Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study

Leijenaar, Ralph T.H.; Bogowicz, Marta; Jochems, Arthur; Hoebers, Frank; Wesseling, Frederik; Huang, Szu-Han; Chan, Biu; Waldron, John; O’Sullivan, Brian; Rietveld, D.; Leemans, C. René; Brakenhoff, Ruud H.; Riesterer, Oliver; Tanadini‐Lang, Stephanie; Gückenberger, Matthias; Ikenberg, Kristian; Lambin, Philippe

doi:10.1259/bjr.20170498

Cited by 126 publications

(133 citation statements)

References 37 publications

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“…CT radiomics has previously been evaluated for prediction of overall survival and HPV status [22][23][24][25] . The performance of the distributed HPV models (AUC 0.73-0.80) is comparable with previously published results (AUC 0.70-0.80).…”

Section: Discussionmentioning

confidence: 99%

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

Bogowicz

Jochems

Deist

et al. 2020

Sci Rep

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A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (Roc) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10 −7). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.

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Section: Discussionmentioning

confidence: 99%

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

Bogowicz

Jochems

Deist

et al. 2020

Sci Rep

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“…76,77 Overall, prevalence of HPV+ and HPVÀ cases (positive/negative = 0.62/0.38 for 116+ vs 71À) in our dataset was considered reasonable, and was higher than in other similar studies. [11][12][13][14] While investigators have found radiomic features to be relatively insensitive to specific image acquisition techniques among different scanners and institutions for patients, 57,78 others have shown this to be a potentially problematic issue requiring more detailed investigation. 22,79 Textural features require interpolation to isotropic voxel spacing to be rotationally invariant, and to allow comparison between image data from different samples, cohorts, or batches.…”

Section: Discussionmentioning

confidence: 99%

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

et al. 2020

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Purpose To perform radiomic analysis of primary tumors extracted from pretreatment contrast‐enhanced computed tomography (CE‐CT) images for patients with oropharyngeal cancers to identify discriminant features and construct an optimal classifier for the characterization and prediction of human papilloma virus (HPV) status. Materials and methods One hundred and eighty seven patients with oropharyngeal cancers with known HPV status (confirmed by immunohistochemistry—p16 protein testing) were retrospectively studied as follows: Group A: 95 patients (19HPV− and 76HPV+) from the MICAII grand challenge. Group B: 92 patients (52HPV− and 40HPV+) from our institution. Radiomic features (172) were extracted from pretreatment diagnostic CE‐CT images of the gross tumor volume (GTV). Levene and Kolmogorov–Smirnov's tests with absolute biserial correlation (>0.48) were used to identify the discriminant features between the HPV+ and HPV− groups. The discriminant features were used to train and test eight different classifiers. Area under receiver operating characteristic (AUC), positive predictive and negative predictive values (PPV and NPV, respectively) were used to evaluate the performance of the classifiers. Principal component analysis (PCA) was applied on the discriminant feature set and seven PCs were used to train and test a generalized linear model (GLM) classifier. Results Among 172 radiomic features only 12 radiomic features (from 3 categories) were significantly different (P < 0.05, |BSC| > 0.48) between the HPV+ and HPV− groups. Among the eight classifiers trained and applied for prediction of HPV status, the GLM showed the highest performance for each discriminant feature and the combined 12 features: AUC/PPV/NPV = 0.878/0.834/0.811. The GLM high prediction power was AUC/PPV/NPV = 0.849/0.731/0.788 and AUC/PPV/NPV = 0.869/0.807/0.870 for unseen test datasets for groups A and B, respectively. After eliminating the correlation among discriminant features by applying PCA analysis, the performance of the GLM was improved by 3.3%, 2.2%, and 1.8% for AUC, PPV, and NPV, respectively. Conclusions Results imply that GTV's for HPV+ patients exhibit higher intensities, smaller lesion size, greater sphericity/roundness, and higher spatial intensity‐variation/heterogeneity. Results are suggestive that radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on contrast‐enhanced diagnostic CT datasets may be potentially used for classification of HPV status.

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“…Z-score standardization was performed to normalize the MRI signal from different images. Lastly, the images were resampled to a common isotropic resolution of 2 mm (as in [ 39 ]) using B-spline interpolation. Intensity values of the MRI were discretized using 32-bins histogram discretization.…”

Section: Methodsmentioning

confidence: 99%

Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients

Bologna

Corino

Calareso

et al. 2020

Cancers

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Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.

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Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study

Abstract: This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.

Cited by 126 publications

References 37 publications

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients

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