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Xie Z, Yang Y, Niu Z, Mao G, Zhu X, Xu Z, Yang D, Wang H, Wang J. Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study. Quant Imaging Med Surg 2024; 14:5151-5163. [PMID: 39022285 PMCID: PMC11250286 DOI: 10.21037/qims-23-1631] [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: 11/16/2023] [Accepted: 05/27/2024] [Indexed: 07/20/2024]
Abstract
Background Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. Methods A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. Results LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Conclusions Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.
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Affiliation(s)
- Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Yang Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Zhongfeng Niu
- Department of radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guoqun Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Xiandi Zhu
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Zhihua Xu
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Dengfa Yang
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, China
| | - Hui Wang
- School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
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Yichu S, Fei L, Ying L, Youyou X. Potential of radiomics analysis and machine learning for predicting brain metastasis in newly diagnosed lung cancer patients. Clin Radiol 2024; 79:e807-e816. [PMID: 38395696 DOI: 10.1016/j.crad.2024.01.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/05/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
AIM To explore the potential of utilising radiomics analysis and machine-learning models that incorporate intratumoural and peritumoural regions of interest (ROIs) for predicting brain metastasis (BM) in newly diagnosed lung cancer patients. MATERIALS AND METHODS The study comprised 183 lung cancer patients (training cohort: n=146; validation cohort: n=37) whose radiomics features were extracted from plain computed tomography (CT) images of the primary lesion. Four machine-learning algorithms (logistic regression [LR], support vector machine [SVM], k-nearest neighbour algorithm [KNN], and random forest [RF]) were employed to develop predictive models. Model diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, and clinical utility was evaluated using decision curve analysis (DCA). Finally, the radiomics model's generalisation ability was further validated in the prediction of metachronous brain metastasis (MBM). RESULTS After feature screening, 22 radiomics features were identified as highly predictive, of which nine were derived from the peritumour region. All four machine-learning models demonstrated predictive capability, with SVM showing superior efficiency and robustness. The area under the ROC curve (AUC) of SVM was 0.918 in the training cohort and 0.901 in the validation cohort. DCA indicated the highest net benefit. Furthermore, the time-dependent ROC curve exhibited predictive efficacy for MBM occurrence across 1-, 2-, and 3-year follow-up periods, with all AUC values exceeding 0.7. CONCLUSION The optimal SVM model integrating intratumoural and peritumoural radiomics features was confirmed and defined as an imaging biomarker for predicting BM in newly diagnosed lung cancer patients, underscoring its potential to significantly impact clinical diagnosis and treatment.
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Affiliation(s)
- S Yichu
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/ Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, China
| | - L Fei
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/ Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, China
| | - L Ying
- Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang City, Jiangsu Province, 222000, China
| | - X Youyou
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/ Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, China.
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Jiang X, Luo C, Peng X, Zhang J, Yang L, Liu LZ, Cui YF, Liu MW, Miao L, Jiang JM, Ren JL, Yang XT, Li M, Zhang L. Incidence rate of occult lymph node metastasis in clinical T 1-2N 0M 0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research. Respir Res 2024; 25:226. [PMID: 38811960 PMCID: PMC11138070 DOI: 10.1186/s12931-024-02852-9] [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: 01/03/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.
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Affiliation(s)
- Xu Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Chao Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xin Peng
- Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, 610031, China
- Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Jing Zhang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Lin Yang
- Department of Pathology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Li-Zhi Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Yan-Fen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Meng-Wen Liu
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lei Miao
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiu-Ming Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, 100176, China
| | - Xiao-Tang Yang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China.
| | - Meng Li
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Li Zhang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, 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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Ren H, Wang Q, Xiao Z, Mo R, Guo J, Hide GR, Tu M, Zeng Y, Ling C, Li P. Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-00967-5. [PMID: 38565729 DOI: 10.1007/s10278-024-00967-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/01/2023] [Accepted: 11/14/2023] [Indexed: 04/04/2024]
Abstract
This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.
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Affiliation(s)
- He Ren
- Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Qiubo Wang
- Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhengguang Xiao
- Department of Radiology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Runwei Mo
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200030, China
| | - Jiachen Guo
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gareth Richard Hide
- Department of Surgery, Faculty of Health Sciences Medical School, University of the Witwatersrand, Parktown, Johannesburg, South Africa
| | - Mengting Tu
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yanan Zeng
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Chen Ling
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Ping Li
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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Zhan PC, Yang T, Zhang Y, Liu KY, Li Z, Zhang YY, Liu X, Liu NN, Wang HX, Shang B, Chen Y, Jiang HY, Zhao XT, Shao JH, Chen Z, Wang XD, Wang K, Gao JB, Lyu PJ. Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study. Eur Radiol 2024; 34:1280-1291. [PMID: 37589900 DOI: 10.1007/s00330-023-10108-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/07/2023] [Accepted: 06/29/2023] [Indexed: 08/18/2023]
Abstract
OBJECTIVES To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA). METHODS The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value. RESULTS A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001). CONCLUSIONS The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment. CLINICAL RELEVANCE STATEMENT A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes. KEY POINTS • The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.
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Affiliation(s)
- Peng-Chao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuan Zhang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Ke-Yan Liu
- Zhengzhou University Medical College, Zhengzhou, 450052, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Yu-Yuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Na-Na Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Hui-Xia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Bo Shang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiang-Tian Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Jing-Hai Shao
- Department of Radiology, He Nan Sui Xian People's Hospital, Shangqiu, 476000, China
| | - Zhe Chen
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Xin-Dong Wang
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Kang Wang
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China.
| | - Pei-Jie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China.
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7
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Zhou T, Yang M, Xiong W, Zhu F, Li Q, Zhao L, Zhao Z. The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. Transl Cancer Res 2024; 13:202-216. [PMID: 38410219 PMCID: PMC10894356 DOI: 10.21037/tcr-23-1324] [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: 07/26/2023] [Accepted: 11/29/2023] [Indexed: 02/28/2024]
Abstract
Background The identification of different subtypes of early-stage lung invasive adenocarcinoma before surgery contributes to the precision treatment. Radiomics could be one of the effective and noninvasive identification methods. The value of peritumoral radiomics in predicting the subtypes of early-stage lung invasive adenocarcinoma perhaps clinically useful. Methods This retrospective study included 937 lung adenocarcinomas which were randomly divided into the training set (n=655) and testing set (n=282) with a ratio of 7:3. This study used the univariate and multivariate analysis to choose independent clinical predictors. Radiomics features were extracted from 18 regions of interest (1 intratumoral region and 17 peritumoral regions). Independent and conjoint prediction models were constructed based on radiomics and clinical features. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE). Significant differences between areas under the ROC (AUCs) were estimated using in the Delong test. Results Patient age, smoking history, carcinoembryonic antigen (CEA), lesion location, length, width and clinic behavior were the independent predictors of differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. The highest AUC value among the 19 independent models was obtained for the PTV0~+3 radiomics model with 0.849 for the training set and 0.854 for the testing set. As the peritumoral distance increased, the predictive power of the models decreased. The radiomics-clinical conjoint model was statistically significantly different from the other models in the Delong test (P<0.05). Conclusions The intratumoral and peritumoral regions contained a wealth of clinical information. The diagnostic efficacy of intra-peritumoral radiomics combined clinical model was further improved, which was particularly important for preoperative staging and treatment decision-making.
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Affiliation(s)
- Tong Zhou
- School of Medicine, Shaoxing University, Shaoxing, China
| | - Minxia Yang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Wanrong Xiong
- School of Medicine, Shaoxing University, Shaoxing, China
| | - Fandong Zhu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Qianling Li
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Li Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
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8
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Li S, Yang Z, Li Y, Zhao N, Yang Y, Zhang S, Jiang M, Wang J, Sun H, Xie Z. Preoperative prediction of vasculogenic mimicry in lung adenocarcinoma using a CT radiomics model. Clin Radiol 2024; 79:e164-e173. [PMID: 37940444 DOI: 10.1016/j.crad.2023.09.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/01/2023] [Accepted: 09/29/2023] [Indexed: 11/10/2023]
Abstract
AIM To develop and validate a non-invasive computed tomography (CT)-based radiomics model for predicting vasculogenic mimicry (VM) status in lung adenocarcinoma (LA). MATERIALS AND METHODS Two hundred and three patients with LA were enrolled retrospectively and grouped into training and test groups with a ratio of 7:3. Uni- and multivariate logistic regression analyses were performed in the training cohort to screen the independent clinical and radiological factors for VM, and the clinical model was then established. A radiomics model was established based on the rad-scores through support vector machine (SVM). A radiomics nomogram model was subsequently constructed by combining the rad-score with clinical-radiological factors. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) were conducted to evaluate the performance of the three models. RESULTS Nine selected radiomics features were selected for the radiomics model and the maximum length and spiculation sign were constructed for the clinical model. The radiomics nomogram model integrating the maximum length, spiculation sign, and rad-score yielded the best AUC in both the training (AUC = 0.925) and test cohorts (AUC = 0.978), in comparison with the radiomics model (AUC = 0.907 and 0.964, in both the training and test cohorts) and the clinical model (AUC = 0.834 and 0.836 in both training and test cohorts). CONCLUSIONS The CT-based radiomics nomogram model showed satisfying discriminating performance for preoperatively and non-invasively predicting VM expression status in LA patients.
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Affiliation(s)
- S Li
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China; Department of Medical Imaging Diagnostics, Bengbu Medical College, Bengbu, China
| | - Z Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Y Li
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - N Zhao
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Y Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - S Zhang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - M Jiang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - J Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - H Sun
- Department of Radiology, Zhongshan Hospital, Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.
| | - Z Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China; Department of Medical Imaging Diagnostics, Bengbu Medical College, Bengbu, China.
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Pan F, Feng L, Liu B, Hu Y, Wang Q. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol 2023; 14:1295511. [PMID: 38027000 PMCID: PMC10646419 DOI: 10.3389/fphar.2023.1295511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.
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Affiliation(s)
- Feng Pan
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
- Department of CT, Jilin Province FAW General Hospital, Changchun, China
| | - Li Feng
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baocai Liu
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Hu
- Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
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10
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Zhang L, Yang Y, Wang T, Chen X, Tang M, Deng J, Cai Z, Cui W. Intratumoral and peritumoral MRI-based radiomics prediction of histopathological grade in soft tissue sarcomas: a two-center study. Cancer Imaging 2023; 23:103. [PMID: 37885031 PMCID: PMC10601231 DOI: 10.1186/s40644-023-00622-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
OBJECTIVES This study aims to develop a model based on intratumoral and peritumoral radiomics from fat-suppressed T2-weighted(FS-T2WI) images to predict the histopathological grade of soft tissue sarcoma (STS). METHODS This retrospective study included 160 patients with STS from two centers, of which 82 were low-grade and 78were high-grade. Radiomics features were extracted and selected from the region of tumor mass volume (TMV) and peritumoral tumor volume (PTV) respectively. The TMV, PTV, and combined(TM-PTV) radiomics models were established in the training cohort (n = 111)for the prediction of histopathological grade. Finally, a radiomics nomogram was constructed by combining the TM-PTV radiomics signature (Rad-score) and the selected clinical-MRI predictor. The ROC and calibration curves were used to determine the performance of the TMV, PTV, and TM-PTV models in the training and validation cohort (n = 49). The decision curve analysis (DCA) and calibration curves were used to investigate the clinical usefulness and calibration of the nomogram, respectively. RESULTS The TMV model, PTV model, and TM-PTV model had AUCs of 0.835, 0.879, and 0.917 in the training cohort and 0.811, 0.756, 0.896 in the validation cohort. The nomogram, including the TM-PTV signatures and peritumoral hyperintensity, achieved good calibration and discrimination with a C-index of 0.948 (95% CI, 0.906 to 0.990) in the training cohort and 0.921 (95% CI, 0.840 to 0.995) in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION The proposed model based on intratumoral and peritumoral radiomics showed good performance in distinguishing low-grade from high-grade STSs.
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Affiliation(s)
- Liyuan Zhang
- Department of Plastic Surgery, Sichuan Provincial People's Hospital, School of Medicine,University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China
| | - Yang Yang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610000, People's Republic of China
| | - Ting Wang
- Department of Plastic Surgery, The First People's Hospital of Yibin, Yibin, 644000, People's Republic of China
| | - Xi Chen
- Sichuan College of Traditional Chinese Medicine, Mianyang, 621000, People's Republic of China
| | - Mingyue Tang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610000, People's Republic of China
| | - Junnan Deng
- Department of Plastic Surgery, Sichuan Provincial People's Hospital, School of Medicine,University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China
| | - Zhen Cai
- Department of Plastic Surgery, Sichuan Provincial People's Hospital, School of Medicine,University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China.
| | - Wei Cui
- Department of Plastic Surgery, Sichuan Provincial People's Hospital, School of Medicine,University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China.
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11
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Zeng C, Zhang W, Liu M, Liu J, Zheng Q, Li J, Wang Z, Sun G. Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer. Front Oncol 2023; 13:1096364. [PMID: 37293586 PMCID: PMC10246750 DOI: 10.3389/fonc.2023.1096364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Objective This study aimed to establish a predictive model for occult lymph node metastasis (LNM) in patients with clinical stage I-A non-small cell lung cancer (NSCLC) based on contrast-enhanced CT. Methods A total of 598 patients with stage I-IIA NSCLC from different hospitals were randomized into the training and validation group. The "Radiomics" tool kit of AccuContour software was employed to extract the radiomics features of GTV and CTV from chest-enhanced CT arterial phase pictures. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to reduce the number of variables and develop GTV, CTV, and GTV+CTV models for predicting occult lymph node metastasis (LNM). Results Eight optimal radiomics features related to occult LNM were finally identified. The receiver operating characteristic (ROC) curves of the three models showed good predictive effects. The area under the curve (AUC) value of GTV, CTV, and GTV+CTV model in the training group was 0.845, 0.843, and 0.869, respectively. Similarly, the corresponding AUC values in the validation group were 0.821, 0.812, and 0.906. The combined GTV+CTV model exhibited a better predictive performance in the training and validation group by the Delong test (p<0.05). Moreover, the decision curve showed that the combined GTV+CTV predictive model was superior to the GTV or CTV model. Conclusion The radiomics prediction models based on GTV and CTV can predict occult LNM in patients with clinical stage I-IIA NSCLC preoperatively, and the combined GTV+CTV model is the optimal strategy for clinical application.
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Affiliation(s)
- Chao Zeng
- Hebei Key Laboratory of Medical-industrial Integration Precision Medicine, Clinical Medicine College, Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei, China
| | - Wei Zhang
- Department of Radiotherapy, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Meiyue Liu
- Hebei Key Laboratory of Medical-industrial Integration Precision Medicine, Clinical Medicine College, Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei, China
| | - Jianping Liu
- Department of Chemoradiation, Tangshan People’s Hospital, Tangshan, Hebei, China
| | - Qiangxin Zheng
- Hebei Key Laboratory of Medical-industrial Integration Precision Medicine, Clinical Medicine College, Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei, China
| | - Jianing Li
- Hebei Key Laboratory of Medical-industrial Integration Precision Medicine, Clinical Medicine College, Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei, China
| | - Zhiwu Wang
- Department of Chemoradiation, Tangshan People’s Hospital, Tangshan, Hebei, China
| | - Guogui Sun
- Hebei Key Laboratory of Medical-industrial Integration Precision Medicine, Clinical Medicine College, Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei, China
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12
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Chen QL, Li MM, Xue T, Peng H, Shi J, Li YY, Duan SF, Feng F. Radiomics nomogram integrating intratumoural and peritumoural features to predict lymph node metastasis and prognosis in clinical stage IA non-small cell lung cancer: a two-centre study. Clin Radiol 2023; 78:e359-e367. [PMID: 36858926 DOI: 10.1016/j.crad.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 01/24/2023] [Accepted: 02/03/2023] [Indexed: 02/18/2023]
Abstract
AIM To investigate the value of a radiomics nomogram integrating intratumoural and peritumoural features in predicting lymph node metastasis and overall survival (OS) in patients with clinical stage IA non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS This study retrospectively enrolled 199 patients (training cohort: 71 patients from Affiliated Tumour Hospital of Nantong University; internal validation cohort: 46 patients from Affiliated Tumour Hospital of Nantong University; external validation cohort: 82 patients from the public database). CT radiomics models were constructed based on four volumes of interest: gross tumour volume (GTV), gross and 3 mm peritumoural volume (GPTV3), gross and 6 mm peritumoural volume (GPTV6), and gross and 9 mm peritumoural volume (GPTV9). The optimal radiomics signature was further combined with independent clinical predictors to develop a nomogram. Univariable and multivariable Cox regression analysis were applied to determine the relationship between factors and OS. RESULTS GPTV6 radiomics yielded better performance than GTV, GPTV3, and, GPTV9 radiomics in the training (area under the curve [AUC], 0.81), internal validation (AUC, 0.79), and external validation cohorts (AUC, 0.71), respectively. The nomogram integrating GPTV6 radiomics and spiculation improved predictive ability, with AUCs of 0.85, 0.80, and 0.74 in three cohorts, respectively. Pathological lymph node metastasis, nomogram-predicted lymph node metastasis, and pleural indentation were independent risk predictors of OS (p<0.05). CONCLUSIONS The nomogram integrating GPTV6 radiomics features and independent clinical predictors performed well in predicting lymph node metastasis and prognosis in patients with clinical stage IA NSCLC.
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Affiliation(s)
- Q-L Chen
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China
| | - M-M Li
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China
| | - T Xue
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China
| | - H Peng
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China
| | - J Shi
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China
| | - Y-Y Li
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China
| | - S-F Duan
- GE Healthcare China, Shanghai City 210000, China
| | - F Feng
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226001, PR China.
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Ren H, Xiao Z, Ling C, Wang J, Wu S, Zeng Y, Li P. Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma. Quant Imaging Med Surg 2023; 13:237-248. [PMID: 36620176 PMCID: PMC9816727 DOI: 10.21037/qims-22-491] [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: 05/16/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
Background Lung cancer is one of the most serious cancers in the world. Subtypes of lung adenocarcinoma can be quickly distinguished by analyzing 3D radiomic signatures and radiological features. Methods This study included 493 patients from 3 hospitals with a total of 506 lesions confirmed as minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), or invasive adenocarcinoma (IAC). After segmenting the lesion area, 3D radiomic signatures were extracted using the PyRadiomics package v. 3.0.1 implemented in Python (https://pyradiomics.readthedocs.io/en/latest/index.html), and the corresponding radiological features were collected. Subsequently, the top 100 features were identified by feature screening methods, including the Spearman rank correlation and minimum redundancy maximum relevance (mRMR) feature selection, and the top 10 features were determined by the least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating 3D radiomic signatures and radiological features in the prediction system. The nomogram was evaluated from multiple perspectives and tested on the validation cohort. Results The model combined 3 radiological features and seven 3D radiomic signatures. The area under the curve (AUC) of the model was 0.877 (95% CI: 0.829-0.925) in the training cohort, 0.864 (95% CI: 0.789-0.940) in the testing cohort, and 0.836 (95% CI: 0.749-0.924) in the validation cohort. The nomogram applied in all 3 cohorts showed reliable accuracy and calibration. The decision curve also demonstrated the clinical effectiveness of the nomogram. Conclusions In this study, a nomogram-based model combining 3D radiomic signatures and radiological features was developed. Its performance in identifying IAC and MIA/AIS was satisfactory and had clinical value.
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Affiliation(s)
- He Ren
- Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Zhengguang Xiao
- Department of Radiology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Ling
- Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Jiayi Wang
- Anesthesiology Department of Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shiyu Wu
- Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Yanan Zeng
- Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Ping Li
- Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, Shanghai, China
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14
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Radiomics for Detection of the EGFR Mutation in Liver Metastatic NSCLC. Acad Radiol 2022; 30:1039-1046. [PMID: 35907759 DOI: 10.1016/j.acra.2022.06.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 12/09/2022]
Abstract
RATIONALE AND OBJECTIVES The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis. MATERIALS AND METHODS A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra- and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating characteristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs. RESULTS A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by combination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the training set. CONCLUSION The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.
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Kothari G, Woon B, Patrick CJ, Korte J, Wee L, Hanna GG, Kron T, Hardcastle N, Siva S. The impact of inter-observer variation in delineation on robustness of radiomics features in non-small cell lung cancer. Sci Rep 2022; 12:12822. [PMID: 35896707 PMCID: PMC9329346 DOI: 10.1038/s41598-022-16520-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/11/2022] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ‘NSCLC-Radiomics’ and ‘NSCLC-Radiomics-Interobserver1’ (‘Interobserver’). For ‘NSCLC-Radiomics’, we created an additional set of manual contours for 92 patients, and for ‘Interobserver’, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (‘NSCLC-Radiomics’) to 0.85 (‘Interobserver’—semi-automated). The median ICC for the ‘NSCLC-Radiomics’, ‘Interobserver’ (manual) and ‘Interobserver’ (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ‘NSCLC-Radiomics’ dataset compared to the ‘Interobserver’ dataset. Survival analysis showed similar separation of curves for three of four RF apart from ‘original_shape_Compactness2’, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features’ prognostic capability.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia. .,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.
| | - Beverley Woon
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Radiology, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Cameron J Patrick
- Statistical Consulting Centre, University of Melbourne, Parkville, Australia
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Department of Biomedical Engineering, School of Chemical and Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Leonard Wee
- Department of Radiotherapy (MAASTRO), GROW School of Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Clinical Data Science, Maastricht University, Maastricht, The Netherlands
| | - Gerard G Hanna
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Nicholas Hardcastle
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia
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