Baseline peripheral blood leukocytosis: Biological marker predicts outcome in oropharyngeal cancer, regardless of HPV-status

To study the prognostic value of abnormalities in baseline complete blood count in patients with oropharyngeal cancer (OPC) treated with (chemo) radiation. The prognostic value of baseline complete blood count on outcome in 234 patients with OPC treated between 2010 and 2015 was examined in multivariate analysis together with other conventional prognostic variables including HPV-status, tumor stage, tumor and nodal size. The 3-year overall survival (OS), disease-free survival (DFS), locoregional control (LRC), and distant control (DC) of the whole group were 74%, 64%, 79%, and 88%, respectively. Leukocytosis and HPV-status were the only significant prognosticators for OS and DFS at the multivariate analysis. Patients without leukocytosis had a significantly better DC compared to those with leukocytosis (92% and 70%, respectively, p  < 0.001). Patients with HPV-negative OPC had significantly worse LRC compared to HPV-positive patients (67% and 90%, respectively, p  < 0.001). The 3-year OS in HPV-positive group with leukocytosis compared to those without leukocytosis were 69% and 95%, respectively (p  < 0.001). The figures for HPV-negative patients were 41% vs. 61%, respectively (p = 0.010). This is the first study to date reporting the independent impact of leukocytosis and HPV-status on outcome of patients with OPC. The poor outcome of patients with leukocytosis is mainly caused by the worse DC. The significant impact of leukocytosis on outcome was even more pronounced in HPV-positive patients. These biomarkers could help identifying patients with poor prognosis at baseline requiring intensification of local and/or systemic treatment while treatment de-intensification might be offered to the low-risk grou

FDG-PET/CT improves detection of residual disease and reduces the need for examination under anaesthesia in oropharyngeal cancer patients treated with (chemo-)radiation

Purpose: Early detection of residual disease (RD) after (chemo)radiation for oropharyngeal (OPC) is crucial. Surveillance of neck nodes with FDG-PET/CT has been studied extensively, whereas its value for local RD remains less clear. We aim to evaluate the diagnostic value of post-treatment FDG-PET/CT in detecting local RD and the outcome of patients with local RD. Methods: A cohort (n = 352) of consecutively treated OPC patients at our institute between 2010 and 2017 was evaluated. Patients that underwent FDG-PET/CT at 3 months post-treatment (n = 94) were classified as having complete (CMR) or partial metabolic response (PMR). PMR was defined as visually detectable metabolic activity above the background of surrounding normal tissues. Primary endpoint was diagnostic accuracy in detecting local RD. Results: Local RD was seen in 19/352 patients (5%), all of them were HPV negative. The FDG-PET/CT had a sensitivity of 100% (8/8), specificity 85% (73/86), PPV 38% (8/21), NPV 100% (73/73), and accuracy 86%. Patients with local RD had significantly worse OS at 2 years, compared to those without (10 versus 88%, P < 0.001). In multivariable analysis, local RD remained a significant predictive factor for death with a hazard ratio of 11.9 (95% CI 5.8–24.3). The number of patients that underwent PET/CT increased over time (P < 0.001), whereas the number of patients that underwent EUA declined (P = 0.072). Conclusion: FDG-PET/CT has excellent performance for the detection of RD, with the sensitivity and negative predictive value approaching 100%. Due to these excellent results is examination under anaesthesia today in the vast majority of the PET-negative cases not necessary anymore

Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer

Purpose: Laborious and time-consuming tumor segmentations are one of the factors that impede adoption of radiomics in the clinical routine. This study investigates model performance using alternative tumor delineation strategies in models predictive of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC). Methods: Of 153 OPSCC patients, HPV status was determined using p16/p53 immunohistochemistry. MR-based radiomic features were extracted within 3D delineations by an inexperienced observer, experienced radiologist or radiation oncologist, and within a 2D delineation of the largest axial tumor diameter and 3D spheres within the tumor. First, logistic regression prediction models were constructed and tested separately for each of these six delineation strategies. Secondly, the model trained on experienced delineations was tested using these delineation strategies. The latter methodology was repeated with the omission of shape features. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. Results: Models constructed and tested using single-slice delineations (AUC/Sensitivity/Specificity: 0.84/0.75/0.84) perform better compared to 3D experienced observer delineations (AUC/Sensitivity/Specificity: 0.76/0.76/0.71), where models based on 4 mm sphere delineations (AUC/Sensitivity/Specificity: 0.77/0.59/0.71) show similar performance. Similar performance was found when experienced and largest diameter delineations (AUC/Sens/Spec: 0.76/0.75/0.65 vs 0.76/0.69/0.69) was used to test the model constructed using experienced delineations without shape features. Conclusion: Alternative delineations can substitute labor and time intensive full tumor delineations in a model that predicts HPV status in OPSCC. These faster delineations may improve adoption of radiomics in the clinical setting. Future research should evaluate whether these alternative delineations are valid in other radiomics models

Clinical variables and magnetic resonance imaging-based radiomics predict human papillomavirus status of oropharyngeal cancer

Background: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables. Methods: Seventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC). Results: Model performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors. Conclusion: Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available

Simple delineations cannot substitute full 3d tumor delineations for MR-based radiomics prediction of locoregional control in oropharyngeal cancer

BACKGROUND AND PURPOSE: Manual delineation of head and neck tumor contours for radiomics analyses is tedious and time consuming. This study investigates if fast or readily available tumor contours can substitute full tumor contours by an experienced observer for an MR-based radiomics model to predict locoregional control (LRC) in oropharyngeal squamous cell carcinoma (OPSCC) tumors. MATERIALS AND METHODS: Radiomic features were extracted from postcontrast T1-weighted MRIs of 177 OPSCC primary tumors using six different manual delineation strategies. LRC prediction models based on recursive feature elimination combined with logistic regression were built. Models were trained and tested on data from each separate delineation. Additionally, the model derived from segmentations from the experienced reader was tested by each of the alternative delineations. Complementary, this was repeated with removal of size and shape features. Model performance was evaluated using area under the curve (AUC). RESULTS: Prediction performance of the experienced radiologist tumor delineation (AUC: 0.74) was superior compared to all other delineations when trained and tested (AUCs: 0.41-0.56) or trained on experienced delineations and tested (AUC: 0.56-0.67) on alternative segmentations. Removal of size and shape features considerably decreases prediction performance (AUC: 0.54). Applying the model based on expert delineations to spherical or single slice delineations makes prediction worthless since these models predict one class. CONCLUSION: Fast or readily available contours cannot substitute full expert tumor delineations in radiomics models predictive of LRC in OPSCC

Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models

Objectives: New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors. Methods: 177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup. Results: A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734–0.757], OS: 0.744 [0.735–0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697–0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729–0.750]), but not for OS prediction (AUC: 0.654 [0.646–0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction. Conclusion: Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS

Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer

PURPOSE: Laborious and time-consuming tumor segmentations are one of the factors that impede adoption of radiomics in the clinical routine. This study investigates model performance using alternative tumor delineation strategies in models predictive of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: Of 153 OPSCC patients, HPV status was determined using p16/p53 immunohistochemistry. MR-based radiomic features were extracted within 3D delineations by an inexperienced observer, experienced radiologist or radiation oncologist, and within a 2D delineation of the largest axial tumor diameter and 3D spheres within the tumor. First, logistic regression prediction models were constructed and tested separately for each of these six delineation strategies. Secondly, the model trained on experienced delineations was tested using these delineation strategies. The latter methodology was repeated with the omission of shape features. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. RESULTS: Models constructed and tested using single-slice delineations (AUC/Sensitivity/Specificity: 0.84/0.75/0.84) perform better compared to 3D experienced observer delineations (AUC/Sensitivity/Specificity: 0.76/0.76/0.71), where models based on 4 mm sphere delineations (AUC/Sensitivity/Specificity: 0.77/0.59/0.71) show similar performance. Similar performance was found when experienced and largest diameter delineations (AUC/Sens/Spec: 0.76/0.75/0.65 vs 0.76/0.69/0.69) was used to test the model constructed using experienced delineations without shape features. CONCLUSION: Alternative delineations can substitute labor and time intensive full tumor delineations in a model that predicts HPV status in OPSCC. These faster delineations may improve adoption of radiomics in the clinical setting. Future research should evaluate whether these alternative delineations are valid in other radiomics models