Localise to segment: crop to improve organ at risk segmentation accuracy

Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest. We investigate the accuracy improvements brought about by such a localisation stage by comparing to a single-stage baseline network trained on full resolution images. We find that localisation approaches can improve both training time and stability and a two stage process involving both a localisation and organ segmentation network provides a significant increase in segmentation accuracy for the spleen, pancreas and heart from the Medical Segmentation Decathlon dataset. We also observe increased benefits of localisation for smaller organs. Source code that recreates the main results is available at \href{https://github.com/Abe404/localise_to_segment}{this https URL}

Survival and failure types after radiation therapy of vulvar cancer

Background and purpose: Describe the survival rates and distribution of events on competing failure types in vulvar carcinoma after treatment with chemoradiation (CRT) or radiation (RT) alone. Material and methods: We included patients with vulvar carcinoma treated with CRT or RT between 2009 and 2014. Survival was estimated using the Kaplan-Meier method. We performed a competing risk analysis and included five competing events: loco-regional failure (LRF), distant metastasis, LRF plus distant metastasis, and death without evidence of disease, with the remaining patients denoted alive without evidence of disease. Results: 87 patients were treated. Progression free survival (PFS) and overall survival (OS) at 3 years were 40% and 57%, respectively. 41.3% of patients relapsed, most often loco-regionally. We saw significantly worse PFS and OS for patients older than 68 (p = 0.011/p = 0.010) and for patients treated with definitive RT (p = 0.004/p = 0.005). Competing risk analysis showed increased risk of LRF, and that death was most often related to vulvar cancer. Death without disease recurrence was less frequent, even in the elderly. Conclusions: LRF was the most common event. PFS and OS were inferior for elderly patients and patients treated definitively. A better understanding of these differences may be used to define risk adapted treatment strategies

Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation

Locoregional recurrences (LRR) are still a frequent site of treatment failure for head and neck squamous cell carcinoma (HNSCC) patients. Identification of high risk subvolumes based on pretreatment imaging is key to biologically targeted radiation therapy. We investigated the extent to which a Convolutional neural network (CNN) is able to predict LRR volumes based on pre-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the potential to identify biological high risk volumes using CNNs. For 37 patients who had undergone primary radiotherapy for oropharyngeal squamous cell carcinoma, five oncologists contoured the relapse volumes on recurrence CT scans. Datasets of pre-treatment FDG-PET/CT, gross tumour volume (GTV) and contoured relapse for each of the patients were randomly divided into training (n=23), validation (n=7) and test (n=7) datasets. We compared a CNN trained from scratch, a pre-trained CNN, a SUVmax threshold approach, and using the GTV directly. The SUVmax threshold method included 5 out of the 7 relapse origin points within a volume of median 4.6 cubic centimetres (cc). Both the GTV contour and best CNN segmentations included the relapse origin 6 out of 7 times with median volumes of 28 and 18 cc respectively. The CNN included the same or greater number of relapse volume POs, with significantly smaller relapse volumes. Our novel findings indicate that CNNs may predict LRR, yet further work on dataset development is required to attain clinically useful prediction accuracy

A modeling study of functional magnetic resonance imaging to individualize target definition of seminal vesicles for external beam radiotherapy

Background Pre-treatment magnetic resonance imaging (MRI) can give patient-specific evaluation of 25 suspected pathologically-involved volumes in the seminal vesicles (SV) in prostate cancer patients. By 26 targeting this suspicious volume we hypothesize that radiotherapy is more efficient without introducing more 27 toxicity. In this study we evaluate the concept of using MRI-defined target volumes in terms of tumor 28 control probability (TCP) and rectal normal tissue complication probability (NTCP). Materials and methods Twenty-one high-risk prostate cancer patients were included. Pre-treatment CT 30 images, T2 weighted (T2w) MRI and two multi-parametric MRI were acquired. Overlap between a 31 suspicious volume in the SV observed on T2w images and a suspicious volume observed on either multi-32 parametric MRI was assumed to reflect a true malignant region (named “MRI positive”). In addition the 33 entire SV on the CT-scan was delineated. Three treatment plans of 2Gyx39 fractions were generated per 34 patient: one covering the MRI positive volume in SV and prostate with margin of 11 mm to the MRI positive 35 in the SV and two plans covering prostate and SV using 11mm and 7mm SV margin, respectively. All plans 36 prescribed the same PTV mean dose. Rectal NTCP grade≥2 was evaluated with the Lyman-Kutcher-Burman 37 model and TCP was estimated by a logistic model using the combined MRI positive volume in SV and 38 prostate as region-of-interest. Results 14/21 patients were classified as MRI positive, 6 of which had suspicious volumes in all three MRI 40 modalities. On average TCP for the plan covering prostate and the MRI positive volume was 3% higher (up 41 to 11%) than the two other plans which was statistically significant. The increased TCP was obtained without 42 increasing rectal NTCP grade≥2. Conclusion Using functional MRI for individualized target delineation in the seminal vesicles may improve 44 the treatment outcome in radiotherapy of prostate cancer without increasing the rectal toxicity.</p