Exploring the effects of extended interval dosing of natalizumab and drug concentrations on brain atrophy in multiple sclerosis.

BACKGROUND: Extended interval dosing (EID) of natalizumab treatment is increasingly used in multiple sclerosis. Besides the clear anti-inflammatory effect, natalizumab is considered to have neuroprotective properties as well. OBJECTIVES: This study aimed to study the longitudinal effects of EID compared to standard interval dosing (SID) and natalizumab drug concentrations on brain atrophy. METHODS: Patients receiving EID or SID of natalizumab with a minimum radiological follow-up of 2 years were included. Changes in brain atrophy measures over time were derived from clinical routine 3D-Fluid Attenuated Inversion Recovery (FLAIR)-weighted magnetic resonance imaging (MRI) scans using SynthSeg. RESULTS: We found no differences between EID (n = 32) and SID (n = 50) for whole brain (-0.21% vs -0.16%, p = 0.42), ventricular (1.84% vs 1.13%, p = 0.24), and thalamic (-0.32% vs -0.32%, p = 0.97) annualized volume change over a median follow-up of 3.2 years. No associations between natalizumab drug concentration and brain atrophy rate were found. CONCLUSION: We found no clear evidence that EID compared to SID or lower natalizumab drug concentrations have a negative impact on the development of brain atrophy over time.

Use of gadolinium-based contrast agents in multiple sclerosis: a review by the ESMRMB-GREC and ESNR Multiple Sclerosis Working Group.

Magnetic resonance imaging (MRI) is the most sensitive technique for detecting inflammatory demyelinating lesions in multiple sclerosis (MS) and plays a crucial role in diagnosis and monitoring treatment effectiveness, and for predicting the disease course. In clinical practice, detection of MS lesions is mainly based on T2-weighted and contrast-enhanced T1-weighted sequences. Contrast-enhancing lesions (CEL) on T1-weighted sequences are related to (sub)acute inflammation, while new or enlarging T2 lesions reflect the permanent footprint from a previous acute inflammatory demyelinating event. These two types of MRI features provide redundant information, at least in regular monitoring of the disease. Due to the concern of gadolinium deposition after repetitive injections of gadolinium-based contrast agents (GBCAs), scientific organizations and regulatory agencies in Europe and North America have proposed that these contrast agents should be administered only if clinically necessary. In this article, we provide data on the mode of action of GBCAs in MS, the indications of the use of these agents in clinical practice, their value in MS for diagnostic, prognostic, and monitoring purposes, and their use in specific populations (children, pregnant women, and breast-feeders). We discuss imaging strategies that achieve the highest sensitivity for detecting CELs in compliance with the safety regulations established by different regulatory agencies. Finally, we will briefly discuss some alternatives to the use of GBCA for detecting blood-brain barrier disruption in MS lesions. CLINICAL RELEVANCE STATEMENT: Although use of GBCA at diagnostic workup of suspected MS is highly valuable for diagnostic and prognostic purposes, their use in routine monitoring is not mandatory and must be reduced, as detection of disease activity can be based on the identification of new or enlarging lesions on T2-weighted images. KEY POINTS: • Both the EMA and the FDA state that the use of GBCA in medicine should be restricted to clinical scenarios in which the additional information offered by the contrast agent is required. • The use of GBCA is generally recommended in the diagnostic workup in subjects with suspected MS and is generally not necessary for routine monitoring in clinical practice. • Alternative MRI-based approaches for detecting acute focal inflammatory MS lesions are not yet ready to be used in clinical practice.

The impact of image contrast, resolution and reader expertise on black hole identification in Multiple Sclerosis.

OBJECTIVES: In the neuroradiological work-up of Multiple Sclerosis (MS), the detection of "black holes" (BH) represent an information of undeniable importance. Nevertheless, different sequences can be used in clinical practice to evaluate BH in MS. Aim of this study was to investigate the possible impact of different sequences, resolutions, and levels of expertise on the intra- and inter-rater reliability identification of BH in MS. METHODS: Brain MRI scans of 85 MS patients (M/F = 22/63; mean age = 36.0 ± 10.2 years) were evaluated in this prospective single-center study. The acquisition protocol included a 3 mm SE-T1w sequence, a 1 mm 3D-GrE-T1w sequence from which a resliced 3 mm sequence was also obtained. Images were evaluated independently by two readers of different expertise at baseline and after a wash-out period of 30 days. The intraclass correlation coefficient (ICC) was calculated as an index of intra and inter-reader reliability. RESULTS: For both readers, the intra-reader ICC analysis showed that the 3 mm SE-T1w and 3 mm resliced GrE-T1w images achieved an excellent performance (both with an ICC ≥ 0.95), while 1 mm 3D-GrE-T1w scans achieved a moderate one (ICC < 0.90). The inter-reader analysis showed that each of the three sequences achieved a moderate performance (all ICCs < 0.90). CONCLUSIONS: The 1 mm 3D-GrE-T1w sequence seems to be prone to a greater intra-reader variability compared to the 3 mm SE-T1w, with this effect being driven by the higher spatial resolution of the first sequence. To ensure reliability levels comparable with the standard SE-T1w in BH count, an assessment on a 3 mm resliced GrE-T1w sequence should be recommended.

External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.

OBJECTIVES: To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. METHODS: Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. RESULTS: Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). CONCLUSION: The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. KEY POINTS: • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.

Association between age and inflammatory disease activity on magnetic resonance imaging in relapse onset multiple sclerosis during long-term follow-up.

BACKGROUND AND PURPOSE: Inflammatory disease activity in multiple sclerosis (MS) decreases with advancing age. Previous work found a decrease in contrast-enhancing lesions (CELs) with age. Here, we describe the relation of age and magnetic resonance imaging (MRI) measures of inflammatory disease activity during long-term follow-up in a large real-world cohort of people with relapse onset MS. METHODS: We investigated MRI data from the long-term observational Amsterdam MS cohort. We used logistic regression models and negative binomial generalized estimating equations to investigate the associations between age and radiological disease activity after a first clinical event. RESULTS: We included 1063 participants and 10,651 cranial MRIs. Median follow-up time was 6.1 years (interquartile range = 2.4-10.9 years). Older participants had a significantly lower risk of CELs on baseline MRI (40-50 years vs. <40 years: odds ratio [OR] = 0.640, 95% confidence interval [CI] = 0.45-0.90; >50 years vs. <40 years: OR = 0.601, 95% CI = 0.33-1.08) and a lower risk of new T2 lesions or CELs during follow-up (40-50 years vs. <40 years: OR = 0.563, 95% CI = 0.47-0.67; >50 years vs. <40 years: OR = 0.486, 95% CI = 0.35-0.68). CONCLUSIONS: Greater age is associated with a lower risk of inflammatory MRI activity at baseline and during long-term follow-up. In patients aged >50 years, a less aggressive treatment strategy might be appropriate compared to younger patients.

MRI Assessment of Swallow Muscle Activation with the Swallow Exercise Aid and with Conventional Exercises in Healthy Volunteers: An Explorative Biomechanical Study.

Swallowing muscle strength exercises are effective in restoring swallowing function. In order to perform the exercises with progressive load, the swallow exercise aid (SEA) was developed. Precise knowledge on which muscles are activated with swallowing exercises, especially with the SEA, is lacking. This knowledge would aid in optimizing the training program to target the relevant swallowing muscles, if necessary. Three healthy volunteers performed the three SEA exercises (chin tuck against resistance, jaw opening against resistance and effortful swallow) and three conventional exercises [conventional effortful swallow (cES), Shaker and Masako] in supine position inside an MRI scanner. Fast muscle functional MRI scans (generating quantitative T2-maps) were made immediately before and after the exercises. Median T2 values at rest and after exercise were compared to identify activated muscles. After the three SEA exercises, the suprahyoid, infrahyoid, sternocleidomastoid, and lateral pterygoid muscles showed significant T2 value increase. After the Shaker, the lateral pterygoid muscles did not show such an increase, but the three other muscle groups did. The cES and Masako caused no significant increase in any of these muscle groups. During conventional (Shaker) exercises, the suprahyoid, infrahyoid, and sternocleidomastoid muscles are activated. During the SEA exercises, the suprahyoid, infrahyoid, sternocleidomastoid, and lateral pterygoid muscles are activated. The findings of this explorative study further support the potential of the SEA to improve swallowing rehabilitation.

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.

Spinal Cord Imaging in Multiple Sclerosis and Related Disorders.

Spinal cord MRI plays an important role in the diagnosis and prognosis of multiple sclerosis (MS) and related disorders. The ANATOMICAL, pathologic, imaging and prognostic consideriations for the spinal cord for MS and the most important other demyelinating disorders, neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein-associated disease, are reviewed. Finally, differential diagnostic considerations of spinal cord MRI in MS and related disorders are discussed.

Scanner-specific optimisation of automated lesion segmentation in MS.

BACKGROUND & OBJECTIVE: Automatic lesion segmentation techniques on MRI scans of people with multiple sclerosis (pwMS) could support lesion detection and segmentation in trials and clinical practice. However, knowledge on their reliability across scanners is limited, hampering clinical implementation. The aim of this study was to investigate the within-scanner repeatability and between-scanner reproducibility of lesion segmentation tools in pwMS across three different scanners and examine their accuracy compared to manual segmentations with and without optimization. METHODS: 30 pwMS underwent a scan and rescan on three MRI scanners. GE Discovery MR750 (3.0 T), Siemens Sola (1.5 T) and Siemens Vida (3.0 T)). 3D-FLuid Attenuated Inversion Recovery (3D-FLAIR) and 3D T1-weighted scans were acquired on each scanner. Lesion segmentation involved preprocessing and automatic segmentation using the Lesion Segmentation Toolbox (LST) and nicMSlesions (nicMS) as well as manual segmentation. Both automated segmentation techniques were used with default settings, and with settings optimized to match manual segmentations for each scanner specifically and combined for the three scanners. LST settings were optimized by adjusting the threshold to improve the Dice similarity coefficient (DSC) for each scanner separately and a combined threshold for all scanners. For nicMS the last layers were retrained, once with the multi-scanner data to represent a combined optimization and once separately for each scanner for scanner specific optimization. Volumes and counts were extracted. DSC was calculated for accuracy, and reliability was assessed using intra-class correlation coefficients (ICC). Differences in DSC between software was tested with a repeated measures ANOVA and when appropriate post-hoc paired t-tests using Bonferroni correction. RESULTS: Scanner-specific optimization significantly improved DSC for LST compared to default and combined settings, except for the GE scanner. NicMS showed significantly higher DSC for both the scanner-specific and combined optimization than default. Within-scanner repeatability was excellent (ICC>0.9) for volume and counts. Between-scanner ICC for volume between Vida and Sola was higher (0.94-0.99) than between GE MR750 and Vida or Sola (0.18-0.93), with improved ICCs for nicMS scanner-specific (0.87-0.93) compared to others (0.18-0.79). This was not present for Sola vs. Vida where all ICCs were excellent (>0.94). CONCLUSION: Scanner-specific optimization strategies proved effective in mitigating inter-scanner variability, addressing the issue of insufficient reproducibility and accuracy found with default settings.

9-HODE associates with thalamic atrophy and predicts white matter damage in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is characterized by extensive tissue damage leading to a range of complex symptoms, including physical disability and cognitive dysfunction. Recent work has indicated the clinical relevance of bioactive lipid mediators (LMs), which are known to orchestrate inflammation and its resolution and are deregulated in MS. However, it is unknown whether LM profiles relate to white matter (WM) damage. OBJECTIVES: To investigate the potential association between plasma-derived LMs and MRI-quantified WM damage using fractional anisotropy (FA) and grey matter (GM) atrophy in dimethyl fumarate-treated relapsing remitting MS (RRMS) patients. METHODS: Severity of FA-based WM damage and GM atrophy was determined in RRMS patients (n = 28) compared to age- and sex-matched controls (n = 31) at treatment initiation (baseline) and after 6 months. Plasma LMs were assessed using HPLC-MS/MS and baseline LMs were correlated to changes in FA and brain volumes. RESULTS: We observed significant WM damage in RRMS patients (mean age 41.4 [SD 9.1]) at baseline and follow-up (z-score=-0.33 and 0.31, respectively) compared to controls (mean age 41.9 [SD 9.5]; p < 0.001 for both comparisons). Patients with severe WM damage showed a decline of thalamic volume (p = 0.02), and this decline correlated (r = 0.51, p < 0.001) with lower baseline levels of 9-HODE. This LM also predicted FA worsening (beta = 0.14, p < 0.001) over time at 6 months. CONCLUSION: Despite the relatively small sample size, lower baseline levels of the LM 9-HODE correlated with more thalamic atrophy and predicted subsequent worsening of WM damage in RRMS patients.

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis.

BACKGROUND: Digital monitoring of people with multiple sclerosis (PwMS) using smartphone-based monitoring tools is a promising method to assess disease activity and progression. OBJECTIVE: To study cross-sectional and longitudinal associations between active and passive digital monitoring parameters and MRI volume measures in PwMS. METHODS: In this prospective study, 92 PwMS were included. Clinical tests [Expanded Disability Status Scale (EDSS), Timed 25 Foot Walk test (T25FW), 9-Hole Peg Test (NHPT), and Symbol Digit Modalities Test (SDMT)] and structural MRI scans were performed at baseline (M0) and 12-month follow-up (M12). Active monitoring included the smartphone-based Symbol Digit Modalities Test (sSDMT) and 2 Minute Walk Test (s2MWT), while passive monitoring was based on smartphone keystroke dynamics (KD). Linear regression analyses were used to determine cross-sectional and longitudinal relations between digital and clinical outcomes and brain volumes, with age, disease duration and sex as covariates. RESULTS: In PwMS, both sSDMT and SDMT were associated with thalamic volumes and lesion volumes. KD were related to brain, ventricular, thalamic and lesion volumes. No relations were found between s2MWT and MRI volumes. NHPT scores were associated with lesion volumes only, while EDSS and T25FW were not related to MRI. No longitudinal associations were found for any of the outcome measures between M0 and M12. CONCLUSION: Our results show clear cross-sectional correlations between digital biomarkers and brain volumes in PwMS, which were not all present for conventional clinical outcomes, supporting the potential added value of digital monitoring tools.

Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging.

Background and purpose: Contouring oropharyngeal primary tumors in radiotherapy is currently done manually which is time-consuming. Autocontouring techniques based on deep learning methods are a desirable alternative, but these methods can render suboptimal results when the structure to segment is considerably smaller than the rest of the image. The purpose of this work was to investigate different strategies to tackle the class imbalance problem in this tumor site. Materials and methods: A cohort of 230 oropharyngeal cancer patients treated between 2010 and 2018 was retrospectively collected. The following magnetic resonance imaging (MRI) sequences were available: T1-weighted, T2-weighted, 3D T1-weighted after gadolinium injection. Two strategies to tackle the class imbalance problem were studied: training with different loss functions (namely: Dice loss, Generalized Dice loss, Focal Tversky loss and Unified Focal loss) and implementing a two-stage approach (i.e. splitting the task in detection and segmentation). Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The network trained with the Generalized Dice Loss yielded a median Dice of 0.54, median 95th HD of 10.6 mm and median MSD of 2.4 mm but no significant differences were observed among the different loss functions (p-value > 0.7). The two-stage approach resulted in a median Dice of 0.64, median HD of 8.7 mm and median MSD of 2.1 mm, significantly outperforming the end-to-end 3D U-Net (p-value < 0.05). Conclusion: No significant differences were observed when training with different loss functions. The two-stage approach outperformed the end-to-end 3D U-Net.

The association between blood MxA mRNA and long-term disease activity in early multiple sclerosis.

Background: Myxovirus resistance protein A (MxA) is a protein that is upregulated by interferon-beta. Homeostatic MxA mRNA levels are potentially correlated with inflammatory disease activity in multiple sclerosis (MS) and could have an important role in MS pathology. Aim: To investigate the association between myxovirus resistance protein A (MxA) mRNA levels in blood and disease activity and progression in MS over a long-term follow-up period. Methods: Baseline blood MxA mRNA levels were determined in a prospective cohort of 116 untreated patients with a clinically isolated syndrome (CIS) or early relapsing remitting MS (RRMS), and related to long-term relapses, radiological disease activity, clinical scores [Expanded Disability Status Scale (EDSS), timed-25-foot walk (T25FW), 9-hole-peg test (9HPT)], MS type, and disease modifying therapy (DMT) use. Results: Low MxA mRNA levels were associated with the occurrence of ≥9 T2-lesions on MRI imaging and the occurrence of relapses during long-term follow-up (median 11 years, IQR 5.91-13.69 years). MxA mRNA levels were not associated with EDSS, T25FW, 9HPT, and MS subtype. Conclusion: Baseline MxA mRNA levels are associated with long-term development of T2-lesions on MRI-scans in our cohort. This confirms the relevance of the endogenous interferon-beta system in the occurrence of MS disease activity.

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.

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.

Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning.

BACKGROUND AND PURPOSE: Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semi-automatic approach for tumor segmentation that is expected to save time in the clinic. MATERIALS AND METHODS: We included 171 OPSCC patients retrospectively from 2010 until 2015. For all patients the following MRI sequences were available: T1-weighted, T2-weighted and 3D T1-weighted after gadolinium injection. We trained a 3D UNet using the entire images and images with reduced context, considering only information within clipboxes around the tumor. We compared the performance using different combinations of MRI sequences as input. Finally, a semi-automatic approach by two human observers defining clipboxes around the tumor was tested. Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). RESULTS: The 3D UNet trained with full context and all sequences as input yielded a median Dice of 0.55, HD of 8.7 mm and MSD of 2.7 mm. Combining all MRI sequences was better than using single sequences. The semi-automatic approach with all sequences as input yielded significantly better performance (p < 0.001): a median Dice of 0.74, HD of 4.6 mm and MSD of 1.2 mm. CONCLUSION: Reducing the amount of context around the tumor and combining multiple MRI sequences improved the segmentation performance. A semi-automatic approach was accurate and clinically feasible.

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.

The impact of anatomical changes during photon or proton based radiation treatment on tumor dose in glioblastoma dose escalation trials.

PURPOSE/OBJECTIVE: Most dose-escalation trials in glioblastoma patients integrate the escalated dose throughout the standard course by targeting a specific subvolume. We hypothesize that anatomical changes during irradiation may affect the dose coverage of this subvolume for both proton- and photon-based radiotherapy. MATERIAL AND METHODS: For 24 glioblastoma patients a photon- and proton-based dose escalation treatment plan (of 75 Gy/30 fr) was simulated on the dedicated radiotherapy planning MRI obtained before treatment. The escalated dose was planned to cover the resection cavity and/or contrast enhancing lesion on the T1w post-gadolinium MRI sequence. To analyze the effect of anatomical changes during treatment, we evaluated on an additional MRI that was obtained during treatment the changes of the dose distribution on this specific high dose region. RESULTS: The median time between the planning MRI and additional MRI was 26 days (range 16-37 days). The median time between the planning MRI and start of radiotherapy was relatively short (7 days, range 3-11 days). In 3 patients (12.5%) changes were observed which resulted in a substantial deterioration of both the photon and proton treatment plans. All these patients underwent a subtotal resection, and a decrease in dose coverage of more than 5% and 10% was observed for the photon- and proton-based treatment plans, respectively. CONCLUSION: Our study showed that only for a limited number of patients anatomical changes during photon or proton based radiotherapy resulted in a potentially clinically relevant underdosage in the subvolume. Therefore, volume changes during treatment are unlikely to be responsible for the negative outcome of dose-escalation studies.

Prognostic functional MR imaging parameters in head and neck squamous cell carcinoma: A systematic review.

OBJECTIVE: Functional MR imaging has demonstrated potential for predicting treatment response. This systematic review gives an extensive overview of the current level of evidence for pre-treatment MR-based perfusion and diffusion imaging parameters that are prognostic for treatment outcome in head and neck squamous cell carcinoma (HNSCC) (PROSPERO registrationCRD42020210689). MATERIALS AND METHODS: According to the PRISMA statements, Medline, Embase and Scopus were queried for articles with a maximum date of October 19th, 2020. Studies investigating the predictive performance of pre-treatment MR-based perfusion and/or diffusion imaging parameters in HNSCC treatment response were included. All prognosticators were extracted from the primary tumor. Risk of bias was assessed using the QUIPS tool. Results were summarized in tables and forest plots. RESULTS: 31 unique studies met the inclusion criteria; among them, 11 articles described perfusion (n = 529 patients) and 28 described diffusion (n = 1626 patients) MR-imaging, eight studies were included in both categories. Higher Ktrans and Kep were associated with better treatment response for OS and DFS, respectively. Study findings for Vp and Ve were inconsistent or not significant. High-level controversy was observed between studies examining the MR diffusion parameters mean and median ADC. CONCLUSION: For HNSCC patients, the accurate and consistent results of pre-treatment MR-based perfusion parameters Ktrans and Kep are potential for clinical applicability predictive of OS and DFS and treatment decision guidance. Significant heterogeneity in study designs might affect high discrepancy in study results for parameters extracted from diffusion imaging. Furthermore, recommendations for future research were summarized.

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.