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Lu Q, Liu Z, He W, Chu X. Retracted article: Protective effects of ulinastatin on rats with acute lung injury induced by lipopolysaccharide. Bioengineered 2024; 15:1987083. [PMID: 34637694 PMCID: PMC10813561 DOI: 10.1080/21655979.2021.1987083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 09/24/2021] [Indexed: 10/20/2022] Open
Abstract
Qitong Lu, Zhiyong Liu, Wei He and Xin Chu. Protective effects of ulinastatin on rats with acute lung injury induced by lipopolysaccharide. Bioengineered. 2021 Oct. doi: 10.1080/21655979.2021.1987083.Since publication, significant concerns have been raised about the compliance with ethical policies for human research and the integrity of the data reported in the article.When approached for an explanation, the authors provided some original data but were not able to provide all the necessary supporting information. As verifying the validity of published work is core to the scholarly record's integrity, we are retracting the article. All authors listed in this publication have been informed.We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted.'
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Affiliation(s)
- Qitong Lu
- Department of Cardiothoracic Surgery, Zhongda Hospital, Southeast University, Nanjing, P. R. China
| | - Zhiyong Liu
- Department of Cardiothoracic Surgery, Zhongda Hospital, Southeast University, Nanjing, P. R. China
| | - Wei He
- Department of Cardiothoracic Surgery, Zhongda Hospital, Southeast University, Nanjing, P. R. China
| | - Xin Chu
- Department of Cardiothoracic Surgery, Zhongda Hospital, Southeast University, Nanjing, P. R. China
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Cao L, Yang H, Yao D, Cai H, Wu H, Yu Y, Zhu L, Xu W, Liu Y, Li J. Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas. Oncol Lett 2024; 28:340. [PMID: 38855505 PMCID: PMC11157660 DOI: 10.3892/ol.2024.14472] [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: 10/18/2023] [Accepted: 04/26/2024] [Indexed: 06/11/2024] Open
Abstract
The aim of the present study was to develop and evaluate a clinical-imaging-radiomic nomogram based on pre-enhanced computed tomography (CT) for pre-operative differentiation lipid-poor adenomas (LPAs) from metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas (AIs). A total of 196 consecutive patients with lung cancer, who underwent initial chest or abdominal pre-enhanced CT scan with small hyperattenuating AIs, were included. The patients were randomly divided into a training cohort with 71 cases of LPAs and 66 cases of metastases, and a testing cohort with 31 cases of LPAs and 28 cases of metastases. Plain CT radiological and clinical features were evaluated, including sex, age, size, pre-enhanced CT value (CTpre), shape, homogeneity and border. A total of 1,316 radiomic features were extracted from the plain CT images of the AIs, and the significant features selected by the least absolute shrinkage and selection operator were used to establish a Radscore. Subsequently, a clinical-imaging-radiomic model was developed by multivariable logistic regression incorporating the Radscore with significant clinical and imaging features. This model was then presented as a nomogram. The performance of the nomogram was assessed by calibration curves and decision curve analysis (DCA). A total of 4 significant radiomic features were incorporated in the Radscore, which yielded notable area under the receiver operating characteristic curves (AUCs) of 0.920 in the training dataset and 0.888 in the testing dataset. The clinical-imaging-radiomic nomogram incorporating the Radscore, CTpre, sex and age revealed favourable differential diagnostic performance (AUC: Training, 0.968; testing, 0.915) and favourable calibration curves. The nomogram was revealed to be more useful than the Radscore and the clinical-imaging model in clinical practice by DCA. The clinical-imaging-radiomics nomogram based on initial plain CT images by integrating the Radscore and clinical-imaging factors provided a potential tool to effectively differentiate LPAs from metastases in patients with lung cancer with small hyperattenuating AIs.
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Affiliation(s)
- Lixiu Cao
- Department of Nuclear Medical Imaging, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China
| | - Haoxuan Yang
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050010, P.R. China
| | - Deshun Yao
- Department of Oncology Surgery, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China
| | - Haifeng Cai
- Department of Oncology Surgery, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China
| | - Huijing Wu
- Department of Nuclear Medical Imaging, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China
| | - Yixing Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China
| | - Lei Zhu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300000, P.R. China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300000, P.R. China
| | - Yongliang Liu
- Department of Neurosurgery, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China
| | - Jingwu Li
- Department of Tumor Surgery, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China
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Feng B, Ma C, liu Y, Hu Q, Lei Y, Wan M, Lin F, Cui J, Long W, Cui E. Deep learning vs. robust federal learning for distinguishing adrenal metastases from benign lesions with multi-phase CT images. Heliyon 2024; 10:e25655. [PMID: 38371957 PMCID: PMC10873667 DOI: 10.1016/j.heliyon.2024.e25655] [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: 12/20/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 02/20/2024] Open
Abstract
Background Differentiating adrenal adenomas from metastases poses a significant challenge, particularly in patients with a history of extra-adrenal malignancy. This study investigates the performance of three-phase computed tomography (CT) based robust federal learning algorithm and traditional deep learning for distinguishing metastases from benign adrenal lesions. Material and methods This retrospective analysis includes 1187 instances who underwent three-phase CT scans between January 2008 and March 2021, comprising 720 benign lesions and 467 metastases. Utilizing the three-phase CT images, both a Robust Federal Learning Signature (RFLS) and a traditional Deep Learning Signature (DLS) were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Their diagnostic capabilities were subsequently validated and compared using metrics such as the Areas Under the Receiver Operating Curve (AUCs), Net Reclassification Improvement (NRI), and Decision Curve Analysis (DCA). Results Compared with DLS, the RFLS showed better capability in distinguishing metastases from benign adrenal lesions (average AUC: 0.816 vs.0.798, NRI = 0.126, P < 0.072; 0.889 vs.0.838, NRI = 0.209, P < 0.001; 0.903 vs.0.825, NRI = 0.643, p < 0.001) in the four-testing cohort, respectively. DCA showed that the RFLS added more net benefit than DLS for clinical utility. Moreover, Comparison with state-of-the-art federal learning methods, the results once again confirmed that the RFLS significantly improved the diagnostic performance based on three-phase CT (AUC: AP, 0.727 vs. 0.757 vs. 0.739 vs. 0.796; PCP, 0.781 vs. 0.851 vs. 0.790 vs. 0.882; VP, 0.789 vs. 0.814 vs. 0.779 vs. 0.886). Conclusion RFLS was superior to DLS for preoperative distinguishing metastases from benign adrenal lesions with multi-phase CT Images.
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Affiliation(s)
- Bao Feng
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
- Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, 541004, China
| | - Changyi Ma
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
| | - Yu liu
- Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, 541004, China
| | - Qinghui Hu
- Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, 541004, China
| | - Yan Lei
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
| | - Meiqi Wan
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, 518035, China
| | - Jin Cui
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China
- Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, Guangzhou, 510620, China
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Ma C, Feng B, Lin F, Lei Y, Xu K, Cui J, Liu Y, Long W, Cui E. Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists. Eur J Radiol 2023; 169:111169. [PMID: 37956572 DOI: 10.1016/j.ejrad.2023.111169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 10/05/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVES To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions. MATERIALS AND METHODS This retrospective two-center study included 1146 adrenal lesions from 1059 patients who underwent multiphase CT scanning between January 2008 and March 2021. The study encompassed 564 surgically confirmed adenomas, along with 135 benign lesions and 447 metastases confirmed by observation. DL models based on multiphase CT images were developed, validated and tested. The diagnostic performance of the classification models, imaging phases and radiologists with or without DL were compared using accuracy (ACC) and receiver operating characteristic (ROC) curves. Integrated discrimination improvement (IDI) analysis and the DeLong test were used to compare the area under the curve (AUC) among models. Decision curve analysis (DCA) was used to assess the clinical usefulness of the predictive models. RESULTS The DL signature based on LASSO (DLSL) had a higher AUC than that of the other classification models (IDI > 0, P < 0.05). Furthermore, the precontrast phase (PCP)-based DLSL performed best in the independent external validation (AUC = 0.881, ACC = 82.9 %) and clinical test cohorts (AUC = 0.790, ACC = 70.4 %), outperforming DLSL based on the other single-phase or three-phase images (IDI > 0, P < 0.05). DCA demonstrated that PCP-based DLSL provided a higher net benefit (0.01-0.95). The diagnostic performance led to statistically significant improvements when radiologists incorporated the DL model, with the AUC improving by 0.056-0.159 and the ACC improving by 0.069-0.178 (P < 0.05). CONCLUSION The DL model based on PCP CT images performed acceptably in differentiating adrenal metastases from benign lesions, and it may assist radiologists in accurate tumor staging for patients with a history of extra-adrenal malignancy.
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Affiliation(s)
- Changyi Ma
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen 529030, PR China
| | - Bao Feng
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin 541000, PR China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, PR China
| | - Yan Lei
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi 563006, PR China
| | - Kuncai Xu
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin 541000, PR China
| | - Jin Cui
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen 529030, PR China
| | - Yu Liu
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin 541000, PR China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen 529030, PR China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen 529030, PR China; Zunyi Medical University, 1 Xiaoyuan Road, Zunyi 563006, PR China; Guangdong Medical University, 2 Wenming East Road, 524023, PR China; Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, PR China.
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Stanzione A, Cuocolo R, Bombace C, Pesce I, Mainolfi CG, De Giorgi M, Delli Paoli G, La Selva P, Petrone J, Camera L, Klain M, Del Vecchio S, Cuocolo A, Maurea S. Prediction of 2-[ 18F]FDG PET-CT SUVmax for Adrenal Mass Characterization: A CT Radiomics Feasibility Study. Cancers (Basel) 2023; 15:3439. [PMID: 37444549 DOI: 10.3390/cancers15133439] [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: 04/17/2023] [Revised: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Indeterminate adrenal masses (AM) pose a diagnostic challenge, and 2-[18F]FDG PET-CT serves as a problem-solving tool. Aim of this study was to investigate whether CT radiomics features could be used to predict the 2-[18F]FDG SUVmax of AM. METHODS Patients with AM on 2-[18F]FDG PET-CT scan were grouped based on iodine contrast injection as CT contrast-enhanced (CE) or CT unenhanced (NCE). Two-dimensional segmentations of AM were manually obtained by multiple operators on CT images. Image resampling and discretization (bin number = 16) were performed. 919 features were calculated using PyRadiomics. After scaling, unstable, redundant, and low variance features were discarded. Using linear regression and the Uniform Manifold Approximation and Projection technique, a CT radiomics synthetic value (RadSV) was obtained. The correlation between CT RadSV and 2-[18F]FDG SUVmax was assessed with Pearson test. RESULTS A total of 725 patients underwent PET-CT from April 2020 to April 2021. In 150 (21%) patients, a total of 179 AM (29 bilateral) were detected. Group CE consisted of 84 patients with 108 AM (size = 18.1 ± 4.9 mm) and Group NCE of 66 patients with 71 AM (size = 18.5 ± 3.8 mm). In both groups, 39 features were selected. No statisticallyf significant correlation between CT RadSV and 2-[18F]FDG SUVmax was found (Group CE, r = 0.18 and p = 0.058; Group NCE, r = 0.13 and p = 0.27). CONCLUSIONS It might not be feasible to predict 2-[18F]FDG SUVmax of AM using CT RadSV. Its role as a problem-solving tool for indeterminate AM remains fundamental.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Baronissi, Italy
| | - Claudia Bombace
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Ilaria Pesce
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Ciro Gabriele Mainolfi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Marco De Giorgi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Gregorio Delli Paoli
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Pasquale La Selva
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Jessica Petrone
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Luigi Camera
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Michele Klain
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
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Wang G, Kang B, Cui J, Deng Y, Zhao Y, Ji C, Wang X. Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study. Eur Radiol 2023; 33:1873-1883. [PMID: 36264313 DOI: 10.1007/s00330-022-09182-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 12/07/2022]
Abstract
OBJECTIVES To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population. METHODS This retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong's test, calibration curve, and decision curve. RESULTS Lesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits. CONCLUSION Unenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations. KEY POINTS • All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population's adrenal benign lesions and metastases. • Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models. • Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.
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Affiliation(s)
- Gongzheng Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Jingjing Cui
- United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100094, China
| | - Yan Deng
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Yun Zhao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Congshan Ji
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China. .,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China. .,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
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Zhu H, Wu M, Wei P, Tian M, Zhang T, Hu C, Han Z. A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study. Front Oncol 2023; 13:1086039. [PMID: 37152026 PMCID: PMC10154461 DOI: 10.3389/fonc.2023.1086039] [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: 11/03/2022] [Accepted: 04/05/2023] [Indexed: 05/09/2023] Open
Abstract
Objective This study aimed to investigate the application of modified region-of-interest (ROI) segmentation method in unenhanced computed tomography in the radiomics model of adrenal lipid-poor adenoma, and to evaluate the diagnostic performance using an external medical institution data set and select the best ROI segmentation method. Methods The imaging data of 135 lipid-poor adenomas and 102 non-adenomas in medical institution A and 30 lipid-poor adenomas and 43 non-adenomas in medical institution B were retrospectively analyzed, and all cases were pathologically or clinically confirmed. The data of Institution A builds the model, and the data of Institution B verifies the diagnostic performance of the model. Semi-automated ROI segmentation of tumors was performed using uAI software, using maximum area single-slice method (MAX) and full-volume method (ALL), as well as modified single-slice method (MAX_E) and full-volume method (ALL_E) to segment tumors, respectively. The inter-rater correlation coefficients (ICC) was performed to assess the stability of the radiomics features of the four ROI segmentation methods. The area under the curve (AUC) and at least 95% specificity pAUC (Partial AUC) were used as measures of the diagnostic performance of the model. Results A total of 104 unfiltered radiomics features were extracted using each of the four segmentation methods. In the ROC analysis of the radiomics model, the AUC value of the model constructed by MAX was 0.925, 0.919, and 0.898 on the training set, the internal validation set, and the external validation set, respectively, and the AUC value of MAX_E was 0.937, 0.931, and 0.906, respectively. The AUC value of ALL was 0.929, 0.929, and 0.918, and the AUC value of ALL_E was 0.942, 0.926, and 0.927, respectively. In all samples, the pAUCs of MAX, MAX_E, ALL, and ALL_E were 0.021, 0.025, 0.018, and 0.028, respectively. Conclusion The diagnostic performance of the radiomics model constructed based on the full-volume method was better than that of the model based on the single-slice method. The model constructed using the ALL_E method had a stronger generalization ability and the highest AUC and pAUC value.
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Affiliation(s)
- Hanlin Zhu
- Department of Radiology, Hangzhou Ninth People’s Hospital, Hangzhou, China
| | - Mengwei Wu
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Peiying Wei
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Tian
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tong Zhang
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunfeng Hu
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhijiang Han,
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Cao L, Xu W. Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article. Medicine (Baltimore) 2022; 101:e30856. [PMID: 36197274 PMCID: PMC9509040 DOI: 10.1097/md.0000000000030856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-operative differentiation of "early stage" adrenal metastases from lipid-poor adenomas (LPAs). This retrospective study included 188 patients who underwent abdominal CECT (training cohort: LPAs, 68; metastases, 64; validation cohort: LPAs, 29; metastases, 27). Abdominal CECT included plain, arterial, portal, and venous imaging. Clinical and CECT radiological features were assessed and significant features were selected. Radiomic features of the adrenal lesions were extracted from four-phase CECT images. Significant radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. The clinical-radiological, unenhanced radiomics, arterial radiomics, portal radiomics, venous radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher than those of the clinical-radiological model (0.788) in the testing dataset. The AUC of the combined radiomics model (incorporating plain and venous radiomics features) was further improved to 0.953, which was significantly higher than portal radiomics model (P = .033) and clinical-radiological model (P = .009), with the highest accuracy (89.13%) and a relatively stable sensitivity (91.67%) and specificity (86.36%). As the optimal model, the combined radiomics model based on biphasic CT images is effective enough to differentiate "early stage" adrenal metastases from LPAs by reducing the radiation dose.
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Affiliation(s)
- Lixiu Cao
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin, China
- Department of ECT, Tangshan People’s Hospital, Tangshan, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin, China
- *Correspondence: Wengui Xu, Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, No. 1 Huanhu West Road, Hexi District, Tianjin 300060, China (e-mail: )
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Zhang H, Lei H, Pang J. Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis. Front Oncol 2022; 12:975183. [PMID: 36119492 PMCID: PMC9478189 DOI: 10.3389/fonc.2022.975183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives(1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.MethodsPubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman’s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks’ funnel plot.ResultsTwenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93–0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85–0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.ConclusionsThe methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .
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O'Shea A, Kilcoyne A, McDermott E, O'Grady M, McDermott S. Can radiomic feature analysis differentiate adrenal metastases from lipid-poor adenomas on single-phase contrast-enhanced CT abdomen? Clin Radiol 2022; 77:e711-e718. [DOI: 10.1016/j.crad.2022.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022]
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Crimì F, Quaia E, Cabrelle G, Zanon C, Pepe A, Regazzo D, Tizianel I, Scaroni C, Ceccato F. Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review. Int J Mol Sci 2022; 23:ijms23020637. [PMID: 35054823 PMCID: PMC8776161 DOI: 10.3390/ijms23020637] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 12/19/2022] Open
Abstract
Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.
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Affiliation(s)
- Filippo Crimì
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Emilio Quaia
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Giulio Cabrelle
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Chiara Zanon
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Alessia Pepe
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Daniela Regazzo
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
| | - Irene Tizianel
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
| | - Carla Scaroni
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
| | - Filippo Ceccato
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
- Correspondence: ; Tel.: +39-049-8211323; Fax: +39-049-657391
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