Biologically adapted radiation therapy

PET/SPECT/Spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part II: Biologically guided radiotherapy

BACKGROUND

This study addresses the technical gap between clinical radiation therapy (RT) and preclinical small-animal RT, hindering the comprehensive validation of innovative clinical RT approaches in small-animal models of cancer and the translation of preclinical RT studies into clinical practices.

PURPOSE

The main aim was to explore the feasibility of biologically guided RT implemented within a small-animal radiation therapy (SART) platform, with integrated quad-modal on-board positron emission tomography (PET), single-photon emission computed tomography, photon-counting spectral CT, and cone-beam CT (CBCT) imaging, in a Monte Carlo model as a proof-of-concept.

METHODS

We developed a SART workflow employing quad-modal imaging guidance, integrating multimodal image-guided RT and emission-guided RT (EGRT). The EGRT algorithm was outlined using positron signals from a PET radiotracer, enabling near real-time adjustments to radiation treatment beams for precise targeting in the presence of a 2-mm setup error. Molecular image-guided RT, incorporating a dose escalation/de-escalation scheme, was demonstrated using a simulated phantom with a dose painting plan. The plan involved delivering a low dose to the CBCT-delineated planning target volume (PTV) and a high dose boosted to the highly active biological target volume (hBTV) identified by the 18F-PET image. Additionally, the Bayesian eigentissue decomposition method illustrated the quantitative decomposition of radiotherapy-related parameters, specifically iodine uptake fraction and virtual noncontrast (VNC) electron density, using a simulated phantom with Kidney1 and Liver2 inserts mixed with an iodine contrast agent at electron fractions of 0.01-0.02.

RESULTS

EGRT simulations generated over 4,000 beamlet responses in dose slice deliveries and illustrated superior dose coverage and distribution with significantly lower doses delivered to normal tissues, even with a 2-mm setup error introduced, demonstrating the robustness of the novel EGRT scheme compared to conventional image-guided RT. In the dose-painting plan, doubling the dose to the hBTV while maintaining a low dose for the PTV resulted in an organ-at-risk (OAR) dose comparable to the low-dose treatment for the PTV alone. Furthermore, the decomposition of radiotherapy-related parameters in Kidney1 and Liver2 inserts, including iodine uptake fractions and VNC electron densities, exhibited average relative errors of less than 1.0% and 2.5%, respectively.

CONCLUSIONS

The results demonstrated the successful implementation of biologically guided RT within the proposed quad-model image-guided SART platform, with potential applications in preclinical RT and adaptive RT studies.

PET/SPECT/spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part I: Quad-modal imaging

BACKGROUND

In spite of the tremendous potential of game-changing biological image- and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent need for a highly integrated small-animal radiation therapy (SART) platform that can seamlessly combine therapeutic and diagnostic capabilities to comprehensively enhance RT for cancer treatment.

PURPOSE

We investigated a highly integrated quad-modal on-board imaging configuration combining positron emission tomography (PET), single-photon emission computed tomography (SPECT), photon-counting spectral CT, and cone-beam computed tomography (CBCT) in a SART platform using a Monte Carlo model as a proof-of-concept.

METHODS

The quad-modal on-board imaging configuration of the SART platform was designed and evaluated by using the GATE Monte Carlo code. A partial-ring on-board PET imaging subsystem, utilizing advanced semiconductor thallium bromide detector technology, was designed to achieve high sensitivity and spatial resolution. On-board SPECT, photon-counting spectral-CT, and CBCT imaging were performed using a single cadmium zinc telluride flat detector panel. The absolute peak sensitivity and scatter fraction of the PET subsystem were estimated by using simulated phantoms described in the NEMA NU-4 standard. The spatial resolution of the PET image of the platform was evaluated by imaging a simulated micro-Derenzo hot-rod phantom. To evaluate the quantitative imaging capability of the system's spectral CT, the Bayesian eigentissue decomposition (ETD) method was utilized to quantitatively decompose the virtual noncontrast (VNC) electron densities and iodine contrast agent fractions in the Kidney1 inserts mixed with the iodine contrast agent within the simulated phantoms. The performance of the proposed quad-model imaging in the platform was validated by imaging a simulated phantom with multiple imaging probes, including an iodine contrast agent and radioisotopes of 18F and 99mTc.

RESULTS

The PET subsystem demonstrated an absolute peak sensitivity of 18.5% at the scanner center, with an energy window of 175-560 KeV, and a scatter fraction of only 3.5% for the mouse phantom, with a default energy window of 480-540 KeV. The spatial resolution of PET on-board imaging exceeded 1.2 mm. All imaging probes were identified clearly within the phantom. The PET and SPECT images agreed well with the actual spatial distributions of the tracers within the phantom. Average relative errors on electron density and iodine contrast agent fraction in the Kidney1 inserts were less than 3%. High-quality PET images, SPECT images, spectral-CT images (including iodine contrast agent fraction images and VNC electron density images), and CBCT images of the simulated phantom demonstrated the comprehensive multimodal imaging capability of the system.

CONCLUSIONS

The results demonstrated the feasibility of the proposed quad-modal imaging configuration in a SART platform. The design incorporates anatomical, molecular, and functional information about tumors, thereby facilitating successful translation of preclinical studies into clinical practices.

Effect of synthetic CT on dose-derived toxicity predictors for MR-only prostate radiotherapy

Objectives

Toxicity-driven adaptive radiotherapy (RT) is enhanced by the superior soft tissue contrast of magnetic resonance (MR) imaging compared with conventional computed tomography (CT). However, in an MR-only RT pathway synthetic CTs (sCT) are required for dose calculation. This study evaluates 3 sCT approaches for accurate rectal toxicity prediction in prostate RT.

Methods

Thirty-six patients had MR (T2-weighted acquisition optimized for anatomical delineation, and T1-Dixon) with same day standard-of-care planning CT for prostate RT. Multiple sCT were created per patient using bulk density (BD), tissue stratification (TS, from T1-Dixon) and deep-learning (DL) artificial intelligence (AI) (from T2-weighted) approaches for dose distribution calculation and creation of rectal dose volume histograms (DVH) and dose surface maps (DSM) to assess grade-2 (G2) rectal bleeding risk.

Results

Maximum absolute errors using sCT for DVH-based G2 rectal bleeding risk (risk range 1.6% to 6.1%) were 0.6% (BD), 0.3% (TS) and 0.1% (DL). DSM-derived risk prediction errors followed a similar pattern. DL sCT has voxel-wise density generated from T2-weighted MR and improved accuracy for both risk-prediction methods.

Conclusions

DL improves dosimetric and predicted risk calculation accuracy. Both TS and DL methods are clinically suitable for sCT generation in toxicity-guided RT, however, DL offers increased accuracy and offers efficiencies by removing the need for T1-Dixon MR.

Advances in knowledge

This study demonstrates novel insights regarding the effect of sCT on predictive toxicity metrics, demonstrating clear accuracy improvement with increased sCT resolution. Accuracy of toxicity calculation in MR-only RT should be assessed for all treatment sites where dose to critical structures will guide adaptive-RT strategies.

Clinical trial registration number

Patient data were taken from an ethically approved (UK Health Research Authority) clinical trial run at Guy's and St Thomas' NHS Foundation Trust. Study Name: MR-simulation in Radiotherapy for Prostate Cancer. ClinicalTrials.gov Identifier: NCT03238170.

First-in-human technique translation of oxygen-enhanced MRI to an MR Linac system in patients with head and neck cancer

BACKGROUND AND PURPOSE

Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system.

MATERIALS AND METHODS

MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T1) was measured alongside the change in 1/T1 (termed ΔR1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems.

RESULTS

Baseline T1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR1 significantly increased (p < 0.0001) in healthy participants demonstrating OE-MRI feasibility. ΔR1 repeatability coefficients (RC) were 0.023-0.040 s-1 across both MR systems. The tumour ΔR1 RC was 0.013 s-1 and the within-subject coefficient of variation (wCV) was 25% on the diagnostic MR. Tumour ΔR1 RC was 0.020 s-1 and wCV was 33% on the MR Linac. ΔR1 magnitude and time-course trends were similar on both systems.

CONCLUSION

We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy.

Clinical use of positron emission tomography for radiotherapy planning - Medical physics considerations

PET/CT imaging plays an increasing role in radiotherapy treatment planning. The aim of this article was to identify the major use cases and technical as well as medical physics challenges during integration of these data into treatment planning. Dedicated aspects, such as (i) PET/CT-based radiotherapy simulation, (ii) PET-based target volume delineation, (iii) functional avoidance to optimized organ-at-risk sparing and (iv) functionally adapted individualized radiotherapy are discussed in this article. Furthermore, medical physics aspects to be taken into account are summarized and presented in form of check-lists.

Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration

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Magnetic resonance imaging was used to derive dose-painting prescriptions in glioma.

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Dose prescriptions derived from magnetic resonance imaging are highly repeatable.

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Dose-painting plans are more repeatable than their dose prescriptions.

Background and purpose

Glioblastoma (GBM) patients have a dismal prognosis. Tumours typically recur within months of surgical resection and post-operative chemoradiation. Multiparametric magnetic resonance imaging (mpMRI) biomarkers promise to improve GBM outcomes by identifying likely regions of infiltrative tumour in tumour probability (TP) maps. These regions could be treated with escalated dose via dose-painting radiotherapy to achieve higher rates of tumour control. Crucial to the technical validation of dose-painting using imaging biomarkers is the repeatability of the derived dose prescriptions. Here, we quantify repeatability of dose-painting prescriptions derived from mpMRI.

Materials and methods

TP maps were calculated with a clinically validated model that linearly combined apparent diffusion coefficient (ADC) and relative cerebral blood volume (rBV) or ADC and relative cerebral blood flow (rBF) data. Maps were developed for 11 GBM patients who received two mpMRI scans separated by a short interval prior to chemoradiation treatment. A linear dose mapping function was applied to obtain dose-painting prescription (DP) maps for each session. Voxel-wise and group-wise repeatability metrics were calculated for parametric, TP and DP maps within radiotherapy margins.

Results

DP maps derived from mpMRI were repeatable between imaging sessions (ICC > 0.85). ADC maps showed higher repeatability than rBV and rBF maps (Wilcoxon test, p = 0.001). TP maps obtained from the combination of ADC and rBF were the most stable (median ICC: 0.89).

Conclusions

Dose-painting prescriptions derived from a mpMRI model of tumour infiltration have a good level of repeatability and can be used to generate reliable dose-painting plans for GBM patients.

Integrated MRI-guided radiotherapy - opportunities and challenges

MRI can help to categorize tissues as malignant or non-malignant both anatomically and functionally, with a high level of spatial and temporal resolution. This non-invasive imaging modality has been integrated with radiotherapy in devices that can differentially target the most aggressive and resistant regions of tumours. The past decade has seen the clinical deployment of treatment devices that combine imaging with targeted irradiation, making the aspiration of integrated MRI-guided radiotherapy (MRIgRT) a reality. The two main clinical drivers for the adoption of MRIgRT are the ability to image anatomical changes that occur before and during treatment in order to adapt the treatment approach, and to image and target the biological features of each tumour. Using motion management and biological targeting, the radiation dose delivered to the tumour can be adjusted during treatment to improve the probability of tumour control, while simultaneously reducing the radiation delivered to non-malignant tissues, thereby reducing the risk of treatment-related toxicities. The benefits of this approach are expected to increase survival and quality of life. In this Review, we describe the current state of MRIgRT, and the opportunities and challenges of this new radiotherapy approach.

Rates of MRI simulator utilisation in a tertiary cancer therapy centre

Magnetic resonance imaging (MRI) is increasingly being integrated into the radiation oncology workflow, due to its improved soft tissue contrast without additional exposure to ionising radiation. A review of MRI utilisation according to evidence based departmental guidelines was performed. Guideline utilisation rates were calculated to be 50% (true utilisation rate was 46%) of all new cancer patients treated with adjuvant or curative intent, excluding simple skin and breast cancer patients. Guideline utilisation rates were highest in the lower gastrointestinal and gynaecological subsites, with the lowest being in the upper gastrointestinal and thorax subsites. Head and neck (38% vs 45%) and CNS (46% vs 67%) cancers had the largest discrepancy between true and guideline utilisation rates due to unnamed reasons and non-contemporaneous diagnostic imaging respectively. This report outlines approximate MRI utilisation rates in a tertiary radiation oncology service and may help guide planning for future departments contemplating installation of an MRI simulator.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Photon and Proton Dose Painting Based on Oxygen Distribution – Feasibility Study and Tumour Control Probability Assessment

Solid tumours may present hypoxic sub-regions of increased radioresistance. Hypoxia quantification requires of clinically implementable, non-invasive and reproducible techniques as positron emission tomography (PET). PET-based dose painting strategies aiming at targeting those sub-regions may be limited by the resolution gap between the PET imaging resolution and the smaller scale at which hypoxia occurs. The ultimate benefit of the usage of dose painting may be reached if the planned dose distribution can be performed and delivered consistently. This study aimed at assessing the feasibility of two PET-based dose painting strategies using two beam qualities (photon or proton beams) in terms of tumour control probability (TCP), accounting for underlying oxygen distribution at sub-millimetre scale.A tumour oxygenation model at submillimetre scale was created consisting of three regions with different oxygen partial pressure distributions, being hypoxia decreasing from core to periphery. A published relationship between uptake and oxygen partial pressure was used and a PET image of the tumour was simulated. The fundamental effects that limit the PET camera resolution were considered by processing the uptake distribution with a Gaussian 3D filter and re-binning to a PET image voxel size of 2 mm. Prescription doses to overcome tumour hypoxia were calculated based on the processed images, and planned using robust optimisation.Normal tissue complication probabilities and TCPs after the delivery of the planned doses were calculated for the nominal plan and the lowest bounds of the dose volume histograms resulting from the robust scenarios planned, taking into account the underlying oxygenation at submillimetre scale. Results were presented for the two beam qualities and the two dose painting strategies: by contours (DPBC) and by using a voxel grouping-based approach (DPBOX).In the studied case, DPBOX outperforms DPBC with respect to TCP regardless the beam quality, although both dose painting strategy plans demonstrated robust target coverage.

Manual and semi-automated delineation of locally advanced rectal cancer subvolumes with diffusion-weighted MRI

OBJECTIVES

To evaluate interobserver agreement for T₂ weighted (T₂W) and diffusion-weighted MRI (DW-MRI) contours of locally advanced rectal cancer (LARC); and to evaluate manual and semi-automated delineations of restricted diffusion tumour subvolumes.

METHODS

20 cases of LARC were reviewed by 2 radiation oncologists and 2 radiologists. Contours of gross tumour volume (GTV) on T₂W, DW-MRI and co-registered T₂W/DW-MRI were independently delineated and compared using Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics of interobserver agreement. Restricted diffusion subvolumes within GTVs were manually delineated and compared to semi-automatically generated contours corresponding to intratumoral apparent diffusion coefficient (ADC) centile values.

RESULTS

Observers were able to delineate subvolumes of restricted diffusion with moderate agreement (DSC 0.666, MDA 1.92 mm). Semi-automated segmentation based on the 40th centile intratumoral ADC value demonstrated moderate average agreement with consensus delineations (DSC 0.581, MDA 2.44 mm), with errors noted in image registration and luminal variation between acquisitions. A small validation set of four cases with optimised planning MRI demonstrated improvement (DSC 0.669, MDA 1.91 mm).

CONCLUSION

Contours based on co-registered T₂W and DW-MRI could be used for delineation of biologically relevant tumour subvolumes. Semi-automated delineation based on patient-specific intratumoral ADC thresholds may standardise subvolume delineation if registration between acquisitions is sufficiently accurate.

ADVANCES IN KNOWLEDGE

This is the first study to evaluate the feasibility of semi-automated diffusion-based subvolume delineation in LARC. This approach could be applied to dose escalation or 'dose painting' protocols to improve delineation reproducibility.

Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis

Background

Automatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer.

Methods

Head&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (k_trans, v_e) and diffusion (ADC) measurements at 3 time points before and during radiochemotherapy. The 7 different MRI contrasts (input channels) and manually defined gross tumor volumes (primary tumor and lymph node metastases) were used to train CNNs for lesion segmentation. A reference CNN with all input channels was compared to individually trained CNNs where one of the input channels was left out to identify which MRI contrast contributes the most to the tumor segmentation task. A statistical analysis was employed to account for random fluctuations in the segmentation performance.

Results

The CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSC_GTV-T = 5.7% for primary tumor and ΔDSC_GTV-Ln = 5.8% for lymph node metastases compared to the network containing all input channels. Overall, the ADC input channel showed the least impact on segmentation performance, with ΔDSC_GTV-T = 2.4% for primary tumor and ΔDSC_GTV-Ln = 2.2% respectively.

Conclusions

We developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance.

Trial registration

The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction

Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (k_trans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use.

Holistic View on Cell Survival and DNA Damage: How Model-Based Data Analysis Supports Exploration of Dynamics in Biological Systems

In this work, a method is established to calibrate a model that describes the basic dynamics of DNA damage and repair. The model can be used to extend planning for radiotherapy and hyperthermia in order to include the biological effects. In contrast to “syntactic” models (e.g., describing molecular kinetics), the model used here describes radiobiological semantics, resulting in a more powerful model but also in a far more challenging calibration. Model calibration is attempted from clonogenic assay data (doses of 0–6 Gy) and from time-resolved comet assay data obtained within 6 h after irradiation with 6 Gy. It is demonstrated that either of those two sources of information alone is insufficient for successful model calibration, and that both sources of information combined in a holistic approach are necessary to find viable model parameters. Approximate Bayesian computation (ABC) with simulated annealing is used for parameter search, revealing two aspects that are beneficial to resolving the calibration problem: (1) assessing posterior parameter distributions instead of point-estimates and (2) combining calibration runs from different assays by joining posterior distributions instead of running a single calibration run with a combined, computationally very expensive objective function.

PET/CT in radiation oncology

The progressive integration of positron emission tomography/computed tomography (PET/CT) imaging in radiation therapy has its rationale in the biological intertumoral and intratumoral heterogeneity of malignant lesions that require the individual adjustment of radiation dose to obtain an effective local tumor control in cancer patients. PET/CT provides information on the biological features of tumor lesions such as metabolism, hypoxia, and proliferation that can identify radioresistant regions and be exploited to optimize treatment plans. Here, we provide an overview of the basic principles of PET-based target volume selection and definition using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) and then we focus on the emerging strategies of dose painting and adaptive radiotherapy using different tracers. Previous studies provided consistent evidence that integration of ¹⁸F-FDG PET/CT in radiotherapy planning improves delineation of target volumes and reduces the uncertainties and variabilities of anatomical delineation of tumor sites. PET-based dose painting and adaptive radiotherapy are feasible strategies although their clinical implementation is highly demanding and requires strong technical, computational, and logistic efforts. Further prospective clinical trials evaluating local tumor control, survival, and toxicity of these emerging strategies will promote the full integration of PET/CT in radiation oncology.

Effect of radiochemotherapy on T2* MRI in HNSCC and its relation to FMISO PET derived hypoxia and FDG PET

BACKGROUND

To assess the effect of radiochemotherapy (RCT) on proposed tumour hypoxia marker transverse relaxation time (T2*) and to analyse the relation between T2* and ¹⁸F-misonidazole PET/CT (FMISO-PET) and ¹⁸F-fluorodeoxyglucose PET/CT (FDG-PET).

METHODS

Ten patients undergoing definitive RCT for squamous cell head-and-neck cancer (HNSCC) received repeat FMISO- and 3 Tesla T2*-weighted MRI at weeks 0, 2 and 5 during treatment and FDG-PET at baseline. Gross tumour volumes (GTV) of tumour (T), lymph nodes (LN) and hypoxic subvolumes (HSV, based on FMISO-PET) and complementary non-hypoxic subvolumes (nonHSV) were generated. Mean values for T2* and SUVmean FDG were determined.

RESULTS

During RCT, marked reduction of tumour hypoxia on FMISO-PET was observed (T, LN), while mean T2* did not change significantly. At baseline, mean T2* values within HSV-T (15 ± 5 ms) were smaller compared to nonHSV-T (18 ± 3 ms; p = 0.051), whereas FDG SUVmean (12 ± 6) was significantly higher for HSV-T (12 ± 6) than for nonHSV-T (6 ± 3; p = 0.026) and higher for HSV-LN (10 ± 4) than for nonHSV-LN (5 ± 2; p ≤ 0.011). Correlation between FMISO PET and FDG PET was higher than between FMSIO PET and T2* (R² for GTV-T (FMISO/FDG) = 0.81, R² for GTV-T (FMISO/T2*) = 0.32).

CONCLUSIONS

Marked reduction of tumour hypoxia between week 0, 2 and 5 found on FMISO PET was not accompanied by a significant T2*change within GTVs over time. These results suggest a relation between tumour oxygenation status and T2* at baseline, but no simple correlation over time. Therefore, caution is warranted when using T2* as a substitute for FMISO-PET to monitor tumour hypoxia during RCT in HNSCC patients.

TRIAL REGISTRATION

DRKS, DRKS00003830 . Registered 23.04.2012.

Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool

Functional imaging techniques provide radiobiological information that can be included into tumour control probability (TCP) models to enable individualized outcome predictions in radiotherapy. However, functional imaging and the derived radiobiological information are influenced by uncertainties, translating into variations in individual TCP predictions. In this study we applied a previously developed analytical tool to quantify dose and TCP uncertainty bands when initial cell density is estimated from MRI-based apparent diffusion coefficient maps of eleven patients. TCP uncertainty bands of 16% were observed at patient level, while dose variations bands up to 8 Gy were found at voxel level for an iso-TCP approach.