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 (), and apparent diffusion coefficient (ADC) measurements.

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.

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.

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 F-misonidazole PET/CT (FMISO-PET) and F-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.

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.

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

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). 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.

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.