The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review

Reiazi, Reza; Abbas, Engy; Famiyeh, Petra; Rezaie, Aria; Kwan, Jennifer; Patel, Tirth; Bratman, Scott V.; Tadic, Tony; Liu, Feifei; Haibe‐Kains, Benjamin

doi:10.1016/j.compbiomed.2021.104400

Cited by 44 publications

(28 citation statements)

References 58 publications

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“…Beyond blood-based biomarkers, assessment of additional non-invasive biomarkers, for instance, radiomic evaluation of quantitative features on routine radiological imaging, as well as interrogation of other body substances such as urine or stool for microbiome analysis, may offer a holistic understanding of the interactions between host, tumor, and the microenvironment. 150 With efforts to this end already underway, and a wealth of further possibilities, the field of circulating biomarkers for disease monitoring appears promising.…”

Section: Discussionmentioning

confidence: 99%

Circulating Biomarkers for Therapeutic Monitoring of Anti-cancer Agents

2022

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Circulating biomarkers have emerged as valuable surrogates for evaluating disease states in solid malignancies. Their relative ease of access and rapid turnover has bolstered clinical applications in monitoring treatment efficacy and cancer progression. In this review, the roles of various circulating biomarkers in monitoring treatment response are described. Non-specific markers of disease burden, tumor markers (eg CA 125, CEA, PSA, etc.), circulating tumor cells, nucleic acids, exosomes, and metabolomic arrays are highlighted. Specifically, the discovery of each of these markers is reviewed, with examples illustrating their use in influencing treatment decisions, and barriers to their application noted where these exist. Finally, opportunities for future work using these circulating biomarkers are discussed.

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Section: Discussionmentioning

confidence: 99%

Circulating Biomarkers for Therapeutic Monitoring of Anti-cancer Agents

2022

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“…The computation of radiomic features relies on lesion segmentation -that is, the location and subsequent delineation of boundaries of the said lesion in a medical image [14] . In the context of radiation therapy, manual segmentation is routinely performed by experts to define the treatment target and normal structures [14] . Similar to the choice and measurement of target lesions in RECIST 1.1, manual delineation is time-consuming and subjective.…”

Section: Radiomics To the Rescuementioning

confidence: 99%

Radiomics in sarcoma trials: a complement to RECIST for patient assessment

Geady¹,

Shultz²,

Razak³

et al. 2022

J Cancer Metastasis Treat

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Radiological imaging has a critical role in the diagnosis of sarcomas and in evaluating therapy response assessment. The current gold standard for response assessment in solid tumors is the Response Evaluation Criteria in Solid Tumors, which evaluates changes in tumor size as a surrogate endpoint for therapeutic efficacy. However, tumors may undergo necrosis, changes in vascularization or become cystic in response to therapy, with no significant volume changes; thus, size assessments alone may not be adequate. Such morphological changes may give rise to radiographic phenotypes that are not easily detected by human operators. Fortunately, recent advances in high-performance computing and machine learning algorithms have enabled deep analysis of radiological images to extract features that can provide richer information about intensity, shape, size or volume, and texture of tumor phenotypes. There is growing evidence to suggest that these image-derived or “radiomic features” are sensitive to biological processes such as necrosis and glucose metabolism. Thus, radiomics could prove to be a critical tool for assessing treatment response and may present an integral complement to existing response criteria, opening new avenues for patient assessment in sarcoma trials.

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“…While these differences typically have little clinical impacts for routine radiological interpretation, they introduce biases when analyzed numerically to extract meaningful data 6 . This hampers advancement of reproducible feature extraction pipelines, a critical pre-requisite for clinical translation 7 .…”

Section: Introductionmentioning

confidence: 99%

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for validation of computed tomography post-processing and inference algorithms

Shapira

Donovan

Mei

et al. 2022

Preprint

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BackgroundRadiomics and other modern clinical decision-support algorithms are emerging as the next frontier for diagnostic and prognostic medical imaging. However, heterogeneities in image characteristics due to variations in imaging systems and protocols hamper the advancement of reproducible feature extraction pipelines. There is a growing need for realistic patient-based phantoms that accurately mimic human anatomy and disease manifestations to provide consistent ground-truth targets when comparing different feature extraction or image cohort normalization techniques.Materials and MethodsPixelPrint was developed for 3D-printing lifelike lung phantoms for computed tomography (CT) by directly translating clinical images into printer instructions that control the density on a voxel-by-voxel basis. CT datasets of three COVID-19 pneumonia patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Linear mixed models were utilized to evaluate effect sizes of evaluating phantom as opposed to patient images. Finally, PixelPrint’s reproducibility was evaluated by producing four phantoms from the same clinical images.ResultsEstimated mean differences between patient and phantom images were small (0.03-0.29, using a 1-5 scale). Effect size assessment with respect to rating variabilities revealed that the effect of having a phantom in the image is within one-third of the inter- and intra-reader variabilities. PixelPrint’s production reproducibility tests showed high correspondence among four phantoms produced using the same patient images, with higher similarity scores between high-dose scans of the different phantoms than those measured between clinical-dose scans of a single phantom.ConclusionsWe demonstrated PixelPrint’s ability to produce lifelike 3D-printed CT lung phantoms reliably. These can provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols, as well as for optimizing scan protocols with realistic patient-based phantoms.

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The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review

Cited by 44 publications

References 58 publications

Circulating Biomarkers for Therapeutic Monitoring of Anti-cancer Agents

Circulating Biomarkers for Therapeutic Monitoring of Anti-cancer Agents

Radiomics in sarcoma trials: a complement to RECIST for patient assessment

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for validation of computed tomography post-processing and inference algorithms

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