Biological imaging for individualized therapy in radiation oncology: part II medical and clinical aspects.

Positron emission tomography and multiparametric MRI provide crucial information concerning tumor extent and normal tissue anatomy. Moreover, they are able to visualize biological characteristics of the tumor, which can be considered in the radiation treatment planning and monitoring. In this review we discuss the impact of biological imaging positron emission tomography and multiparametric MRI for radiation oncology, based on the data of the literature and on the experience of our own institution in this field.

Mathematical Description of Changes in Tumour Oxygenation from Repeated Functional Imaging.

Functional imaging of tumour hypoxia has been suggested as a tool for refining target definition and treatment optimization in radiotherapy. The approach, however, has been slow to be adopted clinically as most of the studies on the topic do not take into account the in-treatment changes of hypoxia. The present study aimed to introduce a function that quantifies the changes of oxygen distributions in repeated PET images taken during treatment. The proposed approach for determining the reoxygenation function was tested for feasibility on patients with head and neck cancer, repeatedly imaged with FMISO PET during radiotherapy. Reoxygenation functions were derived by solving the convolution between functions describing the oxygen distributions of successive images. The method was found to be mathematically feasible. The results indicate that the reoxygenation functions describing the change in oxygenation have distinct shapes prompting the hypothesis that oxygenation changes reflected by them might have predictive power for treatment outcome. Future studies on a larger patient population to search for predictive correlations based on the reoxygenation function are planned.

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 (ktrans), 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.

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.

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

Following the publication of this article [1], the authors noticed that figures 2, 3, 4 and 5 were in the incorrect order and thus had incorrect captions.

Development and Validation of a Gene Signature for Patients with Head and Neck Carcinomas Treated by Postoperative Radio(chemo)therapy.

Purpose: The aim of this study was to identify and independently validate a novel gene signature predicting locoregional tumor control (LRC) for treatment individualization of patients with locally advanced HPV-negative head and neck squamous cell carcinomas (HNSCC) who are treated with postoperative radio(chemo)therapy (PORT-C).Experimental Design: Gene expression analyses were performed using NanoString technology on a multicenter training cohort of 130 patients and an independent validation cohort of 121 patients. The analyzed gene set was composed of genes with a previously reported association with radio(chemo)sensitivity or resistance to radio(chemo)therapy. Gene selection and model building were performed comparing several machine-learning algorithms.Results: We identified a 7-gene signature consisting of the three individual genes HILPDA, CD24, TCF3, and one metagene combining the highly correlated genes SERPINE1, INHBA, P4HA2, and ACTN1 The 7-gene signature was used, in combination with clinical parameters, to fit a multivariable Cox model to the training data (concordance index, ci = 0.82), which was successfully validated (ci = 0.71). The signature showed improved performance compared with clinical parameters alone (ci = 0.66) and with a previously published model including hypoxia-associated genes and cancer stem cell markers (ci = 0.65). It was used to stratify patients into groups with low and high risk of recurrence, leading to significant differences in LRC in training and validation (P < 0.001).Conclusions: We have identified and validated the first hypothesis-based gene signature for HPV-negative HNSCC treated by PORT-C including genes related to several radiobiological aspects. A prospective validation is planned in an ongoing prospective clinical trial before potential application in clinical trials for patient stratification. Clin Cancer Res; 24(6); 1364-74. ©2018 AACR.

Comparison of detection methods for HPV status as a prognostic marker for loco-regional control after radiochemotherapy in patients with HNSCC.

OBJECTIVE: To compare six HPV detection methods in pre-treatment FFPE tumour samples from patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who received postoperative (N = 175) or primary (N = 90) radiochemotherapy. MATERIALS AND METHODS: HPV analyses included detection of (i) HPV16 E6/E7 RNA, (ii) HPV16 DNA (PCR-based arrays, A-PCR), (iii) HPV DNA (GP5+/GP6+ qPCR, (GP-PCR)), (iv) p16 (immunohistochemistry, p16 IHC), (v) combining p16 IHC and the A-PCR result and (vi) combining p16 IHC and the GP-PCR result. Differences between HPV positive and negative subgroups were evaluated for the primary endpoint loco-regional control (LRC) using Cox regression. RESULTS: Correlation between the HPV detection methods was high (chi-squared test, p < 0.001). While p16 IHC analysis resulted in several false positive classifications, A-PCR, GP-PCR and the combination of p16 IHC and A-PCR or GP-PCR led to results comparable to RNA analysis. In both cohorts, Cox regression analyses revealed significantly prolonged LRC for patients with HPV positive tumours irrespective of the detection method. CONCLUSIONS: The most stringent classification was obtained by detection of HPV16 RNA, or combining p16 IHC with A-PCR or GP-PCR. This approach revealed the lowest rate of recurrence in patients with tumours classified as HPV positive and therefore appears most suited for patient stratification in HPV-based clinical studies.