Radiomics-based quantitative biomarker discovery: development of a robust image processing infrastructure

Hwang, Darryl; Varghese, Bino; Chang, Michael W.; Deng, Christopher; Ugweze, Chidubem; Cen, Steven; Duddalwar, Vinay

doi:10.1117/12.2256829

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Future studies might focus on the application of this tool in masses from other sites of the body and using it in combination with other radiomic tumors metrics 48 derived from CECT that can be used to separate malignant from benign renal masses or even tumor subtyping within a sample population.…”

Section: Discussionmentioning

confidence: 99%

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

Varghese

Chen

Hwang

et al. 2018

BJR

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Section: Discussionmentioning

confidence: 99%

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

Varghese

Chen

Hwang

et al. 2018

BJR

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“…Machine-learning algorithms have even shown promise in segregating COVID-19 from other causes of viral pneumonia, with an area-under-the-curve of 0.78 on external validation testing sets [ 43 ]. While quantitative imaging metrics and machine-learning algorithms have for some time shown applicability in a wide range of pathologies ranging from soft-tissue masses to renal cancer, integration of radiomics analyses into clinical workflows is still in its infancy [ [44] , [45] , [46] ]. Nevertheless, surging demand for subspecialty-trained cardiothoracic radiologists may necessitate more widespread implementation of computer-aided detection systems as the need for rapid risk prioritization continues to rise [ 47 ].…”

Section: Lessons From Chinamentioning

confidence: 99%

Coronavirus Disease 2019 (COVID-19) diagnostic technologies: A country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic

2020

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Since first report of a novel coronavirus in December of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has crippled healthcare systems around the world. While many initial screening protocols centered around laboratory detection of the virus, early testing assays were thought to be poorly sensitive in comparison to chest computed tomography, especially in asymptomatic disease. Coupled with shortages of reverse transcription polymerase chain reaction (RT-PCR) testing kits in many parts of the world, these regions instead turned to the use of advanced imaging as a first-line screening modality. However, in contrast to previous Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome coronavirus epidemics, chest X-ray has not demonstrated optimal sensitivity to be of much utility in first-line screening protocols. Though current national and international guidelines recommend for the use of RT-PCR as the primary screening tool for suspected cases of COVID-19, institutional and regional protocols must consider local availability of resources when issuing universal recommendations. Successful containment and social mitigation strategies worldwide have been thus far predicated on unified governmental responses, though the underlying ideologies of these practices may not be widely applicable in many Western nations. As the strain on the radiology workforce continues to mount, early results indicate a promising role for the use of machine-learning algorithms as risk stratification schema in the months to come.

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“…Radiomics is defined as the conversion of medical imaging into multi-dimensional mineable data for clinical decision support to bolster accurate diagnosis, prognostication, and prediction of treatment response [4,[23][24][25][26][27][28]. In comparison with standard biopsy techniques, radiomics analysis offers the advantage of being able to non-invasively quantify heterogeneity of entire tumor volumes at given time points of interest, which in theory should allow for better characterization of chemotherapeutic response than use of sizebased criteria alone [6, 20-22, 26, 27, 29-31].…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with standard biopsy techniques, radiomics analysis offers the advantage of being able to non-invasively quantify heterogeneity of entire tumor volumes at given time points of interest, which in theory should allow for better characterization of chemotherapeutic response than use of sizebased criteria alone [6, 20-22, 26, 27, 29-31]. Radiomics has already been successfully applied to a variety of clinical applications related to STS, including stratification of benign from malignant soft tissue neoplasms, prediction of histologic grade, and assessment of metastatic risk [27,[31][32][33][34], though lack of standardized protocols has hindered widespread adoption of radiomics workflows in clinical practice [16,24,25,35].…”

Section: Introductionmentioning

confidence: 99%

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach

et al. 2023

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Objectives To evaluate the performance of machine learning–augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. Methods Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning–augmented radiomics analyses. Results Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22–0.58) and 0.44 (95% CI 0.26–0.62) for RF and AdaBoost, respectively. Conclusion Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

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Radiomics-based quantitative biomarker discovery: development of a robust image processing infrastructure

Cited by 4 publications

References 6 publications

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

Coronavirus Disease 2019 (COVID-19) diagnostic technologies: A country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach

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