Biology subjects

Shukla-Dave, A.

Publications and source records attributed to Shukla-Dave, A..

4 recordsLinked to original sources

bioRxiv2024

Cancer radiomic feature variations due to reconstruction kernel choice and integral tube current.

PurposeRadiological cancer imaging features, or radiomics features, can be derived to diagnose disease or predict treatment response. However, variability between vendors, scanners, protocols, and even reconstruction software versions is an obstacle to the clinical use of radiomics features. This study aimed to characterize the impact of kernel reconstruction differences and integral tube current settings on radiomic features extracted from computed tomography (CT) scans. MethodsRadiomic features were extracted from CT scans of a 3D-printed phantom with five imprinted tumors using the CERR software system, resulting in 282 features. Batch effects were assessed via principal component analysis (PCA) and correlation measures. Robustness was measured using the concordance correlation coefficient (CCC) and Pearson correlation coefficient. Statistical analysis was performed using R software. ResultsPCA identified two clusters comprised of Standard, ASIRs, ASIRV, and soft kernels in one, and Lung and Bone Kernels in the other. Features displayed a gradient from ASIR10 to ASIR50 and ASIRV1 to ASIRV5 in terms of nearness to Standard Kernel feature values. Feature correlation matrices revealed little change in ASIRs, ASIRVs, and the Standard Kernel, but showed significant changes in Bone and Lung Kernel results. Combat-algorithm correction improved robustness, particularly in first-order statistic features, and mitigated batch effects due to the ASIRs and the standard kernel. Forty (40) out of 282 features were identified as robust. However, Combat-based correction performed poorly in harmonizing Bone and Lung reconstruction kernels. ConclusionsThe robustness of means and median radiomic features across kernel reconstruction choices, in contrast to the lack of robustness in many other radiomic features, suggests that kernel reconstruction effects are not well-addressed by current harmonization methods.

bioinformatics↗

bioRxiv2019

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

PurposeTo construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation. Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network.\n\nMethodsWe propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution.\n\nResultsCommon radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data.\n\nConclusionsWe showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

biophysics↗

bioRxiv2019

Library of model implementations for sharing deep-learning image segmentation and outcomes models

An open-source library of implementations for deep-learning based image segmentation and outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with Computational Environment for Radiological Research (CERR) software platform. CERR is a natural choice to centralize model implementations due to its comprehensiveness, popularity, and ease of use. CERR provides well-validated feature extraction for radiotherapy dosimetry and radiomics with fine control over the calculation settings. This allows users to select the appropriate feature calculation used in the model derivation. Models for automatic image segmentation are distributed via Singularity containers, with seamless i/o to and from CERR. Singularity containers allow for segmentation models to be deployed with a variety of scientific computing architectures. Deployment of models is driven by JSON configuration file, making it convenient to plug-in models. Models from the library can be called programmatically for batch evaluation. The library includes implementations for popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from Image Biomarker Standardization features found to be important across multiple sites and image modalities. Deep learning-based image segmentation models include state of the art networks such as Deeplab and other problem-specific architectures. The library is distributed as GNU-copyrighted software at https://www.github.com/cerr/CERR.

bioinformatics↗

bioRxiv2019

Optimal Mass Transport Kinetic Modeling for Head and Neck DCE-MRI: Initial Analysis

Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and under-went DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ({Phi}F) and backward flux ({Phi}B). OMT-derived {Phi}F was compared with the volume transfer constant for CA, Ktrans, derived from the Extended Tofts Model (ETM). The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 {+/-} 0.0091 (min-1) whereas the mean advective component was 0.0052{+/-}0.0086 (min-1) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67-18.76% and 12.76-30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ({Phi}F) is comparable with the ETM-derived Ktrans. This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.

systems biology↗