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Purpose: To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy., Material and Methods: A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV, ADC mean and clinical factors., Results: The best model was defined by 800-6 mm 2 /s and correlated significantly to treatment outcome (p = 0.003). Optimal HRS cut-off value was found to be 5.8 cm 3 according to ROC-analysis. This HRS demonstrated highly significant stratification potential (p < 0.001, bootstrap AUC ≥ 0.84) similar to GTV size (p < 0.001, AUC ≥ 0.79), in contrast to ADC mean (p = 0.361, AUC = 0.53)., Conclusions: A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Tumor hypoxia and other microenvironmental factors are key determinants of treatment resistance. Hypoxia positron emission tomography (PET) and functional magnetic resonance imaging (MRI) are established prognostic imaging modalities to identify radiation resistance in head-and-neck cancer (HNC). The aim of this preclinical study was to develop a multi-parametric imaging parameter specifically for focal radiotherapy (RT) dose escalation using HNC xenografts of different radiation sensitivities., Methods: A total of eight human HNC xenograft models were implanted into 68 immunodeficient mice. Combined PET/MRI using dynamic [18F]-fluoromisonidazole (FMISO) hypoxia PET, diffusion-weighted (DW), and dynamic contrast-enhanced MRI was carried out before and after fractionated RT (10 × 2 Gy). Imaging data were analyzed on voxel-basis using principal component (PC) analysis for dynamic data and apparent diffusion coefficients (ADCs) for DW-MRI. A data- and hypothesis-driven machine learning model was trained to identify clusters of high-risk subvolumes (HRSs) from multi-dimensional (1-5D) pre-clinical imaging data before and after RT. The stratification potential of each 1D to 5D model with respect to radiation sensitivity was evaluated using Cohen's d-score and compared to classical features such as mean/peak/maximum standardized uptake values (SUV mean/peak/max ) and tumor-to-muscle-ratios (TMR peak/max ) as well as minimum/valley/maximum/mean ADC., Results: Complete 5D imaging data were available for 42 animals. The final preclinical model for HRS identification at baseline yielding the highest stratification potential was defined in 3D imaging space based on ADC and two FMISO PCs ([Formula: see text]). In 1D imaging space, only clusters of ADC revealed significant stratification potential ([Formula: see text]). Among all classical features, only ADC valley showed significant correlation to radiation resistance ([Formula: see text]). After 2 weeks of RT, FMISO&#95;c1 showed significant correlation to radiation resistance ([Formula: see text])., Conclusion: A quantitative imaging metric was described in a preclinical study indicating that radiation-resistant subvolumes in HNC may be detected by clusters of ADC and FMISO using combined PET/MRI which are potential targets for future functional image-guided RT dose-painting approaches and require clinical validation., (© 2023. The Author(s).)

Background and Purpose: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET., Materials and Methods: Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [ 18 F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures.Secondly, a pre-defined [ 18 F]FMISO-PET tumour-to-muscle-ratio (TMR peak  ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [ 18 F]FMISO-PET was performed using area-under-the-curve (AUC) and Kaplan-Meier analysis including log-rank test., Results: The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [ 18 F]FMISO TMR peak reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [ 18 F]FMISO-PET significant differences were observed (p = 0.02)., Conclusions: No direct correlation of patient stratification using [ 18 F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: DT and DZ declare institutional collaborations including financial support with the companies Siemens Healthineers (2014–2019), Elekta AB, Philips and PTW Freiburg without any direct relation to this study. In the past 5 years, MB attended an advisory board meeting of MERCK KGaA (Darmstadt), for which the University of Dresden received a travel grant. He further received funding for his research projects and for educational grants to the University of Dresden by Teutopharma GmbH (2011–2015), IBA (2016), Bayer AG (2016–2018), Merck KGaA (2014–2030), Medipan GmbH (2014–2018). For the German Cancer Research Center (DKFZ, Heidelberg) MB is on the supervisory boards of HI-STEM gGmbH (Heidelberg). MB, as former chair of OncoRay (Dresden) and present CEO and Scientific Chair of the German Cancer Research Center (DKFZ, Heidelberg), signed/signs contracts for his institute(s) and for the staff for research funding and/or collaborations with a multitude of companies worldwide. MB confirms that none of the above funding sources were involved in the design of this study, the preparation of this paper, the materials used, or the collection, analysis, and interpretation of data., (© 2020 The Author(s).)

Purpose: The relation between functional imaging and intrapatient genetic heterogeneity remains poorly understood. The aim of our study was to investigate spatial sampling and functional imaging by FDG-PET/MRI to describe intrapatient tumour heterogeneity., Methods: Six patients with oropharyngeal cancer were included in this pilot study. Two tumour samples per patient were taken and sequenced by next-generation sequencing covering 327 genes relevant in head and neck cancer. Corresponding regions were delineated on pretherapeutic FDG-PET/MRI images to extract apparent diffusion coefficients and standardized uptake values., Results: Samples were collected within the primary tumour (n = 3), within the primary tumour and the involved lymph node (n = 2) as well as within two independent primary tumours (n = 1). Genetic heterogeneity of the primary tumours was limited and most driver gene mutations were found ubiquitously. Slightly increasing heterogeneity was found between primary tumours and lymph node metastases. One private predicted driver mutation within a primary tumour and one in a lymph node were found. However, the two independent primary tumours did not show any shared mutations in spite of a clinically suspected field cancerosis. No conclusive correlation between genetic heterogeneity and heterogeneity of PET/MRI-derived parameters was observed., Conclusion: Our limited data suggest that single sampling might be sufficient in some patients with oropharyngeal cancer. However, few driver mutations might be missed and, if feasible, spatial sampling should be considered. In two independent primary tumours, both lesions should be sequenced. Our data with a limited number of patients do not support the concept that multiparametric PET/MRI features are useful to guide biopsies for genetic tumour characterization.

Objective: To investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning., Material and Methods: In PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced. Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans., Results: PSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7-66.0) for manual and 65.3 Gy (62.5-65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D 2% 67.0 Gy (66.5-67.5) vs. 66.1 Gy (64.7-66.5, p = 0.016). All other evaluated parameters (PTV D 98% and D 2% , rectum V 40Gy and V 60Gy , bladder D 2% and V 60Gy ) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with -0.82 (-16.43-1.08) vs. 0.91 (-5.98-6.25)., Conclusion: PSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Purpose: To review the potential and challenges of integrating diffusion weighted magnetic resonance imaging (DWI) into radiotherapy (RT)., Content: Details related to image acquisition of DWI for RT purposes are discussed, along with the challenges with respect to geometric accuracy and the robustness of quantitative parameter extraction. An overview of diffusion- and perfusion-related parameters derived from mono- and bi-exponential models is provided, and their role as potential RT biomarkers is discussed. Recent studies demonstrating potential of DWI in different tumor sites such as the head and neck, rectum, cervix, prostate, and brain, are reviewed in detail., Conclusion: DWI has shown promise for RT outcome prediction, response assessment, as well as for tumor delineation and characterization in several cancer types. Geometric and quantification robustness is challenging and has to be addressed adequately. Evaluation in larger clinical trials with well designed imaging protocol and advanced analysis models is needed to develop the optimal strategy for integrating DWI in RT.

Background and Purpose: Functional PET/MRI has great potential to improve radiotherapy planning (RTP). However, data integration requires imaging with radiotherapy-specific patient positioning. Here, we investigated the feasibility and image quality of radiotherapy-customized PET/MRI in head-and-neck cancer (HNC) patients using a dedicated hardware setup., Material and Methods: Ten HNC patients were examined with simultaneous PET/MRI before treatment, with radiotherapy and diagnostic scan setup, respectively. We tested feasibility of radiotherapy-specific patient positioning and compared the image quality between both setups by pairwise image analysis of 18 F-FDG-PET, T1/T2-weighted and diffusion-weighted MRI. For image quality assessment, similarity measures including average symmetric surface distance (ASSD) of PET and MR-based tumor contours, MR signal-to-noise ratio (SNR) and mean apparent diffusion coefficient (ADC) value were used., Results: PET/MRI in radiotherapy position was feasible - all patients were successfully examined. ASSD (median/range) of PET and MR contours was 0.6 (0.4-1.2) and 0.9 (0.5-1.3) mm, respectively. For T2-weighted MRI, a reduced SNR of -26.2% (-39.0--11.7) was observed with radiotherapy setup. No significant difference in mean ADC was found., Conclusions: Simultaneous PET/MRI in HNC patients using radiotherapy positioning aids is clinically feasible. Though SNR was reduced, the image quality obtained with a radiotherapy setup meets RTP requirements and the data can thus be used for personalized RTP., (Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: PET imaging may be used to personalize radiotherapy (RT) by identifying radioresistant tumor subvolumes for RT dose escalation. Using the tracers [ 18 F]-fluorodeoxyglucose (FDG) and [ 18 F]-fluoromisonidazole (FMISO), different aspects of tumor biology can be visualized. FDG depicts various biological aspects, e.g., proliferation, glycolysis and hypoxia, while FMISO is more hypoxia specific. In this study, we analyzed size and overlap of volumes based on the two markers for head-and-neck cancer patients (HNSCC)., Material and Methods: Twenty five HNSCC patients underwent a CT scan, as well as FDG and dynamic FMISO PET/CT prior to definitive radio-chemotherapy in a prospective FMISO dose escalation study. Three PET-based subvolumes of the primary tumor (GTV prim ) were segmented: a highly FDG-avid volume V FDG , a hypoxic volume on the static FMISO image acquired four hours post tracer injection (V H ) and a retention/perfusion volume (V M ) using pharmacokinetic modeling of dynamic FMISO data. Absolute volumes, overlaps and distances to agreement (DTA) were evaluated., Results: Sizes of PET-based volumes and the GTV prim are significantly different (GTV prim >V FDG >V H >V M ; p < .05). V H is covered by V FDG or DTAs are small (mean coverage 74.4%, mean DTA 1.4 mm). Coverage of V M is less pronounced. With respect to V FDG and V H , the mean coverage is 48.7% and 43.1% and the mean DTA is 5.3 mm and 6.3 mm, respectively. For two patients, DTAs were larger than 2 cm., Conclusions: Hypoxic subvolumes from static PET imaging are typically covered by or in close proximity to highly FDG-avid subvolumes. Therefore, dose escalation to FDG positive subvolumes should cover the static hypoxic subvolumes in most patients, with the disadvantage of larger volumes, resulting in a higher risk of dose-limiting toxicity. Coverage of subvolumes from dynamic FMISO PET is less pronounced. Further studies are needed to explore the relevance of mismatches in functional imaging.

Purpose: Tumor hypoxia is a major cause of radiation resistance, often present in various solid tumors. Dynamic [ 18 F]-fluoromisonidazole (FMISO) PET imaging is able to reliably assess tumor hypoxia. Comprehensive characterization of tumor microenvironment through FMISO-PET and dynamic contrast enhanced (DCE) MR multimodality imaging might be a valuable alternative to the dynamic FMISO-PET acquisition. The aim of this work was to explore the correlation between the FMISO-PET and DCE-MRI kinetic parameters., Methods: This study was done on head and neck cancer patients (N = 6), who were imaged dynamically with FMISO-PET and DCE-MRI on the same day. Images were registered and analyzed for kinetics on a voxel basis. FMISO-PET images were analyzed with the two-tissue compartment three rate-constant model. Additionally, tumor-to-muscle ratio (TMR) maps were evaluated. DCE-MRI was analyzed with the extended Tofts model. Voxel-wise Pearson's coefficients were calculated for each patient to assess pairwise parameter correlations., Results: Median correlations between FMISO uptake parameters and DCE-MRI kinetic parameters varied across the parameter pairs in the range from -0.05 to 0.71. The highest median correlation of r = 0.71 was observed for the pair V b -v p , while the K 1 -K trans median correlation was r = 0.45. Median correlation coefficients for the K 1 -v p and the K i -K trans pairs were r = 0.42 and r = 0.32, respectively. Correlations between FMISO uptake rate parameter K i and DCE-MRI kinetic parameters varied substantially across the patients, whereas correlations between the FMISO and DCE-MRI vascular parameters were consistently high. Median TMR-K 1 and TMR-K trans correlations were r = 0.52 and r = 0.46, respectively, but varied substantially across the patients., Conclusions: Based on this clinical evidence, we can conclude that the vascular fraction parameters obtained through DCE-MRI kinetic analysis or FMISO kinetic analysis measure the same biological property, while other kinetic parameters are unrelated. These results might be useful in the design of future clinical trials involving FMISO-PET/DCE-MR multimodality imaging for the assessment of tumor microenvironment., (© 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The phase behavior of protein solutions is affected by additives such as crowder molecules or salts. In particular, upon addition of multivalent counterions, a reentrant condensation can occur; i.e., protein solutions are stable for low and high multivalent ion concentrations but aggregating at intermediate salt concentrations. The addition of monovalent ions shifts the phase boundaries to higher multivalent ion concentrations. This effect is found to be reflected in the protein interactions, as accessed via small-angle X-ray scattering. Two simulation schemes (a Monte Carlo sampling of the counterion binding configurations using the detailed protein structure and an analytical coarse-grained binding model) reproduce the shifts of the experimental phase boundaries. The results support a consistent picture of the protein interactions responsible for the phase behavior. The repulsive Coulomb interaction is varied by the binding of multivalent counterions and additionally screened by any increase of the ionic strength. The attractive interaction is induced by the binding of multivalent ions, most likely due to ion bridging between protein molecules. The overall picture of these competing interactions provides interesting insight into possible mechanisms for tailoring interactions in solutions via salt effects.

Background: Combined positron emission tomography (PET)/magnetic resonance imaging (MRI) is highly promising for biologically individualized radiotherapy (RT). Hence, the purpose of this work was to develop an accurate and robust registration strategy to integrate combined PET/MR data into RT treatment planning. Material and methods. Eight patient datasets consisting of an FDG PET/computed tomography (CT) and a subsequently acquired PET/MR of the head and neck (HN) region were available. Registration strategies were developed based on CT and MR data only, whereas the PET components were fused with the resulting deformation field. Following a rigid registration, deformable registration was performed with a transform parametrized by B-splines. Three different optimization metrics were investigated: global mutual information (GMI), GMI combined with a bending energy penalty (BEP) for regularization (GMI+ BEP) and localized mutual information with BEP (LMI+ BEP). Different quantitative registration quality measures were developed, including volumetric overlap and mean distance measures for structures segmented on CT and MR as well as anatomical landmark distances. Moreover, the local registration quality in the tumor region was assessed by the normalized cross correlation (NCC) of the two PET datasets., Results: LMI+ BEP yielded the most robust and accurate registration results. For GMI, GMI+ BEP and LMI+ BEP, mean landmark distances (standard deviations) were 23.9 mm (15.5 mm), 4.8 mm (4.0 mm) and 3.0 mm (1.0 mm), and mean NCC values (standard deviations) were 0.29 (0.29), 0.84 (0.14) and 0.88 (0.06), respectively., Conclusion: Accurate and robust multimodal deformable image registration of CT and MR in the HN region can be performed using a B-spline parametrized transform and LMI+ BEP as optimization metric. With this strategy, biologically individualized RT based on combined PET/MRI in terms of dose painting is possible.