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Shi L, Zhao J, Wei Z, Wu H, Sheng M. Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis. Front Oncol 2024; 14:1381217. [PMID: 39381037 PMCID: PMC11458374 DOI: 10.3389/fonc.2024.1381217] [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: 02/09/2024] [Accepted: 09/05/2024] [Indexed: 10/10/2024] Open
Abstract
Objectives The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412851, identifier CRD42023412851.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China
| | - Huiqun Wu
- Medical School, Nantong University, Nantong, China
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People’s Hospital, Nantong, China
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Wang N, Dai M, Jing F, Liu Y, Zhao Y, Zhang Z, Wang J, Zhang J, Wang Y, Zhao X. Value of 18F-FDG PET/CT-based radiomics features for differentiating primary lung cancer and solitary lung metastasis in patients with colorectal adenocarcinoma. Int J Radiat Biol 2024:1-9. [PMID: 39288285 DOI: 10.1080/09553002.2024.2404465] [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: 03/30/2024] [Revised: 08/20/2024] [Accepted: 09/03/2024] [Indexed: 09/19/2024]
Abstract
OBJECTIVE To investigate the value and applicability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in differentiating primary lung cancer (PLC) from solitary lung metastasis (SLM) in patients with colorectal cancer (CRC). MATERIALS AND METHODS This retrospective study included 103 patients with CRC and solitary pulmonary nodules (SPNs). The least absolute shrinkage and selection operator (LASSO) was used to screen for optimal radiomics features and establish a PET/CT radiomics model. PET/CT Visual and complex models (combining radiomics with PET/CT visual features) were developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the predictive value and diagnostic efficiency of the models. RESULTS The AUC of the PET/CT radiomics model for differentiating PLC from SLM was 0.872 (95% CI: 0.806-0.939), which was not different from that of the visual (0.829 [95% CI: 0.749-0.908; p = .352]). However, the AUC of the complex model (0.936 [95% CI:0.892-0.981]) was significantly higher than that of the PET/CT radiomics (p = .005) and visual model (p = .001). The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of PET/CT radiomics for differentiating PLC from SLM were 0.720, 0.887, 0.806, 0.857, and 0.770, respectively. CONCLUSION PET/CT radiomics can effectively distinguish PLC and SLM in patients with CRC and SPNs and guide the implementation of personalized treatment.
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Affiliation(s)
- Na Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei, China
| | - Meng Dai
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei, China
| | - Fenglian Jing
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yunuan Liu
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yan Zhao
- Department of Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei, China
| | - Yingchen Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei, China
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Liang CH, Liu YC, Wan YL, Yun CH, Wu WJ, López-González R, Huang WM. Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images. Cancers (Basel) 2021; 13:cancers13225600. [PMID: 34830759 PMCID: PMC8615829 DOI: 10.3390/cancers13225600] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/28/2021] [Accepted: 11/05/2021] [Indexed: 01/23/2023] Open
Abstract
Simple Summary Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer. Traditional risk factors including age, male gender, smoking status, and emphysema have been reported. However, there are only limited data on radiomics features from HRCT images useful for risk stratification of IPF patients for lung cancer. In this study, we found that texture-based radiomics features can be differentiated between IPF patients with and without cancer development, and their diagnostic accuracy is not inferior to that of traditional risk factors. By combining radiomics features and traditional risk factors, the diagnostic accuracy can be improved. Abstract Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66–0.73 (sensitivity of 80.0–85.0% and specificity of 54.2–59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients.
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Affiliation(s)
- Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei City 112, Taiwan;
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan
| | - Yung-Chi Liu
- Department of Diagnostic Radiology, Xiamen Chang Gung Hospital, Xiamen 361028, China;
- Department of Imaging Technology Division, Xiamen Chang Gung Hospital, Xiamen 361028, China
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
| | - Chun-Ho Yun
- Department of Radiology, Mackay Memorial Hospital, Taipei City 104, Taiwan;
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
| | - Wen-Jui Wu
- Division of Pulmonary and Critical Care Medicine, Mackay Memorial Hospital, Taipei City 104, Taiwan;
| | | | - Wei-Ming Huang
- Department of Radiology, Mackay Memorial Hospital, Taipei City 104, Taiwan;
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
- Correspondence:
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Shen H, Chen L, Liu K, Zhao K, Li J, Yu L, Ye H, Zhu W. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes. Quant Imaging Med Surg 2021; 11:2918-2932. [PMID: 34249623 DOI: 10.21037/qims-20-1182] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/03/2021] [Indexed: 01/06/2023]
Abstract
Background This study classifies lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion-based radiomics features extracted from positron emission tomography/computed tomography (PET/CT) images. Methods In this study, the standard 18F-fluorodeoxyglucose (FDG) PET/CT images of 150 patients with lung ADC and 100 patients with SCC were retrospectively collected from the PET Center of the First Affiliated Hospital, College of Medicine, Zhejiang University. First, the 3D feature vector of each tumor voxel (whose basis is PET value, CT value, and CT local dominant orientation) was extracted. Using K-means individual clustering and population clustering, each tumor was divided into 4 subregions that reflect intratumoral regional heterogeneity. Next, based on each subregion, 385 radiomics features were extracted. Clinical features including age, gender, and smoking history were included. Thus, there were a total of 1,543 features extracted from PET/CT images and clinical reports. Statistical tests were then used to eliminate irrelevant and redundant features, and the recursive feature elimination (RFE) algorithm was used to select the best feature subset to classify SCC and ADC. Finally, 7 types of classifiers were tested to achieve the optimized model for the classification: support vector machine (SVM) with linear kernel, SVM with radial basis function kernel (SVM-RBF), random forest, logistic regression, Gaussian process classifier, linear discriminant analysis, and the AdaBoost classifier. Furthermore, 5-fold cross-validation was applied to obtain the sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. Results Our model exhibited the best performance with the subregion radiomics features and SVM-RBF classifier, with a 5-fold cross-validation sensitivity, specificity, accuracy, and AUC of 0.8538, 0.8758, 0.8623, and 0.9155, respectively. The interquartile range feature from subregion 2 of CT and the gender feature from the clinical reports are the 2 optimized features that achieved the highest comprehensive score. Conclusions Our proposed model showed that SCC and ADC could be classified successfully using PET/CT images, which could be a promising tool to assist radiologists or medical physicists during diagnosis. The subregion-based method illustrated that non-small cell lung cancer (NSCLC) depicts intratumoral regional heterogeneity on both CT and PET images. By defining these heterogeneities through a subregion-based method, the diagnostic performance was improved. The 3D feature vector (whose basis is PET value, CT value, and CT local dominant orientation) showed superiority in reflecting NSCLC intratumoral regional heterogeneity.
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Affiliation(s)
- Hui Shen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Ling Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kanfeng Liu
- PET Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kui Zhao
- PET Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Lijuan Yu
- The Affiliated Cancer Hospital of Hainan Medical University, Haikou, China
| | - Hongwei Ye
- MinFound Medical System Co., Ltd, Shaoxing, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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Shao X, Niu R, Shao X, Jiang Z, Wang Y. Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules. EJNMMI Res 2020; 10:80. [PMID: 32661639 PMCID: PMC7359213 DOI: 10.1186/s13550-020-00668-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/02/2020] [Indexed: 11/12/2022] Open
Abstract
Background To establish and validate 18F-fluorodeoxyglucose (18F-FDG) PET/CT-based radiomics model and use it to predict the intermediate-high risk growth patterns in early invasive adenocarcinoma (IAC). Methods Ninety-three ground-glass nodules (GGNs) from 91 patients with stage I who underwent a preoperative 18F-FDG PET/CT scan and histopathological examination were included in this study. The LIFEx software was used to extract 52 PET and 49 CT radiomic features. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop radiomics signatures. We used the receiver operating characteristics curve (ROC) to compare the predictive performance of conventional CT parameters, radiomics signatures, and the combination of these two. Also, a nomogram based on conventional CT indicators and radiomics signature score (rad-score) was developed. Results GGNs were divided into lepidic group (n = 18) and acinar-papillary group (n = 75). Four radiomic features (2 for PET and 2 for CT) were selected to calculate the rad-score, and the area under the curve (AUC) of rad-score was 0.790, which was not significantly different as the attenuation value of the ground-glass opacity component on CT (CTGGO) (0.675). When rad-score was combined with edge (joint model), the AUC increased to 0.804 (95% CI [0.699–0.895]), but which was not significantly higher than CTGGO (P = 0.109). Furthermore, the decision curve of joint model showed higher clinical value than rad-score and CTGGO, especially under the purpose of screening for intermediate-high risk growth patterns. Conclusion PET/CT-based radiomics model shows good performance in predicting intermediate-high risk growth patterns in early IAC. This model provides a useful method for risk stratification, clinical management, and personalized treatment.
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Affiliation(s)
- Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Zhenxing Jiang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. .,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China.
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Wu G, Xie R, Li Y, Hou B, Morelli JN, Li X. Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor. Medicine (Baltimore) 2020; 99:e18742. [PMID: 31914093 PMCID: PMC6959892 DOI: 10.1097/md.0000000000018742] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
To investigate the feasibility of histogram analysis with computed tomography angiography (CTA) in distinguishing between soft tissue sarcomas and benign soft tissue tumors. Fourty nine patients (23 men, mean age = 44.3 years, age range = 25-64) with pathologically-confirmed soft tissue sarcoma (n = 24) or benign soft tissue tumors (n = 25) in the lower extremities undergoing CTA for tumor evaluation were retrospectively analyzed. Two radiologists separately performed histogram analyses of CT density with CTA images by drawing a region of interest (ROI). The 10th (P10), 25th (P25), 50th (P50), 75th (P75), 90th percentiles (P90), mean, and standard deviations (SD) of measured tumor density were obtained along with measurements of the absolute value of kurtosis (AVK), absolute value of skewness (AVS), and inhomogeneity for each tumor. Intra-class correlation coefficients (ICC) were calculated to determine inter- and intra-reader variability in parameter measurements. The Mann-Whitney U test was used to compare histogram parameters between soft tissue sarcomas and benign soft tissue tumors. Receiver operator characteristic (ROC) curves were constructed to evaluate the accuracy of tumor discrimination. ICC was greater than 0.7 for AVS, AVK, and inhomogeneity, and >0.9 for mean, SD, and all percentile measures. There was no significant difference in P10, P25, P50, P75, P90, mean, or SD between soft tissue sarcomas and benign tumors (P > .05). AVS, AVK, and inhomogeneity were significantly higher in soft tissue sarcomas (P < .05). Areas under the curve (AUC) were 0.81, 0.83, and 0.84 for AVS, AVK, and inhomogeneity respectively. AUC were below 0.6 for mean, SD, and all percentiles.Skewness, kurtosis, and inhomogeneity measurements derived from histogram analysis from CTA distinguish between soft tissue sarcomas and benign soft tissue tumors.
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Affiliation(s)
- Gang Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yitong Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bowen Hou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Xiaoming Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Maemoto H, Ishigami K, Iraha S, Arashiro K, Kusada T, Ganaha F, Murayama S. Analyses of size and computed tomography densitometry parameters for prediction of keloid recurrence after postoperative electron beam radiation therapy. Skin Res Technol 2019; 26:125-131. [DOI: 10.1111/srt.12775] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 08/22/2019] [Indexed: 11/27/2022]
Affiliation(s)
- Hitoshi Maemoto
- Department of Radiology Graduate School of Medical Science University of the Ryukyus Okinawa Japan
| | - Kousei Ishigami
- Department of Radiology Graduate School of Medical Science University of the Ryukyus Okinawa Japan
| | - Shiro Iraha
- Department of Radiology Okinawa Prefectural Nanbu Medical Center and Children’s Medical Center Okinawa Japan
| | | | - Takeaki Kusada
- Department of Radiology Graduate School of Medical Science University of the Ryukyus Okinawa Japan
| | - Fumikiyo Ganaha
- Department of Radiology Okinawa Prefectural Nanbu Medical Center and Children’s Medical Center Okinawa Japan
| | - Sadayuki Murayama
- Department of Radiology Graduate School of Medical Science University of the Ryukyus Okinawa Japan
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Lu J, Hu D, Tang H, Hu X, Shen Y, Li Z, Peng Y, Kamel I. Assessment of tumor heterogeneity: Differentiation of periampullary neoplasms based on CT whole-lesion histogram analysis. Eur J Radiol 2019; 115:1-9. [PMID: 31084752 DOI: 10.1016/j.ejrad.2019.03.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE To investigate the utility of whole-lesion histogram analysis from multidetector computed tomography (MDCT) for discrimination of duodenal adenocarcinoma (DAC), pancreatic ductal adenocarcinoma (PDAC) and gastrointestinal stromal tumor (GIST) around the periampullary area. MATERIALS AND METHODS 171 patients suspicious of periampullary tumors were examined by MDCT (arterial and venous phases) and treated with surgery. A total of 74 patients were finally included in this retrospective study (26 DACs, 20 PDACs, and 28 GISTs). The interobserver agreement was evaluated by intra-class correlation coefficient (ICC) test between two radiologists. Volumetric histogram analysis based on CT Kinetics software was performed on enhanced MDCT images that recorded different histogram parameters of arterial and venous phases, including mean, median, 10th, 25th, 75th, and 90th percentiles, as well as skewness, kurtosis and entropy. The extracted histogram parameters were compared between DAC, PDAC and GIST respectively by Mann-Whitney U tests with Bonferroni corrections. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic ability of each significant parameter and the area under the curve (AUC) was calculated. RESULTS The whole-lesion CT histogram analysis demonstrated significant differences between DAC, PDAC, and GIST with different histogram features on both arterial and venous phase scans (all P < 0.05). In the ROC analysis, the 90th percentile of venous phase demonstrated the highest AUC of 0.854 (P < 0.001) for discriminating DAC from PDAC. Excellent discriminators of periampullary tumors were noted among the histogram features, namely the 90th percentile of arterial phase, which demonstrated AUCs of 0.809 and 0.936 (P < 0.001) respectively for distinguishing DAC and PDAC from GIST. CONCLUSION The whole-lesion CT histogram analysis could be useful for differential diagnosis of DAC, PDAC and GIST arising from the periampullary area. Further assessment is warranted to investigate the clinical role of histogram analysis based on MDCT.
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Affiliation(s)
- Jingyu Lu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Hao Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Yang Peng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei, 430030, PR China.
| | - Ihab Kamel
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.
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Utility of Metabolic Parameters on FDG PET/CT in the Classification of Early-Stage Lung Adenocarcinoma. Clin Nucl Med 2019; 44:560-565. [DOI: 10.1097/rlu.0000000000002591] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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