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Xu ZY, Huang LW, Yang YJ, Cai ZP, Chen ML, Lu RL, Ouyang YX, Hong ZK, Huang WJ, Xu ZF. Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: A multicenter study. Cancer Med 2024; 13:e7407. [PMID: 38899534 DOI: 10.1002/cam4.7407] [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: 02/29/2024] [Revised: 06/03/2024] [Accepted: 06/07/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVES To investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation. METHODS From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application. RESULTS Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145). CONCLUSIONS ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.
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Affiliation(s)
- Zhen-Yu Xu
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Lin-Wen Huang
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Yun-Jun Yang
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Zhi-Ping Cai
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Mei-Lin Chen
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Rui-Liang Lu
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Yong-Xi Ouyang
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Zhen-Kai Hong
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Wei-Jun Huang
- Department of Ultrasound, The First People's Hospital of Foshan, Foshan, China
| | - Zhi-Feng Xu
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
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Lu Y, Liu H, Liu Q, Wang S, Zhu Z, Qiu J, Xing W. CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors. Front Oncol 2023; 13:1118351. [PMID: 36969052 PMCID: PMC10036756 DOI: 10.3389/fonc.2023.1118351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/23/2023] [Indexed: 03/12/2023] Open
Abstract
ObjectiveThis study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models.MethodsIn this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer–Lemeshow test. DeLong’s test was utilized for comparing the AUCs.ResultsThe radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively.ConclusionOur study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.
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Chen F, Ge Y, Li S, Liu M, Wu J, Liu Y. Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland. Dentomaxillofac Radiol 2023; 52:20220009. [PMID: 36367128 PMCID: PMC9974237 DOI: 10.1259/dmfr.20220009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA). METHODS A total of 189 patients with PA (n = 112), WT (n = 53) and BCA (n = 24) were divided into a training set (n = 133) and a test set (n = 56). The radiomics features were extracted from plain CT and contrast-enhanced CT images. After dimensionality reduction, plain CT, multiphase-enhanced CT, integrated radiomics signature models and radiomics score (Rad-score) were established and calculated. The receiver operating characteristic (ROC) curve analysis was taken for the assessment of the model performance, and then comparison was conducted among these models. Decision curve analysis (DCA) was adopted to assess the clinical benefits of the models. Diagnostic performances including sensitivity, specificity, and accuracy of the radiologists were evaluated. RESULTS Seven, nine, fourteen, and fourteen optimal features were used to constructed plain scan, arterial phase, venous phase, and integrated radiomics signature models, respectively. ROC analysis showed these four models were able to differentiate PA from BCA and WT, with the area under the ROC curve (AUC) values of 0.79, 0.90, 0.87, and 0.94 in the training set, and 0.79, 0.89, 0.86, and 0.94 in the test set, respectively. The integrated model had better diagnostic performance than single-phase radiomics model, but it had similar diagnostic performance to that of the radiomics model based on the arterial phase (p > 0.05). The sensitivity, specificity, and accuracy in the diagnosis of PA were 0.86, 0.46, and 0.70 for the non-subspecialized radiologist and 0.88, 0.77, and 0.84 for the subspecialized radiologist, respectively. Six venous phase parameters were finally selected in differentiating WT from BCA. The predictive effect of the model was favorable, with AUC value of 0.95, sensitivity of 0.96, specificity of 0.83, and accuracy of 0.92. The sensitivity, specificity, and accuracy in the diagnosis between WT and BCA were 0.26, 0.87, and 0.45 for the non-subspecialized radiologist and 0.85, 0.58, and 0.77 for the subspecialized radiologist, respectively. CONCLUSION The CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.
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Affiliation(s)
- Fangfang Chen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | | | - Shuang Li
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Mengqiu Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Jiaoyan Wu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Ying Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
- GE Healthcare, Shanghai, China
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Correspondence:
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The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment. Diagnostics (Basel) 2022; 12:diagnostics12123002. [PMID: 36553009 PMCID: PMC9777175 DOI: 10.3390/diagnostics12123002] [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: 10/24/2022] [Revised: 11/16/2022] [Accepted: 11/29/2022] [Indexed: 12/04/2022] Open
Abstract
Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.
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Ibrahim A, Lu L, Yang H, Akin O, Schwartz LH, Zhao B. The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model. APPLIED SCIENCES (BASEL, SWITZERLAND) 2022; 12:9824. [PMID: 37091743 PMCID: PMC10121203 DOI: 10.3390/app12199824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
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Affiliation(s)
- Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Correspondence:
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
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Zheng YL, Zheng YN, Li CF, Gao JN, Zhang XY, Li XY, Zhou D, Wen M. Comparison of Different Machine Models Based on Multi-Phase Computed Tomography Radiomic Analysis to Differentiate Parotid Basal Cell Adenoma From Pleomorphic Adenoma. Front Oncol 2022; 12:889833. [PMID: 35903689 PMCID: PMC9315155 DOI: 10.3389/fonc.2022.889833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveThis study explored the value of different radiomic models based on multiphase computed tomography in differentiating parotid pleomorphic adenoma (PA) and basal cell tumor (BCA) concerning the predominant phase and the optimal radiomic model.MethodsThis study enrolled 173 patients with pathologically confirmed parotid tumors (training cohort: n=121; testing cohort: n=52). Radiomic features were extracted from the nonenhanced, arterial, venous, and delayed phases CT images. After dimensionality reduction and screening, logistic regression (LR), K-nearest neighbor (KNN) and support vector machine (SVM) were applied to develop radiomic models. The optimal radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was performed to analyze clinical-radiological characteristics and to identify variables for developing a clinical model. A combined model was constructed by integrating clinical and radiomic features. Model performances were assessed by ROC curve analysis.ResultsA total of 1036 radiomic features were extracted from each phase of CT images. Sixteen radiomic features were considered valuable by dimensionality reduction and screening. Among radiomic models, the SVM model of the arterial and delayed phases showed superior predictive efficiency and robustness (AUC, training cohort: 0.822, 0.838; testing cohort: 0.752, 0.751). The discriminatory capability of the combined model was the best (AUC, training cohort: 0.885; testing cohort: 0.834).ConclusionsThe diagnostic performance of the arterial and delayed phases contributed more than other phases. However, the combined model demonstrated excellent ability to distinguish BCA from PA, which may provide a non-invasive and efficient method for clinical decision-making.
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Affiliation(s)
- Yun-lin Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi-neng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chuan-fei Li
- Department of Gastroenterology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jue-ni Gao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin-yu Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin-yi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Di Zhou
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Di Zhou, ; Ming Wen,
| | - Ming Wen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Di Zhou, ; Ming Wen,
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Yu Q, Wang A, Gu J, Li Q, Ning Y, Peng J, Lv F, Zhang X. Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors. Front Oncol 2022; 12:913898. [PMID: 35847942 PMCID: PMC9280642 DOI: 10.3389/fonc.2022.913898] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). Methods This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. Results Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). Conclusion Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.
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Affiliation(s)
- Qiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Anran Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinming Gu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Quanjiang Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Youquan Ning
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Juan Peng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Juan Peng,
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Wen B, Zhang Z, Zhu J, Liu L, Li Y, Huang H, Zhang Y, Cheng J. Apparent Diffusion Coefficient Map–Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors. Front Oncol 2022; 12:830496. [PMID: 35747827 PMCID: PMC9210443 DOI: 10.3389/fonc.2022.830496] [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: 12/07/2021] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe magnetic resonance imaging (MRI) findings may overlap due to the complex content of parotid gland tumors and the differentiation level of malignant tumor (MT); consequently, patients may undergo diagnostic lobectomy. This study assessed whether radiomics features could noninvasively stratify parotid gland tumors accurately based on apparent diffusion coefficient (ADC) maps.MethodsThis study examined diffusion-weighted imaging (DWI) obtained with echo planar imaging sequences. Eighty-eight benign tumors (BTs) [54 pleomorphic adenomas (PAs) and 34 Warthin tumors (WTs)] and 42 MTs of the parotid gland were enrolled. Each case was randomly divided into training and testing cohorts at a ratio of 7:3 and then was compared with each other, respectively. ADC maps were digitally transferred to ITK SNAP (www.itksnap.org). The region of interest (ROI) was manually drawn around the whole tumor margin on each slice of ADC maps. After feature extraction, the Synthetic Minority Oversampling TEchnique (SMOTE) was used to remove the unbalance of the training dataset. Then, we applied the normalization process to the feature matrix. To reduce the similarity of each feature pair, we calculated the Pearson correlation coefficient (PCC) value of each feature pair and eliminated one of them if the PCC value was larger than 0.95. Then, recursive feature elimination (RFE) was used to process feature selection. After that, we used linear discriminant analysis (LDA) as the classifier. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the ADC.ResultsThe LDA model based on 13, 8, 3, and 1 features can get the highest area under the ROC curve (AUC) in differentiating BT from MT, PA from WT, PA from MT, and WT from MT on the validation dataset, respectively. Accordingly, the AUC and the accuracy of the model on the testing set achieve 0.7637 and 73.17%, 0.925 and 92.31%, 0.8077 and 75.86%, and 0.5923 and 65.22%, respectively.ConclusionThe ADC-based radiomics features may be used to assist clinicians for differential diagnosis of PA and WT from MTs.
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Affiliation(s)
- Baohong Wen
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zanxia Zhang
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Zhu
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Liang Liu
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yinhua Li
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haoyu Huang
- Advanced Technical Support, Philips Healthcare, Shanghai, China
| | - Yong Zhang
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Jingliang Cheng,
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CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors. Eur Radiol 2022; 32:6953-6964. [PMID: 35484339 DOI: 10.1007/s00330-022-08830-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/03/2022] [Accepted: 04/20/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES This study aimed to explore and validate the value of different radiomics models for differentiating benign and malignant parotid tumors preoperatively. METHODS This study enrolled 388 patients with pathologically confirmed parotid tumors (training cohort: n = 272; test cohort: n = 116). Radiomics features were extracted from CT images of the non-enhanced, arterial, and venous phases. After dimensionality reduction and selection, radiomics models were constructed by logistic regression (LR), support vector machine (SVM), and random forest (RF). The best radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. A combined model was constructed by incorporating radiomics and clinical features. Model performances were assessed by ROC curve analysis, and decision curve analysis (DCA) was used to estimate the models' clinical values. RESULTS In total, 2874 radiomic features were extracted from CT images. Ten radiomics features were deemed valuable by dimensionality reduction and selection. Among radiomics models, the SVM model showed greater predictive efficiency and robustness, with AUCs of 0.844 in the training cohort; and 0.840 in the test cohort. Ultimate clinical features constructed a clinical model. The discriminatory capability of the combined model was the best (AUC, training cohort: 0.904; test cohort: 0.854). Combined model DCA revealed optimal clinical efficacy. CONCLUSIONS The combined model incorporating radiomics and clinical features exhibited excellent ability to distinguish benign and malignant parotid tumors, which may provide a noninvasive and efficient method for clinical decision making. KEY POINTS The current study is the first to compare the value of different radiomics models (LR, SVM, and RF) for preoperative differentiation of benign and malignant parotid tumors. A CT-based combined model, integrating clinical-radiological and radiomics features, is conducive to distinguishing benign and malignant parotid tumors, thereby improving diagnostic performance and aiding treatment.
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors. Curr Opin Otolaryngol Head Neck Surg 2021; 30:107-113. [PMID: 34907957 DOI: 10.1097/moo.0000000000000782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. RECENT FINDINGS The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy. SUMMARY All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.
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Zhao J, Sun L, Sun K, Wang T, Wang B, Yang Y, Wu C, Sun X. Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule. Front Oncol 2021; 11:759840. [PMID: 34858836 PMCID: PMC8630666 DOI: 10.3389/fonc.2021.759840] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/18/2021] [Indexed: 01/11/2023] Open
Abstract
Objective To establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability. Materials and Methods A total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit. Results We extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors-maximum diameter, lobulation, and pleural retraction-were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram. Conclusions The radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.
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Affiliation(s)
- Jiabi Zhao
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lin Sun
- Department of Radiation Medicine, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Ke Sun
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Tingting Wang
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Bin Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Yang
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
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Lv Y, Xie X, Pu L, Wang Q, Pu S, Ai C, Liu Y, Chen J, Xiang B. Molecular Characteristics of Choledochal Cysts in Children: Transcriptome Sequencing. Front Genet 2021; 12:709340. [PMID: 34413880 PMCID: PMC8369577 DOI: 10.3389/fgene.2021.709340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/01/2021] [Indexed: 02/05/2023] Open
Abstract
A choledochal cyst (CC) is a common congenital biliary disease in children, yet the underlying molecular bases for the cystic and fusiform clinical subtypes are unknown. RNA sequencing (RNA-seq) has been performed on 22 high-quality CC samples, including 12 cystic CC and 10 fusiform CC samples, to search for molecular features. Weighted gene co-expression network analysis (WGCNA) was performed to identify key modules associated with clinical subtypes. Bioinformatic analyses were conducted to elucidate potential mechanisms. Then, we constructed protein–protein interaction (PPI) networks to identify candidate hub genes related to CC. Finally, we used the support vector machine (SVM) to eliminate redundant features and screen out the hub genes. The selected gene expression was determined in CC patients through quantitative real-time polymerase chain reaction (PCR). A total of 6,463 genes were found to be aberrantly expressed between cystic CC and fusiform CC. Twelve co-expression modules that correlated with clinical subtypes of CC were identified and assigned representative colors. Among the 12 modules, the blue module was considered the key module. Two functionally distinct sets of dysregulated genes have been identified in two major subtypes, metabolism-related genes in cystic CC and immune-related genes in fusiform CC. A total of 20 candidate hub genes that were correlated with clinical subtypes were found in the blue module. In addition, we found ERBB2 and WNT11 that have not been studied in CC and verified their differential expression in CC through quantitative real-time PCR experiments. For the first time, we have described the transcriptome characteristics of CC. These results suggest that cystic CC and fusiform CC have different molecular mechanisms. The bi-omics-identified novel candidate genes and pathways might be helpful for personalized treatment and are of great clinical significance for CC.
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Affiliation(s)
- Yong Lv
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaolong Xie
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Lihui Pu
- Department of Critical Care, West China Hospital, Sichuan University, Chengdu, China
| | - Qi Wang
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Siyu Pu
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Chengbo Ai
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Liu
- Department of Rheumatology and Immunology, Rare Diseases Center, Institute of Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Chen
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Xiang
- Laboratory of Pediatric Surgery, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, China
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Wei P, Shao C, Tian M, Wu M, Wang H, Han Z, Hu H. Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors. Cancer Manag Res 2021; 13:5423-5431. [PMID: 34262350 PMCID: PMC8275037 DOI: 10.2147/cmar.s319466] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 06/25/2021] [Indexed: 11/29/2022] Open
Abstract
Objective To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors. Materials and Methods MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs. Results The area under the curve (AUC) of tumor to parotid gland SIR (SIRP), tumor to spinal cord SIR (SIRC), and tumor to buccal subcutaneous fat SIR (SIRF) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIRP, SIRC, and SIRF for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIRP, SIRC, and SIRF for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively. Conclusion SIRP, SIRC, and SIRF on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIRP and SIRC for differentiating between PAs and MTs, and SIRF for differentiating between WTs and MTs had relatively high diagnostic efficiency.
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Affiliation(s)
- Peiying Wei
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.,Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Chang Shao
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Min Tian
- The Fourth Clinical Medical College, Zhejiang Traditional Chinese Medicine University, Hangzhou, People's Republic of China
| | - Mengwei Wu
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, People's Republic of China
| | - Haibin Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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