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Sun K, Yu S, Wang Y, Jia R, Shi R, Liang C, Wang X, Wang H. Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor. BMC Med Imaging 2024; 24:44. [PMID: 38355484 PMCID: PMC10868069 DOI: 10.1186/s12880-024-01219-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the model's performance. METHODS Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One experienced radiologist manually delineated the volume of interest and then resampled the voxel size of the images to 0.5 × 0.5 × 0.5 mm3, 1 × 1 × 1 mm3, and 2 × 2 × 2 mm3, respectively. Radiomics features were extracted using PyRadiomics, resulting in 1218 features from each phase image. The datasets were randomly divided into training set (n = 66) and validation set (n = 28) at a 7:3 ratio. After applying multiple feature selection methods, the optimal features were screened. Radial basis kernel function-based support vector machine (RBF-SVM) was used as the classifier, and model performance was evaluated using the area under the receiver operating curve (AUC) analysis, as well as accuracy, sensitivity, and specificity. RESULTS The combined phase model performed better than the other phase models, and the resampling method of 0.5 × 0.5 × 0.5 mm3 achieved the highest performance with an AUC of 0.953 (0.881-1), accuracy of 0.929, sensitivity of 0.938, and specificity of 0.917 in the validation set. The Delong test showed no significant difference in AUCs among the three resampling methods, with p > 0.05. CONCLUSIONS Radiomics can effectively differentiate between HP and GISTs on CT images, and the diagnostic performance of radiomics is minimally affected by different resampling methods.
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Affiliation(s)
- Kui Sun
- Department of General Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, 100191, Beijing, China
| | - Shuxia Yu
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, No. 324, 250021, Jinan, China
| | - Ying Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, NO. 324, 250021, Jinan, Shandong, China
| | - Rongze Jia
- Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Shandong Provincial Hospital of Traditional Chinese Medicine, Jing Shi Road, No. 16369, 250014, Jinan, China
| | - Rongchao Shi
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing Wu Road, No. 324, 250021, Jinan, China
| | - Changhu Liang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, NO. 324, 250021, Jinan, Shandong, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, NO. 324, 250021, Jinan, Shandong, China.
| | - Haiyan Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, NO. 324, 250021, Jinan, Shandong, China.
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Yang Y, Chen F, Liang H, Bai Y, Wang Z, Zhao L, Ma S, Niu Q, Li F, Xie T, Cai Y. CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors. Front Oncol 2023; 13:1166988. [PMID: 37333811 PMCID: PMC10272725 DOI: 10.3389/fonc.2023.1166988] [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: 02/15/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Objective To investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis. Materials and methods From 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images). Manual segmentation was the gold standard and serves as ground truth. Seven CNN-based models including DeepLabV3+, UNet, UNet++, UNet3+, SegNet, MultilResUNet and Attention UNet were used for automatic segmentation. Python 3.7.0 and Pyradiomics package 3.0.1 were used for radiomic feature extraction. Performance of all approaches was evaluated by the metrics of mean intersection over union (mIOU), dice similarity coefficient (DSC), precision, and recall. Reliability and reproducibility of radiomics features were evaluated by the Pearson coefficient and the intraclass correlation coefficient (ICC). Results All seven CNN-based models achieved good performance with the mIOU, DSC, precision and recall ranging between 81.97%-93.04%, 78.67%-92.70%, 93.92%-97.56%, and 85.29%-95.17%, respectively. The average Pearson coefficients ranged from 0.81 to 0.95, and the average ICCs ranged from 0.77 to 0.92. The UNet++ model showed the best performance with the mIOU, DSC, precision and recall of 93.04%, 92.70%, 97.43% and 95.17%, respectively. For ccRCC, AML and other subtypes, the reliability and reproducibility of radiomic analysis derived from automatically segmented CEUS images were excellent, with the average Pearson coefficients of 0.95, 0.96 and 0.96, and the average ICCs for different subtypes were 0.91, 0.93 and 0.94, respectively. Conclusion This retrospective single-center study showed that the CNN-based models had good performance on automatic segmentation of CEUS images for renal tumors, especially the UNet++ model. The radiomics features extracted from automatically segmented CEUS images were feasible and reliable, and further validation by multi-center research is necessary.
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Affiliation(s)
- Yin Yang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fei Chen
- Department of Pediatrics, Jiahui International Hospital, Shanghai, China
| | - Hongmei Liang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Bai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Wang
- School of Computer Science and Technology, Taiyuan Normal University, Taiyuan, China
| | - Lei Zhao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sai Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinghua Niu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianwu Xie
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Yingyu Cai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Brunese MC, Fantozzi MR, Fusco R, De Muzio F, Gabelloni M, Danti G, Borgheresi A, Palumbo P, Bruno F, Gandolfo N, Giovagnoni A, Miele V, Barile A, Granata V. Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma. Diagnostics (Basel) 2023; 13:diagnostics13081488. [PMID: 37189589 DOI: 10.3390/diagnostics13081488] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. METHODS The PubMed database was searched for papers published in the English language no earlier than October 2022. RESULTS We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. CONCLUSIONS It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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Affiliation(s)
- Maria Chiara Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100 Campobasso, Italy
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100 Campobasso, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria delle Marche", 60121 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L'Aquila, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L'Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria delle Marche", 60121 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy
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