An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images

We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patient had four pre-operative MR scans and manually drawn tumor contours. For automatic tumor segmentation, a 3D convolutional neural network (CNN) was trained and validated using 122 glioma patients. The trained model was applied to the remaining 163 GBM patients to generate tumor contours. The handcrafted and DL-based radiomic features were extracted from auto-contours using explicitly designed algorithms and a pre-trained CNN respectively. 163 GBM patients were randomly split into training (n=122) and testing (n=41) sets for survival analysis. Cox regression models with regularization techniques were trained to construct the handcrafted and DL-based signatures. The prognostic power of the two signatures was evaluated and compared. The 3D CNN achieved an average Dice coefficient of 0.85 across 163 GBM patients for tumor segmentation. The handcrafted signature achieved a C-index of 0.64 (95% CI: 0.55-0.73), while the DL-based signature achieved a C-index of 0.67 (95% CI: 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into two prognostically distinct groups (p-value<0.01, HR=2.80, 95% CI: 1.26-6.24). The proposed 3D CNN generated accurate GBM tumor contours from four MR images. The DL-based signature resulted in better GBM survival prediction, in terms of higher C-index and significant patient stratification, than the handcrafted signature. The proposed automatic radiomic workflow demonstrated the potential of improving patient stratification and survival prediction in GBM patients

Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks

Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose a site-agnostic, 3D dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset

L-shaped association of serum calcium with all-cause and CVD mortality in the US adults: A population-based prospective cohort study

BackgroundCalcium is involved in many biological processes, but the impact of serum calcium levels on long-term mortality in general populations has been rarely investigated.MethodsThis prospective cohort study analyzed data from the National Health and Nutrition Examination Survey (1999–2018). All-cause mortality, cardiovascular disease (CVD) mortality, and cancer mortality were obtained through linkage to the National Death Index. Survey-weighted multivariate Cox regression was performed to compute hazard ratios (HRs) and 95% confidential intervals (CIs) for the associations of calcium levels with risks of mortality. Restricted cubic spline analyses were performed to examine the non-linear association of calcium levels with all-cause and disease-specific mortality.ResultsA total of 51,042 individuals were included in the current study. During an average of 9.7 years of follow-up, 7,592 all-cause deaths were identified, including 2,391 CVD deaths and 1,641 cancer deaths. Compared with participants in the first quartile (Q1) of serum calcium level [≤2.299 mmol/L], the risk of all-cause mortality was lower for participants in the second quartile (Q2) [2.300–2.349 mmol/L], the third quartile (Q3) [2.350–2.424 mmol/L] and the fourth quartile (Q4) [≥2.425 mmol/L] with multivariable-adjusted HRs of 0.81 (95% CI, 0.74–0.88), 0.78 (95% CI, 0.71–0.86), and 0.80 (95% CI, 0.73, 0.88). Similar associations were observed for CVD mortality, with HRs of 0.82 (95% CI, 0.71–0.95), 0.87 (95% CI, 0.74–1.02), and 0.83 (95% CI, 0.72, 0.97) in Q2–Q4 quartile. Furthermore, the L-shaped non-linear associations were detected for serum calcium with the risk of all-cause mortality. Below the median of 2.350 mmol/L, per 0.1 mmol/L higher serum calcium was associated with a 24% lower risk of all-cause mortality (HR: 0.76, 95% CI, 0.70–0.83), however, no significant changes were observed when serum calcium was above the median. Similar L-shaped associations were detected for serum calcium with the risk of CVD mortality with a 25% reduction in the risk of CVD death per 0.1 mmol/L higher serum calcium below the median (HR: 0.75, 95% CI, 0.65–0.86).ConclusionL-shaped associations of serum calcium with all-cause and CVD mortality were observed in US adults, and hypocalcemia was associated with a higher risk of all-cause mortality and CVD mortality

Improving the Quantitative Interpretation of Multi-parametric MRI (mp-MRI) in Prostate Cancer

Purpose Prostate cancer (PCa) is the second leading cause of cancer-related death in men in the United States. The accurate diagnosis of PCa is crucial for proper treatment decision. Although biopsy is still the gold standard for diagnosis, it is limited to low sensitivity and invasiveness. On the other hand, as a non-invasive imaging tool, multi-parametric MRI (mp-MRI) has excellent potential in PCa diagnosis such as detection and stratification of aggressiveness. The mp-MRI includes both anatomical and functional information to be able to provide a comprehensive characterization of the tissue. However, diagnosis with mp-MRI is limited to inconsistent and qualitative interpretation. Clinically, the evaluation of mp-MRI is often through a standardized scoring system, PIRADS v2, which can lead to high inter- and intra-observer variability, and with a large amount of data for each case, the diagnosis process can be time-consuming. In order to get a more consistent quantitative evaluation, there are mainly two ways to utilize algorithms to help the diagnosis. The first one is creating quantitative biomarkers through mathematical models proposed based on assumption and understanding of physics and physiology, such as pharmacokinetic models for quantitative dynamic contrast-enhanced (DCE) MRI. The second one is using a machine learning technique to train a system with existing data to get diagnosis prediction on new data. The purpose of this work is to improve the quantitative interpretation of mp-MRI in PCa diagnosis regarding consistency and accuracy.Methods To evaluate existing B1+ estimation techniques to achieve a more consistent pre-contrast T1 estimation for quantitative DCE- MRI, 21 volunteers were prospectively recruited and scanned twice on two 3T MRI scanners, resulting in 84 variable flip angle (VFA) T1 exams. Two B1+ mapping techniques, including reference region variable flip angle (RR-VFA) and saturated turbo FLASH (satTFL), were used for B1+ correction, and T1 maps with and without B1+ correction were tested for the intra-scanner repeatability and inter-scanner reproducibility. Volumetric regions of interest were drawn on the transition zone, peripheral zone of the prostate and the obturator internus left and right muscles in the corresponding slices. The average T1 within each ROI for each scan was compared for both intra- and inter-scanner variability using concordance correlation coefficient and Bland-Altman plot. To simplify B1+ compensation for quantitative DCE MRI in clinical and clinical research settings, an analytical B1+ correction method is proposed using a Taylor series approximation to the steady-state spoiled gradient echo signal equation. The proposed approach only requires B1+ maps and uncorrected pharmacokinetic (PK) parameters as the input, and was evaluated using a prostate digital reference object (DRO) and 82 in-vivo prostate DCE-MRI cases. The approximated analytical correction was compared with the ground truth PK parameters in simulation, and compared with the reference numerical correction in in-vivo experiments, using percentage error as the metric. To develop a deep transfer learning (DTL) based model to distinguish indolent lesions from clinically significant PCa lesions using multiparametric MRI, 140 patients with 3T mp-MRI and whole-mount histopathology (WMHP) were included as the study cohort with IRB approval. The DTL based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL based model with the same deep learning (DL) model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC.Results Both RR-VFA-corrected T1 and satTFL-corrected T1 showed higher intra- and inter-scanner correlation (0.89/0.87 and 0.87/0.84 respectively) than VFA T1 (0.84 and 0.74). Bland-Altman plots showed that VFA T1 had a wider 95% limits of agreement and a larger range of T1 for each tissue compared to T1 with B1+ correction. The prostate DRO results show that the proposed approach provides residual error less than 0.4% for both Ktrans and ve, compared to the ground truth. This noise-free residual error was smaller than the noise-induced error using the reference numerical correction, which had a minimum error of 2.1�4.3% with baseline SNR of 234.5. For the 82 in-vivo cases, percentage error compared to the reference numerical correction method had a mean of 0.1% (95% central range of [0.0%, 0.2%]) across the prostate volume. After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL based model, DL model without transfer learning and PIRADS v2 score &gt; 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]) and 0.711 (CI [0.575, 0.847]), respectively in the testing set. Conclusion The application of B1+ correction (both RR-VFA and satTFL) to VFA T1 results in more repeatable and reproducible T1 estimation than VFA T1. This can potentially provide improved quantification of the prostate DCE-MRI parameters. The approximated analytical B1+ correction method provides comparable results with less than 0.3% error within 95% central range, compared to reference numerical B1+ correction. The proposed method is a practical solution for B1+ correction in prostate DCE-MRI due to its simple implementation. The DTL based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score &gt; 4 in discriminating clinically significant lesions in the testing set. The DeLong test indicated that the DTL based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89)

A adaptive reduced-dimensional discrete element method for non-dissipative explicit dynamic responses of granular materials with high-frequency noises

We present a dimensional-reduction framework based on proper orthogonal decomposition (POD) for non-dissipative explicit dynamic discrete element method (DEM) simulations. Through Galerkin projection, we introduce a finite dimensional space with less number of degree of freedoms such that the discrete element simulations are not only faster but also free of high-frequency noises. Since this method requires no injection of artificial or numerical damping, there is no need to tune damping parameters. The suppression of high-frequency responses allows larger time step for faster explicit integration. To capture the highly nonlinear behaviors due to particle rearrangement, an automatic mode-update scheme is formulated such that the most efficient basis can be used to predict mechanical responses. Numerical examples including, the wave propagation simulations and uniaxial extension and compression tests are used to demonstrate the capacity of the reduced order model

A adaptive reduced-dimensional discrete element method for non-dissipative explicit dynamic responses of granular materials with high-frequency noises

We present a dimensional-reduction framework based on proper orthogonal decomposition (POD) for non-dissipative explicit dynamic discrete element method (DEM) simulations. Through Galerkin projection, we introduce a finite dimensional space with less number of degree of freedoms such that the discrete element simulations are not only faster but also free of high-frequency noises. Since this method requires no injection of artificial or numerical damping, there is no need to tune damping parameters. The suppression of high-frequency responses allows larger time step for faster explicit integration. To capture the highly nonlinear behaviors due to particle rearrangement, an automatic mode-update scheme is formulated such that the most efficient basis can be used to predict mechanical responses. Numerical examples including, the wave propagation simulations and uniaxial extension and compression tests are used to demonstrate the capacity of the reduced order model