta previa and secondly to differentiate different categories of PAS disorders. METHODS The study participants were comprised of 99 patients with placenta previa, including 16 patients with placenta accreta, 51 patients with increta, 8 patients with percreta and 24 patients without PAS disorders between 28 + 0 and 39 + 6 weeks. IVIM MRI was performed on a 1.5 T scanner. Perfusion fraction (f), pseudodiffusion coefficient (D*) and diffusion coefficient (D) were calculated. RESULTS Women with PAS disorders had a higher perfusion fraction (p = 0.019) than women without the disease. Multiple comparisons showed perfusion fraction in patients without PAS disorders was significantly lower than in patients with placenta accreta and percreta(P = 0.018 and 0.033 respectively), but was not lower than in patients with increta(p = 1). CONCLUSION Patients with placenta accreta and percreta differed in placental perfusion fraction from women with increta and without PAS disorders.
Collapse
|
23
|
Wu Q, Yao K, Liu Z, Li L, Zhao X, Wang S, Shang H, Lin Y, Wen Z, Zhang X, Tian J, Wang M. Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study. EBioMedicine 2019; 50:355-365. [PMID: 31767539 PMCID: PMC6921361 DOI: 10.1016/j.ebiom.2019.11.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/04/2019] [Accepted: 11/07/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD). METHODS A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application. FINDINGS Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844-0.933) and 0.832 (95% CI, 0.746-0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort. INTERPRETATION The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.
Collapse
Affiliation(s)
- Qingxia Wu
- Department of Medical Imaging, Henan Key Laboratory of Neurological Imaging, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, Henan, China
| | - Kuan Yao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; 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; University of Chinese Academy of Sciences, Beijing, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, China
| | - Xin Zhao
- Department of Radiology, the Third affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Honglei Shang
- Department of Radiology, the Third affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yusong Lin
- Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, China
| | - Zejun Wen
- Department of Radiology, the Third affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaoan Zhang
- Department of Radiology, the Third affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Engineering Research Centre of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, China.
| | - Meiyun Wang
- Department of Medical Imaging, Henan Key Laboratory of Neurological Imaging, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, Henan, China.
| |
Collapse
|