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Lv X, Lu JJ, Song SM, Hou YR, Hu YJ, Yan Y, Yu T, Ye DM. Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study. Clin Otolaryngol 2024; 49:462-474. [PMID: 38622816 DOI: 10.1111/coa.14162] [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: 09/27/2023] [Revised: 01/28/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
INTRODUCTION To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). METHODS A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. RESULTS Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. CONCLUSION We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.
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Affiliation(s)
- Xin Lv
- Department of Oncology, Yingkou Central Hospital, Yingkou, People's Republic of China
| | - Jing-Jing Lu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Si-Meng Song
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Yi-Ru Hou
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Yan-Jun Hu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Yan Yan
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Tao Yu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Dong-Man Ye
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
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Wang H, Chen A, Wang K, Yang H, Wen W, Ren Q, Chen L, Xu X, Zhu Q. CT imaging features of lung ground-glass nodule patients with upgraded intraoperative frozen pathology. Discov Oncol 2024; 15:29. [PMID: 38310621 PMCID: PMC10838864 DOI: 10.1007/s12672-024-00872-x] [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: 10/17/2023] [Accepted: 01/23/2024] [Indexed: 02/06/2024] Open
Abstract
PURPOSE Intraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model. METHODS Collected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted. RESULTS The consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model's Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS. CONCLUSION CT imaging has the capacity to effectively detect patients at risk of upstaging during FS.
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Affiliation(s)
- Hongya Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Aiping Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Kun Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - He Yang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Wei Wen
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Qianrui Ren
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Liang Chen
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Xinfeng Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
| | - Quan Zhu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, 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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Zhuo Y, Shen J, Zhan Y, Tian Y, Yu M, Yang S, Ye P, Fan L, Zhang Z, Shan F. Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study. Quant Imaging Med Surg 2023; 13:6139-6151. [PMID: 37711807 PMCID: PMC10498235 DOI: 10.21037/qims-22-992] [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: 09/19/2022] [Accepted: 06/15/2023] [Indexed: 09/16/2023]
Abstract
Background Broad generalization of radiomics-assisted models may be impeded by concerns about variability. This study aimed to evaluate the merit of combatting batch effect (ComBat) harmonization in reducing the variability of voxel size-related radiomics in both phantom and clinical study in comparison with image resampling correction method. Methods A pulmonary phantom with 22 different types of nodules was scanned by computed tomography (CT) with different voxel sizes. The variability of voxel size-related radiomics features was evaluated using concordance correlation coefficient (CCC), dynamic range (DR), and intraclass correlation coefficient (ICC). ComBat and image resampling compensation methods were used to reduce variability of voxel size-related radiomics. The percentage of robust radiomics features was compared before and after optimization. Pathologically differential diagnosis of invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) (AIS-MIA group) was used for clinical validation in 134 patients. Results Before optimization, the number of excellent features in the phantom and clinical data was 26.12% and 32.31%, respectively. The excellent features were increased after image resampling and ComBat correction. For clinical optimization, the effect of the ComBat compensation method was significantly better than that of image resampling, with excellent features reaching 90.96% and poor features only amounting to 4.96%. In addition, the hierarchical clustering analysis showed that the first-order and shape features had better robustness than did texture features. In clinical validation, the area under the curve (AUC) of the testing set was 0.865 after ComBat correction. Conclusions The ComBat harmonization can optimize voxel size-related CT radiomics variability in pulmonary nodules more efficiently than image resampling harmonization.
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Affiliation(s)
- Yaoyao Zhuo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yi Zhan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Ye Tian
- Department of Radiology, Beilun Second People’s Hospital, Ningbo, China
| | - Mingfeng Yu
- Department of Thoracic Surgery, Beilun Second People’s Hospital, Ningbo, China
| | - Shuyi Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peiyan Ye
- Department of Traditional Chinese Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
- Research Institute of Big Data, 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
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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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Computed tomography radiomics in growth prediction of pulmonary ground-glass nodules. Eur J Radiol 2023; 159:110684. [PMID: 36621209 DOI: 10.1016/j.ejrad.2022.110684] [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: 07/10/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/02/2023]
Abstract
PURPOSE Individualized follow-up of pulmonary ground-glass nodules (GGNs) remains challenging in clinical practice. Accurate prediction of the growth or long-term stability of persistent GGNs is essential to optimize the follow-up intervals. METHODS In this retrospective study, 253 patients with 1115 computed tomography (CT) images were recruited. In total, 1115 CT images were randomized into training (70%) and validation sets (30%). We developed models for the growth or long-term stable prediction of GGNs using radiomics and clinical features. We evaluated the prediction accuracy of the models using receiver operating characteristic (ROC) curve analysis, and the areas under the curve (AUCs) were established. The ROC curves of the models were compared using the DeLong method. RESULTS The growth and stable groups contained 535 and 580 GGNs, respectively. Traditional radiographic features have limited value in the prediction of growth or long-term stability of GGNs. The prediction nomogram model combining radiomics and clinical features (size, location, and age) yielded the best AUC in both the training and validation sets (AUC = 0.843 and 0.824, respectively). The radiomics model outperformed the clinical model in both sets (AUC: 0.836 vs 0.772 and 0.818 vs 0.735, respectively). The radiomics signature and nomogram model achieved similar AUCs (Delong test, training set: P = 0.09; validation set: P = 0.37). CONCLUSIONS We developed and validated a nomogram model combining radiomics signature, size, age, and location to predict the growth or long-term stability of GGNs. The model achieved good performance and may provide a basis for the improvement of follow-up management of GGNs.
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Abstract
Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.
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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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Huang Y, Feng A, Lin Y, Gu H, Chen H, Wang H, Shao Y, Duan Y, Zhuo W, Xu Z. Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features. Radiat Oncol 2022; 17:188. [PMID: 36397060 PMCID: PMC9673306 DOI: 10.1186/s13014-022-02154-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/08/2022] [Indexed: 11/18/2022] Open
Abstract
Background This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution. Methods A total of 140 patients with non-small cell lung cancer who received stereotactic body radiation therapy (SBRT) were retrospectively included in this study. These patients were randomly divided into the training (n = 112) and test (n = 28) sets. Besides, 107 dosiomics features were extracted by Pyradiomics, and 1316 DLR features were extracted by ResNet50. Feature visualization was performed based on Spearman’s correlation coefficients, and feature selection was performed based on the least absolute shrinkage and selection operator. Three different models were constructed based on random forest, including (1) a dosiomics model (a model constructed based on dosiomics features), (2) a DLR model (a model constructed based on DLR features), and (3) a hybrid model (a model constructed based on dosiomics and DLR features). Subsequently, the performance of these three models was compared with receiver operating characteristic curves. Finally, these dosiomics and DLR features were analyzed with Spearman’s correlation coefficients. Results In the training set, the area under the curve (AUC) of the dosiomics, DLR, and hybrid models was 0.9986, 0.9992, and 0.9993, respectively; the accuracy of these three models was 0.9643, 0.9464, and 0.9642, respectively. In the test set, the AUC of these three models was 0.8462, 0.8750, and 0.9000, respectively; the accuracy of these three models was 0.8214, 0.7857, and 0.8571, respectively. The hybrid model based on dosiomics and DLR features outperformed other two models. Correlation analysis between dosiomics features and DLR features showed weak correlations. The dosiomics features that correlated DLR features with the Spearman’s rho |ρ| ≥ 0.8 were all first-order features. Conclusion The hybrid features based on dosiomics and DLR features from 3D dose distribution could improve the performance of RP prediction after SBRT.
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Ding Y, He C, Zhao X, Xue S, Tang J. Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests. Front Med (Lausanne) 2022; 9:936595. [PMID: 36059824 PMCID: PMC9433577 DOI: 10.3389/fmed.2022.936595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.
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Affiliation(s)
- Yizong Ding
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunming He
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojing Zhao
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Song Xue
- Department of Cardiovascular Surgery, Reiji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Tang
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Jian Tang,
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Xiong Z, Jiang Y, Tian D, Zhang J, Guo Y, Li G, Qin D, Li Z. Radiomics for identifying lung adenocarcinomas with predominant lepidic growth manifesting as large pure ground-glass nodules on CT images. PLoS One 2022; 17:e0269356. [PMID: 35749350 PMCID: PMC9231804 DOI: 10.1371/journal.pone.0269356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 05/19/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose To explore the value of radiomics in the identification of lung adenocarcinomas with predominant lepidic growth in pure ground-glass nodules (pGGNs) larger than 10 mm. Methods We retrospectively analyzed CT images of 204 patients with large pGGNs (≥ 10 mm) pathologically diagnosed as minimally invasive adenocarcinomas (MIAs), lepidic predominant adenocarcinomas (LPAs), and non-lepidic predominant adenocarcinomas (NLPAs). All pGGNs in the two groups (MIA/LPA and NLPA) were randomly divided into training and test cohorts. Forty-seven patients from another center formed the external validation cohort. Baseline features, including clinical data and CT morphological and quantitative parameters, were collected to establish a baseline model. The radiomics model was built with the optimal radiomics features. The combined model was developed using the rad_score and independent baseline predictors. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The differential diagnosis performance of the models was compared with three radiologists (with 20+, 10+, and 3 years of experience) in the test cohort. Results The radiomics (training AUC: 0.833; test AUC: 0.804; and external validation AUC: 0.792) and combined (AUC: 0.849, 0.820, and 0.775, respectively) models performed better for discriminating than the baseline model (AUC: 0.756, 0.762, and 0.725, respectively) developed by tumor location and mean CT value of the whole nodule. The DeLong test showed that the AUCs of the combined and radiomics models were significantly increased in the training cohort. The highest AUC value of the radiologists was 0.600. Conclusion The application of CT radiomics improved the identification performance of lung adenocarcinomas with predominant lepidic growth appearing as pGGNs larger than 10 mm.
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Affiliation(s)
- Ziqi Xiong
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yining Jiang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Di Tian
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Jingyu Zhang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Guosheng Li
- Department of Pathology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Dongxue Qin
- Department of Radiology, the Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Zhiyong Li
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- * E-mail:
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Qian L, Zhou Y, Zeng W, Chen X, Ding Z, Shen Y, Qian Y, Tosi D, Silva M, Han Y, Fu X. A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules. Transl Lung Cancer Res 2022; 11:1132-1144. [PMID: 35832446 PMCID: PMC9271446 DOI: 10.21037/tlcr-22-395] [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: 03/28/2022] [Accepted: 06/16/2022] [Indexed: 11/06/2022]
Abstract
Background Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals. Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection. Methods Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017-December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them. Results The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort. The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and external validation cohort. Conclusions Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low- and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection.
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Affiliation(s)
- Liqiang Qian
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yinjie Zhou
- Department of Thoracic Surgery, Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital), Ningbo, China
| | - Wanqin Zeng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoke Chen
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Zhengping Ding
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yujia Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yifeng Qian
- National Clinical Research Center for Oral Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Davide Tosi
- Thoracic Surgery and Lung Transplant Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Yuchen Han
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
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13
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Li Y, Liu J, Yang X, Xu H, Qing H, Ren J, Zhou P. Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening. Br J Radiol 2022; 95:20211048. [PMID: 34995082 PMCID: PMC10993960 DOI: 10.1259/bjr.20211048] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/16/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). METHODS A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. RESULTS The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801-0.935), 0.929 (95% CI, 0.862-0.971), 0.941 (95% CI, 0.876-0.978), and 0.884 (95% CI, 0.805-0.938) in the primary cohort and 0.897 (95% CI, 0.768-0.968), 0.933 (95% CI, 0.815-0.986), 0.901 (95% CI, 0.773-0.970), and 0.828 (95% CI, 0.685-0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). CONCLUSION The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be pre-operative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. ADVANCES IN KNOWLEDGE The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be pre-operative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
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14
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Zhu M, Yang Z, Zhao W, Wang M, Shi W, Cheng Z, Ye C, Zhu Q, Liu L, Liang Z, Chen L. Predicting Ki-67 labeling index level in early-stage lung adenocarcinomas manifesting as ground-glass opacity nodules using intra-nodular and peri-nodular radiomic features. Cancer Med 2022; 11:3982-3992. [PMID: 35332684 PMCID: PMC9636499 DOI: 10.1002/cam4.4719] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 03/10/2022] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
Objectives To explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground‐glass opacity nodules (GGO) with high‐ and low Ki‐67 expression levels. Materials and Methods From January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models. Results Seven hundred and sixty‐nine patients with 769 GGOs were included in this study. Two hundred and forty‐five GGOs were confirmed to be of high Ki‐67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high‐ and low Ki‐67 LI groups (p < 0.05), and spiculation sign and maximum 2D diameter of the nodule were further confirmed to be risk factors for Ki‐67 LI. The radiomic model established using SVM exhibited an AUC of 0.731 in the validation cohort, which was higher than that of the clinical‐radiographic model (AUC = 0.675). Moreover, radiomic model combining both intra‐ and peri‐nodular features showed better diagnostic efficacy than using intra‐nodular features alone (AUC = 0.731 and 0.720, respectively). Conclusions The established radiomic model exhibited good diagnostic efficacy in differentiating between lung adenocarcinoma GGOs with high and low Ki‐67 LI, which was higher than the clinical‐radiographic model. Peri‐nodular radiomic features showed added benefits to the radiomic model. As a novel noninvasive method, radiomics have the potential to be applied in the preliminary classification of Ki‐67 expression level in lung adenocarcinoma GGOs.
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Affiliation(s)
- Minghui Zhu
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China.,Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhen Yang
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Wei Zhao
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Miaoyu Wang
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Wenjia Shi
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Zhenshun Cheng
- Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Cheng Ye
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Qiang Zhu
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Lu Liu
- Department of Nutrition, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Zhixin Liang
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Liangan Chen
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
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15
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Li M, Ruan Y, Feng Z, Sun F, Wang M, Zhang L. Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2 cm or Less: A Multicenter Study. Front Oncol 2021; 11:788424. [PMID: 34926304 PMCID: PMC8674565 DOI: 10.3389/fonc.2021.788424] [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: 10/02/2021] [Accepted: 11/15/2021] [Indexed: 11/16/2022] Open
Abstract
Purpose To construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images. Materials and Methods We retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability. Results Both models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability. Conclusion Nodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.
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Affiliation(s)
- Meirong Li
- Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Yachao Ruan
- Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Zhan Feng
- Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Fangyu Sun
- Department of Radiology, Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, China
| | - Minhong Wang
- Department of Radiology, First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Liang Zhang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
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16
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Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021; 26:85-96. [PMID: 34902259 DOI: 10.1080/24699322.2021.1994014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.
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Affiliation(s)
- Travis L Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lily V Saadat
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alice Wei
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
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17
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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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18
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Peeken JC, Neumann J, Asadpour R, Leonhardt Y, Moreira JR, Hippe DS, Klymenko O, Foreman SC, von Schacky CE, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Woodruff HC, Lambin P, Nyflot MJ, Gersing AS, Combs SE. Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics. Cancers (Basel) 2021; 13:1929. [PMID: 33923697 PMCID: PMC8073388 DOI: 10.3390/cancers13081929] [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: 03/01/2021] [Revised: 04/13/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.
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Affiliation(s)
- Jan C. Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, 85764 München, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands; (H.C.W.); (P.L.)
| | - Jan Neumann
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Rebecca Asadpour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
| | - Yannik Leonhardt
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Joao R. Moreira
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Daniel S. Hippe
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
| | - Olena Klymenko
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
| | - Sarah C. Foreman
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Claudio E. von Schacky
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Matthew B. Spraker
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63110, USA;
| | - Stephanie K. Schaub
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
| | - Carolin Knebel
- Department of Orthopedics and Sports Orthopedics, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany;
| | - Nina A. Mayr
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
| | - Henry C. Woodruff
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands; (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, 6229 HX Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands; (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, 6229 HX Maastricht, The Netherlands
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Alexandra S. Gersing
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, 85764 München, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
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19
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Wu G, Jochems A, Refaee T, Ibrahim A, Yan C, Sanduleanu S, Woodruff HC, Lambin P. Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging 2021; 48:3961-3974. [PMID: 33693966 PMCID: PMC8484174 DOI: 10.1007/s00259-021-05242-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/03/2021] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. METHODS Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. CONCLUSION The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."
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Affiliation(s)
- Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands. .,Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. .,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Chenggong Yan
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Sebastian Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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20
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Adachi T, Nakamura M, Shintani T, Mitsuyoshi T, Kakino R, Ogata T, Ono T, Tanabe H, Kokubo M, Sakamoto T, Matsuo Y, Mizowaki T. Multi-institutional dose-segmented dosiomic analysis for predicting radiation pneumonitis after lung stereotactic body radiation therapy. Med Phys 2021; 48:1781-1791. [PMID: 33576510 DOI: 10.1002/mp.14769] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/02/2021] [Accepted: 02/05/2021] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To predict radiation pneumonitis (RP) grade 2 or worse after lung stereotactic body radiation therapy (SBRT) using dose-based radiomic (dosiomic) features. METHODS This multi-institutional study included 247 early-stage nonsmall cell lung cancer patients who underwent SBRT with a prescribed dose of 48-70 Gy at an isocenter between June 2009 and March 2016. Ten dose-volume indices (DVIs) were used, including the mean lung dose, internal target volume size, and percentage of entire lung excluding the internal target volume receiving greater than x Gy (x = 5, 10, 15, 20, 25, 30, 35, and 40). A total of 6,808 dose-segmented dosiomic features, such as shape, first order, and texture features, were extracted from the dose distribution. Patients were randomly partitioned into two groups: model training (70%) and test datasets (30%) over 100 times. Dosiomic features were converted to z-scores (standardized values) with a mean of zero and a standard deviation (SD) of one to put different variables on the same scale. The feature dimension was reduced using the following methods: interfeature correlation based on Spearman's correlation coefficients and feature importance based on a light gradient boosting machine (LightGBM) feature selection function. Three different models were developed using LightGBM as follows: (a) a model with ten DVIs (DVI model), (b) a model with the selected dosiomic features (dosiomic model), and (c) a model with ten DVIs and selected dosiomic features (hybrid model). Suitable hyperparameters were determined by searching the largest average area under the curve (AUC) value in the receiver operating characteristic curve (ROC-AUC) via stratified fivefold cross-validation. Each of the final three models with the closest the ROC-AUC value to the average ROC-AUC value was applied to the test datasets. The classification performance was evaluated by calculating the ROC-AUC, AUC in the precision-recall curve (PR-AUC), accuracy, precision, recall, and f1-score. The entire process was repeated 100 times with randomization, and 100 individual models were developed for each of the three models. Then the mean value and SD for the 100 random iterations were calculated for each performance metric. RESULTS Thirty-seven (15.0%) patients developed RP after SBRT. The ROC-AUC and PR-AUC values in the DVI, dosiomic, and hybrid models were 0.660 ± 0.054 and 0.272 ± 0.052, 0.837 ± 0.054 and 0.510 ± 0.115, and 0.846 ± 0.049 and 0.531 ± 0.116, respectively. For each performance metric, the dosiomic and hybrid models outperformed the DVI models (P < 0.05). Texture-based dosiomic feature was confirmed as an effective indicator for predicting RP. CONCLUSIONS Our dose-segmented dosiomic approach improved the prediction of the incidence of RP after SBRT.
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Affiliation(s)
- Takanori Adachi
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Shintani
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takamasa Mitsuyoshi
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Radiation Oncology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Ryo Kakino
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Ogata
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Radiation Oncology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Tomohiro Ono
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroaki Tanabe
- Department of Radiological Technology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Masaki Kokubo
- Department of Radiation Oncology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Takashi Sakamoto
- Department of Radiation Oncology, Kyoto Katsura Hospital, Kyoto, Japan
| | - Yukinori Matsuo
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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21
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Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging. Clin Lung Cancer 2021; 22:e756-e766. [PMID: 33678583 DOI: 10.1016/j.cllc.2021.02.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patients with spiculation, lobulation, or pleural indentation on CT. PATIENTS AND METHODS We retrospectively recruited 915 patients with solitary solid pulmonary nodules and suspicious signs of malignancy. Data including clinical characteristics and subjective CT findings were obtained. A 3-dimensional U-Net-based DL model was used for tumor segmentation and extraction of 3-dimensional radiomics features. We used the Maximum Relevance and Minimum Redundancy (mRMR) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm to select the intranodular, perinodular, and gross nodular radiomics features. We propose a medical image DL (IDL) model, a clinical image DL (CIDL) model, a radiomics DL (RDL) model, and a clinical image radiomics DL (CIRDL) model to preoperatively differentiate GNs from solid lung cancer. Five-fold cross-validation was used to select and evaluate the models. The prediction performance of the models was evaluated using receiver operating characteristic and calibration curves. RESULTS The CIRDL model achieved the best performance in differentiating between GNs and solid lung cancer (area under the curve [AUC] = 0.9069), which was significantly higher compared with the IDL (AUC = 0.8322), CIDL (AUC = 0.8652), intra-RDL (AUC = 0.8583), peri-RDL (AUC = 0.8259), and gross-RDL (AUC = 0.8705) models. CONCLUSION The proposed CIRDL model is a noninvasive diagnostic tool to differentiate between granuloma nodules and solid lung cancer nodules and reduce the need for invasive diagnostic and surgical procedures.
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Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions. Eur Radiol 2021; 31:4848-4859. [PMID: 33404696 DOI: 10.1007/s00330-020-07519-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 09/27/2020] [Accepted: 11/13/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE To assess the diagnostic performance of radiomic analysis using high temporal resolution (HTR)-dynamic contrast enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions. MATERIALS AND METHODS We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semi-quantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences was calculated and called "kinetic textural parameters." Statistical analysis by LASSO logistic regression and cross validation was performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis. RESULTS We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed, 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC = 0.876 (sensitivity = 0.98, specificity = 0.52, accuracy = 0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC = 0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC = 0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n = 2). CONCLUSION A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis. KEY POINTS • Radiomic analysis using HTR-DCE is of better diagnostic performance (AUC = 0.876) than conventional breast MRI reading with BI-RADS (AUC = 0.831) (p < 0.001). • A radiomic malignant probability score under 19.5% gives a negative predictive value of 100% while a malignant probability score over 81% gives a positive predictive value of 100%. • Kinetic textural features extracted from HTR-DCE-MRI have a major role to play in distinguishing benign from malignant breast lesions.
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O’Farrell AC, Jarzabek MA, Lindner AU, Carberry S, Conroy E, Miller IS, Connor K, Shiels L, Zanella ER, Lucantoni F, Lafferty A, White K, Meyer Villamandos M, Dicker P, Gallagher WM, Keek SA, Sanduleanu S, Lambin P, Woodruff HC, Bertotti A, Trusolino L, Byrne AT, Prehn JHM. Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models. Cancers (Basel) 2020; 12:cancers12102978. [PMID: 33066609 PMCID: PMC7602510 DOI: 10.3390/cancers12102978] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 10/09/2020] [Indexed: 12/24/2022] Open
Abstract
Resistance to chemotherapy often results from dysfunctional apoptosis, however multiple proteins with overlapping functions regulate this pathway. We sought to determine whether an extensively validated, deterministic apoptosis systems model, 'DR_MOMP', could be used as a stratification tool for the apoptosis sensitiser and BCL-2 antagonist, ABT-199 in patient-derived xenograft (PDX) models of colorectal cancer (CRC). Through quantitative profiling of BCL-2 family proteins, we identified two PDX models which were predicted by DR_MOMP to be sufficiently sensitive to 5-fluorouracil (5-FU)-based chemotherapy (CRC0344), or less responsive to chemotherapy but sensitised by ABT-199 (CRC0076). Treatment with ABT-199 significantly improved responses of CRC0076 PDXs to 5-FU-based chemotherapy, but showed no sensitisation in CRC0344 PDXs, as predicted from systems modelling. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) scans were performed to investigate possible early biomarkers of response. In CRC0076, a significant post-treatment decrease in mean standard uptake value was indeed evident only in the combination treatment group. Radiomic CT feature analysis of pre-treatment images in CRC0076 and CRC0344 PDXs identified features which could phenotypically discriminate between models, but were not predictive of treatment responses. Collectively our data indicate that systems modelling may identify metastatic (m)CRC patients benefitting from ABT-199, and that 18F-FDG-PET could independently support such predictions.
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Affiliation(s)
- Alice C. O’Farrell
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Monika A. Jarzabek
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Andreas U. Lindner
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.U.L.); (S.C.); (F.L.); (M.M.V.)
| | - Steven Carberry
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.U.L.); (S.C.); (F.L.); (M.M.V.)
| | - Emer Conroy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (E.C.); (W.M.G.)
| | - Ian S. Miller
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Kate Connor
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Liam Shiels
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Eugenia R. Zanella
- Candiolo Cancer Institute—FPO IRCCS, Candiolo, 10060 Torino, Italy; (E.R.Z.); (A.B.); (L.T.)
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Federico Lucantoni
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.U.L.); (S.C.); (F.L.); (M.M.V.)
| | - Adam Lafferty
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Kieron White
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
| | - Mariangela Meyer Villamandos
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.U.L.); (S.C.); (F.L.); (M.M.V.)
| | - Patrick Dicker
- Department of Epidemiology and Public Health Medicine, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland;
| | - William M. Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (E.C.); (W.M.G.)
| | - Simon A. Keek
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (S.A.K.); (S.S.); (P.L.); (H.C.W.)
| | - Sebastian Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (S.A.K.); (S.S.); (P.L.); (H.C.W.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (S.A.K.); (S.S.); (P.L.); (H.C.W.)
- Department of Radiology and Nuclear Imaging, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (S.A.K.); (S.S.); (P.L.); (H.C.W.)
- Department of Radiology and Nuclear Imaging, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands
| | - Andrea Bertotti
- Candiolo Cancer Institute—FPO IRCCS, Candiolo, 10060 Torino, Italy; (E.R.Z.); (A.B.); (L.T.)
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Livio Trusolino
- Candiolo Cancer Institute—FPO IRCCS, Candiolo, 10060 Torino, Italy; (E.R.Z.); (A.B.); (L.T.)
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Annette T. Byrne
- Precision Cancer Medicine Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.C.O.); (M.A.J.); (I.S.M.); (K.C.); (L.S.); (A.L.); (K.W.); (A.T.B.)
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (E.C.); (W.M.G.)
| | - Jochen H. M. Prehn
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland; (A.U.L.); (S.C.); (F.L.); (M.M.V.)
- Correspondence: ; Tel.: +353-1-402-2255
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