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Background and purpose: In head and neck squamous cell carcinoma (HNSCC) (chemo)radiotherapy is increasingly used to preserve organ functionality. The purpose of this study was to identify predictive pretreatment DWI-and 18 F-FDG-PET/CT-parameters for treatment failure (TF), locoregional recurrence (LR) and death in HNSCC patients treated by (chemo)radiotherapy. Materials and methods: We retrospectively included 134 histologically proven HNSCC patients treated with (chemo)radiotherapy between 2012-2017. In 58 patients pre-treatment DWI and 18 F-FDG-PET/CT were performed, in 31 patients DWI only and in 45 patients 18 F-FDG-PET/CT only. Primary tumor (PT) and largest lymph node (LN) metastasis were quantitatively assessed for TF, LR and death. Multivariate analysis was performed for 18 F-FDG-PET/CT and DWI separately and thereafter combined. In patients with both imaging modalities, positive and negative predictive value in TF and differences in LR and death, were assessed. Results: Mean follow-up was 25.6 months (interquartile-range; 14.0-37.1 months). Predictors of treatment failure, corrected for TNM-stage and HPV-status, were SUV max-PT , ADC max-PT , total lesion glycolysis (TLG-LN), ADCp20-LN (P = 0.049, P = 0.024, P = 0.031, P = 0.047, respectively). TLG-PT was predictive for LR (P = 0.003). Metabolic active tumor volume (MATV-PT) (P = 0.003), ADC GTV-PT (P < 0.001), ADCSD (P = 0.048) were significant predictors for death. In patients with both imaging modalities SUV max-PT remained predictive for treatment failure (P = 0.049), TLG-LN for LR (P = 0.003) and ADC GTV-PT for death (P < 0.001). Higher predictive value for treatment failure was found for the combination of SUV max-PT and ADC max-PT , compared to either one separately. Conclusion: Both DWI-and 18 F-FDG-PET/CT-parameters appear to have predictive value for treatment failure, locoregional recurrence and death. Combining SUV max-PT and ADC max-PT resulted in better prediction of treatment failure compared to single parameter assessment.

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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Background Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. Methods Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients’ outcome. Results Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). Conclusions Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. Trial registration Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946

Objectives To assess (I) correlations between diffusion-weighted (DWI), intravoxel incoherent motion (IVIM), dynamic contrast-enhanced (DCE) MRI, and 18 F-FDG-PET/CT imaging parameters capturing tumor characteristics and (II) their predictive value of locoregional recurrence-free survival (LRFS) and overall survival (OS) in patients with head and neck squamous cell carcinoma (HNSCC) treated with (chemo)radiotherapy. Methods Between 2014 and 2018, patients with histopathologically proven HNSCC, planned for curative (chemo) radiotherapy, were prospectively included. Pretreatment clinical, anatomical, and functional imaging parameters (obtained by DWI/IVIM, DCE-MRI, and 18 F-FDG-PET/CT) were extracted for primary tumors (PT) and lymph node metastases. Correlations and differences between parameters were assessed. The predictive value of LRFS and OS was assessed, performing univariable, multivariable Cox and CoxBoost regression analyses. Results In total, 70 patients were included. Significant correlations between 18 F-FDG-PET parameters and DWI-/DCE volume parameters were found ( r > 0.442, p < 0.002). The combination of HPV (HR = 0.903), intoxications (HR = 1.065), PT ADC GTV (HR = 1.252), K trans (HR = 1.223), and V e (HR = 1.215) was predictive for LRFS (C-index = 0.546; p = 0.023). N-stage (HR = 1.058), HPV positivity (HR = 0.886), hypopharyngeal tumor location (HR = 1.111), ADC GTV (HR = 1.102), ADC mean (HR = 1.137), D* (HR = 0.862), K trans (HR = 1.106), V e (HR = 1.195), SUV max (HR = 1.094), and TLG (HR = 1.433) were predictive for OS (C-index = 0.664; p = 0.046). Conclusions Functional imaging parameters, performing DWI/IVIM, DCE-MRI, and 18 F-FDG-PET/CT, yielded complementary value in capturing tumor characteristics. More specific, intoxications, HPV-negative status, large tumor volume-related parameters, high permeability (K trans ), and high extravascular extracellular space (V e ) parameters were predictive for adverse locoregional recurrence-free survival and adverse overall survival. Low cellularity (high ADC) and high metabolism (high SUV) were additionally predictive for decreased overall survival. These different predictive factors added to estimated locoregional and overall survival. Key Points • Parameters of DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT were able to capture complementary tumor characteristics. • Multivariable analysis revealed that intoxications, HPV negativit...

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