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Zhao B, Li B, Guo H, Zhao Q, Zhang X, Zhao H, Xue W, Li W, Duan G, Xu S. The correlation between KRAS and TP53 gene mutations and early growth of pulmonary nodules. J Cardiothorac Surg 2024; 19:376. [PMID: 38926874 PMCID: PMC11200870 DOI: 10.1186/s13019-024-02927-0] [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: 02/21/2024] [Accepted: 06/15/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE The purpose of this study is to investigate whether gene mutations can lead to the growth of malignant pulmonary nodules. METHODS Retrospective analysis was conducted on patients with pulmonary nodules at Hebei Provincial People's Hospital, collecting basic clinical information such as gender, age, BMI, and hematological indicators. According to the inclusion and exclusion criteria, 85 patients with malignant pulmonary nodules were selected for screening, and gene mutation testing was performed on all patient tissues to explore the relationship between gene mutations and the growth of malignant pulmonary nodules. RESULTS There is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules (P < 0.05), while there is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules in the subgroup of invasive malignant pulmonary nodules (P < 0.05). CONCLUSION Mutations in the TP53 gene can lead to the growth of malignant pulmonary nodules and are correlated with the degree of invasion of malignant pulmonary nodules.
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Affiliation(s)
- Bin Zhao
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, 050057, People's Republic of China
- Graduate School, Hebei Medical University, Shijiazhuang, 050011, People's Republic of China
| | - Bin Li
- Hebei Bio-High Technology Development CO., LTD, Shijiazhuang, 050011, People's Republic of China
| | - Haoxin Guo
- Graduate School, Hebei Medical University, Shijiazhuang, 050011, People's Republic of China
| | - Qingtao Zhao
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, 050057, People's Republic of China
| | - Xiaopeng Zhang
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, 050057, People's Republic of China
| | - Huanfen Zhao
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, 050057, People's Republic of China
| | - Wenfei Xue
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, 050057, People's Republic of China
| | - Wei Li
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, 050057, People's Republic of China
| | - Guochen Duan
- Department of Thoracic Surgery, Children's Hospital of Hebei Province, Shijiazhuang, 050000, People's Republic of China.
| | - Shun Xu
- Department of Thoracic Surgery, The First Hospital of China Medical University, Shenyang, 110000, People's Republic of China
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Tang EK, Wu YJ, Chen CS, Wu FZ. Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time. Quant Imaging Med Surg 2024; 14:3983-3996. [PMID: 38846271 PMCID: PMC11151246 DOI: 10.21037/qims-23-1759] [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: 12/12/2023] [Accepted: 04/22/2024] [Indexed: 06/09/2024]
Abstract
Background Prediction of subsolid nodule (SSN) interval growth is crucial for clinical management and decision making in lung cancer screening program. To the best of our knowledge, no study has investigated whether volume doubling time (VDT) is an independent factor for predicting SSN interval growth, or whether its predictive power is better than that of traditional semantic methods, such as nodular diameter or type. This study aimed to investigate whether VDT could provide added value in predicting the long-term natural course of SSNs (<3 cm) regarding stage shift. Methods This retrospective study enrolled 132 patients with spectrum lesions of lung adenocarcinoma who underwent two consecutive computed tomography (CT) examinations before surgical tissue proofing between 2012 and 2021 in Kaohsiung Veterans General Hospital. The VDTs were manually calculated from the volumetric segmentation using Schwartz's approximation formula. We utilized logistic regression to identify predictors associated with stage shift progression based on the VDT parameter. Results The average duration of follow-up period was 3.629 years. A VDT-based nomogram model (model 2) based on CT semantic features, clinical characteristics, and the VDT parameter yielded an area under the curve (AUC) of 0.877 [95% confidence interval (CI): 0.807-0.928]. Compared with model 1 (CT semantic features and clinical characteristics), model 2 exhibited the better predictive performance for stage shift (AUC model 1: 0.833 versus AUC model 2: 0.877, P=0.047). In model 2, significant predictors of stage shift growth included initial nodule size [odds ratio (OR) =4.074, 95% CI: 1.368-12.135; P=0.012], SSN classification (OR =0.042; 95% CI: 0.006-0.288; P=0.001), follow-up period (OR =1.692, 95% CI: 1.337-2.140; P<0.001), and VDT classification (OR =2.327, 95% CI: 1.368-3.958; P=0.002). For the stage shift, the mean progression time for the VDT (>400 d) group was 7.595 years, and median progression time was 7.430 years. Additionally, a VDT ≤400 d is an important prognostic factor associated with aggressive growth behavior with a stage shift. Conclusions VDT is crucial for predicting SSN stage shift growth irrespective of clinical and CT semantic features. This highlights its significance in informing follow-up protocols and surgical planning, emphasizing its prognostic value in predicting SSN growth.
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Affiliation(s)
- En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung
- Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung
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Wu FZ, Wu YJ, Chen CS, Tang EK. Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features. Acad Radiol 2023; 30:2856-2869. [PMID: 37080884 DOI: 10.1016/j.acra.2023.02.033] [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: 11/15/2022] [Revised: 12/23/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
RATIONALES AND OBJECTIVES To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas. MATERIALS AND METHODS A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis. RESULTS The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth. CONCLUSION Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.
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Affiliation(s)
- Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, 70, Lien-hai Road, Kaohsiung 80424, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
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Fang J, Wang J, Li A, Yan Y, Liu H, Li J, Yang H, Hou Y, Yang X, Yang M, Liu J. Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3602-3613. [PMID: 37471191 DOI: 10.1109/tmi.2023.3297209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.
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Mayer R, Turkbey B, Choyke PL, Simone CB. Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study. Diagnostics (Basel) 2023; 13:3238. [PMID: 37892059 PMCID: PMC10605733 DOI: 10.3390/diagnostics13203238] [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: 09/22/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.
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Affiliation(s)
- Rulon Mayer
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Oncoscore, Garrett Park, MD 20896, USA
| | - Baris Turkbey
- National Institutes of Health, Bethesda, MD 20892, USA; (B.T.); (P.L.C.)
| | - Peter L. Choyke
- National Institutes of Health, Bethesda, MD 20892, USA; (B.T.); (P.L.C.)
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Huang W, Zhang H, Ge Y, Duan S, Ma Y, Wang X, Zhou X, Zhou T, Tu W, Wang Y, Liu S, Dong P, Fan L. Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography. J Thorac Imaging 2023; 38:304-314. [PMID: 37423615 DOI: 10.1097/rti.0000000000000725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
PURPOSE Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. MATERIALS AND METHODS Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. RESULTS The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. CONCLUSION The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.
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Affiliation(s)
- Wenjun Huang
- School of Medical Imaging, Weifang Medical University
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Hanxiao Zhang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu
| | - Yanming Ge
- School of Medical Imaging, Weifang Medical University
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai
| | - Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province
| | - Xiaoling Wang
- Department of Radiology, Deyang People's Hospital, Deyang, Sichuan Province, China
| | - Xiuxiu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Taohu Zhou
- School of Medical Imaging, Weifang Medical University
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Yun Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
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Liu YC, Liang CH, Wu YJ, Chen CS, Tang EK, Wu FZ. Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns. Diagnostics (Basel) 2023; 13:2674. [PMID: 37627933 PMCID: PMC10453827 DOI: 10.3390/diagnostics13162674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management.
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Affiliation(s)
- Yung-Chi Liu
- Department of Radiology, Xiamen Chang Gung Hospital, Xiamen 361028, China;
- Department of Imaging Technology Division, Xiamen Chang Gung Hospital, Xiamen 361028, China
- Department of Healthcare Administration Department, Xiamen Chang Gung Hospital, Xiamen 361028, China
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112304, Taiwan;
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Education, National Sun Yat-Sen University, Kaohsiung 804241, Taiwan
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Lee S, Jung JY, Nam Y, Jung CK, Lee SY, Lee J, Shin SH, Chung YG. Diagnosis of Marginal Infiltration in Soft Tissue Sarcoma by Radiomics Approach Using T2-Weighted Dixon Sequence. J Magn Reson Imaging 2023; 57:752-760. [PMID: 35808915 DOI: 10.1002/jmri.28331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Determination of preoperative soft tissue sarcoma (STS) margin is crucial for patient prognosis. PURPOSE To evaluate diagnostic performance of radiomics model using T2-weighted Dixon sequence for infiltration degree of STS margin. STUDY TYPE Retrospective. POPULATION Seventy-two STS patients consisted of training (n = 58) and test (n = 14) sets. FIELD STRENGTH/SEQUENCE A 3.0 T; T2-weighted Dixon images. ASSESSMENT Pathologic result of marginal infiltration in STS (circumscribed margin; n = 27, group 1, focally infiltrative margin; n = 31, group 2-A, diffusely infiltrative margin; n = 14, group 2-B) was the reference standard. Radiomic volume and shape (VS) and other (T2) features were extracted from entire tumor volume and margin, respectively. Twelve radiomics models were generated using four combinations of classifier algorithms (R, SR, LR, LSR) and three different inputs (VS, T2, VS + T2 [VST2] features) to differentiate the three groups. Three radiologists (reader 1, 2, 3) analyzed the marginal infiltration with 6-scale confidence score. STATISTICAL TESTS Area under the receiver operating characteristic curve (AUC) and concordance rate. RESULTS Averaged AUCs of R, SR, LR, LSR models were 0.438, 0.466, 0.438, 0.466 using VS features, 0.596, 0.584, 0.814, 0.815 using T2 features, and 0.581, 0.587, 0.821, 0.821 using VST2 features, respectively. The LR and LSR models constructed with T2 or VST2 features showed higher AUC and concordance rate compared to radiologists' analysis (AUC; 0.730, 0.675, 0.706, concordance rate; 0.46, 0.43, 0.47 in reader 1, 2, 3). DATA CONCLUSION Radiomics model constructed with features from tumor margin on T2-weighted Dixon sequence is a promising method for differentiating infiltration degree of STS margin. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Seungeun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea
| | - Chan-Kwon Jung
- Department of Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jooyeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.,Department of Applied Statistics, Hanyang University, Seoul, Republic of Korea
| | - Seung-Han Shin
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yang-Guk Chung
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Choi J, Zo S, Kim JH, Oh YJ, Ahn JH, Kim M, Lee K, Lee HY. Nondiagnostic, radial-probe endobronchial ultrasound-guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy. Thorac Cancer 2022; 14:177-185. [PMID: 36408780 PMCID: PMC9834694 DOI: 10.1111/1759-7714.14730] [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: 08/26/2022] [Revised: 10/29/2022] [Accepted: 10/31/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES This study investigated whether radiomic features extracted from radial-probe endobronchial ultrasound (radial EBUS) images can assist in decision-making for subsequent clinical management in cases with indeterminate pathologic results. METHODS A total of 494 patients who underwent radial EBUS biopsy for lung nodules between January 2017 and December 2018 were allocated to our training set. For the validation set, 229 patients with radial EBUS biopsy results from January 2019 to April 2020 were used. A multivariate logistic regression analysis was used for feature selection and prediction modeling. RESULTS In the training set, 157 (67 benign and 90 malignant) of 212 patients pathologically diagnosed as indeterminate were analyzed. In the validation set, 213 patients were diagnosed as indeterminate, and 158 patients (63 benign and 95 malignant) were included in the analysis. The performance of the radiomics-added model, which considered satellite nodules, linear arc, shape, patency of vessels and bronchi, echogenicity, spiculation, C-reactive protein, and minimum histogram, was 0.929 for the training set and 0.877 for the validation set, whereas the performance of the model without radiomics was 0.910 and 0.891, respectively. CONCLUSION Although the next diagnostic step for indeterminate lung biopsy results remains controversial, integrating various factors, including radiomic features from radial EBUS, might facilitate decision-making for subsequent clinical management.
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Affiliation(s)
- Jihwan Choi
- Department of Digital HealthSAIHST, Sungkyunkwan UniversitySeoulSouth Korea,Hongseong‐gun Public Health CenterHongseong‐gunSouth Korea
| | - Sungmin Zo
- Division of Pulmonary and Critical Care Medicine, Department of MedicineSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
| | - Jong Hoon Kim
- Industrial Biomaterial Research Center, Korea Research Institute of Bioscience and BiotechnologyDaejeonSouth Korea
| | - You Jin Oh
- Department of Health Sciences and TechnologySAIHST, Sungkyunkwan UniversitySeoulSouth Korea
| | - Joong Hyun Ahn
- Biomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center
| | - Myoungkyoung Kim
- Department of Radiology and Center for Imaging ScienceSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Department of MedicineSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
| | - Ho Yun Lee
- Department of Health Sciences and TechnologySAIHST, Sungkyunkwan UniversitySeoulSouth Korea,Department of Radiology and Center for Imaging ScienceSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
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Guo H, Xue W, Zhao Q, Zhao H, Hu Z, Zhang X, Duan G. Correlation and significance of COX-2, Ki67, VEGF and other immune indexes with the growth of malignant pulmonary nodules. J Cardiothorac Surg 2022; 17:290. [DOI: 10.1186/s13019-022-02039-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 11/05/2022] [Indexed: 11/17/2022] Open
Abstract
Abstract
Objective
This study intends to explore the factors affecting the growth of pulmonary nodules in the natural process by immunohistochemical method.
Methods
40 cases of pulmonary nodules followed up for more than 3 years were divided into growth group (n = 20) and stable group (n = 20). The expressions of cyclooxygenase-2 (COX-2), Ki67, vascular endothelial growth factor (VEGF), CD44V6, epidermal growth factor receptor (EGFR), double microsome 2 (MDM2) and transforming growth factor (TGF)-β1 in pulmonary nodules were detected by immunohistochemical method so as to explore the relationship between it and the growth of pulmonary nodules.
Results
Compared with stable pulmonary nodules, the positive rates of COX-2, Ki67 and VEGF in the growth group were 85%, 80% and 55%, respectively. There was significant difference between the stable group and the growth group (P < 0.05). The correlation between other indexes and the growth of pulmonary nodules was not statistically significant (Pcd44v6 = 0.104;PEGFR = 0.337; PMDM2 = 0.49; PTGF-β1 = 0.141). In the subgroup of patients with non-invasive lung cancer, there was a correlation between VEGF and the growth of pulmonary nodules (P < 0.05).
Conclusion
The high expression of COX-2, Ki67 and VEGF proteins may be significantly related to the growth of pulmonary nodules, and VEGF may be an important factor affecting the growth of malignant pulmonary nodules. This study intends to provide a research direction for further searching for the essential causes of the growth of pulmonary nodules.
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Prognostic Evaluation Based on Dual-Time 18F-FDG PET/CT Radiomics Features in Patients with Locally Advanced Pancreatic Cancer Treated by Stereotactic Body Radiation Therapy. JOURNAL OF ONCOLOGY 2022; 2022:6528865. [PMID: 35874634 PMCID: PMC9303166 DOI: 10.1155/2022/6528865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/18/2022] [Indexed: 12/24/2022]
Abstract
Background 18F-FDG PET/CT is widely used in the prognosis evaluation of tumor patients. The radiomics features can provide additional information for clinical prognostic assessment. Purpose Purpose is to explore the prognostic value of radiomics features from dual-time 18F-FDG PET/CT images for locally advanced pancreatic cancer (LAPC) patients treated with stereotactic body radiation therapy (SBRT). Materials and Methods This retrospective study included 70 LAPC patients who received early and delayed 18F-FDG PET/CT scans before SBRT treatment. A total of 1188 quantitative imaging features were extracted from dual-time PET/CT images. To avoid overfitting, the univariate analysis and elastic net were used to obtain a sparse set of image features that were applied to develop a radiomics score (Rad-score). Then, the Harrell consistency index (C-index) was used to evaluate the prognosis model. Results The Rad-score from dual-time images contains six features, including intensity histogram, morphological, and texture features. In the validation cohort, the univariate analysis showed that the Rad-score was the independent prognostic factor (p < 0.001, hazard ratio [HR]: 3.2). And in the multivariate analysis, the Rad-score was the only prognostic factor (p < 0.01, HR: 4.1) that was significantly associated with the overall survival (OS) of patients. In addition, according to cross-validation, the C-index of the prognosis model based on the Rad-score from dual-time images is better than the early and delayed images (0.720 vs. 0.683 vs. 0.583). Conclusion The Rad-score based on dual-time 18F-FDG PET/CT images is a promising noninvasive method with better prognostic value.
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12
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Mayer R, Simone CB, Turkbey B, Choyke P. Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume. Quant Imaging Med Surg 2022; 12:1096-1108. [PMID: 35111607 DOI: 10.21037/qims-21-466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/03/2021] [Indexed: 12/15/2022]
Abstract
Background Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness. Methods Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs. Results From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction. Conclusions Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.
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Affiliation(s)
- Rulon Mayer
- University of Pennsylvania, Philadelphia, PA, USA.,Oncoscore, Garrett Park, MD, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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13
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Mayer R, Simone CB, Turkbey B, Choyke P. Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging. Quant Imaging Med Surg 2021; 11:4235-4244. [PMID: 34603979 DOI: 10.21037/qims-21-24] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/22/2021] [Indexed: 01/25/2023]
Abstract
Background Proliferating cancer cells interacting with their microenvironment affects a tumor's spatial shape. Elongation or roundness (eccentricity) of lung, skin, and breast cancers indicates the cancer's relative aggressiveness. Non-invasive determination of the prostate tumor's shape should provide meaningful input for prognostication and clinical management. There are currently few studies of prostate tumor shape, therefore this study examines the relationship between a prostate tumor's eccentricity, derived from spatially registered multi-parametric MRI and histology slides, and Gleason scores. Methods A total of 26 consecutive patients were enrolled in the study. Median patient age was 60 years (range, 49 to 75 years), median PSA was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL, and median Gleason score was 7 (range, 6 to 9). Multi-parametric MRI (T1, T2, Diffusion, Dynamic Contrast Enhanced) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Also, tumor shape was computed from the histology slides. Blobbing, labeling, and calculation of eccentricity using moments of inertia were applied to the multi-parametric MRI and histology slides. The eccentricity measurements were compared to the Gleason scores from 25 patients. Results From histology slides analysis: the correlation coefficient between the eccentricity for the largest blob and a weighted average eccentricity against the Gleason score ranged from -0.67 to -0.78 for all 18 patients whose tumor volume exceeded 1.0 cc. From multi-parametric MRI analysis: the correlation coefficient between the eccentricity for the largest blob for varying thresholds against the Gleason score ranged from -0.60 to -0.66 for all 25 patients showing contrast uptake in the Dynamic Contrast Enhancement (DCE) MRI. Conclusions Spherical shape prostate adenocarcinoma shows a propensity for higher Gleason score. This novel finding follows lung and breast adenocarcinomas but depart from other primary tumor types. Analysis of multi-parametric MRI can non-invasively determine the prostate tumor's morphology and add critical information for prognostication and disease management. Eccentricity of smaller tumors (<1.0 cc) from MP-MRI correlates well with Gleason score, unlike eccentricity measured using histology of wholemount prostatectomy.
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Affiliation(s)
- Rulon Mayer
- University of Pennsylvania, Philadelphia, PA, USA.,OncoScore, Garrett Park, MD, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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Tan M, Ma W, Sun Y, Gao P, Huang X, Lu J, Chen W, Wu Y, Jin L, Tang L, Kuang K, Li M. Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics. Front Oncol 2021; 11:658138. [PMID: 33937070 PMCID: PMC8082461 DOI: 10.3389/fonc.2021.658138] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/22/2021] [Indexed: 01/15/2023] Open
Abstract
Objectives To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. Methods From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. Results Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set. Conclusions The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.
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Affiliation(s)
- Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Xuemei Huang
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Jinjuan Lu
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Wufei Chen
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yue Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Lin Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
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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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