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Luo L, Wang X, Xie H, Liang H, Gao J, Li Y, Xia Y, Zhao M, Shi F, Shen C, Duan X. Role of [ 18F]-PSMA-1007 PET radiomics for seminal vesicle invasion prediction in primary prostate cancer. Comput Biol Med 2024; 183:109249. [PMID: 39388841 DOI: 10.1016/j.compbiomed.2024.109249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 09/23/2024] [Accepted: 10/03/2024] [Indexed: 10/12/2024]
Abstract
PURPOSE The purpose of this study is to investigate the diagnostic utility of [18F]-PSMA-1007 PET radiomics combined with machine learning methods to predict seminal vesicle invasion (SVI) after radical prostatectomy (RP) in prostate cancer (PCa) patients. METHODS This is a post hoc retrospective analysis for a prospective clinical trial that included a consecutive sample of PCa patients (n = 140) who had [18F]-PSMA-1007 PET/CT prior to RP. The intraprostatic lesion's volume of interest (VOI) was semi-automatically sketched using a threshold of 40 % maximum standardized uptake value (SUVmax), namely 40%SUVmax-VOI, and seminal vesicle glands were manually contoured, namely SV-VOI. Models were built using a variety of machine learning methods such as logistic regression, random forest, and support vector machine. The area under the receiver operating characteristic curve (AUC) was calculated for different models, and the prediction performances of radiomics models were compared against the radiologists' assessment. Kaplan-Meier analysis was utilized to assess the effectiveness of selected radiomics features to determine the progression-free survival (PFS) probability. RESULTS The training set had 112 patients and the test set had 28 patients. The highest AUC for the PET radiomics model of 40%SUVmax-VOI and the PET radiomics model of SV-VOI were 0.85 and 0.96 in the test set, respectively. The PET radiomics model of SV-VOI had a significantly higher AUC compared to the radiologists' assessment (P < 0.05). The Kaplan-Meier analysis showed that PET radiomics features were associated with PFS in patients with PCa. CONCLUSION Radiomics models developed by preoperative [18F]-PSMA-1007 PET were proven useful in predicting SVI, and PSMA PET radiomics features were correlated with PFS, suggesting that the PSMA PET radiomics might be an accurate tool for PCa characterization.
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Affiliation(s)
- Liang Luo
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinyi Wang
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hongjun Xie
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hua Liang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jungang Gao
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yang Li
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Mengmeng Zhao
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Cong Shen
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyi Duan
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Zhuo LY, Hao JW, Song ZJ, Meng H, Wang TD, Yang LL, Yang ZM, Ma JM, Shen D, Cui JJ, Chen WJ, Yang W, Zang LL, Wang JN, Yin XP. Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study. Sci Rep 2024; 14:22978. [PMID: 39362944 PMCID: PMC11450145 DOI: 10.1038/s41598-024-74251-5] [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: 05/04/2024] [Accepted: 09/24/2024] [Indexed: 10/05/2024] Open
Abstract
The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.
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Affiliation(s)
- Li-Yong Zhuo
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Jia-Wei Hao
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Zi-Jun Song
- Department of Critical Care Medicine, Baoding First Central Hospital, Lianchi District, No. 320, Changcheng North Street (Qianwei Road), Baoding, 071000, China
| | - Huan Meng
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Tian-Da Wang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Lu-Lu Yang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Zi-Mei Yang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Jia-Mei Ma
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Dan Shen
- Department of Urology, the Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, China
| | - Jing-Jing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd.Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Wen-Jing Chen
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd.Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Wei Yang
- Department of Pulmonary and Critical Care Medicine, Baoding First Central Hospital, Lianchi District, No. 320, Changcheng North Street (Qianwei Road), Baoding, 071000, China
| | - Li-Li Zang
- Department of Radiology, Baoding Children's Hospital, No. 103, East Baihua Road, Baoding, 071000, China
| | - Jia-Ning Wang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China.
| | - Xiao-Ping Yin
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China.
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Pan L, Wang X, Ge X, Ye H, Zhu X, Feng Q, Wang H, Shi F, Ding Z. Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics. BMC Med Imaging 2024; 24:246. [PMID: 39285327 PMCID: PMC11404009 DOI: 10.1186/s12880-024-01424-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 09/09/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND This study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals. METHODS A total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation. RESULTS On the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p < 0.05). The Area Under the Curve (AUC) values for the three models (Gradient Boosting Decision Tree, Gaussian Process, and Random Forest) are 0.90, 0.87, and 0.86, respectively. After testing with DeLong and McNemar methods, these three models all exhibit good performance in distinguishing CTN from normal individuals. CONCLUSIONS Radiomics can aid in the clinical diagnosis of CTN, and it is a more objective approach. It serves as a reliable neurobiological indicator for the clinical diagnosis of CTN and the assessment of changes in the trigeminal nerve in patients with CTN.
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Affiliation(s)
- Lei Pan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China
| | - Xuechun Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, 701 Yunjin Road, Shanghai, 200030, China
| | - Xiuhong Ge
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China
| | - Haiqi Ye
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China
| | - Xiaofen Zhu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China
| | - Qi Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China
| | - Haibin Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, 701 Yunjin Road, Shanghai, 200030, China.
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China.
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Liu W, Yang Y, Wang X, Li C, Liu C, Li X, Wen J, Lin X, Qin J. A Comprehensive Model Outperformed the Single Radiomics Model in Noninvasively Predicting the HER2 Status in Patients with Breast Cancer. Acad Radiol 2024:S1076-6332(24)00481-1. [PMID: 39122586 DOI: 10.1016/j.acra.2024.07.048] [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: 06/02/2024] [Revised: 07/23/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop predictive models based on conventional magnetic resonance imaging (cMRI) and radiomics features for predicting human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC) and compare their performance. MATERIALS AND METHODS A total of 287 patients with invasive BC in our hospital were retrospectively analyzed. All patients underwent preoperative breast MRI consisting of fat-suppressed T2-weighted imaging, axial dynamic contrast-enhanced MRI, and diffusion-weighted imaging sequences. From these sequences, radiomics features were derived. Three distinct models were established utilizing cMRI features, radiomics features, and a comprehensive model that amalgamated both. The predictive capabilities of these models were assessed using the receiver operating characteristic curve analysis. The comparative performance was then determined through the DeLong test and net reclassification improvement (NRI). RESULTS In a randomized split, the 287 patients with BC were allotted to either training (234; 46 HER2-zero, 107 HER2-low, 81 HER2-positive) or test (53; 8 HER2-zero, 27 HER2-low, 18 HER2-positive) at an 8:2 ratio. The mean area under the curve (AUCs) for cMRI, radiomics, and comprehensive models predicting HER2 status were 0.705, 0.819, and 0.859 in training set and 0.639, 0.797, and 0.842 in test set, respectively. DeLong's test indicated that the combined model's AUC surpassed the radiomics model significantly (p < 0.05). NRI analysis verified superiority of the combined model over the radiomics for BC HER2 prediction (NRI 25.0) in the test set. CONCLUSION The comprehensive model based on the combination of cMRI and radiomics features outperformed the single radiomics model in noninvasively predicting the three-tiered HER2 status in patients with BC.
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Affiliation(s)
- Weimin Liu
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Yiqing Yang
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Xiaohong Wang
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Chao Li
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Chen Liu
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Xiaolei Li
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Junzhe Wen
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Xue Lin
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Jie Qin
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China.
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Si J, Wang H, Xie M, Yang Y, Li J, Wang F, Chen X, He L. The value of radiomics features of the spleen as surrogates for differentiating subtypes of common pediatric lymphomas. Quant Imaging Med Surg 2024; 14:5630-5641. [PMID: 39143994 PMCID: PMC11320520 DOI: 10.21037/qims-24-122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/12/2024] [Indexed: 08/16/2024]
Abstract
Background Lymphoma is a common malignant tumor in children. The pathologic subtyping of lymphoma is high complex, and the treatment options vary. The different pathologic subtypes of lymphomas have no significant differences on computed tomography (CT) images. As it is a hematologic disease, patients with lymphoma often show abnormalities in the spleen, and so the aim of this study was to construct a model for differentiating Burkitt lymphoma (BL) from lymphoblastic lymphoma through the extraction of radiomic features of the spleen from CT images. This could provide an efficient, noninvasive method that can differentiate the common pathological subtypes in patients with pediatric lymphoma. Methods The clinical data and imaging data of 48 patients with lymphoblastic lymphoma and 61 patients with BL were retrospectively analyzed. The dataset was divided into a training set (n=76) and a test set (n=33) through complete randomization. Radiomics features of the spleen were separately extracted from CT images in the noncontrast enhanced, arterial, and venous phases. These phase-specific features were integrated to construct fusion models. Three classifiers, quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM), were employed to build the models. Results The fusion model exhibited superior performance compared to individual models. There was no significant difference between the fusion models constructed by QDA and LR in either the training set or the test set. Among the four fusion models constructed with the SVM classifier, SVM_4 emerged as the best performing model. The area under the curve, sensitivity, specificity, and F1-score of the SVM_4 model were 0.967 [95% confidence interval (CI): 0.935-0.998], 0.86, 0.97, and 0.913 in the training set, respectively, and 0.754 (95% CI: 0.584-0.924), 0.611, 0.867, and 0.71 in the test set, respectively. Conclusions The radiomics features of the spleen demonstrated the capability to distinguish between the two most common lymphoma subtypes in pediatric patients. This noninvasive approach holds promise for efficient and accurate discrimination.
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Affiliation(s)
- Jiajun Si
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Haoru Wang
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Mingye Xie
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Yanlin Yang
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Jun Li
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xin Chen
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Ling He
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
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Song C, Li W, Cui J, Miao Q, Liu Y, Zhang Z, Nie S, Zhou M, Chai R. Pre-operative prediction of histopathological growth patterns of colorectal cancer liver metastasis using MRI-based radiomic models. Abdom Radiol (NY) 2024:10.1007/s00261-024-04290-z. [PMID: 39069557 DOI: 10.1007/s00261-024-04290-z] [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: 01/25/2024] [Revised: 03/10/2024] [Accepted: 03/11/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE Histopathological growth patterns (HGPs) of colorectal liver metastases (CRLMs) have prognostic value. However, the differentiation of HGPs relies on postoperative pathology. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomic model to predict HGP pre-operatively, following the latest guidelines. METHODS This retrospective study included 93 chemotherapy-naïve patients with CRLMs who underwent contrast-enhanced liver MRI and a partial hepatectomy between 2014 and 2022. Radiomic features were extracted from the tumor zone (RTumor), a 2-mm outer ring (RT+2), a 2-mm inner ring (RT-2), and a combined ring (R2+2) on late arterial phase MRI images. Analysis of variance method (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection. Logistic regression with five-fold cross-validation was used for model construction. Receiver operating characteristic curves, calibrated curves, and decision curve analyses were used to assess model performance. DeLong tests were used to compare different models. RESULTS Twenty-nine desmoplastic and sixty-four non-desmoplastic CRLMs were included. The radiomic models achieved area under the curve (AUC) values of 0.736, 0.906, 0.804, and 0.794 for RTumor, RT-2, RT+2, and R2+2, respectively, in the training cohorts. The AUC values were 0.713, 0.876, 0.785, and 0.777 for RTumor, RT-2, RT+2, and R2+2, respectively, in the validation cohort. RT-2 exhibited the best performance. CONCLUSION The MRI-based radiomic models could predict HGPs in CRLMs pre-operatively.
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Affiliation(s)
- Chunlin Song
- Department of Radiology, First Hospital of China Medical University, 155 Nanjing St, Shenyang, 110001, China
| | - Wenhui Li
- Institute of Cancer Research, First Hospital of China Medical University, Shenyang, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence, Beijing, China
| | - Qi Miao
- Department of Radiology, First Hospital of China Medical University, 155 Nanjing St, Shenyang, 110001, China
| | - Yi Liu
- Department of Radiology, Cancer Hospital of China Medical University, Shenyang, China
| | - Zitian Zhang
- Department of Radiology, First Hospital of China Medical University, 155 Nanjing St, Shenyang, 110001, China
| | - Siru Nie
- Department of Pathology, The First Hospital of China Medical University, Shenyang, China
| | - Meihong Zhou
- Department of Radiology, Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Ruimei Chai
- Department of Radiology, First Hospital of China Medical University, 155 Nanjing St, Shenyang, 110001, China.
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Jiang C, Wang T, Pan Y, Ding Z, Shen D. Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT. Med Image Anal 2024; 95:103194. [PMID: 38749304 DOI: 10.1016/j.media.2024.103194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/20/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024]
Abstract
Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at https://github.com/jiang-cw/SDTGAN.
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Affiliation(s)
- Caiwen Jiang
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Tianyu Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China; Zhejiang University School of Medicine, Hangzhou, China
| | - Yongsheng Pan
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
| | - Dinggang Shen
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, 201210, China.
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Zhang R, Wei Y, Wang D, Chen B, Sun H, Lei Y, Zhou Q, Luo Z, Jiang L, Qiu R, Shi F, Li W. Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images. Eur Radiol 2024; 34:4218-4229. [PMID: 38114849 DOI: 10.1007/s00330-023-10518-1] [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: 06/20/2023] [Revised: 09/18/2023] [Accepted: 11/11/2023] [Indexed: 12/21/2023]
Abstract
OBJECTIVES To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings. MATERIALS AND METHODS Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians. RESULTS There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician. CONCLUSION The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules. CLINICAL RELEVANCE STATEMENT The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis. KEY POINTS • According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Denian Wang
- Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Lei
- General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Zhuang Luo
- Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Li Jiang
- Department of Respiratory and Critical Care Medicine, the Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Rong Qiu
- Department of Respiratory and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
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Liu X, Li H, Wang S, Yang S, Zhang G, Xu Y, Yang H, Shan F. CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study. Front Oncol 2024; 14:1420213. [PMID: 38952551 PMCID: PMC11215045 DOI: 10.3389/fonc.2024.1420213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/03/2024] [Indexed: 07/03/2024] Open
Abstract
Purpose To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making. Methods A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor. Results In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002). Conclusion The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.
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Affiliation(s)
- Xiaoyu Liu
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Hongjian Li
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shan Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Guobin Zhang
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yonghua Xu
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital of Fudan University, Fudan University, Shanghai, China
| | - Hanfeng Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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Chen X, Wang H, Xia Y, Shi F, He L, Liu E. The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma. Discov Oncol 2024; 15:201. [PMID: 38822860 PMCID: PMC11144178 DOI: 10.1007/s12672-024-01067-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/28/2024] [Indexed: 06/03/2024] Open
Abstract
OBJECTIVE Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China.
| | - Enmei Liu
- Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, 400014, China.
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11
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Meng H, Wang TD, Zhuo LY, Hao JW, Sui LY, Yang W, Zang LL, Cui JJ, Wang JN, Yin XP. Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia. Front Med (Lausanne) 2024; 11:1409477. [PMID: 38831994 PMCID: PMC11146305 DOI: 10.3389/fmed.2024.1409477] [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: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.
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Affiliation(s)
- Huan Meng
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Tian-Da Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Li-Yong Zhuo
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Jia-Wei Hao
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Lian-yu Sui
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Wei Yang
- Department of Radiology, Baoding First Central Hospital, Baoding, China
| | - Li-Li Zang
- Department of Radiology, Baoding Children's Hospital, Baoding, China
| | - Jing-Jing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Beijing, China
| | - Jia-Ning Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Xiao-Ping Yin
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
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12
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Jiang W, Pan X, Luo Q, Huang S, Liang Y, Zhong X, Zhang X, Deng W, Lv Y, Chen L. Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus. Front Med (Lausanne) 2024; 11:1328687. [PMID: 38707184 PMCID: PMC11069320 DOI: 10.3389/fmed.2024.1328687] [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: 10/27/2023] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open
Abstract
Objective To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus. Materials and methods In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model. Results Seven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73-1), 0.881 (0.715-1), and 0.922 (0.804-1) for the respective models. Conclusion Radiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.
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Affiliation(s)
- Wei Jiang
- Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Qunzhi Luo
- Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China
| | - Shiqi Huang
- Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China
| | - Yuhong Liang
- Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China
| | - Xixi Zhong
- Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China
| | - Xianjie Zhang
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Wei Deng
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yaping Lv
- Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
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13
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Wei S, Gou X, Zhang Y, Cui J, Liu X, Hong N, Sheng W, Cheng J, Wang Y. Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics. Clin Exp Metastasis 2024; 41:143-154. [PMID: 38416301 DOI: 10.1007/s10585-024-10275-5] [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: 10/10/2023] [Accepted: 01/24/2024] [Indexed: 02/29/2024]
Abstract
Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.
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Affiliation(s)
- Shengcai Wei
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China
| | - Xinyi Gou
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China
| | - Yinli Zhang
- Department of Pathology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd, Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Xiaoming Liu
- Department of Research and Development, Beijing United Imaging Research Institute of Intelligent Imaging, Yongteng North Road, Haidian District, Beijing, 100089, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China
| | - Weiqi Sheng
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China.
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China.
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Xing Z, Zhu Z, Jiang Z, Zhao J, Chen Q, Xing W, Pan L, Zeng Y, Liu A, Ding J. Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:444-454. [PMID: 38343222 PMCID: PMC11031534 DOI: 10.1007/s10278-023-00946-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 04/20/2024]
Abstract
To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data.
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Affiliation(s)
- Zhaoyu Xing
- Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Zuhui Zhu
- Department of Radiology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu, China
| | - Zhenxing Jiang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Jingshi Zhao
- Department of Radiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qin Chen
- Department of Radiology, People's Hospital of Pengzhou, Chengdu, Sichuan, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Liang Pan
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Jiule Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
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Huang D, Lin C, Jiang Y, Xin E, Xu F, Gan Y, Xu R, Wang F, Zhang H, Lou K, Shi L, Hu H. Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy. Front Oncol 2024; 14:1348678. [PMID: 38585004 PMCID: PMC10996281 DOI: 10.3389/fonc.2024.1348678] [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: 12/03/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
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Affiliation(s)
- Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yangyang Jiang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Enhui Xin
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rui Xu
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
- DUT-RU Co-Research Center of Advanced Information Computing Technology (ICT) for Active Life, Dalian University of Technology, Dalian, Liaoning, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haiping Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Wang Y, Shang Y, Guo Y, Hai M, Gao Y, Wu Q, Li S, Liao J, Sun X, Wu Y, Wang M, Tan H. Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer. Front Oncol 2024; 14:1357145. [PMID: 38567148 PMCID: PMC10985134 DOI: 10.3389/fonc.2024.1357145] [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: 12/17/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.
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Affiliation(s)
- Yunxia Wang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yiyan Shang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yaxin Guo
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Menglu Hai
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University &Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Yang Gao
- Heart Center, People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence Co., Ltd., Beijing, China
| | - Shunian Li
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jun Liao
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaojuan Sun
- School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yaping Wu
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hongna Tan
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Zhang Z, Ding Z, Chen F, Hua R, Wu J, Shen Z, Shi F, Xu X. Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning. Int J Gen Med 2024; 17:739-750. [PMID: 38463439 PMCID: PMC10923240 DOI: 10.2147/ijgm.s446531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/12/2024] [Indexed: 03/12/2024] Open
Abstract
Background Cerebral small vessel disease lacks specific clinical manifestations, and extraction of valuable features from multimodal images is expected to improve its diagnostic accuracy. In this study, we used deep learning techniques to segment cerebral small vessel disease imaging markers in multimodal magnetic resonance images and analyze them with clinical risk factors. Methods and results We recruited 211 lacunar stroke patients and 83 control patients. The patients' cerebral small vessel disease markers were automatically segmented using a V-shaped bottleneck network, and the number and volume were calculated after manual correction. The segmentation results of the V-shaped bottleneck network for white matter hyperintensity and recent small subcortical infarction were in high agreement with the ground truth (DSC>0.90). In small lesion segmentation, cerebral microbleed (average recall=0.778; average precision=0.758) and perivascular spaces (average recall=0.953; average precision=0.923) were superior to lacunar infarct (average recall=0.339; average precision=0.432) in recall and precision. Binary logistic regression analysis showed that age, systolic blood pressure, and total cerebral small vessel disease load score were independent risk factors for lacunar stroke (P<0.05). Ordered logistic regression analysis showed age was positively correlated with cerebral small vessel disease load score and total cholesterol was negatively correlated with cerebral small vessel disease score (P<0.05). Conclusion Lacunar stroke patients exhibited higher cerebral small vessel disease imaging markers, and age, systolic blood pressure, and total cerebral small vessel disease score were independent risk factors for lacunar stroke patients. V-shaped bottleneck network segmentation network based on multimodal deep learning can segment and quantify various cerebral small vessel disease lesions to some extent.
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Affiliation(s)
- Zhiliang Zhang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, People’s Republic of China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, People’s Republic of China
| | - Fenyang Chen
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, People’s Republic of China
| | - Rui Hua
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China
| | - Zhefan Shen
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, People’s Republic of China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China
| | - Xiufang Xu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, People’s Republic of China
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Miao Q, Wang X, Cui J, Zheng H, Xie Y, Zhu K, Chai R, Jiang Y, Feng D, Zhang X, Shi F, Tan X, Fan G, Liang K. Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging. Comput Biol Med 2024; 171:108125. [PMID: 38340439 DOI: 10.1016/j.compbiomed.2024.108125] [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: 10/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.
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Affiliation(s)
- Qi Miao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xuechun Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Bejing, China
| | - Haoxin Zheng
- Department of Computer Science, University of California, Los Angeles, USA
| | - Yan Xie
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Kexin Zhu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Ruimei Chai
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yuanxi Jiang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Dongli Feng
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiaodong Tan
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
| | - Keke Liang
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
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Zhou L, Yang W, Liu Y, Li J, Zhao M, Liu G, Zhang J. Correlations between cognitive reserve, gray matter, and cerebrospinal fluid volume in healthy elders and mild cognitive impairment patients. Front Neurol 2024; 15:1355546. [PMID: 38497043 PMCID: PMC10941649 DOI: 10.3389/fneur.2024.1355546] [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: 12/15/2023] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Objective To explore the effect of cognitive reserve (CR) on brain volume and cerebrospinal fluid (CSF) in patients with mild cognitive impairment (MCI) and healthy elders (HE). Methods 31 HE and 50 MCI patients were collected in this study to obtain structural MRI, cognitive function, and composite CR scores. Educational attainment, leisure time, and working activity ratings from two groups were used to generate cognitive reserve index questionnaire (CRIq) scores. The different volumes of brain regions and CSF were obtained using uAI research portal in both groups, which were taken as the regions of interest (ROI), the correlation analysis between ROIs and CRIq scores were conducted. Results The scores of CRIq, CRIq-leisure time, and CRIq-education in HE group were significantly higher than patients in MCI group, and the montreal cognitive assessment (MoCA) and minimum mental state examination (MMSE) scores were positively correlated with the CRIq, CRIq-education in both groups, and were positively correlated with CRIq-leisure time in MCI group. The scores of auditory verbal learning test (AVLT) and verbal fluency test (VFT) were also positively correlated with CRIq, CRIq-leisure time, and CRIq-education in MCI group, but the score of AVLT was only positively correlated with CRIq in HE group. Moreover, in MCI group, the volume of the right middle cingulate cortex and the right parahippocampal gyrus were negatively correlated with the CRIq, and the volume of CSF, peripheral CSF, and third ventricle were positively correlated with the CRIq-leisure time score. The result of mediation analysis suggested that right parahippocampal gryus mediated the main effect of the relationship between CRIq and MoCA score in MCI group. Conclusion People with higher CR show better levels of cognitive function, and MCI patients with higher CR showed more severe volume atrophy of the right middle cingulate cortex and the right parahippocampal gyrus, but more CSF at a given level of global cognition.
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Affiliation(s)
- Liang Zhou
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wenxia Yang
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yang Liu
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jiachen Li
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Mengmeng Zhao
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Guangyao Liu
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
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Wang M, Xi Y, Wang L, Chen H, Jiang F, Ding Z. Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma. Radiat Oncol 2024; 19:26. [PMID: 38418994 PMCID: PMC10900635 DOI: 10.1186/s13014-024-02417-6] [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: 09/18/2022] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Xerostomia is one of the most common side effects in nasopharyngeal carcinoma (NPC) patients after chemoradiotherapy. To establish a Delta radiomics model for predicting xerostomia secondary to chemoradiotherapy for NPC based on magnetic resonance T1-weighted imaging (T1WI) sequence and evaluate its diagnostic efficacy. METHODS Clinical data and Magnetic resonance imaging (MRI) data before treatment and after induction chemotherapy (IC) of 255 NPC patients with stage III-IV were collected retrospectively. Within one week after CCRT, the patients were divided into mild (92 cases) and severe (163 cases) according to the grade of xerostomia. Parotid glands in T1WI sequence images before and after IC were delineated as regions of interest for radiomics feature extraction, and Delta radiomics feature values were calculated. Univariate logistic analysis, correlation, and Gradient Boosting Decision Tree (GBDT) methods were applied to reduce the dimension, select the best radiomics features, and establish pretreatment, post-IC, and Delta radiomics xerostomia grading predictive models. The receiver operating characteristic (ROC) curve and decision curve were drawn to evaluate the predictive efficacy of different models. RESULTS Finally, 15, 10, and 12 optimal features were selected from pretreatment, post-IC, and Delta radiomics features, respectively, and a xerostomia prediction model was constructed with AUC values of 0.738, 0.751, and 0.843 in the training set, respectively. Only age was statistically significant in the clinical data of both groups (P < 0.05). CONCLUSION Delta radiomics can predict the degree of xerostomia after chemoradiotherapy for NPC patients and it has certain guiding significance for clinical early intervention measures.
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Affiliation(s)
- Mengze Wang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Yuzhen Xi
- Department of Radiology, 903 RD Hospital of PLA, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Haonan Chen
- Department of Radiology, Zhejiang Hospital, Hangzhou, China
| | - Feng Jiang
- Department of Head and Neck Radiotherapy, Zhejiang Province Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, China.
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China.
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Fu L, Wang W, Lin L, Gao F, Yang J, Lv Y, Ge R, Wu M, Chen L, Liu A, Xin E, Yu J, Cheng J, Wang Y. Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics. Front Med (Lausanne) 2024; 11:1334062. [PMID: 38384418 PMCID: PMC10880444 DOI: 10.3389/fmed.2024.1334062] [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: 11/06/2023] [Accepted: 01/11/2024] [Indexed: 02/23/2024] Open
Abstract
Objective High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images. Methods This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test. Results The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model. Conclusion The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.
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Affiliation(s)
- Le Fu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjing Wang
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingling Lin
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Feng Gao
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunyun Lv
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ruiqiu Ge
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meixuan Wu
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Aie Liu
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Enhui Xin
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jianli Yu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiejun Cheng
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
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Cheng Y, Yang L, Wang Y, Kuang L, Pan X, Chen L, Cao X, Xu Y. Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids. Quant Imaging Med Surg 2024; 14:1803-1819. [PMID: 38415139 PMCID: PMC10895146 DOI: 10.21037/qims-23-916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/06/2023] [Indexed: 02/29/2024]
Abstract
Background The heterogeneity of uterine fibroids in magnetic resonance imaging (MRI) is complex for a subjective visual evaluation, therefore it is difficult for an accurate prediction of the efficacy of high intensity focused ultrasound (HIFU) ablation in fibroids before the treatment. The purpose of this study was to set up a radiomics model based on MRI T2-weighted imaging (T2WI) for predicting the efficacy of HIFU ablation in uterine fibroids, and it would be used in preoperative screening of the fibroids for achieving high non-perfused volume ratio (NPVR). Methods A total of 178 patients with uterine fibroids were consecutively enrolled and treated with ultrasound-guided HIFU under conscious sedation between February 2017 and December 2021. Among them, 96 patients with 108 uterine fibroids with high ablation efficacy (NPVR ≥80%, h_NPVR) and 82 patients with 92 fibroids with lower ablation efficacy (NPVR <80%, l_NPVR) were retrospectively analyzed. The transverse T2WI images of fibroids were selected, and the fibroids were delineated slice by slice using ITK-SNAP software. The radiomics analysis was performed to find the imaging biomarker for the construction of a predicting model for the evaluation of the ablation efficacy, including the feature extraction, feature selection and model construction. The prediction model was built by logistic regression and assessed by receiver operating characteristic (ROC) curve, and the prediction efficiency of the two models was compared by Delong test. The ratio of the training set to the testing set was 8:2. Results The logistic regression model showed that the mean area under the curve (AUC) of the training set was 0.817 [95% confidence interval (CI): 0.755-0.882], and the testing set was 0.805 (95% CI: 0.670-0.941), respectively, which indicated a strong classification ability. The Delong test showed that there was no significant difference in the area under the ROC curve between the training set and testing set (P>0.05). Conclusions The radiomics model based on T2WI is feasible and effective for predicting the efficacy of HIFU ablation in treatment of uterine fibroids.
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Affiliation(s)
- Yu Cheng
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Lixia Yang
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Yiran Wang
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Lanqiong Kuang
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiaohuan Cao
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yonghua Xu
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
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Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering (Basel) 2024; 11:50. [PMID: 38247927 PMCID: PMC10813496 DOI: 10.3390/bioengineering11010050] [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: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVE Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. METHODS In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.
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Affiliation(s)
| | | | | | | | | | - Wei Wei
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116014, China (S.W.); (Y.F.)
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Peng T, Zeng X, Li Y, Li M, Pu B, Zhi B, Wang Y, Qu H. A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening. Osteoporos Int 2024; 35:117-128. [PMID: 37670164 PMCID: PMC10786975 DOI: 10.1007/s00198-023-06900-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023]
Abstract
This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong correlation with QCT results, showing promise for expanding osteoporosis screening and reducing unnecessary radiation and costs. PURPOSE To explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set. METHODS A total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated. RESULTS The ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship. CONCLUSION Using deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.
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Affiliation(s)
- Tao Peng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2Nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China.
| | - Xiaohui Zeng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2Nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Yang Li
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, China
| | - Man Li
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, China
| | - Bingjie Pu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2Nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Biao Zhi
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2Nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Yongqin Wang
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2Nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China
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