Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings

Diffeomorphic Registration of Images with Variable Contrast Enhancement

Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacement fields, which is a necessary condition for these to be physical. However, such methods are based on the matching of intensities and are not suitable for registering images with different contrast enhancement. In such cases, a registration method based on the local phase like the Morphons has to be used. In this paper, a diffeomorphic version of the Morphons registration method is proposed and compared to conventional Morphons, Demons, and diffeomorphic Demons. The method is validated in the context of radiotherapy for lung cancer patients on several 4D respiratory-correlated CT scans of the thorax with and without variable contrast enhancement

High-intensity aerobic interval training and resistance training are feasible in rectal cancer patients undergoing chemoradiotherapy: a feasibility randomized controlled study

Background: There has been growing evidence of the benefits of high-intensity aerobic interval training (HIIT) and resistance training (RES) for populations with cancer. However, these two modalities have not yet been performed alone in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (NACRT). Therefore, this study aimed to determine the feasibility of HIIT and RES in rectal cancer patients undergoing NACRT. Materials and methods: Rectal cancer patients set to undergo NACRT were randomly assigned to HIIT intervention, RES intervention, or the usual care. Feasibility of HIIT and RES was assessed by measuring recruitment rate, adherence (retention rate, attendance rate, and exercise sessions duration and intensity), and adverse events. Endpoints (changes in fatigue, health-related quality of life, depression, daytime sleepiness, insomnia, sleep quality, functional exercise capacity, and executive function) were assessed at baseline and at week 5. Results: Among the 20 eligible patients, 18 subjects were enrolled and completed the study, yielding a 90% recruitment rate and 100% retention rate. Attendance at exercise sessions was excellent, with 92% in HIIT and 88% in RES. No exercise-related adverse events occurred. Conclusion: This study demonstrated that HIIT and RES are feasible in rectal cancer patients undergoing NACRT. Trial registration: www.clinicaltrials.gov, NCT03252821 (date of registration: March 30, 2017)

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

peer reviewedDetection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours

Adaptive biological image-guided radiation therapy in pharyngo-laryngeal squamous cell carcinoma

In recent years, the impressive progress performed in imaging, computational and technological fields have made possible the emergence of image-guided radiation therapy (IGRT) and adaptive radiation therapy (ART). The accuracy in radiation dose delivery reached by IMRT offers the possibility to increase locoregional dose-intensity, potentially overcoming the poor tumor control achieved by standard approaches. However, before implementing such a technique in clinical routine, a particular attention has to be paid at the target volumes definition and delineation procedures to avoid inadequate dosage to TVs/OARs. In head and neck squamous cell carcinoma (HNSCC), the GTV is typically defined on CT acquired prior to treatment. However, providing functional information about the tumor, FDG-PET might advantageously complete the classical CT-Scan to better define the TVs. Similarly, re-imaging the tumor with optimal imaging modality might account for the constantly changing anatomy and tumor shape occurring during the course of fractionated radiotherapy. Integrating this information into the treatment planning might ultimately lead to a much tighter dose distribution. From a methodological point of view, the delineation of TVs on anatomical or functional images is not a trivial task. Firstly, the poor soft tissue contrast provided by CT comes out of large interobserver variability in GTV delineation. In this regard, we showed that the use of consistent delineation guidelines significantly improved consistency between observers, either with CT and with MRI. Secondly, the intrinsic characteristics of PET images, including the blur effect and the high level of noise, make the detection of the tumor edges arduous. In this context, we developed specific image restoration tools, i.e. edge-preserving filters for denoising, and deconvolution algorithms for deblurring. This procedure restores the image quality, allowing the use of gradient-based segmentation techniques. This method was validated on phantom and patient images, and proved to be more accurate and reliable than threshold-based methods. Using these segmentation methods, we proved that GTVs significantly shrunk during radiotherapy in patients with HNSCC, whatever the imaging modality used (MRI, CT, FDG-PET). No clinically significant difference was found between CT and MRI, while FDG-PET provided significantly smaller volumes than those based on anatomical imaging. Refining the target volume delineation by means of functional and sequential imaging ultimately led to more optimal dose distribution to TVs with subsequent soft tissue sparing. In conclusion, we demonstrated that a multi-modality-based adaptive planning is feasible in HN tumors and potentially opens new avenues for dose escalation strategies. As a high level of accuracy is required by such approach, the delineation of TVs however requires a special care.(MED 3) -- UCL, 200