The Image Biomarker Standardization Initiative: Standardized                     Quantitative Radiomics for High-Throughput Image-based                     Phenotyping

Zwanenburg, Alex; Vallières, Martin; Abdalah, Mahmoud A.; Aerts, Hugo J.W.L.; Andrearczyk, Vincent; Apte, Aditya; Ashrafinia, Saeed; Bakas, Spyridon; Beukinga, Roelof J.; Boellaard, Ronald; Bogowicz, Marta; Boldrini, Luca; Buvat, Irène; Cook, Gary; Davatzikos, Christos; Depeursinge, Adrien; Desseroit, Marie-Charlotte; Dinapoli, N.; Dinh, Cuong V.; Echegaray, Sebastian; Naqa, Issam El; Fedorov, Andriy; Gatta, Roberto; Gillies, Robert J.; Goh, Vicky; Goetz, Michael; Gückenberger, Matthias; Ha, Sung Min; Hatt, Mathieu; Isensee, Fabian; Lambin, Philippe; Leger, Stefan; Leijenaar, Ralph T.H.; Lenkowicz, Jacopo; Lippert, Fiona; Losnegård, Are; Maier‐Hein, Klaus H.; Morin, Olivier; Müller, Henning; Napel, Sandy; Nioche, Christophe; Orlhac, Fanny; Pati, Sarthak; Pfaehler, Elisabeth; Rahmim, Arman; Rao, Arvind; Scherer, Jonas; Siddique, Muhammad; Sijtsema, Nanna M.; Fernandez, Jairo Socarras; Spezi, Emiliano; Steenbakkers, Roel J.H.M.; Tanadini‐Lang, Stephanie; Thorwarth, Daniela; Troost, Esther G.C.; Upadhaya, Taman; Valentini, Vincenzo; Dijk, Lisanne V. van; Griethuysen, Joost J.M. van; Velden, Floris H. P. van; Whybra, Philip; Richter, Christian; Löck, Steffen

doi:10.1148/radiol.2020191145

Cited by 2,418 publications

(2,029 citation statements)

References 35 publications

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“…The GLSZM analyzes the distance between groups of voxels with similar grey-levels by counting the number of groups of linked voxels, which occur if the neighboring voxel has an identical discretized grey level. SZE focuses on areas of small volume, where the lower the SZE value, the more heterogeneous the intensities in the image (in this case, the ADC map) within the tumor volume are considered to be [18].…”

Section: Discussionmentioning

confidence: 99%

“…Prior to the extraction of radiomics features, wavelet filters were applied to each MRI sequence, thereby creating 8 filtered images with high-pass and low-pass versions of the wavelet basis function coiflet1. Radiomics features were extracted using homemade radiomics code implemented in MatLab ® , following the most up-to-date guidelines and benchmark values of the Image Biomarker Standardisation Initiative (IBSI) [18]. Only the previously identified feature (ADC SZE GLSZM ) [14] was considered in the present study, as explained in the statistical analysis section below.…”

Section: Radiomics Featuresmentioning

confidence: 99%

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External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

Bourbonne

Fournier

Vallières

et al. 2020

Cancers

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Adjuvant radiotherapy after prostatectomy was recently challenged by early salvage radiotherapy, which highlighted the need for biomarkers to improve risk stratification. Therefore, we developed an MRI ADC map-derived radiomics model to predict biochemical recurrence (BCR) and BCR-free survival (bRFS) after surgery. Our goal in this work was to externally validate this radiomics-based prediction model. Experimental Design: A total of 195 patients with a high recurrence risk of prostate cancer (pT3-4 and/or R1 and/or Gleason’s score > 7) were retrospectively included in two institutions. Patients with postoperative PSA (Prostate Specific Antigen) > 0.04 ng/mL or lymph node involvement were excluded. Radiomics features were extracted from T2 and ADC delineated tumors. A total of 107 patients from Institution 1 were used to retrain the previously published model. The retrained model was then applied to 88 patients from Institution 2 for external validation. BCR predictions were evaluated using AUC (Area Under the Curve), accuracy, and bRFS using Kaplan–Meier curves. Results: With a median follow-up of 46.3 months, 52/195 patients experienced BCR. In the retraining cohort, the clinical prediction model (combining the number of risk factors and postoperative PSA) demonstrated moderate predictive power (accuracy of 63%). The radiomics model (ADC-based SZEGLSZM) predicted BCR with an accuracy of 78% and allowed for significant stratification of patients for bRFS (p < 0.0001). In Institution 2, this radiomics model remained predictive of BCR (accuracy of 0.76%) contrary to the clinical model (accuracy of 0.56%). Conclusions: The recently developed MRI ADC map-based radiomics model was validated in terms of its predictive accuracy of BCR and bRFS after prostatectomy in an external cohort.

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Section: Discussionmentioning

confidence: 99%

Section: Radiomics Featuresmentioning

confidence: 99%

External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

Bourbonne

Fournier

Vallières

et al. 2020

Cancers

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“…Radiomics calculations were performed using an in-house developed software implementation (Z-Rad, Python programming language v 2.7.10) which has been compared to established radiomics software. 24,25 A HU range of À300 to 200 HU was applied to exclude lung tissue and bone structures. In total, 1404 radiomic features were calculated, i.e.…”

Section: E Delineation Data Preprocessing and Radiomics Calculationmentioning

confidence: 99%

Comparison of robust to standardized CT radiomics models to predict overall survival for non‐small cell lung cancer patients

et al. 2020

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Background: Radiomics is a promising tool for the identification of new prognostic biomarkers. Radiomic features can be affected by different scanning protocols, often present in retrospective and prospective clinical data. We compared a computed tomography (CT) radiomics model based on a large but highly heterogeneous multicentric image dataset with robust feature pre-selection to a model based on a smaller but standardized image dataset without pre-selection. Materials and methods: Primary tumor radiomics was extracted from pre-treatment CTs of IIIA/N2/IIIB NSCLC patients from a prospective Swiss multicentric randomized trial (n patient = 124, n institution = 14, SAKK 16/00) and a validation dataset (n patient = 31, n institution = 1). Four robustness studies investigating inter-observer delineation variation, motion, convolution kernel, and contrast were conducted to identify robust features using an intraclass correlation coefficient threshold >0.9. Two 12-months overall survival (OS) logistic regression models were trained: (a) on the entire multicentric heterogeneous dataset but with robust feature pre-selection (MCR) and (b) on a smaller standardized subset using all features (STD). Both models were validated on the validation dataset acquired with similar reconstruction parameters as the STD dataset. The model performances were compared using the DeLong test. Results: In total, 113 stable features were identified (n shape = 8, n intensity = 0, n texture = 7, n wavelet = 98). The convolution kernel had the strongest influence on the feature robustness (<20% stable features). The final models of MCR and STD consisted of one and two features respectively. Both features of the STD model were identified as non-robust. MCR did not show performance significantly different from STD on the validation cohort (AUC [95%CI] = 0.72 [0.48-0.95] and 0.79 [0.63-0.95], p = 0.59). Conclusion: Prognostic OS CT radiomics model for NSCLC based on a heterogeneous multicentric imaging dataset with robust feature pre-selection performed equally well as a model on a standardized dataset.

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“…All of the features were verified for stability by image biomarker standardization initiative (IBSI). 13,17 A detailed definition of the features adopted is given in Table S1. The workflow of radiomic feature extraction is illustrated in Figure 2.…”

Section: Imaging and Texture Analysismentioning

confidence: 99%

Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non‐small cell lung carcinoma patients

Kim

Park

et al. 2020

Thoracic Cancer

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Background A single institution retrospective analysis of 124 non‐small cell lung carcinoma (NSCLC) patients was performed to identify whether disease‐free survival (DFS) achieves incremental values when radiomic and genomic data are combined with clinical information. Methods Using the least absolute shrinkage and selection operator (LASSO) Cox regression method, radiomic and genetic features were reduced in number for selection of the most useful prognostic feature. We created four models using only baseline clinical data, clinical data with selected genetic features, clinical data with selected radiomic features, and clinical data with selected genetic and radiomic features together. Multivariate Cox proportional hazards analysis was performed to determine predictors of DFS. Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for DFS prediction by four constructed models at the five‐year time point. Results On precontrast scan, improved discrimination performance was obtained in a merging of selected radiomics and genetics (AUC = 0.8638), compared with clinical data only (AUC = 0.7990), selected genetic features (AUC = 0.8497), and selected radiomic features (AUC = 0.8355). On post‐contrast scan, discrimination performance was improved (AUC = 0.8672) compared with the clinical variables (AUC = 0.7913), and selected genetic features (AUC = 0.8376) and selected radiomic features (AUC = 0.8399) were considered. Conclusions The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating clinicopathologic model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone. Key points Significant findings of the study Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for disease‐free survival (DFS). The discriminative performance for DFS was better when combining radiomic and genetic features compared to clinical data only, selected genetic features, and selected radiomic features. What this study adds The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating a clinicopathological model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Cited by 2,418 publications

References 35 publications

External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

Comparison of robust to standardized CT radiomics models to predict overall survival for non‐small cell lung cancer patients

Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non‐small cell lung carcinoma patients

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