CT respiratory motion synthesis using joint supervised and adversarial learning.

Objective.Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery.Approach.In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude.Main results.Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim).Significance.This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found athttps://github.com/cyiheng/Dynagan.

Evaluation of conventional and deep learning based image harmonization methods in radiomics studies.

Objective.To evaluate the impact of image harmonization on outcome prediction models using radiomics.Approach.234 patients from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset with T1 MRI were enrolled in this study. Images were harmonized to a reference image using histogram matching (HHM) and a generative adversarial network (GAN)-based method (HGAN). 88 radiomics features were extracted on HHM, HGANand original (HNONE) images. Wilcoxon paired test was used to identify features significantly impacted by the harmonization protocol used. Radiomic prediction models were built using feature selection with the Least Absolute Shrinkage and Selection Operator (LASSO) and Kaplan-Meier analysis.Main results.More than 50% of the features (49/88) were statistically modified by the harmonization with HHMand 55 with HGAN(adjustedp-value < 0.05). The contribution of histogram and texture features selected by the LASSO, in comparison to shape features that were not impacted by harmonization, was higher in harmonized datasets (47% for Hnone, 62% for HHMand 71% for HGAN). Both image-based harmonization methods allowed to split patients into two groups with significantly different survival (p<0.05). With the HGANimages, we were also able to build and validate a model using only features impacted by the harmonization (median survivals of 189 versus 437 days,p= 0.006)Significance.Data harmonization in a multi-institutional cohort allows to recover the predictive value of some radiomics features that was lost due to differences in the image properties across centers. In terms of ability to build survival prediction models in the BRATS dataset, the loss of power from impacted histogram and heterogeneity features was compensated by the selection of additional shape features. The harmonization using a GAN-based approach outperformed the histogram matching technique, supporting the interest for the development of new advanced harmonization techniques for radiomic analysis purposes.

Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer.

PURPOSE: (Chemo)-radiotherapy (RT) is the gold standard treatment for patients with locally advanced lung cancer non accessible for surgery. However, current toxicity prediction models rely on clinical and dose volume histograms (DVHs) and remain unsufficient. The goal of this work is to investigate the added predictive value of the radiomics approach applied to dose maps regarding acute and late toxicities in both the lungs and esophagus. METHODS: Acute and late toxicities scored using the CTCAE v4.0 were retrospectively collected on patients treated with RT in our institution. Radiomic features were extracted from 3D dose maps considering Gy values as grey-levels in images. DVH and usual clinical factors were also considered. Three toxicity prediction models (clinical only, clinical + DVH and combined, i.e., including clinical + DVH + radiomics) were incrementally trained using a neural network on 70% of the patients for prediction of grade ≥2 acute and late pulmonary toxicities (APT/LPT) and grade ≥2 acute esophageal toxicities (AET). After bootstrapping (n = 1000), optimal cut-off values were determined based on the Youden Index. The trained models were then evaluated in the remaining 30% of patients using balanced accuracy (BAcc). RESULTS: 167 patients were treated from 2015 to 2018: 78% non small-cell lung cancers, 14% small-cell lung cancers and 8% other histology with a median age at treatment of 66 years. Respectively, 22.2%, 16.8% and 30.0% experienced APT, LPT and AET. In the training set (n = 117), the corresponding BAcc for clinical only/clinical + DVH/combined were 0.68/0.79/0.92, 0.66/0.77/0.87 and 0.68/0.73/0.84. In the testing evaluation (n = 50), these trained models obtained a corresponding BAcc of 0.69/0.69/0.92, 0.76/0.80/0.89 and 0.58/0.73/0.72. CONCLUSION: In patients with a lung cancer treated with RT, radiomic features extracted from 3D dose maps seem to surpass usual models based on clinical factors and DVHs for the prediction of APT and LPT.

Use of radiomics in the radiation oncology setting: Where do we stand and what do we need?

Radiomics is a field that has been growing rapidly for the past ten years in medical imaging and more particularly in oncology where the primary objective is to contribute to personalised and predictive medicine. This short review aimed at providing some insights regarding the potential value of radiomics for cancer patients treated with radiotherapy. Radiomics may contribute to each stage of the patients' management: diagnosis, planning, treatment monitoring and post-treatment follow-up (toxicity and response). However, its applicability in clinical routine is currently hindered by several factors, including lack of automation, standardisation and harmonisation. A major effort must be carried out to automate the workflow, standardise radiomics good practices and carry out large-scale studies before any transfer to daily clinical practice.

One-shot synthesis of a poly(N-isopropylacrylamide)/silica hybrid gel.

Class I hybrid poly(N-isopropylacrylamide)/silica hydrogels, PNIPAM/SiO2, were prepared by a new one shot synthesis. In this approach, the free-radical polymerization of vinyl groups of N-isopropylacrylamide (NIPAM) and the hydrolysis-condensation of alkoxy groups of tetramethoxysilane (TMOS) are performed concomitantly using sodium persulfate and 3-(dimethylamino)-propionitrile, a well-known couple to initiate the organic polymerization. The cross-linker is N,N-methylenebisacrylamide. The kinetic study of mechanical properties from the sol-to-gel state for different ratios of TMOS/NIPAM was investigated by rheological ultrasonic measurements. The thermoresponse of hybrid materials was investigated by differential scanning calorimetry and the measurements showed that hybrid gels present a lower critical solution temperature, which is similar with one of single organic hydrogel.