Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.Comment: 8 pages, 3 figures, submitted to Medical Physics Lette

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency were then evaluated on 1) a digital respiratory phantom, 2) a physical respiratory phantom, and 3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 seconds, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 seconds on the same GPU card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 seconds

Predicting Benefit From Immune Checkpoint Inhibitors in Patients With Non-Small-Cell Lung Cancer by CT-Based Ensemble Deep Learning: A Retrospective Study

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer

Ultra-Scratch-Resistant, Hydrophobic and Transparent Organosilicon-Epoxy-Resin Coating with a Double Cross-Link Structure

In this paper, an ultra-scratch-resistant, hydrophobic and transparent coating was fabricated by the sol–gel method using (3-Glycidyloxypropyl) triethoxysilane (GPTES) and curing agents. When the silanol was condensated, the ring-opening reaction of the epoxy groups also took place, which formed a double-cross-linked network (Si–O–Si and R3N). This network structure restricted the molecule chains from being twisted or dislocated, resulting in a great improvement of the abrasion resistance of the coating. A pencil hardness grade up to 8H was obtained. The coating also showed excellent stability after being soaked in pH = 2 and pH = 12 solutions, seawater and acetone, respectively. In addition, a water contact angle of 121° was obtained by post-treatment with hexamethyldisilazane (HMDS). The average transmittance of the coating reached to 90% in the wavelength range of 400~800 nm, nearly identical to the glass substrate. With multiple desirable properties and a simple fabrication process, this low-cost coating shows great potential in many practical applications

A novel atmospheric pressure hydrolysis without stirring and combustion–calcination process for the fabrication of magnetic Fe3O4/α-Fe2O3 heterostructure nanorods

Atmospheric pressure hydrolysis without stirring and a combustion–calcination method were utilized to fabricate magnetic Fe _3 O _4 / α -Fe _2 O _3 heterogeneous nanorods. First, the β -FeOOH nanorods were fabricated via hydrolysis, and the concentration of Fe ^3+ , hydrolysis temperature, and hydrolysis time were optimized. The optimal fabrication conditions were as follows: a 0.1 M FeCl _3 solution was hydrolyzed at 90 °C for 2 h. The average length and diameter of the β -FeOOH nanorods fabricated under the optimal conditions were approximately 216 and 58 nm, respectively. Subsequently, Fe _3 O _4 / α -Fe _2 O _3 heterogeneous nanorods were fabricated via a combustion–calcination process. The volume of absolute ethanol, calcination temperature, and calcination time were investigated to optimize the fabrication conditions of Fe _3 O _4 / α -Fe _2 O _3 heterogeneous nanorods under the following conditions: absolute ethanol: 50 ml; calcination temperature: 300 °C; and calcination time: 2 h. Magnetic Fe _3 O _4 / α -Fe _2 O _3 heterogeneous nanorods fabricated under optimal conditions were characterized with an average length of 199 nm, an average diameter of 51 nm, a zeta potential of +17.2 mV, and a saturation magnetization of 13 emu·g ^–1