Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method

Hatt, Mathieu; Laurent, Baptiste; Fayad, Hadi; Jaouen, Vincent; Visvikis, Dimitris; Rest, Catherine Cheze Le

doi:10.1007/s00259-017-3865-3

Cited by 45 publications

(26 citation statements)

References 29 publications

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“…A and B for visual examples of the MATVs obtained using the two different segmentation approaches). The volume underestimation observed with the use of the 41% fixed threshold is in agreement with previous published studies [29][30][31][32] . This is most likely due to the highly irregular shapes in combination with the heterogeneous nature of uptake for the considered tumors.…”

Section: Radiomics Analysissupporting

confidence: 92%

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Tixier

Cheze-Le-Rest

Schick

et al. 2020

Sci Rep

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Metabolic images from Positron Emission Tomography (PET) are used routinely for diagnosis, follow-up or treatment planning purposes of cancer patients. in this study we aimed at determining if radiomic features extracted from 18 F-Fluoro Deoxy Glucose (FDG) PET images could mirror tumor transcriptomics. In this study we analyzed 45 patients with locally advanced head and neck cancer (H&N) that underwent FDG-PET scans at the time of diagnosis and transcriptome analysis using RnAs from both cancer and healthy tissues on microarrays. Association between pet radiomics and transcriptomics was carried out with the Genomica software and a functional annotation was used to associate PET radiomics, gene expression and altered biological pathways. We identified relationships between PET radiomics and genes involved in cell-cycle, disease, DNA repair, extracellular matrix organization, immune system, metabolism or signal transduction pathways, according to the Reactome classification. Our results suggest that these FDG PET radiomic features could be used to infer tissue gene expression and cellular pathway activity in H&n cancers. these observations strengthen the value of radiomics as a promising approach to personalize treatments through targeting tumor-specific molecular processes. 18 F-FDG Positron emission tomography (PET) imaging is largely used for diagnostic purposes in several cancer types, allowing accurate disease staging 1,2 , but it is also gaining ground for therapy applications, including monitoring treatment response and planning in the field of external beam radiotherapy 3. Although visual interpretation may be sufficient for diagnosis, a (semi)quantitative analysis of PET data for image guided therapy applications is most frequently necessary. The simplest parameter usually extracted from PET images used in clinical practice is the maximum standardized uptake value (SUV max) corresponding to the highest single voxel intensity within a region of interest. On the one hand, SUV max has often been reported as a biomarker with potential for improving overall patient management, including the prediction of response to therapy and survival in several cancers 4-6. On the other hand, numerous studies have shown that the predictive/prognostic value of SUV max can be limited, particularly when using images to characterize tumors on baseline PET images 7-9. For this reason, there has been an increasing interest in extracting additional parameters from 18 F-FDG PET images with the objective of more fully characterizing the entire tumor uptake. Most studies initially focused on the delineation of the metabolically active tumor volume (MATV) and associated SUV measurements (such as the mean value or total lesion glycolysis) 10,11 , while more recent studies have concentrated on parameters characterizing the shape or activity distribution within the tumor. These parameters, today known as radiomic features 14 , include 1 st-order image features such as (cumulative) intensity histograms, geometrical tumor shape descriptors...

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Section: Radiomics Analysissupporting

confidence: 92%

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Tixier

Cheze-Le-Rest

Schick

et al. 2020

Sci Rep

Self Cite

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“…In [53], tumor heterogeneity evaluated by texture features at CT was a significant predictor of overall survival (OS) in NSCLC, but radiotracer uptake was not. CT-and PET-derived heterogeneity were both significant predictors of OS in [73], while Hatt et al [28] found an association between OS and PET sphericity, although their results also showed dependency on lesion volume and method used for segmentation. Positive association with different survival metrics and PET/CT texture features [37,54,73,74] or CT features alone [32,36,53] were also reported in other studies.…”

Section: Prediction Of Survivalmentioning

confidence: 87%

“…In most cases, their objective is to differentiate between round, smooth, and regular lesions from spiculated, elongated, and irregular ones. Apart from volume, common shape features are compactness, elongation, rectangular fit, spherical disproportion, sphericity, surface area, and surface-to-volume ratio [24,28,29]. Clearly, shape features are more easily assessed at CT than PET due to the higher image resolution of the former.…”

Section: Shape Featuresmentioning

confidence: 99%

PET/CT Radiomics in Lung Cancer: An Overview

et al. 2020

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Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.

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“…Results using percentage thresholds have also been reported: for example, outlining 41% (49)(50)(51) or 25% of the SUV max in individual lesions and then summing them to calculate MTV (14,15). More complex image-processing methods, including gradient thresholds based on changes in the intensity of uptake at the edges of lesions (52); sourceto-background-corrected contours (53); and statistical methods such as clustering (54), fuzzy locally adaptive Bayesian (55), and others, have been proposed but not applied much in lymphoma and possibly have no clinical advantage over simpler methods (56).…”

Section: Which Thresholds Should Be Applied To Segment Mtv?mentioning

confidence: 99%

Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden

2019

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See an invited perspective on this article on page 1094. Increased tumor burden is associated with inferior outcomes in many lymphoma subtypes. Surrogates of tumor burden that are easy to measure, such as the maximum tumor dimension of the bulkiest lesion on CT, have been used as prognostic indices for many years. Recently, total metabolic tumor volume (MTV) and tumor lesion glycolysis have emerged as promising and robust biomarkers of outcome in various lymphomas. The median MTV and the optimal cutoffs to separate patients into risk groups in a study population are, however, highly dependent on the population characteristics and the delineation method used to outline tumor on the PET image. This issue has precluded the use of MTV for risk stratification in trials and clinical practice. Standardization of the methodology is timely to allow the potential for risk adaptation to be explored in addition to response adaptation using PET. Meetings between representatives from research groups active in the field were held under the auspices of the PET International Lymphoma and Myeloma Workshop. A summary of those discussions, which included a review of the literature and a practical assessment of methods used for outlining, including various software options, is presented. Finally, a proposal is made to perform a technical validation of MTV measurement enabling benchmark reference ranges to be derived for published delineation approaches used for outlining with various software. This process would require collation of representative imaging data sets of the most common lymphoma subtypes; agreement on pragmatic criteria for the selection of lesions; generation of a range of MTVs, with consensus to be reached on final contours in a training set; and development of automated software solutions with a set of minimum functionalities to reduce measurement variability. Methods developed in the above training exercise could then be applied to another data set, with a final set of contours and values generated. This final data set would provide a benchmark against which end-users could test their ability to measure MTVs that are consistent with expected values. The data set and automated software solutions could be shared with manufacturers with the aim of including these in standard workflows to allow standardization of MTV measurement across the world.

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Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method

Cited by 45 publications

References 29 publications

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

PET/CT Radiomics in Lung Cancer: An Overview

Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden

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