Background: Poorly differentiated midline carcinoma with a translocation between chromosomes 15 and 19, i.e. t(15;19), has been recognized as a distinct clinical entity for over a decade. This tumor affects young individuals, shows a rapidly fatal clinical course despite intensive therapy. The t(15;19) results in the fusion oncogene BRD4-NUT. Information concerning treatment of this rare disorder is scarce.

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Mycosis fungoides is a disease with manifestation of the skin that has traditionally been treated with electron therapy. In this paper, we present a method of treating the entire skin with megavoltage photons using helical tomotherapy (HT), verified through a phantom study and clinical dosimetric data from our first two treated patients. A whole body phantom was fitted with a wetsuit as bolus, and scanned with computer tomography. We accounted for variations in daily setup using virtual bolus in the treatment plan optimization. Positioning robustness was tested by moving the phantom, and recalculating the dose at different positions. Patient treatments were verified with in vivo film dosimetry and dose reconstruction from daily imaging. Reconstruction of the actual delivered dose to the patients showed similar target dose as the robustness test of the phantom shifted 10 mm in all directions, indicating an appropriate approximation of the anticipated setup variation. In vivo film measurements agreed well with the calculated dose confirming the choice of both virtual and physical bolus parameters. Despite the complexity of the treatment, HT was shown to be a robust and feasible technique for total skin irradiation. We believe that this technique can provide a viable option for Tomotherapy centers without electron beam capability.

Background Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. Methods A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1–4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. Results Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. Conclusions Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.

Objectives Mutation analysis by massive parallel sequencing (MPS) is routinely performed in the clinical management of lung cancer in Sweden. We describe the clinical and mutational profiles of lung cancer patients subjected to the first 1.5 years of treatment predictive MPS testing in an autonomous regional health care region. Methods Tumors from all patients with lung cancer who had an MPS test from January 2015 to June 2016 in the Skåne health care region in Sweden (1.3 million citizens) were included. Six hundred eleven tumors from 599 patients were profiled using targeted sequencing with a 26-gene exon-focused panel. Data on disease patterns and characteristics of the patients subjected to testing were assembled, and correlations between mutational profiles and clinical features were analyzed. Results MPS with the 26-gene panel revealed alterations in 92% of the 611 lung tumors, with the most frequent mutations detected in the nontargetable genes TP53 (62%) and KRAS (37%). Neither KRAS nor TP53 mutations were associated with disease pattern, chemotherapy response, progression-free survival, or overall survival in advanced-stage disease treated with platinum-based doublet chemotherapy as a first-line treatment. Among targetable genes, EGFR driver mutations were detected in 10% of the tumors, and BRAF p.V600 variants in 2.3%. For the 71 never smokers (12%), targetable alterations ( EGFR mutations, BRAF p.V600, MET exon 14 skipping, or ALK/ROS1 rearrangement) were detected in 59% of the tumors. Conclusion Although the increasing importance of MPS as a predictor of response to targeted therapies is indisputable, its role in prognostics or as a predictor of clinical course in nontargetable advanced stage lung cancer requires further investigation.