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Zhang Y, Yang X, Bi F, Wen L, Niu Y, Yang Y, Lin H, Yu X. CT-based radiomics for differentiating peripherally located pulmonary sclerosing pneumocytoma from carcinoid. Med Phys 2024; 51:4219-4230. [PMID: 38507783 DOI: 10.1002/mp.17037] [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: 09/05/2023] [Revised: 01/31/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Pulmonary sclerosing pneumocytoma (PSP) and pulmonary carcinoid (PC) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate benign from malignant pulmonary lesions. However, the value of radiomics based on computed tomography (CT) images to differentiate PSP from PC has not been well explored. PURPOSE We aimed to investigate the feasibility of radiomics in the differentiation between PSP and PC. METHODS Fifty-three PSP and fifty-five PC were retrospectively enrolled and then were randomly divided into the training and test sets. Univariate and multivariable logistic analyses were carried to select clinical predictor related to differential diagnosis of PSP and PC. A total of 1316 radiomics features were extracted from the unenhanced CT (UECT) and contrast-enhanced CT (CECT) images, respectively. The minimum redundancy maximum relevance and the least absolute shrinkage and selection operator were used to select the most significant radiomics features to construct radiomics models. The clinical predictor and radiomics features were integrated to develop combined models. Two senior radiologists independently categorized each patient into PSP or PC group based on traditional CT method. The performances of clinical, radiomics, and combined models in differentiating PSP from PC were investigated by the receiver operating characteristic (ROC) curve. The diagnostic performance was also compared between the combined models and radiologists. RESULTS In regard to differentiating PSP from PC, the area under the curves (AUCs) of the clinical, radiomics, and combined models were 0.87, 0.96, and 0.99 in the training set UECT, and were 0.87, 0.97, and 0.98 in the training set CECT, respectively. The AUCs of the clinical, radiomics, and combined models were 0.84, 0.92, and 0.97 in the test set UECT, and were 0.84, 0.93, and 0.98 in the test set CECT, respectively. In regard to the differentiation between PSP and PC, the combined model was comparable to the radiomics model, but outperformed the clinical model and the two radiologists, whether in the test set UECT or CECT. CONCLUSIONS Radiomics approaches show promise in distinguishing between PSP and PC. Moreover, the integration of clinical predictor (gender) has the potential to enhance the diagnostic performance even further.
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Affiliation(s)
- Yi Zhang
- Graduate Collaborative Training base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Xiaohuang Yang
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Feng Bi
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Lu Wen
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Yue Niu
- Graduate Collaborative Training base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Yanhui Yang
- Graduate Collaborative Training base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, General Electric (GE) Healthcare, Changsha, Hunan, China
| | - Xiaoping Yu
- Graduate Collaborative Training base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
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Tian Z, Cheng Y, Zhao S, Li R, Zhou J, Sun Q, Wang D. Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study. Cancer Imaging 2024; 24:52. [PMID: 38627828 PMCID: PMC11020328 DOI: 10.1186/s40644-024-00697-5] [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: 10/11/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection. METHODS A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors. RESULTS The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649-0.923] vs. 0.822 [0.692-0.952] vs. 0.733 [0.573-0.892] vs. 0.511 [0.359-0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance. CONCLUSIONS The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Zhen Tian
- Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Yifan Cheng
- Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Shuai Zhao
- Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Ruiqi Li
- Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Jiajie Zhou
- Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Qiannan Sun
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, China
| | - Daorong Wang
- Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China.
- Department of General Surgery, Northern Jiangsu People's Hospital, Yangzhou, China.
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, China.
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China.
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Zhu F, Yang C, Zou J, Ma W, Wei Y, Zhao Z. The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis. Acta Radiol 2023; 64:3074-3084. [PMID: 37817511 DOI: 10.1177/02841851231205737] [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] [Indexed: 10/12/2023]
Abstract
Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.
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Affiliation(s)
- Fandong Zhu
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Chen Yang
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Jiajun Zou
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Weili Ma
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Hangzhou, Zhejiang, PR China
| | - Zhenhua Zhao
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
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Shi L, Sheng M, Wei Z, Liu L, Zhao J. CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis. Acad Radiol 2023; 30:3064-3075. [PMID: 37385850 DOI: 10.1016/j.acra.2023.05.026] [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: 04/21/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023]
Abstract
RATIONALE AND OBJECTIVES More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies. MATERIALS AND METHODS PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity. RESULTS In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs. CONCLUSION CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China (M.S.)
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Lei Liu
- Institutes of Intelligence Medicine, Fudan University, Shanghai, China (L.L.)
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China (J.Z.).
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Xu J, Guo J, Yang HQ, Ji QL, Song RJ, Hou F, Liang HY, Liu SL, Tian LT, Wang HX. Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors. Eur Radiol 2023; 33:6781-6793. [PMID: 37148350 DOI: 10.1007/s00330-023-09686-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: 04/22/2022] [Revised: 02/11/2023] [Accepted: 02/26/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT). METHODS Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis. RESULTS The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model. CONCLUSIONS The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.
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Affiliation(s)
- Jun Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jia Guo
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Hai-Qiang Yang
- Institute for Future Shandong Key Laboratory of Industrial Control Technology of Qingdao University, Qingdao, Shandong, China
| | - Qing-Lian Ji
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Rui-Jie Song
- Institute for Future Shandong Key Laboratory of Industrial Control Technology of Qingdao University, Qingdao, Shandong, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Hao-Yu Liang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shun-Li Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Lan-Tian Tian
- Department of Hepatopancreatobiliary & Retroperitoneal Tumour Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
| | - He-Xiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
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Hu Z, Liang H, Zhao H, Hou F, Hao D, Ji Q, Huang C, Xu J, Tian L, Wang H. Preoperative contrast-enhanced CT-based radiomics signature for predicting hypoxia-inducible factor 1α expression in retroperitoneal sarcoma. Clin Radiol 2023; 78:e543-e551. [PMID: 37080804 DOI: 10.1016/j.crad.2023.03.015] [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: 11/14/2022] [Revised: 02/27/2023] [Accepted: 03/19/2023] [Indexed: 04/05/2023]
Abstract
AIM To develop and test a contrast-enhanced computed tomography (CECT)-based radiomics signature (RS) to preoperatively predict hypoxia-inducible factor 1α (HIF-1α) expression in retroperitoneal sarcoma (RPS). MATERIALS AND METHODS This study included 129 patients with RPS retrospectively who underwent CECT, including 64 male and 65 female patients (55 [2-84] years). Participants were divided into a training set comprising 85 patients and a test set comprising 44 patients. Clinical data and CECT findings of all patients were collected. RS construction was performed by the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. The clinical information was analysed by univariate and multivariate logistic regression analysis. The RS and risk factors were included to build a radiomics nomogram. The predictive efficacy of different models was evaluated by accuracy, area under the receiver operating characteristic curve (AUC), and decision curve analysis. RESULTS The RS combined signature was constructed on the basis of multi-phase CECT and had an accuracy of 0.795 and an AUC of 0.719 (95% confidence interval [CI], 0.552-0.886) in the test set, which were higher than that of the radiomics nomogram (accuracy: 0.636; AUC: 0.702 [95% CI, 0.547-0.857]) and the clinical model (accuracy: 0.682; AUC: 0.486 [95% CI, 0.324-0.647]). The decision curve analysis showed that the RS combined signature provided better clinical application than the clinical model and radiomics nomogram. CONCLUSIONS The multi-phase CECT-based RS constructed can be used as a powerful tool for predicting HIF-1α expression in patients with RPS.
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Affiliation(s)
- Z Hu
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - H Liang
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - H Zhao
- Department of Pathology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - F Hou
- Department of Pathology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - D Hao
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - Q Ji
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - C Huang
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, 100089, China
| | - J Xu
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, 100089, China
| | - L Tian
- Department of Hepatopancreatobiliary & Retroperitoneal Tumour Surgery, Affiliated Hospital of Qingdao University, Qingdao, 266003, China.
| | - H Wang
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, 266003, China.
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Wang B, Zhang H, Li W, Fu S, Li Y, Gao X, Wang D, Yang X, Xu S, Wang J, Hou D. Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study. Front Oncol 2023; 13:1037052. [PMID: 37293594 PMCID: PMC10244560 DOI: 10.3389/fonc.2023.1037052] [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: 09/05/2022] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Objective The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. Methods A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient). Results The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation. Conclusion The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.
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Affiliation(s)
- Bing Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Hui Zhang
- Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Wei Li
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Siyun Fu
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Ye Li
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Xiang Gao
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Dongpo Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Xinjie Yang
- Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Shaofa Xu
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Jinghui Wang
- Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Dailun Hou
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, 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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Demircioğlu A. Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 2021; 12:172. [PMID: 34817740 PMCID: PMC8613324 DOI: 10.1186/s13244-021-01115-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Many studies in radiomics are using feature selection methods to identify the most predictive features. At the same time, they employ cross-validation to estimate the performance of the developed models. However, if the feature selection is performed before the cross-validation, data leakage can occur, and the results can be biased. To measure the extent of this bias, we collected ten publicly available radiomics datasets and conducted two experiments. First, the models were developed by incorrectly applying the feature selection prior to cross-validation. Then, the same experiment was conducted by applying feature selection correctly within cross-validation to each fold. The resulting models were then evaluated against each other in terms of AUC-ROC, AUC-F1, and Accuracy. RESULTS Applying the feature selection incorrectly prior to the cross-validation showed a bias of up to 0.15 in AUC-ROC, 0.29 in AUC-F1, and 0.17 in Accuracy. CONCLUSIONS Incorrect application of feature selection and cross-validation can lead to highly biased results for radiomic datasets.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
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