Biology subjects

Oh, J. H.

Publications and source records attributed to Oh, J. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Versatile electroporation protocols enable reproducible CRISPR-RNP delivery across multiple primary mouse cells of the hematopoietic lineage

Genetic engineering of primary hematopoietic cells is essential for mechanistic immunology studies, however the development of cell-based therapies yet remains constrained by two major factors: the fragility of many primary lineages and challenges of viral delivery platforms that are costly, time-intensive, and biologically confounding. Here, we optimized scalable, non-viral CRISPR-Cas9 electroporation workflows using the ExPERT platform across three primary mouse hematopoietic cell types: OT-I CD8 T cells, bone marrow-derived macrophages (BMDMs), and hematopoietic stem cells (HSCs). In activated OT-I CD8 T cells, two electroporation programs supported high-efficiency mRNA and RNP delivery with minimal impact on cell viability or proliferative capacity, with subtle activation-state-dependent sensitivity at higher energy settings. Extending optimization to myeloid and stem cell lineages, we found that BMDMs maintained high viability following electroporation, and a high-performing electroporation program supported robust RNP delivery and efficient target gene knockout while preserving macrophage differentiation. In HSCs, the same program enabled consistent RNP delivery, sustained viability, and reproducible gene knockout. Together, these findings establish ExPERT electroporation as a robust, reproducible, and modular platform for non-viral genome editing across primary mouse hematopoietic lineages, lowering barriers to rapid genetic perturbation for both discovery and translational applications.

cell biology↗

bioRxiv2025

NetFlow: A Framework to Explore Topological Representations of High-Dimensional Biomedical Data

Biomedical datasets are increasingly high-dimensional and multi-modal, theoretically enabling a more comprehensive understanding of biological systems. However, common clustering and dimensionality reduction methods often oversimplify continua and obscure complex, biologically relevant patterns, hindering our ability to meaningfully probe sample relationships. To address this challenge, we developed NetFlow, a computational framework that constructs graph representations of structural relationships between samples by integrating a data-driven, lineage-tracing-inspired pseudo-ordering with sparse similarity networks. NetFlow is suitable for small cohorts, features a flexible, modular design, supports multi-modal data fusion, and provides interactive visualization, enabling nuanced exploration of heterogeneity across biological samples. Applied across uni- and multi-modal cancer datasets, NetFlow refined clinically relevant subtypes, identified biomarker associations, and revealed more informative and structured relational patterns when benchmarked against current approaches. NetFlow thus delivers an interpretable graph representation of sample relatedness that supports precision oncology, hypothesis generation, and general high-dimensional data analysis.

bioinformatics↗

bioRxiv2025

Artificial Intelligence Apps for Medical Image Analysis using pyCERR and Cancer Genomics Cloud

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.

bioinformatics↗

bioRxiv2024

ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems

Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret the large-scale omic data are still needed. To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature-omics (ORCO). ORCO incorporates gene interaction information with omic data into a biological network, and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC, an edge-based measure, indicates network robustness and captures global gene signaling changes in functional cooperation using a consistent information passing measure, thereby helping identify therapeutic targets and regulatory modules in biological systems. This tool can be applicable to any data that can be represented as a network. ORCO is an open-source Python package and publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

bioinformatics↗

bioRxiv2024

High WEE1 expression is independently linked to poor survival in multiple myeloma

Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. We therefore analyzed the MMRF CoMMpass dataset (N=659) and identified a high-risk group (top tertile) and a low-risk group (bottom tertile) based on WEE1 expression sorted in descending order. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M-checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but has not until this time been implicated in MM. PFS was significantly different (p <1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N=341) and 3 (N=214) trials. Our results show WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic pathways.

cancer biology↗

bioRxiv2023

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival. STATEMENT OF SIGNIFICANCEMultiple myeloma has heterogenous clinical outcomes which are not well predicted by current prognostic scoring systems. Global assessment of gene-protein interactions using Ollivier-Ricci Curvature produces clusters of patients with defined prognostic significance, with high-risk groups harboring complex gene dysregulation impacting immune function.

cancer biology↗

bioRxiv2023

Inducing an oxidized redox-balance improves anti-tumor CD8+ T cell function

Cancer immunotherapy using antigen-specific CD8+ T cells depends on long-lasting anti-tumor function of the in vitro expanded T cells. T cell function is intricately linked to the activity of many metabolic pathways directly impacting the ability of CD8+ T cells to kill tumor cells. Metabolic conditioning in vitro better prepares CD8+ T cells for in vivo survival, tumor infiltration and tumor clearance. The mechanism underlying in vitro metabolic conditioning-induced augmented in vivo T cell function remains poorly understood. Here we show that metabolic conditioning of CD8+ effector T cells induces an oxidized cellular redox balance at least in part mediated by increased mitochondrial reactive oxygen species (ROS). This redox shift contributes to enhanced in vivo persistence and tumor clearance. In human tumour-infiltrating T cells, altering the redox balance ex-vivo reinvigorated pro-inflammatory cytokine production. Therefore, we believe that redox alterations present a targetable pathway to increase T cell-based anti-tumor immunotherapy efficacy.

immunology↗

bioRxiv2023

A multi-kingdom genetic barcoding system for precise target clone isolation

Clonal heterogeneity underlies diverse biological processes, including cancer progression, cell differentiation, and microbial evolution. Cell tagging strategies with DNA barcodes have recently enabled analysis of clone size dynamics and clone-restricted transcriptomic landscapes of heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here, we present a new multi-kingdom genetic barcoding system, CloneSelect, in which a target cell clone can be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first barcoded and propagated so their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored throughout the experimental timecourse. This novel CRISPR-barcode genetics platform provides many new ways of analyzing and manipulating mammalian, yeast, and bacterial systems. TeaserA multi-kingdom CRISPR-activatable barcoding system enables the precise isolation of target barcode-labeled clones from a complex cell population.

synthetic biology↗

bioRxiv2022

Technical Note: STRATIS: A Cloud-enabled Software Toolbox for Radiotherapy and Imaging Analysis

PurposeRecent advances in computational resources, including software libraries and hardware, have enabled the use of high-dimensional, multi-modal datasets to build Artificial Intelligence (AI) models and workflows for radiation therapy and image analysis. The purpose of Software Toolbox for RAdioTherapy and Imaging analysiS (STRATIS) is to provide cloud-enabled, easy-to-share software workflows to train and deploy AI models for transparency and multi-institutional collaboration. MethodSTRATIS leverages open source medical image informatics software for application-specific analysis. Jupyter notebooks for AI modeling workflows are provided with Python language as the base kernel. In addition to Python, workflows use software written in other languages, such as MATLAB, GNU-Octave, R, and C++, with the help of bridge libraries. The workflows can be run on a cloud platform, local workstation, or an institutional HPC cluster. Computational environments are provided in the form of publicly available docker images -and build scripts for local Anaconda environments. Utilities provided with STRATIS simplify bookkeeping of associations between imaging objects and allow chaining data processing operations defined via a setting file for AI models. ResultsWorkflows available on STRATIS can be broadly categorized into image segmentation, deformable image registration, and outcomes modeling for radiotherapy toxicity and tumor control using radiomics and dosimetry features. The STRATIS-forge GitHub organization https://www.github.com/stratis-forge hosts build-scripts for Docker and Anaconda as well as Jupyter notebooks for analysis workflows. The software for building environments and workflow notebooks has open source-GNU-GPL copyright, and AI models retain the copyright chosen by their original developers. ConclusionSTRATIS enables researchers to deploy and share AI modeling workflows for radiotherapy and image analysis. STRATIS is publicly available on Terra.bios FireCloud platform with a pre-deployed computational environment and on GitHub organization for users pursuing local deployment.

bioinformatics↗

bioRxiv2022

Automatic identification of drug-induced liver injury literature using natural language processing and machine learning methods

Drug-induced liver injury (DILI) is an adverse hepatic drug reaction that can potentially lead to life-threatening liver failure. Previously published work in the scientific literature on DILI has provided valuable insights for the understanding of hepatotoxicity as well as drug development. However, the manual search of scientific literature in PubMed is laborious. Natural language processing (NLP) techniques have been developed to decipher and understand the meaning of human language by extracting useful information from unstructured text data. In particular, NLP along with artificial intelligence (AI) / machine learning (ML) techniques may allow automatic processing of the DILI literature, but useful methods are yet to be demonstrated. To address this challenge, we have developed an integrated NLP/ML classification model to identify DILI-related literature using only paper titles and abstracts. We used 14,203 publications provided by the Critical Assessment of Massive Data Analysis (CAMDA) challenge, employing word vectorization techniques in NLP coupled with machine learning methods. Classification modeling was performed using 2/3 of the data for training and the remainder for testing in internal validation. The best performance was achieved using a linear support vector machine (SVM) model that combined vectors derived from term frequency-inverse document frequency (TF-IDF) and Word2Vec, achieving an accuracy of 95.0% and an F1-score of 95.0%. The final SVM model built using all 14,203 publications was tested on independent datasets, resulting in accuracies of 92.5%, 96.3%, and 98.3%, and F1-scores of 93.5%, 86.1%, and 75.6% for three test sets (T1-T3). The SVM model was tested on four external validation sets (V1-V4), resulting in accuracies of 92.0%, 96.2%, 98.3%, and 93.1%, and F1-scores of 92.4%, 82.9%, 75.0%, and 93.3%.

bioinformatics↗

bioRxiv2022

Dynamic Network Curvature Analysis of RNA-Seq Data in Sarcoma

In this work, we utilized network features of cancer gene interactomes to cluster pediatric sarcoma tumors and identify candidate therapeutic targets in an unsupervised manner. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks for mathematical analysis. We employed a geometric approach centered on a discrete notion of curvature, which provides a measure of the functional association between genes in the context of their connectivity. Specifically, we adopted a recently proposed dynamic extension of graph curvature to extract features of the non-Euclidean, multiscale structure of genomic networks. We propose a hierarchical clustering approach to reveal preferential gene clustering according to their geometric cooperation which captured the characteristic EWSR1-FLI1 fusion in Ewing sarcoma. We also performed in silico edge perturbations to assess systemic response to simulated interventions quantified by changes in curvature. These results demonstrate that geometric network-based features can be useful for identifying non-trivial gene associations in an agnostic manner.

bioinformatics↗

bioRxiv2021

vWCluster: A Network Based Clustering of Multi-omics Breast Cancer Data Based on Vector-Valued Optimal Transport

In this paper, we present a network-based clustering method based on the vector-valued Wasserstein distance derived from optimal mass transport (OMT) theory. This distance allows for the natural integration of multi-layer representations of data in a given network from which one derives clusters via a hierarchical clustering approach. In this study, we applied the methodology, called vector Wasserstein clustering (vW-cluster), to multi-omics data from the two largest breast cancer studies. The resultant clusters showed significantly different survival rates in Kaplan-Meier analysis in both datasets. CIBERSORT scores were compared among the identified clusters. Out of the 22 CIBERSORT immune cell types, 9 were commonly significantly different in both datasets, suggesting the difference of tumor immune microenvironment in the cluster. vWCluster can aggregate multi-omics data represented as a vectorial form in a network with multiple layers, taking into account the concordant effect of heterogeneous data, and further identify subtypes of tumors with different survival rates.

bioinformatics↗

bioRxiv2021

Geometric Network Analysis Provides Prognostic Information in Patients with High Grade Serous Carcinoma of the Ovary Treated with Immune Checkpoint Inhibitors

PurposeNetwork analysis methods can potentially quantify cancer disturbances in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs), and to rank key genes associated with prognosis. MethodsCopy number alterations (CNAs) from targeted and whole exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein-protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier-Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan-Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. ResultsThe network curvature analysis stratified patients at high risk of mortality with p=0.00047 in Kaplan-Meier analysis. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. ConclusionNetwork curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

cancer biology↗

bioRxiv2020

Entelon(R) (vitis vinifera seed extract) reduces inflammation and calcification in a beagle dog model of intravascular bovine pericardium implantation

ObjectiveInflammation and calcification are major factors responsible for failure of bioprosthetic valve and other substitute heart valve implantations. The objective of this study was to evaluate the anti-inflammatory and anti-calcification effects of Entelon150(R) (consisting of grape-seed extract) in a beagle dog model of intravascular bovine pericardium implantation. MethodsIn total, 8 healthy male beagle dogs were implanted with a bovine pericardium bilaterally in the external jugular veins and divided into two groups. Animals in the Entelon150(R) group (n = 4) were treated with 150 mg of Entelon150(R) twice daily for six weeks after surgery. The negative control (NC) group (n = 4) was treated with 5 ml of saline using the same method. After six weeks, we measured the calcium content, performed histological examination, and performed molecular analysis. ResultsThe calcium content of implanted tissue in the Entelon150(R) group (0.56{+/-}0.14 mg/g) was significantly lower than that in the NC group (1.48{+/-}0.57 mg/g) (p < 0.05). Histopathological examination showed that infiltration of chronic inflammatory cells, such as fibroblasts and macrophages, occurred around the graft in all groups; however, the inflammation level of the implanted tissue in the Entelon150(R) group was significantly lower than that in the NC group. Both immunohistochemical and western blot analyses revealed that bone morphogenetic protein 2 expression was significantly attenuated in the Entelon150(R) group. ConclusionsOur results indicate that Entelon150(R) significantly attenuates post-implantation inflammation and degenerative calcification of the bovine pericardium in dogs. Therefore, Entelon150(R) may increase the longevity of the bovine pericardium after intravascular implantation.

bioengineering↗

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

Network-based clustering for drug sensitivity prediction in cancer cell lines

The study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We show that prior clustering of the heterogenous cell lines and structurally diverse drugs significantly improves the accuracy of the prediction. In addition, it facilities the interpretability of the results and identification of molecular biomarkers which are significant for both clustering of the cell lines and predicting the drug response.

bioinformatics↗

bioRxiv2019

Anatomically Informed 3D Printed CT phantoms: The First Step of a Pipeline To Identify Robust Quantitative Radiomic Features

PurposeThis study investigates the robustness of quantitative radiomic features derived from computed tomography (CT) images of a novel patient informed 3-D printed phantom, which captures the morphological heterogeneity of tumors and normal tissue observed on CT scans.\n\nMethodsUsing a novel voxel-based multi-material three-dimensional (3D) printer, an anthropomorphic phantom that was modeled after diseased tissue seen on 6 patient CT scans was manufactured. Four patients presented with pancreatic adenocarcinoma tumors (PDAC), 1 with non-small cell lung carcinoma (NSCLC) and 1 with advanced stage hepatic cirrhosis. The 5 tumors were segmented, extracted and then imbedded into CT images of the heterogenous portion of the cirrhotic liver. The composite scan of the implanted tumor within the background cirrhotic liver was then 3D printed. The resultant phantom was scanned sequentially, 30 times with a clinical CT scanner using a reference CT protocol. One hundred and four quantitative radiomic features were then extracted from images of each lesion to determine their repeatability. Repeatability of each radiomic feature was evaluated using the within subject coefficient of variation (wCV, %). A feature with a wCV (%) > 10% was considered as being unrepeatable. A subset of the repeatable features that were also found to be prognostic for lung and pancreatic cancers were then assessed for their percent deviation (pDV, %) from reference values. The reference values were those derived from the repeatability portion of this study. The assessment was conducted by re-scanning the phantom with 11 different clinically relevant sets of scanning parameters. Deviation of radiomic features derived from images of each tumor across all sets of scanning parameters was assessed using the percent deviation relative to the reference values.\n\nResultsTwenty nine of the 104 features presented with wCV (%) > 10%. The lack of repeatability was found to depend on tumor type. The only class of radiomic features with a wCV (%) < 10% were those calculated using the neighboring grey level dependence-based matrices (NGLDM). Notably, skewness, first information correlation, cluster shade, Haralick correlation, autocorrelation, busyness, complexity, high gray level zone emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis, short run high grey level emphasis, and valley radiomic features had wCV (%) values > 10% for select tumors within the phantom. Two radiomic features prognostic for NSCLC, energy and grey level non-uniformity, had pDVs (%) that exceeded 30% across all scanning techniques. The pDV (%) for the 4 radiomic features prognostic for PDAC tumors depended on tumor type and selected scanning parameter. Application of the lung kernel caused the largest pDVs (%). Scans acquired with the reduced tube current of 100 mA and reconstructed with the bone kernel yielded pDVs (%) within {+/-} 10%.\n\nConclusionWe demonstrated the feasibility with which patient informed 3D printed phantoms can be manufactured directly from lesions seen on CT scans, and demonstrate their potential use for the assessment of robust quantitative radiomic features.

biophysics↗