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Yang D, Yang Y, Zhao M, Ji H, Niu Z, Hong B, Shi H, He L, Shao M, Wang J. Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique. BMC Cancer 2024; 24:1080. [PMID: 39223592 PMCID: PMC11367849 DOI: 10.1186/s12885-024-12823-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVE To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning. METHODS pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models. RESULTS The patients with pGGNs (360 men, 776 women; mean age, 54.63 ± 12.36 years) included 235 cases of AAH + AIS, 332 cases of MIA, and 569 cases of IAC. In the validation group, the areas under the curve in detecting the invasiveness of pGGNs as a three-category classification (AAH + AIS, MIA, IAC) were 0.8008, 0.8090, 0.8165, and 0.8158 for EfficientNet-b0 2D, dual-head ResNet_3D, 3D_3F, and 3D_19F, respectively, whereas the accuracies were 0.6422, 0.6158, 0.651, and 0.6364, respectively. The Obuchowski index revealed no significant differences in the diagnostic performance of the four models. CONCLUSIONS The dual-head ResNet_3D_3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models.
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Affiliation(s)
- Dengfa Yang
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, 318000, China
| | - Yang Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China
| | - MinYi Zhao
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, 318000, China
| | - Hongli Ji
- Jianpei Technology, Hangzhou, 311202, China
| | - Zhongfeng Niu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Bo Hong
- Jianpei Technology, Hangzhou, 311202, China
| | - Hengfeng Shi
- Department of Radiology, Anqing Municipal Hospital, Anqing, 246004, China
| | - Linyang He
- Jianpei Technology, Hangzhou, 311202, China
| | - Meihua Shao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China.
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Wu T, Gao C, Lou X, Wu J, Xu M, Wu L. Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis. BMC Pulm Med 2024; 24:246. [PMID: 38762472 PMCID: PMC11102161 DOI: 10.1186/s12890-024-03020-x] [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: 12/19/2023] [Accepted: 04/16/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION CRD42022375712.
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Affiliation(s)
- Ting Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, China
| | - Xinjing Lou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, China
| | - Jun Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, China.
| | - Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, China.
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Yang Y, Xu J, Wang W, Ma M, Huang Q, Zhou C, Zhao J, Duan Y, Luo J, Jiang J, Ye L. A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma. BMC Cancer 2024; 24:438. [PMID: 38594670 PMCID: PMC11005224 DOI: 10.1186/s12885-024-12207-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.
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Affiliation(s)
- Yantao Yang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jing Xu
- Department of Dermatology and Venereal Diseases, Yan'an Hospital of Kunming City, Kunming, China
| | - Wei Wang
- Department of Thoracic and Cardiovascular Surgery, Shiyan Taihe Hospital (Hubei University of Medicine), Hubei, Shiyan, China
| | - Mingsheng Ma
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Qiubo Huang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Chen Zhou
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jie Zhao
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Yaowu Duan
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jia Luo
- Department of Pathology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiezhi Jiang
- Department of Radiology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lianhua Ye
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China.
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Zhang J, Hao L, Xu Q, Gao F. Radiomics and Clinical Characters Based Gaussian Naive Bayes (GNB) Model for Preoperative Differentiation of Pulmonary Pure Invasive Mucinous Adenocarcinoma From Mixed Mucinous Adenocarcinoma. Technol Cancer Res Treat 2024; 23:15330338241258415. [PMID: 38819419 PMCID: PMC11143847 DOI: 10.1177/15330338241258415] [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: 11/02/2023] [Revised: 05/03/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024] Open
Abstract
Objective: To develop and validate predictive models based on clinical parameters, and radiomic features to distinguish pulmonary pure invasive mucinous adenocarcinoma (pIMA) from mixed mucinous adenocarcinoma (mIMA) before surgery. Method: From January 2017 to December 2022, 193 pIMA and 111 mIMA were retrospectively analyzed at our hospital in this retrospective study. From contrast-enhanced computed tomography, 1037 radiomic features were extracted. The patients were randomly divided into a training group and a test group (n = 213 and 91, respectively) in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was used to select radiomic features. In this study, 9 machine learning radiomics prediction models were applied. The radiomics score was then calculated based on the best-performing machine learning model adopted. The clinical model was developed using the same machine learning model of radiomics. In the end, a combined model based on clinical factors and radiomics features was developed. The area under the receiver operating characteristic curve (AUC) value and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the prediction model. Results: The combined model established by the Gaussian Naive Bayes machine learning method exhibited the best performance. The AUC of the combined model, clinical model, and radiomics model were 0.81, 0.80, and 0.68 in the training group and 0.91, 0.80, and 0.81 in the test group, respectively. The Brier scores of the combined model were 0.171 and 0.112. The DCA curve also showed that the combined model was beneficial to clinical settings. Conclusion: The combined model integration of radiomics features and clinical parameters may have potential value for the preoperative differentiation of pIMA from mIMA.
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Affiliation(s)
- Junjie Zhang
- Department of Computed Tomography and Magnetic Resonance, Xing Tai People's Hospital, Xing Tai, He Bei, China
| | - Ligang Hao
- Department of Thoracic Surgery, Xing Tai People’s Hospital, Xing Tai, He Bei, China
| | - Qian Xu
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Fengxiao Gao
- Department of Computed Tomography and Magnetic Resonance, Xing Tai People's Hospital, Xing Tai, He Bei, China
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Zhou C, Zhang YF, Guo S, Wang D, Lv HX, Qiao XN, Wang R, Chang DH, Zhao LM, Zhou FH. Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study. Discov Oncol 2023; 14:133. [PMID: 37470865 PMCID: PMC10361451 DOI: 10.1007/s12672-023-00752-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023] Open
Abstract
PURPOSE Prostate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures. METHODS AND MATERIALS We collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan-Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival. RESULT The hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS). CONCLUSIONS Both Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.
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Affiliation(s)
- Chuan Zhou
- The First Clinical Medical College of Lanzhou University, Lanzhou, 73000, China
| | - Yun-Feng Zhang
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Sheng Guo
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Dong Wang
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Hao-Xuan Lv
- The First Clinical Medical College of Lanzhou University, Lanzhou, 73000, China
| | - Xiao-Ni Qiao
- Department of Information Management, Gansu Provincial Hospital, Lanzhou, 730000, China
| | - Rong Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 73000, China
- Department of Nuclear Medicine, Gansu Provincial Hospital, Lanzhou, 730000, China
| | - De-Hui Chang
- Department of Urology, The 940 Hospital of Joint Logistics Support Force of Chinese PLA, Lanzhou, 730000, China
| | - Li-Ming Zhao
- Department of Urology, Second People's Hospital of Gansu Province, Lanzhou, 730000, China
| | - Feng-Hai Zhou
- The First Clinical Medical College of Lanzhou University, Lanzhou, 73000, China.
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China.
- Department of Urology, Gansu Provincial Hospital, Lanzhou, 730000, China.
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Zhang J, Hao L, Qi M, Xu Q, Zhang N, Feng H, Shi G. Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules. BMC Cancer 2023; 23:261. [PMID: 36944978 PMCID: PMC10029225 DOI: 10.1186/s12885-023-10734-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/13/2023] [Indexed: 03/23/2023] Open
Abstract
OBJECTIVE To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). METHOD A total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People's Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models. RESULTS The combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively. CONCLUSION The combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.
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Affiliation(s)
- Junjie Zhang
- Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Department of CT&MR, The First Hospital of Xing Tai, Xing Tai, 054000, He Bei, China
| | - Ligang Hao
- Department of Thoracic Surgery Xing, Tai People's Hospital, Xing Tai, 054000, He Bei, China
| | - MingWei Qi
- Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Qian Xu
- Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China.
| | - Ning Zhang
- Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Hui Feng
- Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
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Zhang H, Wang S, Deng Z, Li Y, Yang Y, Huang H. Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study. PeerJ 2023; 11:e14559. [PMID: 36643621 PMCID: PMC9838201 DOI: 10.7717/peerj.14559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/21/2022] [Indexed: 01/12/2023] Open
Abstract
To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661-0.824]) for the logistic regression, 0.828 (95% CI [0.76-0.896]) for the support vector machine, and 0.917 (95% CI [0.869-0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694-0.913]), 0.726 (95% CI [0.598-0.854]), and 0.874 (95% CI [0.776-0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93.
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Liu C, Zhao W, Xie J, Lin H, Hu X, Li C, Shang Y, Wang Y, Jiang Y, Ding M, Peng M, Xu T, Hu A, Huang Y, Gao Y, Liu X, Liu J, Ma F. Development and validation of a radiomics-based nomogram for predicting a major pathological response to neoadjuvant immunochemotherapy for patients with potentially resectable non-small cell lung cancer. Front Immunol 2023; 14:1115291. [PMID: 36875128 PMCID: PMC9978193 DOI: 10.3389/fimmu.2023.1115291] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 02/02/2023] [Indexed: 02/18/2023] Open
Abstract
Introduction The treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics. Methods A total of 89 eligible participants were included and randomly divided into training (N=64) and validation (N=25) sets. Radiomic features were extracted from tumor volumes of interest in pretreatment CT images. Following data dimension reduction, feature selection, and radiomic signature building, a radiomics-clinical combined nomogram was developed using logistic regression analysis. Results The radiomics-clinical combined model achieved excellent discriminative performance, with AUCs of 0.84 (95% CI, 0.74-0.93) and 0.81(95% CI, 0.63-0.98) and accuracies of 80% and 80% in the training and validation sets, respectively. Decision curves analysis (DCA) indicated that the radiomics-clinical combined nomogram was clinically valuable. Discussion The constructed nomogram was able to predict MPR to neoadjuvant immunochemotherapy with a high degree of accuracy and robustness, suggesting that it is a convenient tool for assisting with the individualized management of patients with potentially resectable NSCLC.
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Affiliation(s)
- Chaoyuan Liu
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Junpeng Xie
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China
| | - Xingsheng Hu
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chang Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Youlan Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yapeng Wang
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yingjia Jiang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Mengge Ding
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Muyun Peng
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Tian Xu
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ao'ran Hu
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yuda Huang
- Department of Ministry of science and technology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yuan Gao
- Department of Basic Science, College of Chiropractic, Logan University, Chester field, MO, United States
| | - Xianling Liu
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China.,Radiology Quality Control Center, Changsha, Hunan, China
| | - Fang Ma
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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10
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Gao J, Qi Q, Li H, Wang Z, Sun Z, Cheng S, Yu J, Zeng Y, Hong N, Wang D, Wang H, Yang F, Li X, Li Y. Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules. Front Oncol 2023; 13:1096453. [PMID: 36910632 PMCID: PMC9996279 DOI: 10.3389/fonc.2023.1096453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Background Tumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness. Methods We identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People's Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves. Results In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004-1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058-3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004-1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259-27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057-9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399-0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750-7.717; p<0.001) were independent predictors for IAC. The areas under the curve (AUCs) in the training and validation cohorts indicated a better discriminative ability of the histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886). Conclusion We developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.
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Affiliation(s)
- Jian Gao
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Qingyi Qi
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Hao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Zhenfan Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Zewen Sun
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Sida Cheng
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Jie Yu
- Department of Thoracic Surgery, Qingdao Women and Children's Hospital, Qingdao, China
| | - Yaqi Zeng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Huiyang Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Feng Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Xiao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Yun Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
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11
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liang L, zhang H, Lei H, Zhou H, Wu Y, shen J. Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters. Technol Cancer Res Treat 2022; 21:15330338221119748. [PMID: 36259167 PMCID: PMC9583213 DOI: 10.1177/15330338221119748] [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] [Indexed: 11/04/2022] Open
Abstract
Objective: To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. Methods: A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Results: Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets (P <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased (P < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a P value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. Conclusion: The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules.
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Affiliation(s)
| | | | | | | | | | - Jiang shen
- Jiang shen, Chongqing Key Laboratory of
Translational Research for Cancer Metastasis and Individualized Treatment,
Chongqing University Cancer Hospital, Chongqing, 400030, China. Emails:
;
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12
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Song Q, Song B, Li X, Wang B, Li Y, Chen W, Wang Z, Wang X, Yu Y, Min X, Ma D. A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification. Cancer Imaging 2022; 22:46. [PMID: 36064495 PMCID: PMC9446567 DOI: 10.1186/s40644-022-00483-1] [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: 03/01/2022] [Accepted: 08/23/2022] [Indexed: 11/10/2022] Open
Abstract
Purpose To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. Methods A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves. Results Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit. Conclusions A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00483-1.
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Affiliation(s)
- Qilong Song
- Department of Radiology, Anhui Chest Hospital, Hefei, China.,Clinical College of Chest, Anhui Medical University, Hefei, China
| | - Biao Song
- Department of Radiology, Anhui Chest Hospital, Hefei, China.,Clinical College of Chest, Anhui Medical University, Hefei, China
| | - Xiaohu Li
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bin Wang
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Yuan Li
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Wu Chen
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Zhaohua Wang
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Xu Wang
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Xuhong Min
- Department of Radiology, Anhui Chest Hospital, Hefei, China. .,Clinical College of Chest, Anhui Medical University, Hefei, China.
| | - Dongchun Ma
- Clinical College of Chest, Anhui Medical University, Hefei, China. .,Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, China.
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13
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Zhang T, Zhang C, Zhong Y, Sun Y, Wang H, Li H, Yang G, Zhu Q, Yuan M. A radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm. Front Oncol 2022; 12:900049. [PMID: 36033463 PMCID: PMC9406823 DOI: 10.3389/fonc.2022.900049] [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: 03/19/2022] [Accepted: 07/20/2022] [Indexed: 12/04/2022] Open
Abstract
Objective To investigate whether radiomics can help radiologists and thoracic surgeons accurately predict invasive adenocarcinoma (IAC) manifesting as part-solid nodules (PSNs) with solid components <6 mm and provide a basis for rational clinical decision-making. Materials and Methods In total, 1,210 patients (mean age ± standard deviation: 54.28 ± 11.38 years, 374 men and 836 women) from our hospital and another hospital with 1,248 PSNs pathologically diagnosed with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or IAC were enrolled in this study. Among them, 1,050 cases from our hospital were randomly divided into a derivation set (n = 735) and an internal validation set (n = 315), 198 cases from another hospital were used for external validation. Each labeled nodule was segmented, and 105 radiomics features were extracted. Least absolute shrinkage and selection operator (LASSO) was used to calculate Rad-score and build the radiomics model. Multivariable logistic regression was conducted to identify the clinicoradiological predictors and establish the clinical-radiographic model. The combined model and predictive nomogram were developed based on identified clinicoradiological independent predictors and Rad-score using multivariable logistic regression analysis. The predictive performances of the three models were compared via receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was performed on both the internal and external validation sets to evaluate the clinical utility of the nomogram. Results The radiomics model showed superior predictive performance than the clinical-radiographic model in both internal and external validation sets (Az values, 0.884 vs. 0.810, p = 0.001; 0.924 vs. 0.855, p < 0.001, respectively). The combined model showed comparable predictive performance to the radiomics model (Az values, 0.887 vs. 0.884, p = 0.398; 0.917 vs. 0.924, p = 0.271, respectively). The clinical application value of the nomogram developed based on the Rad-score, maximum diameter, and lesion shape was confirmed, and DCA demonstrated that application of the Rad-score would be beneficial for radiologists predicting invasive lesions. Conclusions Radiomics has the potential as an independent diagnostic tool to predict the invasiveness of PSNs with solid components <6 mm.
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Affiliation(s)
- Teng Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yan Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Haijie Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Quan Zhu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Quan Zhu, ; Mei Yuan,
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Quan Zhu, ; Mei Yuan,
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14
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Yao F, Ding J, Lin F, Xu X, Jiang Q, Zhang L, Fu Y, Yang Y, Lan L. Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer. Br J Radiol 2022; 95:20211332. [PMID: 35612547 PMCID: PMC10162053 DOI: 10.1259/bjr.20211332] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. METHODS Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. RESULTS The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77-0.90) and 0.82 (95% CI: 0.71-0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. CONCLUSIONS The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and type II EOC. ADVANCES IN KNOWLEDGE This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists non-invasively identify EOC types before surgery.
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Affiliation(s)
- Fei Yao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jie Ding
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Lin
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaomin Xu
- Department of Ultrasound imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qi Jiang
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Li Zhang
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yanqi Fu
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Li Lan
- Department of Ultrasound imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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15
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Wu YJ, Wu FZ, Yang SC, Tang EK, Liang CH. Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics (Basel) 2022; 12:diagnostics12051064. [PMID: 35626220 PMCID: PMC9139351 DOI: 10.3390/diagnostics12051064] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/14/2022] [Accepted: 04/22/2022] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.
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Affiliation(s)
- Yun-Ju Wu
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan;
| | - Fu-Zong Wu
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Correspondence:
| | - Shu-Ching Yang
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan;
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16
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Qiu L, Zhang X, Mao H, Fang X, Ding W, Zhao L, Chen H. Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan. Front Oncol 2022; 11:691112. [PMID: 35059308 PMCID: PMC8765579 DOI: 10.3389/fonc.2021.691112] [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: 04/06/2021] [Accepted: 12/02/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To investigative the diagnostic performance of the morphological model, radiomics model, and combined model in differentiating invasive adenocarcinomas (IACs) from minimally invasive adenocarcinomas (MIAs). Methods This study retrospectively involved 307 patients who underwent chest computed tomography (CT) examination and presented as subsolid pulmonary nodules whose pathological findings were MIAs or IACs from January 2010 to May 2018. These patients were randomly assigned to training and validation groups in a ratio of 4:1 for 10 times. Eighteen categories of morphological features of pulmonary nodules including internal and surrounding structure were labeled. The following radiomics features are extracted: first-order features, shape-based features, gray-level co-occurrence matrix (GLCM) features, gray-level size zone matrix (GLSZM) features, gray-level run length matrix (GLRLM) features, and gray-level dependence matrix (GLDM) features. The chi-square test and F1 test selected morphology features, and LASSO selected radiomics features. Logistic regression was used to establish models. Receiver operating characteristic (ROC) curves evaluated the effectiveness, and Delong analysis compared ROC statistic difference among three models. Results In validation cohorts, areas under the curve (AUC) of the morphological model, radiomics model, and combined model of distinguishing MIAs from IACs were 0.88, 0.87, and 0.89; the sensitivity (SE) was 0.68, 0.81, and 0.83; and the specificity (SP) was 0.93, 0.79, and 0.87. There was no statistically significant difference in AUC between three models (p > 0.05). Conclusion The morphological model, radiomics model, and combined model all have a high efficiency in the differentiation between MIAs and IACs and have potential to provide non-invasive assistant information for clinical decision-making.
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Affiliation(s)
- Lu Qiu
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China.,Department of Radiology, Wuxi Children's Hospital, Nanjing Medical University, Wuxi, China
| | - Xiuping Zhang
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Haixia Mao
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Xiangming Fang
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Wei Ding
- Department of Intervention, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Lun Zhao
- Department of Research and Development, Deepwise Medical Artificial Intelligence Research Institute, Beijing, China
| | - Hongwei Chen
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
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17
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Automatically Predicting Lung Adenocarcinoma Invasiveness. 2022 INTERNATIONAL CONFERENCE ON BIG DATA, INFORMATION AND COMPUTER NETWORK (BDICN) 2022. [DOI: 10.1109/bdicn55575.2022.00048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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19
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Zhuo Y, Zhan Y, Zhang Z, Shan F, Shen J, Wang D, Yu M. Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule. Front Oncol 2021; 11:701598. [PMID: 34712605 PMCID: PMC8546326 DOI: 10.3389/fonc.2021.701598] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/26/2021] [Indexed: 12/15/2022] Open
Abstract
Aim To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN). Materials and Methods A total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model. Results A total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings. Conclusion The radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.
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Affiliation(s)
- Yaoyao Zhuo
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yi Zhan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Institute of Big Data, Fudan University, Shanghai, China.,Fudan University, Shanghai, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Institute of Big Data, Fudan University, Shanghai, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Daoming Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Mingfeng Yu
- Department of Thoracic Surgery, Beilun Second People's Hospital, Zhejiang, China
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20
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Song L, Xing T, Zhu Z, Han W, Fan G, Li J, Du H, Song W, Jin Z, Zhang G. Hybrid Clinical-Radiomics Model for Precisely Predicting the Invasiveness of Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodule. Acad Radiol 2021; 28:e267-e277. [PMID: 32534967 DOI: 10.1016/j.acra.2020.05.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/27/2020] [Accepted: 05/04/2020] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To identify whether the radiomics features of computed tomography (CT) allowed for the preoperative discrimination of the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs) and further to develop and compare different predictive models. MATERIALS AND METHODS We retrospectively included 187 lung adenocarcinomas presenting as pGGNs (66 preinvasive lesions and 121 invasive lesions), which were randomly divided into the training and test sets (8:2). Radiomics features were extracted from non-enhanced CT images. Clinical features, including patient's demographic characteristics, smoking status, and conventional CT features that reflect tumor's morphology and surrounding information were also collected. Intraclass correlation coefficient and ℓ2.1-norm minimization were used to identify influential feature subset which was then used to build three predictive models (clinical, radiomics, and clinical-radiomics models) with the gradient boosting regression tree classifier. The performances of the predictive models were evaluated using the area under the curve (AUC). RESULTS Of the 1409 radiomics features and 27 clinical feature subtypes, 102 features were selected to construct the hybrid clinical-radiomics model, which achieved the best discriminative power (AUC = 0.934 and 0.929 in training and test set). The radiomics model showed comparable predictive performance (AUC = 0.911 and 0.901 in training and test set) compared to the clinical model (AUC = 0.911 and 0.894 in training and test set). CONCLUSION The radiomics model showed good predictive performance in discriminating invasive lesions from preinvasive lesions for lung adenocarcinomas presenting as pGGNs. Its performance can be further improved by adding clinical features.
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Affiliation(s)
- Lan Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Tongtong Xing
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhenchen Zhu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China; 4+4 MD Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Han
- Department of Epidemiology and Health Statistics, Institute of Basic Medicine Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China
| | - Guangda Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Ji Li
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Huayang Du
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Guanglei Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
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21
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Zhang R, Sun H, Chen B, Xu R, Li W. Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. J Thorac Dis 2021; 13:4156-4168. [PMID: 34422345 PMCID: PMC8339772 DOI: 10.21037/jtd-21-80] [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: 01/12/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
Background Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. Methods This study enrolled 5–20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model. Results Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model. Conclusions Predictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Renjie Xu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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22
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Gong J, Liu J, Li H, Zhu H, Wang T, Hu T, Li M, Xia X, Hu X, Peng W, Wang S, Tong T, Gu Y. Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study. Cancers (Basel) 2021; 13:cancers13133300. [PMID: 34209366 PMCID: PMC8269183 DOI: 10.3390/cancers13133300] [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: 04/23/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Prediction of the malignancy and invasiveness of ground glass nodules (GGNs) from computed tomography images is a crucial task for radiologists in risk stratification of early-stage lung adenocarcinoma. In order to solve this challenge, a two-stage deep neural network (DNN) was developed based on the images collected from four centers. A multi-reader multi-case observer study was conducted to evaluate the model capability. The performance of our model was comparable or even more accurate than that of senior radiologists, with average area under the curve values of 0.76 and 0.95 for two tasks, respectively. Findings suggest (1) a positive trend between the diagnostic performance and radiologist’s experience, (2) DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution reduced the model performance in predicting the risks of GGNs. Abstract This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens. A two-stage deep neural network was developed based on the 3D residual network and atrous convolution module to diagnose benign and malignant GGNs (Task1) and classify between invasive adenocarcinoma (IA) and non-IA for these malignant GGNs (Task2). A multi-reader multi-case observer study with six board-certified radiologists’ (average experience 11 years, range 2–28 years) participation was conducted to evaluate the model capability. DNN yielded area under the receiver operating characteristic curve (AUC) values of 0.76 ± 0.03 (95% confidence interval (CI): (0.69, 0.82)) and 0.96 ± 0.02 (95% CI: (0.92, 0.98)) for Task1 and Task2, which were equivalent to or higher than radiologists in the senior group with average AUC values of 0.76 and 0.95, respectively (p > 0.05). With the CT image slice thickness increasing from 1.15 mm ± 0.36 to 1.73 mm ± 0.64, DNN performance decreased 0.08 and 0.22 for the two tasks. The results demonstrated (1) a positive trend between the diagnostic performance and radiologist’s experience, (2) the DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution decreased model performance in predicting the risks of GGNs. Once tested prospectively in clinical practice, the DNN could have the potential to assist doctors in precision diagnosis and treatment of early lung adenocarcinoma.
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Affiliation(s)
- Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jiyu Liu
- Department of Radiology, Shanghai Pulmonary Hospital, 507 Zheng Min Road, Shanghai 200433, China;
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Tingting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Tingdan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xianwu Xia
- Department of Radiology, Municipal Hospital Affiliated to Taizhou University, Taizhou 318000, China;
| | - Xianfang Hu
- Department of Radiology, Huzhou Central Hospital Affiliated Central Hospital of Huzhou University, 1558 Sanhuan North Road, Huzhou 313000, China;
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Correspondence: (S.W.); (T.T.); (Y.G.); Tel.: +86-13818521975 (S.W); +86-18017312912 (T.T.); +86-18017312040 (Y.G.)
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Correspondence: (S.W.); (T.T.); (Y.G.); Tel.: +86-13818521975 (S.W); +86-18017312912 (T.T.); +86-18017312040 (Y.G.)
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, China; (J.G.); (H.L.); (H.Z.); (T.W.); (T.H.); (M.L.); (W.P.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Correspondence: (S.W.); (T.T.); (Y.G.); Tel.: +86-13818521975 (S.W); +86-18017312912 (T.T.); +86-18017312040 (Y.G.)
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Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation. Phys Eng Sci Med 2021; 44:745-754. [PMID: 34075559 DOI: 10.1007/s13246-021-01022-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 05/26/2021] [Indexed: 12/28/2022]
Abstract
The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients' data from Siemens (Vendor 1), and the inner test set was 70 patients' data from the same vendor. The external test set was 221 patients' data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.
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Wu L, Gao C, Ye J, Tao J, Wang N, Pang P, Xiang P, Xu M. The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules. Eur Radiol 2021; 31:9030-9037. [PMID: 34037830 DOI: 10.1007/s00330-021-07948-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). METHODS For this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method. RESULTS The radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423). CONCLUSIONS The radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region. KEY POINTS • The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.
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Affiliation(s)
- Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianfeng Ye
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingying Tao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Neng Wang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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Shen J, Zhuang W, Xu C, Jin K, Chen B, Tian D, Hiley C, Onishi H, Zhu C, Qiao G. Surgery or Non-surgical Treatment of ≤8 mm Non-small Cell Lung Cancer: A Population-Based Study. Front Surg 2021; 8:632561. [PMID: 34124131 PMCID: PMC8187794 DOI: 10.3389/fsurg.2021.632561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 04/14/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Timing for intervention of small indeterminate pulmonary nodules has long been a topic of debate given the low incidence of malignancy and difficulty in obtaining a definite preoperative diagnosis. We sought to determine survival outcomes of surgical and non-surgical managements in non-small cell lung cancer (NSCLC) ≤8 mm, which may provide a reference for prospective decision-making for patients with suspected NSCLC. Method: A total of 1,652 patients with Stage IA NSCLC ≤8 mm were identified from the Surveillance, Epidemiology, and End Results (SEER) database and categorized into surgery and non-surgery groups. Chi-square test, t-test and Mann-Whitney U test were used to compare the baseline characteristics between groups. Survival curves were depicted using Kaplan-Meier method and compared by log-rank test. Cox proportional hazard model was used for univariate and multivariate analyses. Adjustment of confounding factors between groups was performed by propensity score matching. Results: The surgery and non-surgery groups included 1,438 and 208 patients, respectively. Patients in surgery group demonstrated superior survival outcome than patients in non-surgery group both before [overall survival (OS): HR, 16.22; 95% CI, 11.48-22.91, p < 0.001; cancer-specific survival (CSS): HR, 49.6; 95% CI, 31.09-79.11, p < 0.001] and after (OS: HR, 3.12; 95% CI, 2.40-4.05, p < 0.001; CSS: HR, 3.85; 95% CI, 2.74-5.40, p < 0.001) propensity score matching. The 30-day mortality rates were 3.1 and 12.0% in surgery and non-surgery groups, respectively. Multivariate analysis suggested age, sex, race, tumor size, grade, pathological stage were all independent prognostic factors in patients with ≤8 mm NSCLC. A comparison of surgical resections revealed a survival superiority of lobectomy over sub-lobectomy. In terms of CSS, no statistically significant difference was found between segmentectomy and wedge resection. Conclusion: The current SEER database showed better prognosis of surgical resection than non-surgical treatment in patients with ≤8 mm NSCLC. However, the factors that should be essentially included in the proper propensity-matched analysis, such as comorbidity, cardiopulmonary function and performance status were unavailable and the true superiority or inferiority should be examined further by ongoing randomized trial, especially comparing surgery and stereotactic body irradiation.
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Affiliation(s)
- Jianfei Shen
- Department of Cardiothoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Weitao Zhuang
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Congcong Xu
- Department of Cardiothoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ke Jin
- Department of Cardiothoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Baofu Chen
- Department of Cardiothoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Dan Tian
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Crispin Hiley
- Cancer Research United Kingdom (CRUK) Lung Cancer Centre of Excellence, University College London, London, United Kingdom
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Chengchu Zhu
- Department of Cardiothoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Guibin Qiao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Liu X, Zhang L, Chen L, Li J, Wang J, Zhao Y, Liu L, Wu Y. Identification and prioritization of the potent components for combined exposure of multiple persistent organic pollutants associated with gestational diabetes mellitus. JOURNAL OF HAZARDOUS MATERIALS 2021; 409:124905. [PMID: 33412473 DOI: 10.1016/j.jhazmat.2020.124905] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 12/09/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
Persistent organic pollutants (POPs) remain a major point of concern worldwide, and surveillance monitoring of these contaminants presents a significant challenge. Here, we conducted an assessment of combined exposure to multiple POPs components [10 perfluoroalkyl acids (PFAAs), seven polybrominated diphenyl ethers (PBDEs), six polychlorinated biphenyls (PCBs) and 29 dioxin-like compounds (DLCs)] in relation to gestational diabetes mellitus (GDM) risk, and determined the identification and prioritization of potent components in these POPs mixtures. The results indicated a significant mixture effect and the combined exposure index estimated from multiple POPs components was associated with GDM and glucose homeostasis (P < 0.001). Based on the mixture effects on GDM, the procedure of prioritization identified DLCs as the components of the greatest concern, although at the lowest body burden in the population compared with PBDEs, PFAAs, and PCBs. For glucose homeostasis, BDE-153 was the chemical of top-ranked priority of concern. The final effect-based prioritized list of POPs was DLCs > PBDEs >PFAAs > PCBs. This prioritization is important for developing a more cost-effective regulation framework focusing on the POPs components of the greatest concern to human health.
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Affiliation(s)
- Xin Liu
- NHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing 100021, China; Institute of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
| | - Lei Zhang
- NHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing 100021, China
| | - Liangkai Chen
- Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430023, China
| | - Jingguang Li
- NHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing 100021, China.
| | - Jun Wang
- Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China
| | - Yunfeng Zhao
- NHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing 100021, China
| | - Liegang Liu
- Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430023, China
| | - Yongning Wu
- NHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing 100021, China; Institute of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
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Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model. Aging (Albany NY) 2021; 13:12833-12848. [PMID: 33946042 PMCID: PMC8148477 DOI: 10.18632/aging.202954] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/17/2021] [Indexed: 12/15/2022]
Abstract
We constructed a radiomics-clinical model to predict intraventricular hemorrhage (IVH) growth after spontaneous intracerebral hematoma. The model was developed using a training cohort (N=626) and validated with an independent testing cohort (N=270). Radiomics features and clinical predictors were selected using the least absolute shrinkage and selection operator (LASSO) method and multivariate analysis. The radiomics score (Rad-score) was calculated through linear combination of selected features multiplied by their respective LASSO coefficients. The support vector machine (SVM) method was used to construct the model. IVH growth was experienced by 13.4% and 13.7% of patients in the training and testing cohorts, respectively. The Rad-score was associated with severe IVH and poor outcome. Independent predictors of IVH growth included hypercholesterolemia (odds ratio [OR], 0.12 [95%CI, 0.02-0.90]; p=0.039), baseline Graeb score (OR, 1.26 [95%CI, 1.16-1.36]; p<0.001), time to initial CT (OR, 0.70 [95%CI, 0.58-0.86]; p<0.001), international normalized ratio (OR, 4.27 [95%CI, 1.40, 13.0]; p=0.011), and Rad-score (OR, 2.3 [95%CI, 1.6-3.3]; p<0.001). In the training cohort, the model achieved an AUC of 0.78, sensitivity of 0.83, and specificity of 0.66. In the testing cohort, AUC, sensitivity, and specificity were 0.71, 0.81, and 0.64, respectively. This radiomics-clinical model thus has the potential to predict IVH growth.
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Shi L, Shi W, Peng X, Zhan Y, Zhou L, Wang Y, Feng M, Zhao J, Shan F, Liu L. Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter. Front Oncol 2021; 11:618677. [PMID: 33968722 PMCID: PMC8096901 DOI: 10.3389/fonc.2021.618677] [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: 10/18/2020] [Accepted: 03/25/2021] [Indexed: 12/09/2022] Open
Abstract
Purpose To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter. Materials and Methods This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts. Results Five radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion This study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.
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Affiliation(s)
- Lili Shi
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Medical School, Nantong University, Nantong, China
| | - Weiya Shi
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xueqing Peng
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yi Zhan
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Linxiao Zhou
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunpeng Wang
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mingxiang Feng
- Chest Surgery Department, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jinli Zhao
- Radiology Department, Affiliated Hospital of Nantong University, Nantong, China
| | - Fei Shan
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,School of Basic Medical Sciences, and Academy of Engineering and Technology, Fudan University, Shanghai, China
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Tan M, Ma W, Sun Y, Gao P, Huang X, Lu J, Chen W, Wu Y, Jin L, Tang L, Kuang K, Li M. Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics. Front Oncol 2021; 11:658138. [PMID: 33937070 PMCID: PMC8082461 DOI: 10.3389/fonc.2021.658138] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/22/2021] [Indexed: 01/15/2023] Open
Abstract
Objectives To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. Methods From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. Results Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set. Conclusions The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.
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Affiliation(s)
- Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Xuemei Huang
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Jinjuan Lu
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Wufei Chen
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yue Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Lin Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
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Gülbay M, Özbay BO, Mendi BAR, Baştuğ A, Bodur H. A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias? PLoS One 2021; 16:e0246582. [PMID: 33690730 PMCID: PMC7946299 DOI: 10.1371/journal.pone.0246582] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/21/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. METHODS In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. RESULTS Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. CONCLUSION During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.
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Affiliation(s)
- Mutlu Gülbay
- Department of Radiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Bahadır Orkun Özbay
- Department of Infectious Diseases and Clinical Microbiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Bökebatur Ahmet Raşit Mendi
- Department of Radiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Aliye Baştuğ
- Department of Infectious Diseases and Clinical Microbiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Hürrem Bodur
- Department of Infectious Diseases and Clinical Microbiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
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Liu C, Chen H, Sun T, Wang H, Chen B, Wang X. The value of circulating tumor cells with positive centromere probe 8 in the diagnosis of small pulmonary nodules. Transl Oncol 2021; 14:101052. [PMID: 33667891 PMCID: PMC7933811 DOI: 10.1016/j.tranon.2021.101052] [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: 10/09/2020] [Revised: 02/17/2021] [Accepted: 02/19/2021] [Indexed: 11/03/2022] Open
Abstract
Circulating cancer cells (CTCs) can serve as a non-invasive liquid biopsy and provide opportunities for early cancer diagnosis and evaluation. However, the value of CTCs for diagnosis or prognosis of small pulmonary nodules (SPNs) is unclear. Fifty-three patients diagnosed with SPNs with a diameter less than 30 mm by CT examination were enrolled in the study. The CTC numbers, CT examination features, serum tumor marker concentrations, and histopathological characteristics were analyzed. Centromere probe 8 (CEP8) was used as a marker for CTC identification. The CTC numbers were significantly different in patients with malignant and benign SPNs and with early (0/Ⅰa) and advanced (Ⅰb/Ⅱ/Ⅲ) lung cancer stages. ROC analysis showed that the CTC numbers was effective on malignant SNP diagnosis. The combined use of CTCs and the density features of the nodules determined by CT further improved the overall screening, the diagnostic effectiveness for malignant SNPs, and determination of the pTNM (≤Ia vs.>Ia) stage. The CT morphology revealed that large, single, and solid SPNs were associated with significant CTC numbers and the CTC numbers were correlated with malignant histopathology. Using CEP8 as a marker resulted in detection of more CTC numbers in 22 patient samples triple stained for CEP8, EpCAM, and CKs. The CTCs determined by CEP8-positive staining could serve as potential screening and diagnostic markers for malignant SPNs.
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Affiliation(s)
- Caidong Liu
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, 210006 China
| | - Hongling Chen
- Department of Pharmacology, Nanjing Medical University, Nanjing, Jiangsu Province, 210029 China
| | - Tong Sun
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, Jiangsu Province, 211100 China
| | - Haibo Wang
- Cyttelbio Corporation, Beijing, 100176 China
| | - Baoan Chen
- Department of Hematology and Oncology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu Province, 210009 China
| | - Xuerong Wang
- Department of Pharmacology, Nanjing Medical University, Nanjing, Jiangsu Province, 210029 China; Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, Jiangsu Province, 211100 China.
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Ma G, Kang J, Qiao N, Zhang B, Chen X, Li G, Gao Z, Gui S. Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery. Front Oncol 2021; 10:599888. [PMID: 33680925 PMCID: PMC7925821 DOI: 10.3389/fonc.2020.599888] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/30/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose Craniopharyngiomas (CPs) are benign tumors, complete tumor resection is considered to be the optimal treatment. However, although histologically benign, the local invasiveness of CPs commonly contributes to incomplete resection and a poor prognosis. At present, some advocate less aggressive surgery combined with radiotherapy as a more reasonable and effective means of protecting hypothalamus function and preventing recurrence in patients with tight tumor adhesion to the hypothalamus. Hence, if a method can be developed to predict the invasiveness of CP preoperatively, it will help in the development of a more personalized surgical strategy. The aim of the study was to report a radiomics-clinical nomogram for the individualized preoperative prediction of the invasiveness of adamantinomatous CP (ACPs) before surgery. Methods In total, 1,874 radiomics features were extracted from whole tumors on contrast-enhanced T1-weighted images. A support vector machine trained a predictive model that was validated using receiver operating characteristic (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction. Results Eleven features associated with the invasiveness of ACPs were selected by using the least absolute shrinkage and selection operator (LASSO) method. These features yielded area under the curve (AUC) values of 79.09 and 73.5% for the training and test sets, respectively. The nomogram incorporating peritumoral edema and the radiomics signature yielded good calibration in the training and test sets with the AUCs of 84.79 and 76.48%, respectively. Conclusion The developed model yields good performance, indicating that the invasiveness of APCs can be predicted using noninvasive radiological data. This reliable, noninvasive tool can help clinical decision making and improve patient prognosis.
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Affiliation(s)
- Guofo Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jie Kang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ning Qiao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bochao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guilin Li
- Neuropathology Department, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhixian Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules. Sci Rep 2021; 11:3633. [PMID: 33574448 PMCID: PMC7878798 DOI: 10.1038/s41598-021-83167-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 01/25/2021] [Indexed: 12/12/2022] Open
Abstract
Controversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815–0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712–0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.
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Meng F, Guo Y, Li M, Lu X, Wang S, Zhang L, Zhang H. Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules. Transl Oncol 2021; 14:100936. [PMID: 33221688 PMCID: PMC7689413 DOI: 10.1016/j.tranon.2020.100936] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 12/17/2022] Open
Abstract
In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916-0.964) and validation set (AUC, 0.946; 95% CI, 0.907-0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Mingyang Li
- State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China
| | - Xiaoqian Lu
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Shuo Wang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
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Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. 18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg 2021; 11:215-225. [PMID: 33392023 DOI: 10.21037/qims-20-337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. Methods The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. Results The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. Conclusions A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.
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Affiliation(s)
- Lili Wang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tiancheng Li
- PET Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junjie Hong
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingyue Zhang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingli Ouyang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangwu Zheng
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules. Clin Radiol 2020; 76:143-151. [PMID: 33187676 DOI: 10.1016/j.crad.2020.10.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/08/2020] [Indexed: 12/17/2022]
Abstract
AIM To establish a machine-learning model to differentiate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS This retrospective study enrolled 136 patients with histopathologically diagnosed with AIS, MIA, and IAC. All pGGNs were divided randomly into a training and a testing dataset at a ratio of 7 : 3. Radiomics features were extracted based on the unenhanced computed tomography (CT) images derived from the last preoperative CT examination of each patient. The F-test and least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select the most valuable features to establish a support vector machine (SVM) model. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUROC), and the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance of radiologists and the SVM model. RESULTS Six significant radiomics features were selected to develop the SVM model and showed excellent ability to differentiate AIS/MIA from IAC in both the training dataset (AUROC=0.950, 95% confidence interval [CI]: 0.886-0.984) and the testing dataset (AUROC=0.945, 95% CI: 0.826-0.992). Compared with two radiologists, the proposed model possessed significant advantages with higher accuracy (90.24% versus 75.61% and 80.49%), sensitivity (91.67% versus 50% and 75%), and specificity (89.66% versus 86.21% and 82.76%). CONCLUSION A machine-learning model based on radiomics features exhibits superior diagnostic performance in differentiating AIS/MIA from IAC appearing as pGGNs.
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Wu L, Gao C, Xiang P, Zheng S, Pang P, Xu M. CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features. Front Oncol 2020; 10:838. [PMID: 32537436 PMCID: PMC7267037 DOI: 10.3389/fonc.2020.00838] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 04/28/2020] [Indexed: 12/19/2022] Open
Abstract
Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra- and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or peri-nodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Conclusions: The gross radiomic signature of intra- and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.
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Affiliation(s)
- Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Sisi Zheng
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
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Chen H, Liu J, Cheng Z, Lu X, Wang X, Lu M, Li S, Xiang Z, Zhou Q, Liu Z, Zhao Y. Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: A retrospective multicenter study. Eur J Radiol 2020; 129:109066. [PMID: 32502729 DOI: 10.1016/j.ejrad.2020.109066] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 05/07/2020] [Accepted: 05/09/2020] [Indexed: 01/05/2023]
Abstract
PURPOSE To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (≤ 1 year) in osteosarcoma after surgical resection. METHODS This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. RESULTS The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index:0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful. CONCLUSION The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.
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Affiliation(s)
- Haimei Chen
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China.
| | - Jin Liu
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China.
| | - Zixuan Cheng
- Department of Radiology, Guangzhou Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080,China.
| | - Xing Lu
- Sigma Technologies Inc, San Diego, CA 92130, United States.
| | - Xiaohong Wang
- Department of Radiology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510630, China.
| | - Ming Lu
- Department of Oncology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, Guangdong 510630, China.
| | - Shaolin Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong 519000, China.
| | - Zhiming Xiang
- Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, Guangdong 511400, China.
| | - Quan Zhou
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China.
| | - Zaiyi Liu
- Department of Radiology, Guangzhou Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080,China.
| | - Yinghua Zhao
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China.
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Sun Y, Li C, Jin L, Gao P, Zhao W, Ma W, Tan M, Wu W, Duan S, Shan Y, Li M. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. Eur Radiol 2020; 30:3650-3659. [PMID: 32162003 PMCID: PMC7305264 DOI: 10.1007/s00330-020-06776-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/14/2019] [Accepted: 02/24/2020] [Indexed: 12/18/2022]
Abstract
Objectives To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic–radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. Results Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic–radiomics model (AUC 0.77; 95% CI, 0.69–0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62–0.81) and Rad-score (AUC 0.72; 95% CI, 0.63–0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. Conclusions The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. Key Points • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic–radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up. Electronic supplementary material The online version of this article (10.1007/s00330-020-06776-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Cheng Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Wei Zhao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China.,Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Weilan Wu
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | | | - Yuqing Shan
- Department of Radiology, The People's Hospital of Rizhao, Rizhao City, 276800, China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China. .,Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Shanghai, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
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CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists. Eur Radiol 2020; 30:3295-3305. [PMID: 32055949 DOI: 10.1007/s00330-019-06628-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/09/2019] [Accepted: 12/13/2019] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model and expert radiologists. METHODS This study included 525 patients (309 women; median, 62 years) to develop models, and an independent cohort of 101 patients (57 women; median, 66 years) was used for validation. A size-based logistic model and deep learning models using 2.5-dimension (2.5D) and three-dimension (3D) CT images were developed to discriminate IAC from less invasive pathologies. Overall performance, discrimination, and calibration were assessed. Diagnostic performances of the three thoracic radiologists were compared with those of the deep learning model. RESULTS The overall performances of the deep learning models (Brier score, 0.122 for the 2.5D DenseNet and 0.121 for the 3D DenseNet) were superior to those of the size-based logistic model (Brier score, 0.198). The area under the receiver operating characteristic curve (AUC) of the 2.5D DenseNet (0.921) was significantly higher than that of the 3D DenseNet (0.835; p = 0.037) and the size-based logistic model (0.836; p = 0.009). At equally high sensitivities of 90%, the 2.5D DenseNet showed significantly higher specificity (88.2%; all p < 0.05) and positive predictive value (97.4%; all p < 0.05) than other models. Model calibration was poor for all models (all p < 0.05). The 2.5D DenseNet had a comparable performance with the radiologists (AUC, 0.848-0.910). CONCLUSION The 2.5D DenseNet model could be used as a highly sensitive and specific diagnostic tool to differentiate IACs among SSNs for surgical candidates. KEY POINTS • The deep learning model developed using 2.5D DenseNet showed higher overall performance and discrimination than the size-based logistic model for the differentiation of invasive adenocarcinomas among subsolid nodules for surgical candidates. • The 2.5D DenseNet demonstrated a thoracic radiologist-level diagnostic performance and had higher specificity (88.2%) at equal sensitivities (90%) than the size-based logistic model (specificity, 52.9%). • The 2.5D DenseNet could be used to reduce potential overtreatment for the indolent subsolid nodules or to select candidates for sublobar resection instead of the standard lobectomy.
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Zhao W, Wu Y, Xu Y, Sun Y, Gao P, Tan M, Ma W, Li C, Jin L, Hua Y, Liu J, Li M. The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma. Front Oncol 2020; 9:1485. [PMID: 31993370 PMCID: PMC6962353 DOI: 10.3389/fonc.2019.01485] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022] Open
Abstract
Purpose: Up to 50% of Asian patients with NSCLC have EGFR gene mutations, indicating that selecting eligible patients for EGFR-TKIs treatments is clinically important. The aim of the study is to develop and validate radiomics-based nomograms, integrating radiomics, CT features and clinical characteristics, to non-invasively predict EGFR mutation status and subtypes. Materials and Methods: We included 637 patients with lung adenocarcinomas, who performed the EGFR mutations analysis in the current study. The whole dataset was randomly split into a training dataset (n = 322) and validation dataset (n = 315). A sub-dataset of EGFR-mutant lesions (EGFR mutation in exon 19 and in exon 21) was used to explore the capability of radiomic features for predicting EGFR mutation subtypes. Four hundred seventy-five radiomic features were extracted and a radiomics sore (R-score) was constructed by using the least absolute shrinkage and selection operator (LASSO) regression in the training dataset. A radiomics-based nomogram, incorporating clinical characteristics, CT features and R-score was developed in the training dataset and evaluated in the validation dataset. Results: The constructed R-scores achieved promising performance on predicting EGFR mutation status and subtypes, with AUCs of 0.694 and 0.708 in two validation datasets, respectively. Moreover, the constructed radiomics-based nomograms excelled the R-scores, clinical, CT features alone in terms of predicting EGFR mutation status and subtypes, with AUCs of 0.734 and 0.757 in two validation datasets, respectively. Conclusions: Radiomics-based nomogram, incorporating clinical characteristics, CT features and radiomic features, can non-invasively and efficiently predict the EGFR mutation status and thus potentially fulfill the ultimate purpose of precision medicine. The methodology is a possible promising strategy to predict EGFR mutation subtypes, providing the support of clinical treatment scenario.
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Affiliation(s)
- Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China.,Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yuzhi Wu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Ya'nan Xu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Cheng Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yanqing Hua
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Diagnosis and Treatment Center of Small Lung Nodules of Huadong Hospital, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
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Kakino R, Nakamura M, Mitsuyoshi T, Shintani T, Hirashima H, Matsuo Y, Mizowaki T. Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients. Phys Med 2020; 69:176-182. [PMID: 31918370 DOI: 10.1016/j.ejmp.2019.12.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Revised: 11/18/2019] [Accepted: 12/18/2019] [Indexed: 12/18/2022] Open
Abstract
PURPOSE To compare radiomic features extracted from diagnostic computed tomography (CT) images with and without contrast enhancement in delayed phase for non-small cell lung cancer (NSCLC) patients. METHODS Diagnostic CT images from 269 tumors [non-contrast CT, 188 (dataset NE); contrast-enhanced CT, 81 (dataset CE)] were enrolled in this study. Eighteen first-order and seventy-five texture features were extracted by setting five bin width levels for CT values. Reproducible features were selected by the intraclass correlation coefficient (ICC). Radiomic features were compared between datasets NE and CE. Subgroup analyses were performed based on the CT acquisition period, exposure value, and patient characteristics. RESULTS Eighty features were considered reproducible (0.5 ≤ ICC). Twelve of the sixteen first-order features, independent of the bin width levels, were statistically different between datasets NE and CE (p < 0.05), and the p-values of two first-order features depending on the bin width levels were reduced with narrower bin widths. Sixteen out of sixty-two features showed a significant difference, regardless of the bin width (p < 0.05). There were significant differences between datasets NE and CE with older age, lighter body weight, better performance status, being a smoker, larger gross tumor volume, and tumor location at central region. CONCLUSIONS Contrast enhancement in the delayed phase of CT images for NSCLC patients affected some of the radiomic features and the variability of radiomic features due to contrast uptake may depend largely on the patient characteristics.
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Affiliation(s)
- Ryo Kakino
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Research Fellow at Japan Society for the Promotion of Science, Tokyo, Japan
| | - Mitsuhiro Nakamura
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
| | - Takamasa Mitsuyoshi
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Takashi Shintani
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Hideaki Hirashima
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Yukinori Matsuo
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
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Chen PH, Chang KM, Tseng WC, Chen CH, Chao JI. Invasiveness and surgical timing evaluation by clinical features of ground-glass opacity nodules in lung cancers. Thorac Cancer 2019; 10:2133-2141. [PMID: 31571421 PMCID: PMC6825908 DOI: 10.1111/1759-7714.13199] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 08/29/2019] [Accepted: 08/29/2019] [Indexed: 12/17/2022] Open
Abstract
Background The early stages of lung cancer with ground‐glass opacity (GGO) pattern are detectable. However, it remains a challenge for physicians how best to treat GGO nodules as invasive tumors are occasionally found, even in pure GGO nodules. This study identified the invasiveness by the clinical features of the GGO nodules. Methods A retrospective review of patients with resected GGO nodules from August 2015 to February 2019 was performed. A total of 92 patients were enrolled and gender, age, tumor location, operation times, tumor size, histopathologic and radiological findings were analyzed. Results In this study, the sequential of GGO nodules invasiveness was significantly related to the tumor size and solid component. After regrouping the population into preinvasive and invasive groups, the invasiveness was significantly related to tumor size, solid component, tumor volume and maximal computed tomography (CT) value. Conclusions The invasiveness is difficult to evaluate according to the CT features only when the GGO nodules are less than 2 cm and consolidation/tumor ratio (C/T ratio) are less than 0.25. Tumor size and solid component are significant factors for predicting invasiveness. Part‐solid GGO nodules with a diameter greater than 1 cm require surgical consideration due to their high risk of invasiveness.
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Affiliation(s)
- Pai-Hsi Chen
- Department of Surgery, Hsinchu Mackay Memorial Hospital, Hsinchu, Taiwan.,Department and Institute of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Kuo-Ming Chang
- Department of Pathology, Hsinchu Mackay Memorial Hospital, Hsinchu, Taiwan
| | - Wei-Chi Tseng
- Department of Radiology, Hsinchu Mackay Memorial Hospital, Hsinchu, Taiwan
| | - Chien-Hung Chen
- Department and Institute of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Jui-I Chao
- Department and Institute of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, Taiwan.,Center For Intelligent Drug Systems and Smart Bio-devices, National Chiao Tung University, Hsinchu, Taiwan
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Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S. CT radiomics and PET radiomics: ready for clinical implementation? THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:355-370. [PMID: 31527578 DOI: 10.23736/s1824-4785.19.03192-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest. EVIDENCE ACQUISITION Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications. EVIDENCE SYNTHESIS We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling. CONCLUSIONS Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland -
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert S Gabrys
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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