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Lucia F, Louis T, Cousin F, Bourbonne V, Visvikis D, Mievis C, Jansen N, Duysinx B, Le Pennec R, Nebbache M, Rehn M, Hamya M, Geier M, Salaun PY, Schick U, Hatt M, Coucke P, Hustinx R, Lovinfosse P. Multicentric development and evaluation of [ 18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 2024; 51:1097-1108. [PMID: 37987783 DOI: 10.1007/s00259-023-06510-y] [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: 08/29/2023] [Accepted: 11/03/2023] [Indexed: 11/22/2023]
Abstract
PURPOSE To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. METHODS We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence. RESULTS In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). CONCLUSION Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.
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Affiliation(s)
- François Lucia
- Radiation Oncology Department, University Hospital, Brest, France.
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
- Service de Radiothérapie, CHRU Morvan, 2 Avenue Foch, 29609 Cedex, Brest, France.
| | - Thomas Louis
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - Carole Mievis
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Nicolas Jansen
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Romain Le Pennec
- Nuclear Medicine Department, University Hospital, Brest, France
- GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France
| | - Malik Nebbache
- Radiation Oncology Department, University Hospital, Brest, France
| | - Martin Rehn
- Radiation Oncology Department, University Hospital, Brest, France
| | - Mohamed Hamya
- Radiation Oncology Department, University Hospital, Brest, France
| | - Margaux Geier
- Medical Oncology Department, University Hospital, Brest, France
| | - Pierre-Yves Salaun
- Nuclear Medicine Department, University Hospital, Brest, France
- GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Philippe Coucke
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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Cheung BMF. Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study. Radiat Oncol J 2024; 42:4-16. [PMID: 38549380 PMCID: PMC10982060 DOI: 10.3857/roj.2023.00612] [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: 07/11/2023] [Revised: 09/22/2023] [Accepted: 10/10/2023] [Indexed: 04/04/2024] Open
Abstract
PURPOSE Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS). MATERIALS AND METHODS Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion. RESULTS A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction. CONCLUSION Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.
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Deshpande SR, Podder TK, Grubb W, Zhang Y, Zheng Y, Towe C, Linden P, Avril N, Biswas T. Pretreatment and Posttreatment Tumor Metabolic Activity Assessed by FDG-PET/CT as Predictors of Tumor Recurrence and Survival Outcomes in Early-Stage Non-Small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. Adv Radiat Oncol 2024; 9:101313. [PMID: 38260218 PMCID: PMC10801655 DOI: 10.1016/j.adro.2023.101313] [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: 01/13/2023] [Accepted: 06/09/2023] [Indexed: 01/24/2024] Open
Abstract
Purpose Stereotactic body radiation therapy (SBRT) is considered the standard of care for medically inoperable early-stage non-small cell lung cancer. There is mixed evidence on the prognostic significance of tumor metabolic activity assessed by positron emission tomography combined with computed tomography (PET/CT) using F-18 fluorodeoxyglucose (FDG). The objectives of this study were to evaluate the maximum standardized uptake value (SUVmax) pretreatment and at 3 and 6 months after SBRT for prediction of tumor control and survival outcomes. Methods and Materials Consecutive patients from a single institution with T12N0M0 non-small cell lung cancer receiving primary treatment with SBRT with pretreatment FDG-PET/CT (n = 163) and follow-up FDG-PET/CT at 3 or 6 months (n = 71) were included. Receiver operator characteristic analysis was performed to dichotomize variables for Kaplan-Meier survival analysis. Multivariate analysis was performed with Cox proportional hazards regression. Results Median follow-up was 19 months. For the whole cohort, 1-year and 2-year local control, progression-free survival (PFS), and overall survival (OS) were 95.0% and 80.3%, 87.1% and 75.4%, and 67.0% and 49.6% respectively. The following pre-SBRT SUVmax cutoffs were significant: SUV > 4.0 for distant failure-free survival (adjusted hazard ratio [aHR], 3.33, P = .006), >12.3 for PFS (aHR, 2.80, P = .011), and >12.6 for OS (aHR, 3.00, P = .003). SUVmax decreases of at least 45% at 3 months (aHR, 0.15, P = .018), and 53% at 6 months (aHR, 0.12, P = .046) were associated with improved local failure-free survival. Conclusions Pre-SBRT SUVmax cutoffs can predict distant failure, PFS, and OS. At both 3 and 6 months after SBRT, cutoffs for percentage change in SUVmax can potentially stratify risk of local recurrence.
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Affiliation(s)
- Saarang R. Deshpande
- Department of Radiation Oncology, University Hospitals, Seidman Cancer Center, Cleveland, Ohio
- School of Medicine, Case Western Reserve University, Cleveland, Ohio
- Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tarun K. Podder
- Department of Radiation Oncology, University Hospitals, Seidman Cancer Center, Cleveland, Ohio
- School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - William Grubb
- Department of Radiation Oncology, Medical College of Georgia, Augusta, Georgia
| | - Yuxia Zhang
- Department of Radiation Oncology, University Hospitals, Seidman Cancer Center, Cleveland, Ohio
| | - Yiran Zheng
- Department of Radiation Oncology, University Hospitals, Seidman Cancer Center, Cleveland, Ohio
| | - Christopher Towe
- School of Medicine, Case Western Reserve University, Cleveland, Ohio
- Division of Cardiothoracic Surgery, University Hospitals Cleveland Medical Center, Seidman Cancer Center, Cleveland, Ohio
| | - Philip Linden
- School of Medicine, Case Western Reserve University, Cleveland, Ohio
- Division of Cardiothoracic Surgery, University Hospitals Cleveland Medical Center, Seidman Cancer Center, Cleveland, Ohio
| | - Norbert Avril
- School of Medicine, Case Western Reserve University, Cleveland, Ohio
- Department of Radiology, Nuclear Medicine, University Hospitals, Cleveland, Ohio
| | - Tithi Biswas
- Department of Radiation Oncology, University Hospitals, Seidman Cancer Center, Cleveland, Ohio
- School of Medicine, Case Western Reserve University, Cleveland, Ohio
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Yoo J, Lee J, Cheon M, Kim H, Choi YS, Pyo H, Ahn MJ, Choi JY. Radiomics Analysis of 18F-FDG PET/CT for Prognosis Prediction in Patients with Stage III Non-Small Cell Lung Cancer Undergoing Neoadjuvant Chemoradiation Therapy Followed by Surgery. Cancers (Basel) 2023; 15:cancers15072012. [PMID: 37046673 PMCID: PMC10093358 DOI: 10.3390/cancers15072012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/27/2023] [Accepted: 03/27/2023] [Indexed: 03/30/2023] Open
Abstract
We investigated the prognostic significance of radiomic features from 18F-FDG PET/CT to predict overall survival (OS) in patients with stage III NSCLC undergoing neoadjuvant chemoradiation therapy followed by surgery. We enrolled 300 patients with stage III NSCLC who underwent PET/CT at the initial work-up (PET1) and after neoadjuvant concurrent chemoradiotherapy (PET2). Radiomic primary tumor features were subjected to LASSO regression to select the most useful prognostic features of OS. The prognostic significance of the LASSO score and conventional PET parameters was assessed by Cox proportional hazards regression analysis. In conventional PET parameters, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of each PET1 and PET2 were significantly associated with OS. In addition, both the PET1-LASSO score and the PET2-LASSO score were significantly associated with OS. In multivariate Cox regression analysis, only the PET2-LASSO score was an independently significant factor for OS. The LASSO score showed better predictive performance for OS regarding the time-dependent receiver operating characteristic curve and decision curve analysis than conventional PET parameters. Radiomic features from PET/CT were an independent prognostic factor for the estimation of OS in stage III NSCLC. The newly developed LASSO score using radiomic features showed better prognostic results for individualized OS estimation than conventional PET parameters.
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Affiliation(s)
- Jang Yoo
- Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul 05368, Republic of Korea
| | - Jaeho Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Miju Cheon
- Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul 05368, Republic of Korea
| | - Hojoong Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Yong Soo Choi
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Hongryull Pyo
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
- Correspondence: ; Tel.: +82-2-3410-2648; Fax: +82-2-3410-2639
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Chen YH, Chen YC, Lue KH, Chu SC, Chang BS, Wang LY, Li MH, Lin CB. Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma. Ann Nucl Med 2023; 37:139-150. [PMID: 36436112 DOI: 10.1007/s12149-022-01811-y] [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: 03/27/2022] [Accepted: 11/20/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma. METHODS We retrospectively analyzed the 18F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models. RESULTS Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1-3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1-3, p = 0.006, < 0.001, and = 0.224, respectively). CONCLUSION The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.
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Affiliation(s)
- Yu-Hung Chen
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.,Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Yen-Chang Chen
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.,Department of Anatomical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan.
| | - Sung-Chao Chu
- School of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan. .,Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
| | - Bee-Song Chang
- Department of Cardiothoracic Surgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ling-Yi Wang
- Epidemiology and Biostatistics Consulting Center, Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,Graduate Institute of Clinical Pharmacy, Tzu Chi University, Hualien, 97002, Taiwan
| | - Ming-Hsun Li
- Department of Anatomical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Chih-Bin Lin
- Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, 97002, Taiwan
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Chen YH, Lue KH, Chu SC, Chang BS, Lin CB. The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease. Nucl Med Commun 2023; 44:100-107. [PMID: 36437543 DOI: 10.1097/mnm.0000000000001645] [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: 11/29/2022]
Abstract
OBJECTIVE To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease. METHODS We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival. RESULTS Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies. CONCLUSION Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.
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Affiliation(s)
- Yu-Hung Chen
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation
- School of Medicine, College of Medicine, Tzu Chi University
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology
| | - Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology
| | - Sung-Chao Chu
- School of Medicine, College of Medicine, Tzu Chi University
- Departments of Hematology and Oncology
| | | | - Chih-Bin Lin
- Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
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Local Recurrence Risk Score to Predict Relapse after Stereotactic Body Radiation Therapy for Lung Tumors. J Clin Med 2022; 11:jcm11216445. [PMID: 36362674 PMCID: PMC9658057 DOI: 10.3390/jcm11216445] [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: 10/11/2022] [Revised: 10/13/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Background: After stereotactic body radiation therapy (SBRT) for lung tumors, follow-up CT scans remain a pitfall. The early detection of local relapse is essential to propose a new treatment. We aim to create a local recurrence predictive score using pre- and post-therapeutic imaging criteria and test it on a validation cohort. Methods: Between February 2011 and July 2016, lung tumors treated by SBRT with available pretreatment fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) and follow-up CT scans were retrospectively analyzed. The risk factors associated with relapse were identified by univariate logistic regression on a train cohort. The score was created using these factors, merging clinical and imaging criteria associated with local relapse, and then tested on an independent validation cohort. Overall and local relapse-free survival at 1 and 3 years were recorded. Results: Twenty-eight patients were included in the train cohort and ten in the derivation cohort (male 74%, median age 70 ± 12 years). Five variables significantly associated with local recurrence (female gender; sequential enlargement; craniocaudal growing; bulging margins; standardized uptake value (SUVmax > 5.5)) were combined to create the score on five points. With the threshold >2.5/5, the sensitivity and specificity of the score on the validation cohort were 100% and 88%, respectively. Overall survival and local relapse-free survival at 1 and 3 years were 89% and 42%, and 89% and 63%, respectively. Conclusion: The local recurrence risk score created has high sensitivity (100%) and specificity (88%), upon independent validation cohort, to detect local relapse. This score is easy to use in daily clinical practice.
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Zhang R, Hohenforst-Schmidt W, Steppert C, Sziklavari Z, Schmidkonz C, Atzinger A, Kuwert T, Klink T, Sterlacci W, Hartmann A, Vieth M, Förster S. Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer. Nuklearmedizin 2022; 61:385-393. [PMID: 35768005 DOI: 10.1055/a-1816-6950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
PURPOSE To study the relationship between standardized 18F-FDG PET/CT radiomic features and clinicopathological variables and programmed death ligand-1 (PD-L1) expression status in non-small cell lung cancer (NSCLC) patients. METHODS 58 NSCLC patients with preoperative 18F-FDG PET/CT scans and postoperative results of PD-L1 expression were retrospectively analysed. A standardized, open-source software was used to extract 86 radiomic features from PET and low-dose CT images. Univariate analysis and multivariate logistic regression were used to find independent predictors of PD-L1 expression. The Area Under the Curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the ability of variables and their combination in predicting PD-L1 expression. RESULTS Multivariate logistic regression resulted in the PET radiomic feature GLRLM_LGRE (Odds Rate (OR): 0.300 vs 0.114, 95% confidence interval (CI): 0.096-0.931 vs 0.021-0.616, in NSCLC and adenocarcinoma respectively) and the CT radiomic feature GLZLM_SZE (OR: 3.338 vs 7.504, 95%CI: 1.074-10.375 vs 1.382-40.755, in NSCLC and adenocarcinoma respectively), being independent predictors of PD-L1 status. In NSCLC group, after adjusting for gender and histology, the PET radiomic feature GLRLM_LGRE (OR: 0.282, 95%CI: 0.085-0.936) remained an independent predictor for PD-L1 status. In the adenocarcinoma group, when adjusting for gender the PET radiomic feature GLRLM_LGRE (OR: 0.115, 95%CI: 0.021-0.631) and the CT radiomic feature GLZLM_SZE (OR: 7.343, 95%CI: 1.285-41.965) remained associated with PD-L1 expression. CONCLUSION NSCLC and adenocarcinoma with PD-L1 expression show higher tumour heterogeneity. Heterogeneity-related 18F-FDG PET and CT radiomic features showed good ability to non-invasively predict PD-L1 expression.
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Affiliation(s)
- Ruiyun Zhang
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Pathology, Klinikum Bayreuth GmbH, Bayreuth, Germany.,Nuclear Medicine, Klinikum Bayreuth GmbH, Bayreuth, Germany
| | | | | | | | | | - Armin Atzinger
- Nuclear Medicine, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Torsten Kuwert
- Nuclear Medicine, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Thorsten Klink
- Radiology, Universitätsklinikum Würzburg, Wurzburg, Germany.,Medizincampus Oberfranken, Universitätsklinikum Erlangen, Bayreuth, Germany.,Radiology, Klinikum Bayreuth GmbH, Bayreuth, Germany
| | - William Sterlacci
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Pathology, Klinikum Bayreuth GmbH, Bayreuth, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Vieth
- Medizincampus Oberfranken, Universitätsklinikum Erlangen, Bayreuth, Germany.,Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Pathology, Klinikum Bayreuth GmbH, Bayreuth, Germany
| | - Stefan Förster
- Nuclear Medicine, Klinikum rechts der Isar der Technischen Universität München, Munchen, Germany.,Medizincampus Oberfranken, Universitätsklinikum Erlangen, Bayreuth, Germany.,Nuclear Medicine, Klinikum Bayreuth GmbH, Bayreuth, Germany
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9
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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11
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Bundschuh L, Prokic V, Guckenberger M, Tanadini-Lang S, Essler M, Bundschuh RA. A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study. Diagnostics (Basel) 2022; 12:diagnostics12030576. [PMID: 35328128 PMCID: PMC8947476 DOI: 10.3390/diagnostics12030576] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 12/11/2022] Open
Abstract
Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.
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Affiliation(s)
- Lena Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, 53127 Bonn, Germany; (M.E.); (R.A.B.)
- Correspondence: ; Tel.: +49-228-287-16181
| | - Vesna Prokic
- Department of Physics, University Koblenz-Landau, 55118 Koblenz, Germany;
- RheinAhrCampus, University of Applied Science, 56075 Koblenz, Germany
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (M.G.); (S.T.-L.)
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, 53127 Bonn, Germany; (M.E.); (R.A.B.)
| | - Ralph A. Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, 53127 Bonn, Germany; (M.E.); (R.A.B.)
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12
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Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022; 13:22. [PMID: 35124733 PMCID: PMC8817778 DOI: 10.1186/s13244-021-01153-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
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13
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Hu X, Liang X, Antonecchia E, Chiaravallotti A, Chu Q, Han S, Li Z, Wan L, D'Ascenzo N, Schillaci O, Xie Q. 3-D Textural Analysis of 2-[¹⁸F]FDG PET and Ki67 Expression in Nonsmall Cell Lung Cancer. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3051376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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14
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Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm. Clin Oncol (R Coll Radiol) 2021; 34:114-127. [PMID: 34872823 DOI: 10.1016/j.clon.2021.11.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/01/2021] [Accepted: 11/17/2021] [Indexed: 02/06/2023]
Abstract
AIMS Despite the promising results achieved by radiomics prognostic models for various clinical applications, multiple challenges still need to be addressed. The two main limitations of radiomics prognostic models include information limitation owing to single imaging modalities and the selection of optimum machine learning and feature selection methods for the considered modality and clinical outcome. In this work, we applied several feature selection and machine learning methods to single-modality positron emission tomography (PET) and computed tomography (CT) and multimodality PET/CT fusion to identify the best combinations for different radiomics modalities towards overall survival prediction in non-small cell lung cancer patients. MATERIALS AND METHODS A PET/CT dataset from The Cancer Imaging Archive, including subjects from two independent institutions (87 and 95 patients), was used in this study. Each cohort was used once as training and once as a test, followed by averaging of the results. ComBat harmonisation was used to address the centre effect. In our proposed radiomics framework, apart from single-modality PET and CT models, multimodality radiomics models were developed using multilevel (feature and image levels) fusion. Two different methods were considered for the feature-level strategy, including concatenating PET and CT features into a single feature set and alternatively averaging them. For image-level fusion, we used three different fusion methods, namely wavelet fusion, guided filtering-based fusion and latent low-rank representation fusion. In the proposed prognostic modelling framework, combinations of four feature selection and seven machine learning methods were applied to all radiomics modalities (two single and five multimodalities), machine learning hyper-parameters were optimised and finally the models were evaluated in the test cohort with 1000 repetitions via bootstrapping. Feature selection and machine learning methods were selected as popular techniques in the literature, supported by open source software in the public domain and their ability to cope with continuous time-to-event survival data. Multifactor ANOVA was used to carry out variability analysis and the proportion of total variance explained by radiomics modality, feature selection and machine learning methods was calculated by a bias-corrected effect size estimate known as ω2. RESULTS Optimum feature selection and machine learning methods differed owing to the applied radiomics modality. However, minimum depth (MD) as feature selection and Lasso and Elastic-Net regularized generalized linear model (glmnet) as machine learning method had the highest average results. Results from the ANOVA test indicated that the variability that each factor (radiomics modality, feature selection and machine learning methods) introduces to the performance of models is case specific, i.e. variances differ regarding different radiomics modalities and fusion strategies. Overall, the greatest proportion of variance was explained by machine learning, except for models in feature-level fusion strategy. CONCLUSION The identification of optimal feature selection and machine learning methods is a crucial step in developing sound and accurate radiomics risk models. Furthermore, optimum methods are case specific, differing due to the radiomics modality and fusion strategy used.
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Sharma A, Kumar S, Pandey AK, Arora G, Sharma A, Seth A, Kumar R. Haralick texture features extracted from Ga-68 PSMA PET/CT to differentiate normal prostate from prostate cancer: a feasibility study. Nucl Med Commun 2021; 42:1347-1354. [PMID: 34392297 DOI: 10.1097/mnm.0000000000001469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Role of texture parameters on the basis of Ga-68 PSMA PET/CT in prostate cancer (Pca) is largely unexplored. Present work done is a preliminary study that aims to evaluate the role of Haralick texture features on the basis of Ga-68 PSMA PET/CT in Pca in which texture features were used to differentiate between normal prostate and Pca tissue. METHODS The study retrospectively enrolled patients in two groups: group 1 included 30 patients with biopsy-proven adenocarcinoma prostate and median age 64 years (range: 50-82 years) who underwent baseline Ga-68 PSMA PET/CT prior to therapy; group 2 included 24 patients with pathologies other than Pca and median age 53.5 years (range: 18-80 years) who underwent Ga-68 PSMA PET/CT as part of another study in our department. Patients in group 2 did not have any prostate pathology and served as controls for the study. The segmented images of prostate (3-D image) were used to calculate 11 Haralick texture features in MATLAB. SUVmax was also evaluated. All parameters were compared among the two groups using appropriate statistical analysis and P value <0.05 was considered significant. RESULTS All 11 Haralick texture features, as well as SUVmax, were significantly different among Pca and controls (P < 0.05). Among the texture features, contrast was most significant (P value of Mann-Whitney U <0.001) in differentiating Pca from normal prostate with AUROC curve of 82.9% with sensitivity and specificity 83.30% and 73.30%, respectively at cut-off 0.640. SUVmax was also significant with AUROC curve 94.0% and sensitivity and specificity 62.5% and 90%, respectively at cut-off 5.7. A significant negative correlation of SUVmax was observed with contrast. CONCLUSION Haralick texture features have a significant role in differentiating Pca and normal prostate.
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Affiliation(s)
| | | | - Anil Kumar Pandey
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | - Geetanjali Arora
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | | | - Amlesh Seth
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Sciences
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Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021; 34:e107-e122. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/24/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022]
Abstract
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.
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17
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Sharma A, Pandey AK, Sharma A, Arora G, Mohan A, Bhalla AS, Gupta L, Biswal SK, Kumar R. Prognostication Based on Texture Analysis of Baseline 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Nonsmall-Cell Lung Carcinoma Patients Who Underwent Platinum-Based Chemotherapy as First-Line Treatment. Indian J Nucl Med 2021; 36:252-260. [PMID: 34658548 PMCID: PMC8481851 DOI: 10.4103/ijnm.ijnm_20_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 12/24/2022] Open
Abstract
Objective: Our study aims to establish the potential for tumor heterogeneity evaluated using 18F fluorodeoxyglucose positron emission tomography/computed tomography (F-18 FDG PET/CT) texture analysis in nonsmall-cell lung carcinoma (NSCLC) patients who underwent platinum-based chemotherapy to provide an independent marker for overall survival (OS) of more than 1-year. Materials and Methods: A total of 42 patients (34 male and 8 female) with biopsy-proven NSCLC and mean age 55.33 ± 10.71 years who underwent a baseline F-18 FDG PET/CT and received platinum-based chemotherapy as first-line treatment were retrospectively included in the study. Ten first order, 21 s order texture parameters and 7 SUV and metabolic tumor volume (MTV) based metabolic parameters were calculated. All these parameters were compared between the two survival groups based on OS ≥1 year and OS <1 year. Cut-offs of significant parameters were determined using receiver operating characteristic curve analysis. Survival patterns were compared by log-rank test and presented using Kaplan-Meier curves. Cox proportion hazard model was used to determine the independent prognostic marker for 1 year OS. Results: In univariate survival analysis, 3 first order texture parameters (i.e. mean, median, root mean square with hazard ratios [HRs] 2.509 [P = 0.034], 2.590 [P = 0.05], 2.509 [P = 0.034], respectively) and 6 s order texture parameters (i.e. mean, auto correlation, cluster prominence, cluster shade, sum average and sum variance with HRs 2.509 [P = 0.034], 2.509 [P = 0.034], 3.929 [0.007], 2.903 [0.018], 2.954 [0.016] and 2.906 [0.014], respectively) were significantly associated with 1 year OS in these patients. Among the metabolic parameters, only metabolic tumor volume whole-body was significantly associated with 1 year OS. In multivariate survival analysis, cluster prominence came out as the independent predictor of 1 year OS. Conclusion: Texture analysis based on F-18 FDG PET/CT is potentially beneficial in the prediction of OS ≥1 year in NSCLC patients undergoing platinum-based chemotherapy as first-line treatment. Thus, can be used to stratify the patients which will not be benefitted with platinum-based chemotherapy and essentially needs to undergo some other therapy option.
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Affiliation(s)
| | | | - Anshul Sharma
- Department of Nuclear Medicine, AIIMS, New Delhi, India
| | | | - Anant Mohan
- Department of Pulmonary Medicine and Sleep Disorders, AIIMS, New Delhi, India
| | | | - Lalit Gupta
- Department of Radio Diagnosis, AIIMS, New Delhi, India
| | - Shiba Kalyan Biswal
- Department of Pulmonary Medicine and Sleep Disorders, AIIMS, New Delhi, India
| | - Rakesh Kumar
- Department of Nuclear Medicine, AIIMS, New Delhi, India
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18
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Amini M, Nazari M, Shiri I, Hajianfar G, Deevband MR, Abdollahi H, Arabi H, Rahmim A, Zaidi H. Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma. Phys Med Biol 2021; 66. [PMID: 34544053 DOI: 10.1088/1361-6560/ac287d] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/20/2021] [Indexed: 12/23/2022]
Abstract
We developed multi-modality radiomic models by integrating information extracted from18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, School of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver BC, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, CH-1211 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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20
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Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021; 13:97-104. [PMID: 34513082 PMCID: PMC8353717 DOI: 10.17691/stm2021.13.2.11] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
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Affiliation(s)
- A A Litvin
- Professor, Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Deputy Head Physician for Medical Aspects, Regional Clinical Hospital of the Kaliningrad Region, 74 Klinicheskaya St., Kaliningrad, 236016, Russia
| | - D A Burkin
- PhD Student in Information Science and Computer Engineering, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia
| | - A A Kropinov
- Therapeutist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| | - F N Paramzin
- Oncologist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
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21
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Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans. Tomography 2021; 7:301-312. [PMID: 34449727 PMCID: PMC8396250 DOI: 10.3390/tomography7030027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/10/2021] [Accepted: 07/27/2021] [Indexed: 12/29/2022] Open
Abstract
The importance of machine learning (ML) in the clinical environment increases constantly. Differentiation of pathological from physiological tracer-uptake in positron emission tomography/computed tomography (PET/CT) images is considered time-consuming and attention intensive, hence crucial for diagnosis and treatment planning. This study aimed at comparing and validating supervised ML algorithms to classify pathological uptake in prostate cancer (PC) patients based on prostate-specific membrane antigen (PSMA)-PET/CT. Retrospective analysis of 68Ga-PSMA-PET/CTs of 72 PC patients resulted in a total of 77 radiomics features from 2452 manually delineated hotspots for training and labeled pathological (1629) or physiological (823) as ground truth (GT). As the held-out test dataset, 331 hotspots (path.:128, phys.: 203) were delineated in 15 other patients. Three ML classifiers were trained and ranked to assess classification performance. As a result, a high overall average performance (area under the curve (AUC) of 0.98) was achieved, especially to detect pathological uptake (0.97 mean sensitivity). However, there is still room for improvement to detect physiological uptake (0.82 mean specificity), especially for glands. The ML algorithm applied to manually delineated lesions predicts hotspot labels with high accuracy on unseen data and may be an important tool to assist in clinical diagnosis.
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Palani D, Shanmugam S, Govindaraj K. Analysing the possibility of utilizing CBCT radiomics as an independent modality: a phantom study. Asian Pac J Cancer Prev 2021; 22:1383-1391. [PMID: 34048165 PMCID: PMC8408395 DOI: 10.31557/apjcp.2021.22.5.1383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Indexed: 11/25/2022] Open
Abstract
Aim: To verify if computed tomography (CT) radiomics were reproducible by cone beam CT (CBCT) radiomics by using Catphan® 504. Materials and Methods: Catphan® 504 was imaged using the default IGRT OBI CBCT imaging protocols and CT scanner. Seven known density image regions of the phantom were segmented and image feature was extracted by Imaging Biomarker Explorer (IBEX) software. The 49 selected features from four feature categories were analyzed by considering each region of interest (ROI) segment as individual image set. Correlation was studies using interclass correlation coefficient (ICC) and Pearson’s correlation coefficient. Results: The ICC of the three feature categories, namely intensity, GLCM, and GLRLM was significant (p-value<0.05) in comparison with CT, while the ICC of the fourth feature category, NID, was no significant. The average absolute Pearson’s correlation coefficient from the features of the images was as follows: CT: r=0.679±0.257, CBCThead: r=0.707±0.231, CBCTthorax: r=0.643±0.260, and CBCTpelvis: r=0.594±0.276. Conclusion: It seems that the various densities of Catphan® 504 ROI image segments of the CT radiomics are reproducible with CBCT radiomics and CBCT radiomics can be used as an independent modality.
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Affiliation(s)
- Dharmendran Palani
- Research and Development Centre, Bharathiar University, Coimbatore, India
| | - Senthilkumar Shanmugam
- Department of Radiotherapy Government Rajaji Hospital & Madurai Medical College, Madurai, Tamil Nadu, India
| | - Kesavan Govindaraj
- Research and Development Centre, Bharathiar University, Coimbatore, India.,Department of Radiotherapy, Vadamalayan Hospitals Integrated Cancer Centre, Madurai, India
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Sepehri S, Tankyevych O, Upadhaya T, Visvikis D, Hatt M, Cheze Le Rest C. Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040675. [PMID: 33918681 PMCID: PMC8069690 DOI: 10.3390/diagnostics11040675] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 12/23/2022] Open
Abstract
Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular; however, it has been shown that large variations in performance can be obtained by relying on different approaches. The purpose of this study was to evaluate the potential benefit of combining different algorithms into an improved consensus for the final prediction, as it has been shown in other fields. Methods: The evaluation was carried out in the context of the use of radiomics from 18F-FDG PET/CT images for predicting outcome in stage II-III Non-Small Cell Lung Cancer. A cohort of 138 patients was exploited for the present analysis. Eighty-seven patients had been previously recruited retrospectively for another study and were used here for training and internal validation. We also used data from prospectively recruited patients (n = 51) for testing. Three different machine learning pipelines relying on embedded feature selection were trained to predict overall survival (OS) as a binary classification: Support Vector machines (SVMs), Random Forests (RFs), and Logistic Regression (LR). Two different clinical endpoints were investigated: median OS or OS shorter than 6 months. The fusion of the three approaches was implemented using two different strategies: majority voting on the binary outputs or averaging of the output probabilities. Results: Our results confirm previous findings, highlighting that different ML pipelines select different sets of features and reach different classification performances (accuracy in the testing set ranging between 63% and 67% for median OS, and between 75% and 80% for OS < 6 months). Generating a consensus improved the performance for both endpoints; with the probabilities averaging strategy outperforming the majority voting (accuracy of 78% vs. 71% for median OS and 89 vs. 84% for OS < 6 months). Overall, the performance of these radiomic-based models outperformed the standard clinical staging in both endpoints (accuracy of 58% and 53% accuracy in the testing set for each endpoint). Conclusion: Although obtained in a small cohort of patients, our results suggest that a consensus of machine learning algorithms can improve performance in the context of radiomics. The resulting prognostic stratification in the prospective testing cohort is higher than when relying on the clinical stage. This could be of interest for clinical practice as it could help to identify patients with higher risk amongst stage II and III patients, who could benefit from intensified treatment and/or more frequent follow-up after treatment.
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Affiliation(s)
- Shima Sepehri
- LaTIM, INSERM, UMR 1101, University Brest, 29200 Brest, France; (S.S.); (O.T.); (D.V.); (C.C.L.R.)
| | - Olena Tankyevych
- LaTIM, INSERM, UMR 1101, University Brest, 29200 Brest, France; (S.S.); (O.T.); (D.V.); (C.C.L.R.)
- Nuclear Medicine Department, CHU Milétrie, 86021 Poitiers, France;
| | - Taman Upadhaya
- Nuclear Medicine Department, CHU Milétrie, 86021 Poitiers, France;
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University Brest, 29200 Brest, France; (S.S.); (O.T.); (D.V.); (C.C.L.R.)
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, 29200 Brest, France; (S.S.); (O.T.); (D.V.); (C.C.L.R.)
- Correspondence: ; Tel.: +33-2-98-01-81-11
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, University Brest, 29200 Brest, France; (S.S.); (O.T.); (D.V.); (C.C.L.R.)
- Nuclear Medicine Department, CHU Milétrie, 86021 Poitiers, France;
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Huang Y, Zeng H, Chen L, Luo Y, Ma X, Zhao Y. Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:640881. [PMID: 33763374 PMCID: PMC7982462 DOI: 10.3389/fonc.2021.640881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 01/26/2021] [Indexed: 02/05/2023] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients. Method We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients. Results A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models. Conclusion In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.
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Affiliation(s)
- Yeqian Huang
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Zeng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Linyan Chen
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yuling Luo
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ye Zhao
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, China
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Hartmann L, Bundschuh L, Zsótér N, Essler M, Bundschuh RA. Tumor heterogeneity for differentiation between liver tumors and normal liver tissue in 18F-FDG PET/CT. Nuklearmedizin 2021; 60:25-32. [PMID: 33142334 DOI: 10.1055/a-1270-5568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AIM Malignancies show higher spatial heterogeneity than normal tissue. We investigated, if textural parameters from FDG PET describing the heterogeneity function as tool to differentiate between tumor and normal liver tissue. METHODS FDG PET/CT scans of 80 patients with liver metastases and 80 patients with results negative upper abdominal organs were analyzed. Metastases and normal liver tissue were analyzed drawing up to three VOIs with a diameter of 25 mm in healthy liver tissue of the tumoral affected and results negative liver, whilst up to 3 metastases per patient were delineated. Within these VOIs 30 different textural parameters were calculated as well as SUV. The parameters were compared in terms of intra-patient and inter-patient variability (2-sided t test). ROC analysis was performed to analyze predictive power and cut-off values. RESULTS 28 textural parameters differentiated healthy and pathological tissue (p < 0.05) with high sensitivity and specificity. SUV showed ability to differentiate but with a lower significance. 15 textural parameters as well as SUV showed a significant variation between healthy tissues out of tumour infested and negative livers. Mean intra- and inter-patient variability of metastases were found comparable or lower for 6 of the textural features than the ones of SUV. They also showed good values of mean intra- and inter-patient variability of VOIs drawn in liver tissue of patients with metastases and of results negative ones. CONCLUSION Heterogeneity parameters assessed in FDG PET are promising to classify tissue and differentiate malignant lesions usable for more personalized treatment planning, therapy response evaluation and precise delineation of tumors for target volume determination as part of radiation therapy planning.
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Affiliation(s)
- Lynn Hartmann
- Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, Germany
| | - Lena Bundschuh
- Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, Germany
| | | | - Markus Essler
- Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, Germany
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Fan X, Zhang H, Yin Y, Zhang J, Yang M, Qin S, Zhang X, Yu F. Texture Analysis of 18F-FDG PET/CT for Differential Diagnosis Spinal Metastases. Front Med (Lausanne) 2021; 7:605746. [PMID: 33521018 PMCID: PMC7843930 DOI: 10.3389/fmed.2020.605746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 12/07/2020] [Indexed: 11/21/2022] Open
Abstract
Purpose: To evaluate the value of texture analysis for the differential diagnosis of spinal metastases and to improve the diagnostic performance of 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) for spinal metastases. Methods: This retrospective analysis of patients who underwent PET/CT between December 2015 and January 2020 at Shanghai Tenth People's Hospital due to high FDG uptake lesions in the spine included 45 cases of spinal metastases and 44 cases of benign high FDG uptake lesions in the spine. The patients were randomly divided into a training group of 65 and a test group of 24. Seventy-two PET texture features were extracted from each lesion, and the Mann-Whitney U-test was used to screen the training set for texture parameters that differed between the two groups in the presence or absence of spinal metastases. Then, the diagnostic performance of the texture parameters was screened out by receiver operating characteristic (ROC) curve analysis. Texture parameters with higher area under the curve (AUC) values than maximum standardized uptake values (SUVmax) were selected to construct classification models using logistic regression, support vector machines, and decision trees. The probability output of the model with high classification accuracy in the training set was used to compare the diagnostic performance of the classification model and SUVmax using the ROC curve. For all patients with spinal metastases, survival analysis was performed using the Kaplan-Meier method and Cox regression. Results: There were 51 texture parameters that differed meaningfully between benign and malignant lesions, of which four had higher AUC than SUVmax. The texture parameters were input to build a classification model using logistic regression, support vector machine, and decision tree. The accuracy of classification was 87.5, 83.34, and 75%, respectively. The accuracy of the manual diagnosis was 84.27%. Single-factor survival analysis using the Kaplan-Meier method showed that intensity was correlated with patient survival. Conclusion: Partial texture features showed higher diagnostic value for spinal metastases than SUVmax. The machine learning part of the model combined with the texture parameters was more accurate than manual diagnosis. Therefore, texture analysis may be useful to assist in the diagnosis of spinal metastases.
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Affiliation(s)
- Xin Fan
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Han Zhang
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yuzhen Yin
- Shanghai Clinical College, Anhui Medical University, Shanghai, China
| | - Jiajia Zhang
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Mengdie Yang
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shanshan Qin
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaoying Zhang
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fei Yu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
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Ji Y, Qiu Q, Fu J, Cui K, Chen X, Xing L, Sun X. Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer. Cancer Manag Res 2021; 13:307-317. [PMID: 33469373 PMCID: PMC7811450 DOI: 10.2147/cmar.s287128] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/28/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose To investigate the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) textural analysis and to develop a stage-specific PET radiomic prediction model to distinguish lung ADC from SCC. Patients and Methods Patients who were histologically diagnosed with lung ADC or SCC and underwent pretreatment 18F-FDG PET/CT scans were retrospectively identified. Radiomic features were extracted from a semiautomatically outlined tumor region in the Chang-Gung Image Texture Analysis (CGITA) software package. The differences in radiomic parameters between lung ADC and SCC were compared stage-by-stage in 253 consecutive NSCLC patients with stages I to III disease. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. A radiomic signature for each stage was subsequently constructed and evaluated. Then, an individual nomogram incorporating the radiomic signature and clinical risk factors was established and evaluated. The performance of the constructed models was assessed by receiver operating characteristic (ROC) curve analysis, and the nomogram was further validated by calibration curve analysis. Results The performance of the radiomic signature for distinguishing lung ADC and SCC in both the training and validation cohorts was good, with AUCs of 0.883, 0.854, and 0.895 in the training cohort and 0.932, 0.944, and 0.886 in the validation cohort for stages I, II, and III NSCLC, respectively. The radiomic-clinical nomogram integrating radiomic features with independent clinical predictors exhibited more favorable discriminative performance, with AUCs of 0.982, 0.963, and 0.979 in the training cohort and 0.989, 0.984, and 0.978 in the validation cohort for stages I, II, and III, respectively. Conclusion Differences in PET radiomic features between lung ADC and SCC varied in different stages. Stage-specific PET radiomic prediction models provided more favorable performance for discriminating the histological subtype of NSCLC.
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Affiliation(s)
- Yanlei Ji
- Department of Ultrasound Medicine, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People's Republic of China.,Department of Ultrasound Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Jing Fu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong 250014, People's Republic of China
| | - Kai Cui
- Department of PET/CT, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, People's Republic of China
| | - Xia Chen
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
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Chen YH, Wang TF, Chu SC, Lin CB, Wang LY, Lue KH, Liu SH, Chan SC. Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma. PLoS One 2020; 15:e0244502. [PMID: 33370365 PMCID: PMC7769431 DOI: 10.1371/journal.pone.0244502] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/10/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND To investigate the survival prognostic value of the radiomic features of 18F-FDG PET in patients who had EGFR (epidermal growth factor receptor) mutated lung adenocarcinoma and received targeted TKI (tyrosine kinase inhibitor) treatment. METHODS Fifty-one patients with stage III-IV lung adenocarcinoma and actionable EGFR mutation who received first-line TKI were retrospectively analyzed. All patients underwent pretreatment 18F-FDG PET/CT, and we calculated the PET-derived radiomic features. Cox proportional hazard model was used to examine the association between the radiomic features and the survival outcomes, including progression-free survival (PFS) and overall survival (OS). A score model was established according to the independent prognostic predictors and we compared this model to the TNM staging system using Harrell's concordance index (c-index). RESULTS Forty-eight patients (94.1%) experienced disease progression and 41 patients (80.4%) died. Primary tumor SUV entropy > 5.36, and presence of pleural effusion were independently associated with worse OS (both p < 0.001) and PFS (p = 0.001, and 0.003, respectively). We used these two survival predictors to devise a scoring system (score 0-2). Patients with a score of 1 or 2 had a worse survival than those with a score of 0 (HR for OS: 3.6, p = 0.006 for score 1, and HR: 21.8, p < 0.001 for score 2; HR for PFS: 2.2, p = 0.027 for score 1 and HR: 8.8, p < 0.001 for score 2). Our scoring system surpassed the TNM staging system (c-index = 0.691 versus 0.574, p = 0.013 for OS, and c-index = 0.649 versus 0.517, p = 0.004 for PFS). CONCLUSIONS In this preliminary study, combining PET radiomics with clinical risk factors may improve survival stratification in stage III-IV lung adenocarcinoma with actionable EFGR mutation. Our proposed scoring system may assist with optimization of individualized treatment strategies in these patients.
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Affiliation(s)
- Yu-Hung Chen
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Tso-Fu Wang
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Sung-Chao Chu
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Chih-Bin Lin
- Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ling-Yi Wang
- Epidemiology and Biostatistics Consulting Center, Department of Medical Research and Department of Pharmacy, Tzu Chi General Hospital, Hualien, Taiwan
| | - Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Shu-Hsin Liu
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Sheng-Chieh Chan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- * E-mail:
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Multiple Testing, Cut-Point Optimization, and Signs of Publication Bias in Prognostic FDG-PET Imaging Studies of Head and Neck and Lung Cancer: A Review and Meta-Analysis. Diagnostics (Basel) 2020; 10:diagnostics10121030. [PMID: 33271785 PMCID: PMC7761090 DOI: 10.3390/diagnostics10121030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/25/2020] [Accepted: 11/29/2020] [Indexed: 12/23/2022] Open
Abstract
Positron emission tomography (PET) imaging with 2-deoxy-2-[18F]-fluorodeoxyglucose (FDG) was proposed as prognostic marker in radiotherapy. Various uptake metrics and cut points were used, potentially leading to inflated effect estimates. Here, we performed a meta-analysis and systematic review of the prognostic value of pretreatment FDG–PET in head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC), with tests for publication bias. Hazard ratio (HR) for overall survival (OS), disease free survival (DFS), and local control was extracted or derived from the 57 studies included. Test for publication bias was performed, and the number of statistical tests and cut-point optimizations were registered. Eggers regression related to correlation of SUVmax with OS/DFS yielded p = 0.08/p = 0.02 for HNSCC and p < 0.001/p = 0.014 for NSCLC. No outcomes showed significant correlation with SUVmax, when adjusting for publication bias effect, whereas all four showed a correlation in the conventional meta-analysis. The number of statistical tests and cut points were high with no indication of improvement over time. Our analysis showed significant evidence of publication bias leading to inflated estimates of the prognostic value of SUVmax. We suggest that improved management of these complexities, including predefined statistical analysis plans, are critical for a reliable assessment of FDG–PET.
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Alcantara P, Martínez BC, García-Esquinas MG, Belaústegui LG, Bustos A. Evaluation of tumor response after stereotactic body radiation therapy for lung cancer: Role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography. J Clin Transl Res 2020; 6:155-167. [PMID: 33501386 PMCID: PMC7821750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/06/2020] [Indexed: 10/25/2022] Open
Abstract
BACKGROUND Early identification of patients who fail to lung stereotactic body radiation therapy (SBRT) is vital as they can benefit from salvage therapy. Main guidelines recommend computed tomography (CT) to assess response and use of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT only when a local recurrence is suspected in CT. The pattern of radiation-induced lung injury caused by SBRT is different from changes seen after conventional radiation therapy in terms of extent, time of manifestation, and morphologic characteristics, and knowing this is crucial for proper monitoring of the tumor response. In certain cases, it may be difficult to differentiate response from progression or recurrence on CT and, in addition, some changes in CT take a long time to evolve before they are considered suspicious, making early diagnosis difficult. Metabolic changes often precede morphological changes, so 18F-FDG PET/CT quantitative and qualitative metabolic criteria can be useful in assessing early response and detecting relapses. However, the optimal practice for follow-up remains unclear and there is an active search for imaging markers for recurrent disease, including CT texture analysis, biomarker assays, new PET/CT isotopes, and magnetic resonance imaging. AIM The aim of the study was to review the radiological changes that are objectified after pulmonary SBRT and the metabolic changes in 1F-FDG PET/CT, to assess the usefulness of following up patients with 18F-FDG PET/CT. RELEVANCE FOR PATIENTS At present, the evaluation of response and diagnosis of relapse after SBRT are difficult and the incorporation of routine 18F-FDG PET/CT may have value in early diagnosis of relapse when the patient may still benefit from rescue treatment.
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Affiliation(s)
- Pino Alcantara
- Department of Radiation Oncology, Hospital Clinico San Carlos
- Faculty of Medicine, Complutense University of Madrid
| | - Beatriz Cabeza Martínez
- Faculty of Medicine, Complutense University of Madrid
- Department of Radiology, Hospital Clinico San Carlos, Spain
| | - Marta García García-Esquinas
- Department of Nuclear Medicine, Hospital Clinico San Carlos
- Department of Radiology, Hospital Clinico San Carlos, Spain
| | | | - Ana Bustos
- Faculty of Medicine, Complutense University of Madrid
- Department of Radiology, Hospital Clinico San Carlos, Spain
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Kothari G, Korte J, Lehrer EJ, Zaorsky NG, Lazarakis S, Kron T, Hardcastle N, Siva S. A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy. Radiother Oncol 2020; 155:188-203. [PMID: 33096167 DOI: 10.1016/j.radonc.2020.10.023] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND PURPOSE Radiomics allows extraction of quantifiable features from imaging. This study performs a systematic review and meta-analysis of the performance of radiomics based prognostic models in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS A literature review was performed following PRISMA guidelines. Medline, EMBASE and Cochrane databases were searched for articles investigating radiomics features predictive of overall survival (OS) in NSCLC treated with curative intent radiotherapy. A random-effects meta-analysis of Harrell's Concordance Index (C-index) was performed on the performance of radiomics models. RESULTS Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review. There was significant heterogeneity in the methodology for feature selection and model development. Twelve datasets reported the C-index of radiomics based models in predicting OS and were included in the meta-analysis. The C-index random effects estimate was 0.57 (95% CI 0.53-0.62). There was significant heterogeneity (I2 = 70.3%). CONCLUSIONS Based on this review, radiomics based models for lung cancer have to date demonstrated modest prognostic capabilities. Future research should consider using standardised radiomics features, robust feature selection and model development, and deep learning techniques, absolving the need for pre-defined features, to improve imaging-based models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia.
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Department of Biomedical Engineering, School of Engineering, University of Melbourne, Melbourne, Australia
| | - Eric J Lehrer
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Nicholas G Zaorsky
- Department of Radiation Oncology, Penn State Cancer Institute, Hershey, USA; Department of Public Health Sciences, Penn State College of Medicine, Hershey, USA
| | - Smaro Lazarakis
- Health Sciences Library, Peter MacCallum Cancer Centre, Parkville, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia; Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia
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Wu K, Shui Y, Sun W, Lin S, Pang H. Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy. Front Oncol 2020; 10:569435. [PMID: 33178598 PMCID: PMC7594107 DOI: 10.3389/fonc.2020.569435] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/16/2020] [Indexed: 01/27/2023] Open
Abstract
Introduction: This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) treated with stereotactic body radiotherapy (SBRT). Materials and Methods: The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the model. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results: A total of four radiomic features and six clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [area under the curve (AUC) = 0.859 (CI: 0.770–0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641–0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion: This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to assess individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.
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Affiliation(s)
- Kui Wu
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongjie Shui
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenzheng Sun
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Lin
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Haowen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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de la Pinta C, Barrios-Campo N, Sevillano D. Radiomics in lung cancer for oncologists. J Clin Transl Res 2020; 6:127-134. [PMID: 33521373 PMCID: PMC7837741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/12/2020] [Accepted: 06/08/2020] [Indexed: 11/26/2022] Open
Abstract
UNLABELLED Radiomics has revolutionized the world of medical imaging. The aim of this review is to guide oncologists in radiomics and its applications in diagnosis, prediction of response and damage, prediction of survival, and prognosis in lung cancer. In this review, we analyzed published literature on PubMed and MEDLINE with papers published in the last 10 years. We included papers in English language with information about radiomics features and diagnostic, predictive, and prognosis of radiomics in lung cancer. All citations were evaluated for relevant content and validation. RELEVANCE FOR PATIENTS The evolution of technology allows the development of computer algorithms that facilitate the diagnosis and evaluation of response after different oncological treatments and their non-invasive follow-up.
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Affiliation(s)
- Carolina de la Pinta
- 1Department of Radiation Oncology, Ramón y Cajal Hospital, Madrid, Spain,
Corresponding author: Carolina de la Pinta Department of Radiation Oncology, Ramón y Cajal Hospital, Madrid, Spain
| | - Nuria Barrios-Campo
- 2Department of Biomedical Engineering, Madrid Polytechnic University, Madrid, Spain
| | - David Sevillano
- 3Department of Medical Physics, Ramón y Cajal Hospital, Madrid, Spain
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Christie JR, Lang P, Zelko LM, Palma DA, Abdelrazek M, Mattonen SA. Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making. Can Assoc Radiol J 2020; 72:86-97. [PMID: 32735493 DOI: 10.1177/0846537120941434] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)-based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.
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Affiliation(s)
- Jaryd R Christie
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada
| | - Pencilla Lang
- Division of Radiation Oncology, 6221Western University, London, Ontario, Canada
| | - Lauren M Zelko
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada
| | - David A Palma
- Division of Radiation Oncology, 6221Western University, London, Ontario, Canada
| | - Mohamed Abdelrazek
- Department of Medical Imaging, 6221Western University, London, Ontario, Canada
| | - Sarah A Mattonen
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada.,Department of Oncology, 6221Western University, London, Ontario, Canada
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Afshar P, Mohammadi A, Tyrrell PN, Cheung P, Sigiuk A, Plataniotis KN, Nguyen ET, Oikonomou A. [Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer. Sci Rep 2020; 10:12366. [PMID: 32703973 PMCID: PMC7378058 DOI: 10.1038/s41598-020-69106-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 06/26/2020] [Indexed: 12/24/2022] Open
Abstract
Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text] for predicting the OS, DC, and RFS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text], for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients.
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Affiliation(s)
- Parnian Afshar
- Concordia Institute for Information Systems Engineering, Montreal, QC Canada
| | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Montreal, QC Canada
| | - Pascal N. Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | - Patrick Cheung
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Ahmed Sigiuk
- Department of Medical Imaging, University of Toronto, Toronto, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | | | - Elsie T. Nguyen
- Cardiothoracic Imaging Division, Joint Department of Medical Imaging, Toronto General Hospital, Toronto, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
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2-[ 18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2020; 188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
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Bianconi F, Palumbo I, Fravolini ML, Chiari R, Minestrini M, Brunese L, Palumbo B. Texture Analysis on [ 18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types. Mol Imaging Biol 2020; 21:1200-1209. [PMID: 30847822 DOI: 10.1007/s11307-019-01336-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE The study aims to investigate the correlations between positron emission tomography (PET) texture features, X-ray computed tomography (CT) texture features, and histological subtypes in non-small-cell lung cancer evaluated with 2-deoxy-2-[18F]fluoro-D-glucose PET/CT. PROCEDURES We retrospectively evaluated the baseline PET/CT scans of 81 patients with histologically proven non-small-cell lung cancer. Feature extraction and statistical analysis were carried out on the Matlab platform (MathWorks, Natick, USA). RESULTS Intra-CT correlation analysis revealed a strong positive correlation between volume of the lesion (CTvol) and maximum density (CTmax), and between kurtosis (CTkrt) and maximum density (CTmax). A moderate positive correlation was found between volume (CTvol) and average density (CTmean), and between kurtosis (CTkrt) and average density (CTmean). Intra-PET analysis identified a strong positive correlation between the radiotracer uptake (SUVmax, SUVmean) and its degree of variability/disorder throughout the lesion (SUVstd, SUVent). Conversely, there was a strong negative correlation between the uptake (SUVmax, SUVmean) and its degree of uniformity (SUVuni). There was a positive moderate correlation between the metabolic tumor volume (MTV) and radiotracer uptake (SUVmax, SUVmean). Inter (PET-CT) correlation analysis identified a very strong positive correlation between the volume of the lesion at CT (CTvol) and the metabolic volume (MTV), a moderate positive correlation between average tissue density (CTmean) and radiotracer uptake (SUVmax, SUVmean), and between kurtosis at CT (CTkrt) and metabolic tumor volume (MTV). Squamous cell carcinomas had larger volume higher uptake, stronger PET variability and lower uniformity than the other subtypes. By contrast, adenocarcinomas exhibited significantly lower uptake, lower variability and higher uniformity than the other subtypes. CONCLUSIONS Significant associations emerged between PET features, CT features, and histological type in NSCLC. Texture analysis on PET/CT shows potential to differentiate between histological types in patients with non-small-cell lung cancer.
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Affiliation(s)
- Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via G. Duranti 93, 06125, Perugia, Italy.
| | - Isabella Palumbo
- Section of Radiation Oncology, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132, Perugia, Italy
| | - Mario Luca Fravolini
- Department of Engineering, Università degli Studi di Perugia, Via G. Duranti 93, 06125, Perugia, Italy
| | - Rita Chiari
- Department of Medical Oncology, Ospedale Santa Maria della Misericordia, S. Andrea delle Fratte, 06156, Perugia, Italy
| | - Matteo Minestrini
- Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132, Perugia, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", Università degli Studi del Molise, Via Francesco De Sanctis 1, 86100, Campobasso, Italy
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132, Perugia, Italy
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Ijsseldijk MA, Shoni M, Siegert C, Wiering B, van Engelenburg AKC, Tsai TC, Ten Broek RPG, Lebenthal A. Oncologic Outcomes of Surgery Versus SBRT for Non-Small-Cell Lung Carcinoma: A Systematic Review and Meta-analysis. Clin Lung Cancer 2020; 22:e235-e292. [PMID: 32912754 DOI: 10.1016/j.cllc.2020.04.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/21/2020] [Accepted: 04/25/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND The optimal treatment of stage I non-small-cell lung carcinoma is subject to debate. The aim of this study was to compare overall survival and oncologic outcomes of lobar resection (LR), sublobar resection (SR), and stereotactic body radiotherapy (SBRT). METHODS A systematic review and meta-analysis of oncologic outcomes of propensity matched comparative and noncomparative cohort studies was performed. Outcomes of interest were overall survival and disease-free survival. The inverse variance method and the random-effects method for meta-analysis were utilized to assess the pooled estimates. RESULTS A total of 100 studies with patients treated for clinical stage I non-small-cell lung carcinoma were included. Long-term overall and disease-free survival after LR was superior over SBRT in all comparisons, and for most comparisons, SR was superior to SBRT. Noncomparative studies showed superior long-term overall and disease-free survival for both LR and SR over SBRT. Although the papers were heterogeneous and of low quality, results remained essentially the same throughout a large number of stratifications and sensitivity analyses. CONCLUSION Results of this systematic review and meta-analysis showed that LR has superior outcomes compared to SBRT for cI non-small-cell lung carcinoma. New trials are underway evaluating long-term results of SBRT in potentially operable patients.
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Affiliation(s)
- Michiel A Ijsseldijk
- Division of Surgery, Slingeland Ziekenhuis, Doetinchem, The Netherlands; Division of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Melina Shoni
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA
| | - Charles Siegert
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA; Division of Thoracic Surgery, West Roxbury Veterans Administration, West Roxbury, MA
| | - Bastiaan Wiering
- Division of Surgery, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | | | - Thomas C Tsai
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA
| | - Richard P G Ten Broek
- Division of Surgery, Slingeland Ziekenhuis, Doetinchem, The Netherlands; Division of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Abraham Lebenthal
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA; Division of Thoracic Surgery, West Roxbury Veterans Administration, West Roxbury, MA; Harvard Medical School, Boston, MA
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Polverari G, Ceci F, Bertaglia V, Reale ML, Rampado O, Gallio E, Passera R, Liberini V, Scapoli P, Arena V, Racca M, Veltri A, Novello S, Deandreis D. 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival. Cancers (Basel) 2020; 12:cancers12051163. [PMID: 32380754 PMCID: PMC7281558 DOI: 10.3390/cancers12051163] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/21/2020] [Accepted: 04/30/2020] [Indexed: 01/04/2023] Open
Abstract
Objectives: (1.1) to evaluate the association between baseline 18F-FDG PET/CT semi-quantitative parameters of the primary lesion with progression free survival (PFS), overall survival (OS) and response to immunotherapy, in advanced non-small cell lung carcinoma (NSCLC) patients eligible for immunotherapy; (1.2) to evaluate the application of radiomics analysis of the primary lesion to identify features predictive of response to immunotherapy; (1.3) to evaluate if tumor burden assessed by 18F-FDG PET/CT (N and M factors) is associated with PFS and OS. Materials and Methods: we retrospectively analyzed clinical records of advanced NCSLC patients (stage IIIb/c or stage IV) candidate to immunotherapy who performed 18F-FDG PET/CT before treatment to stage the disease. Fifty-seven (57) patients were included in the analysis (F:M 17:40; median age = 69 years old). Notably, 38/57 of patients had adenocarcinoma (AC), 10/57 squamous cell carcinoma (SCC) and 9/57 were not otherwise specified (NOS). Overall, 47.4% patients were stage IVA, 42.1% IVB and 8.8% IIIB. Immunotherapy was performed as front-line therapy in 42/57 patients and as second line therapy after chemotherapy platinum-based in 15/57. The median follow up after starting immunotherapy was 10 months (range: 1.5–68.6). Therapy response was assessed by RECIST 1.1 criteria (CT evaluation every 4 cycles of therapy) in 48/57 patients or when not feasible by clinical and laboratory data (fast disease progression or worsening of patient clinical condition in nine patients). Radiomics analysis was performed by applying regions of interest (ROIs) of the primary tumor delineated manually by two operators and semi-automatically applying a threshold at 40% of SUVmax. Results: (1.1) metabolic tumor volume (MTV) (p = 0.028) and total lesion glycolysis (TLG) (p = 0.035) were significantly associated with progressive vs. non-progressive disease status. Patients with higher values of MTV and TLG had higher probability of disease progression, compared to those patients presenting with lower values. SUVmax did not show correlation with PD status, PFS and OS. MTV (p = 0.027) and TLG (p = 0.022) also resulted in being significantly different among PR, SD and PD groups, while SUVmax was confirmed to not be associated with response to therapy (p = 0.427). (1.2) We observed the association of several radiomics features with PD status. Namely, patients with high tumor volume, TLG and heterogeneity expressed by “skewness” and “kurtosis” had a higher probability of failing immunotherapy. (1.3) M status at 18F-FDG PET/CT was significantly associated with PFS (p = 0.002) and OS (p = 0.049). No significant associations were observed for N status. Conclusions: 18F-FDG PET/CT performed before the start of immunotherapy might be an important prognostic tool able to predict the disease progression and response to immunotherapy in patients with advanced NSCLC, since MTV, TLG and radiomics features (volume and heterogeneity) are associated with disease progression.
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Affiliation(s)
- Giulia Polverari
- Division of Nuclear Medicine, Department of Medical Sciences, University of Turin, AOU Città della Salute e della Scienza, 10126 Turin, Italy; (G.P.); (R.P.); (V.L.); (D.D.)
- PET/CT Center, Affidea IRMET, 10135 Turin, Italy;
| | - Francesco Ceci
- Division of Nuclear Medicine, Department of Medical Sciences, University of Turin, AOU Città della Salute e della Scienza, 10126 Turin, Italy; (G.P.); (R.P.); (V.L.); (D.D.)
- Correspondence:
| | - Valentina Bertaglia
- Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, 10043 Torino, Italy; (V.B.); (M.L.R.); (S.N.)
| | - Maria Lucia Reale
- Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, 10043 Torino, Italy; (V.B.); (M.L.R.); (S.N.)
| | - Osvaldo Rampado
- Medical Physics Unit, S.C. Fisica Sanitaria, A.O.U. Città della Salute e della Scienza, 10135 Turin, Italy; (O.R.); (E.G.)
| | - Elena Gallio
- Medical Physics Unit, S.C. Fisica Sanitaria, A.O.U. Città della Salute e della Scienza, 10135 Turin, Italy; (O.R.); (E.G.)
| | - Roberto Passera
- Division of Nuclear Medicine, Department of Medical Sciences, University of Turin, AOU Città della Salute e della Scienza, 10126 Turin, Italy; (G.P.); (R.P.); (V.L.); (D.D.)
| | - Virginia Liberini
- Division of Nuclear Medicine, Department of Medical Sciences, University of Turin, AOU Città della Salute e della Scienza, 10126 Turin, Italy; (G.P.); (R.P.); (V.L.); (D.D.)
| | - Paola Scapoli
- Nuclear Medicine, Istituto per la Ricerca e la Cura del Cancro (IRCC), 10060 Candiolo, Italy; (P.S.); (M.R.)
| | | | - Manuela Racca
- Nuclear Medicine, Istituto per la Ricerca e la Cura del Cancro (IRCC), 10060 Candiolo, Italy; (P.S.); (M.R.)
| | - Andrea Veltri
- Radiology Unit, Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, 10043 Torino, Italy;
| | - Silvia Novello
- Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, 10043 Torino, Italy; (V.B.); (M.L.R.); (S.N.)
| | - Désirée Deandreis
- Division of Nuclear Medicine, Department of Medical Sciences, University of Turin, AOU Città della Salute e della Scienza, 10126 Turin, Italy; (G.P.); (R.P.); (V.L.); (D.D.)
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[Prognosis factors after lung stereotactic body radiotherapy for non-small cell lung carcinoma]. Cancer Radiother 2020; 24:267-274. [PMID: 32192839 DOI: 10.1016/j.canrad.2019.11.002] [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: 08/19/2019] [Revised: 10/27/2019] [Accepted: 11/05/2019] [Indexed: 10/24/2022]
Abstract
Lung cancer is the fourth most common cancer in France with a prevalence of 30,000 new cases per year. Lobectomy surgery with dissection is the gold standard treatment for T1-T2 localized non-small cell lung carcinoma. A segmentectomy may be proposed to operable patients but fragile from a respiratory point of view. For inoperable patients or patients with unsatisfactory pulmonary function tests, local treatment with stereotactic radiotherapy may be proposed to achieve local control rates ranging from 85 to 95% at 3-5 years. Several studies have examined prognostic factors after stereotaxic pulmonary radiotherapy. We conducted a general review of the literature to identify factors affecting local control.
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Abstract
Quantitative extraction of imaging features from medical scans (‘radiomics’) has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.
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Krarup MMK, Nygård L, Vogelius IR, Andersen FL, Cook G, Goh V, Fischer BM. Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool. Radiother Oncol 2020; 144:72-78. [PMID: 31733491 DOI: 10.1016/j.radonc.2019.10.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/01/2019] [Accepted: 10/17/2019] [Indexed: 02/06/2023]
Abstract
AIM The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. METHODS 18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant. RESULTS Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively). CONCLUSION The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.
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Affiliation(s)
- Marie Manon Krebs Krarup
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Lotte Nygård
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Ivan Richter Vogelius
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark; Faculty of Health and Medical Sciences, Copenhagen University, Denmark.
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Gary Cook
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Vicky Goh
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark; PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
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Konert T, Everitt S, La Fontaine MD, van de Kamer JB, MacManus MP, Vogel WV, Callahan J, Sonke JJ. Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any? PLoS One 2020; 15:e0228793. [PMID: 32097418 PMCID: PMC7041813 DOI: 10.1371/journal.pone.0228793] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/22/2020] [Indexed: 12/14/2022] Open
Abstract
In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients.
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Affiliation(s)
- Tom Konert
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sarah Everitt
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Matthew D. La Fontaine
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jeroen B. van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Michael P. MacManus
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Wouter V. Vogel
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jason Callahan
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- * E-mail:
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Bianconi F, Fravolini ML, Palumbo I, Palumbo B. Shape and Texture Analysis of Radiomic Data for Computer-Assisted Diagnosis and Prognostication: An Overview. LECTURE NOTES IN MECHANICAL ENGINEERING 2020:3-14. [DOI: 10.1007/978-3-030-31154-4_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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Zhang R, Wang C, Cui K, Chen Y, Sun F, Sun X, Xing L. Prognostic Role Of Computed Tomography Textural Features In Early-Stage Non-Small Cell Lung Cancer Patients Receiving Stereotactic Body Radiotherapy. Cancer Manag Res 2019; 11:9921-9930. [PMID: 31819630 PMCID: PMC6883938 DOI: 10.2147/cmar.s220587] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/29/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose The imaging features of patients with early-stage non-small cell lung cancer (NSCLC) receiving stereotactic body radiotherapy (SBRT) are crucial for the decision-making process to establish a treatment plan. The purpose of this study was to predict the clinical outcomes of SBRT from the textural features of pretreatment computed tomography (CT) images. Patients and methods Forty-one early-stage NSCLC patients who received SBRT were included in this retrospective study. In total, 72 textural features were extracted from the pretreatment contrast-enhanced CT images. Survival analysis was used to identify high-risk groups for progression-free survival (PFS) and disease-specific survival (DSS). Receiver operating characteristic (ROC) curve analysis was utilized to estimate the diagnostic abilities of the textural parameters. Univariable and multivariable Cox regression analyses were performed to evaluate the predictors of PFS and DSS. Results Four parameters, including entropy (P=0.003), second angular moment (SAM) (P=0.04), high-intensity long-run emphasis (HILRE) (P=0.046) and long-run emphasis (LRE) (P=0.042), were significant prognostic features for PFS. In addition, contrast (P=0.008), coarseness (P=0.017), low-intensity zone emphasis (LIZE) (P=0.01) and large number emphasis (LNE) (P=0.046) were significant prognostic factors for DSS. In the ROC analysis, the area under the curve (AUC) of coarseness for local recurrence (LR) was 0.722 (0.528–0.916), and the AUC of entropy for lymph node metastasis (LNM) was 0.771 (0.556–0.987). The four highest AUCs for distant metastasis (DM) were 0.885 (0.784–0.985) for LNE, 0.846 (0.733–0.959) for SAM, 0.731 (0.500–0.961) for LRE and 0.731 (0.585–0.876) for contrast. In the multivariable analysis, smoking and entropy were independent prognostic factors for PFS. Conclusion This exploratory study reveals that textual features derived from pretreatment CT scans have prognostic value in early-stage NSCLC patients treated with SBRT.
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Affiliation(s)
- Ran Zhang
- Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China.,Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Changbin Wang
- Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.,Department of Clinical Medicine, Jinan University, Jinan, Shandong, People's Republic of China
| | - Kai Cui
- Department of Clinical Medicine, Jinan University, Jinan, Shandong, People's Republic of China.,Department of Nuclear Medicine, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Yicong Chen
- Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China.,Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Fenghao Sun
- Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.,Department of Clinical Medicine, Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
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Dissaux G, Visvikis D, Da-Ano R, Pradier O, Chajon E, Barillot I, Duvergé L, Masson I, Abgral R, Santiago Ribeiro MJ, Devillers A, Pallardy A, Fleury V, Mahé MA, De Crevoisier R, Hatt M, Schick U. Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: A Multicentric Study. J Nucl Med 2019; 61:814-820. [PMID: 31732678 DOI: 10.2967/jnumed.119.228106] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 09/30/2019] [Indexed: 02/06/2023] Open
Abstract
The aim of this retrospective multicentric study was to develop and evaluate a prognostic 18F-FDG PET/CT radiomic signature in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy (SBRT). Methods: Patients from 3 different centers (n = 27, 29, and 8) were pooled to constitute the training set, whereas the patients from a fourth center (n = 23) were used as the testing set. The primary endpoint was local control. The primary tumor was semiautomatically delineated in the PET images using the fuzzy locally adaptive Bayesian algorithm, and manually in the low-dose CT images. In total, 184 Image Biomarkers Standardization Initiative-compliant radiomic features were extracted. Seven clinical and treatment parameters were included. We used ComBat to harmonize radiomic features extracted from the 4 institutions relying on different PET/CT scanners. In the training set, variables found significant in the univariate analysis were fed into a multivariate regression model, and models were built by combining independent prognostic factors. Results: Median follow-up was 21.1 mo (range, 1.7-63.4 mo) and 25.5 mo (range, 7.7-57.8 mo) in training and testing sets, respectively. In univariate analysis, none of the clinical variables, 2 PET features, and 2 CT features were significantly predictive of local control. The best predictive models in the training set were obtained by combining one feature from PET (Information Correlation 2) and one feature from CT (flatness), reaching a sensitivity of 100% and a specificity of 96%. Another model combining 2 PET features (Information Correlation 2 and strength) reached sensitivity of 100% and specificity of 88%, both with an undefined hazard ratio (P < 0.001). The latter model obtained an accuracy of 0.91 (sensitivity, 100%; specificity, 81%), with a hazard ratio undefined (P = 0.023) in the testing set; however, other models relying on CT radiomic features only or the combination of PET and CT features failed to validate in the testing set. Conclusion: We showed that 2 radiomic features derived from 18F-FDG PET were independently associated with local control in patients with non-small cell lung cancer undergoing SBRT and could be combined in an accurate predictive model. This model could provide local relapse-related information and could be helpful in clinical decision making.
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Affiliation(s)
- Gurvan Dissaux
- Radiation Oncology Department, University Hospital, Brest, France .,LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | | | - Ronrick Da-Ano
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Olivier Pradier
- Radiation Oncology Department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Enrique Chajon
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
| | - Isabelle Barillot
- Department of Radiation Oncology, University Hospital, Tours, France
| | - Loig Duvergé
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
| | - Ingrid Masson
- Department of Radiation Oncology, ICO, Saint-Herblain, France
| | - Ronan Abgral
- Nuclear Medicine Department, University Hospital, Brest, France
| | | | - Anne Devillers
- Nuclear Medicine Department, Centre Eugene Marquis, Rennes, France
| | - Amandine Pallardy
- Nuclear Medicine Department, University Hospital, Nantes, France; and
| | - Vincent Fleury
- Nuclear Medicine Department, ICO, Saint-Herblain, France
| | - Marc-André Mahé
- Department of Radiation Oncology, ICO, Saint-Herblain, France
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
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Mattonen SA, Davidzon GA, Benson J, Leung ANC, Vasanawala M, Horng G, Shrager JB, Napel S, Nair VS. Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology 2019; 293:451-459. [PMID: 31526257 DOI: 10.1148/radiol.2019190357] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Sarah A Mattonen
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Guido A Davidzon
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Jalen Benson
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Ann N C Leung
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Minal Vasanawala
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - George Horng
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Joseph B Shrager
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Sandy Napel
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Viswam S Nair
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
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Werner RA, Ilhan H, Lehner S, Papp L, Zsótér N, Schatka I, Muegge DO, Javadi MS, Higuchi T, Buck AK, Bartenstein P, Bengel F, Essler M, Lapa C, Bundschuh RA. Pre-therapy Somatostatin Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy. Mol Imaging Biol 2019; 21:582-590. [PMID: 30014345 DOI: 10.1007/s11307-018-1252-5] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE Early identification of aggressive disease could improve decision support in pancreatic neuroendocrine tumor (pNET) patients prior to peptide receptor radionuclide therapy (PRRT). The prognostic value of intratumoral textural features (TF) determined by baseline somatostatin receptor (SSTR)-positron emission tomography (PET) before PRRT was analyzed. PROCEDURES Thirty-one patients with G1/G2 pNET were enrolled (G2, n = 23/31). Prior to PRRT with [177Lu]DOTATATE (mean, 3.6 cycles), baseline SSTR-PET computed tomography was performed. By segmentation of 162 (median per patient, 5) metastases, intratumoral TF were computed. The impact of conventional PET parameters (SUVmean/max), imaging-based TF, and clinical parameters (Ki67, CgA) for prediction of both progression-free survival (PFS) and overall survival (OS) after PRRT were evaluated. RESULTS Within a median follow-up of 3.7 years, tumor progression was detected in 21 patients (median, 1.5 years) and 13/31 deceased (median, 1.9 years). In ROC analysis, the TF entropy, reflecting derangement on a voxel-by-voxel level, demonstrated predictive capability for OS (cutoff = 6.7, AUC = 0.71, p = 0.02). Of note, increasing entropy could predict a longer survival (> 6.7, OS = 2.5 years, 17/31), whereas less voxel-based derangement portended inferior outcome (< 6.7, OS = 1.9 years, 14/31). These findings were supported in a G2 subanalysis (> 6.9, OS = 2.8 years, 9/23 vs. < 6.9, OS = 1.9 years, 14/23). Kaplan-Meier analysis revealed a significant distinction between high- and low-risk groups using entropy (n = 31, p < 0.05). For those patients below the ROC-derived threshold, the relative risk of death after PRRT was 2.73 (n = 31, p = 0.04). Ki67 was negatively associated with PFS (p = 0.002); however, SUVmean/max failed in prognostication (n.s.). CONCLUSIONS In contrast to conventional PET parameters, assessment of intratumoral heterogeneity demonstrated superior prognostic performance in pNET patients undergoing PRRT. This novel PET-based strategy of outcome prediction prior to PRRT might be useful for patient risk stratification.
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Affiliation(s)
- Rudolf A Werner
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Harun Ilhan
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Sebastian Lehner
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Ambulatory Healthcare Center Dr. Neumaier & Colleagues, Radiology, Nuclear Medicine, Radiation Therapy, Regensburg, Germany
| | - László Papp
- Department of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | | | - Imke Schatka
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Dirk O Muegge
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Mehrbod S Javadi
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Takahiro Higuchi
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
- Department of Bio Medical Imaging, National Cardiovascular and Cerebral Research Center, Suita, Japan
| | - Andreas K Buck
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Frank Bengel
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Constantin Lapa
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Ralph A Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany.
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