Tumor heterogeneity measurement using [18F] FDG PET/CT shows prognostic value in patients with non-small cell lung cancer

Hughes, Niall; Mou, Tian; O’Regan, Kevin; Murphy, Peter; O’Sullivan, Janet; Wolsztynski, Eric; Huang, Jian; Kennedy, Marcus P.; Eary, Janet F.; O’Sullivan, Finbarr

doi:10.1186/s41824-018-0043-1

Cited by 6 publications

(3 citation statements)

References 13 publications

(14 reference statements)

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“…However, whilst clinically useful, a single SUV value cannot capture all of the relevant information within the tumour. Assessing tumour heterogeneity in SUV values in non-small cell lung carcinoma patients, the latter was found to be an independent predictor of overall survival in NSCLC cancer patients in multivariable analysis in a study by Hughes et al [25]. In their study, tumour heterogeneity was evaluated as the percentage variance unexplained in the tumour region-of-interest uptake values using an ellipsoidally contoured model and a homogenous tumour mass whose voxel intensity is greatest at the centre and diminishes in a monotone fashion as one moves radially towards the periphery of the tumour for comparison.…”

Section: Discussionmentioning

confidence: 99%

Principal component analysis of texture features derived from FDG PET images of melanoma lesions

et al. 2022

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Background: The clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the "bouncing beta" phenomenon which could in part explain why in a similar patient population texture features identified and/or cut-off values of prognostic significance differ from one study to another. Principal component analysis (PCA) is a technique for reducing the dimensionality of large datasets containing highly correlated variables, such as texture feature datasets derived from FDG PET images, increasing data interpretability whilst at the same time minimizing information loss by creating new uncorrelated variables that successively maximize variance. Here, we report on PCA of a texture feature dataset derived from 123 malignant melanoma lesions with a significant range in lesion size using the freely available LIFEx software. Results: Thirty-eight features were derived from all lesions. All features were standardized. The statistical assumptions for carrying out PCA analysis were met. Seven principal components with an eigenvalue > 1 were identified. Based on the "elbow sign" of the Scree plot, only the first five were retained. The contribution to the total variance of these components derived using Varimax rotation was, respectively, 30.6%, 23.6%, 16.1%, 7.4% and 4.1%. The components provided summarized information on the locoregional FDG distribution with an emphasis on high FDG uptake regions, contrast in FDG uptake values (steepness), tumour volume, locoregional FDG distribution with an emphasis on low FDG uptake regions and on the rapidity of changes in SUV intensity between different regions. Conclusions:PCA allowed to reduce the dataset of 38 features to a set of 5 uncorrelated new variables explaining approximately 82% of the total variance contained within the dataset. These principal components may prove more useful for multiple regression analysis considering the relatively low numbers of patients usually included in clinical trials on FDG PET texture analysis. Studies assessing the superior differential diagnostic, predictive or prognostic value of principal components derived using PCA as opposed to the initial texture features in clinical relevant settings are warranted.

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Section: Discussionmentioning

confidence: 99%

Principal component analysis of texture features derived from FDG PET images of melanoma lesions

et al. 2022

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“…Brain tumours typically exhibit numerous genetic mutations, spanning several cellular pathways, that open multiple avenues to oncogenesis no single intervention could conceivably block. It is no surprise that patients with higher levels of tumour heterogeneity—ranging across genetic, 4 , 8 , 9 epigenetic, cellular and imaging characteristics 10 , 11 —exhibit both poorer clinical outcomes and weaker responses to therapy. 5 , 12-14 …”

Section: Introductionmentioning

confidence: 99%

Brain tumour genetic network signatures of survival

Ruffle,

Mohinta,

Pombo

et al. 2023

Brain

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Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.

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“…Here we consider the former approach, analyzing the volumetric uptake in function of an idealized ellipsoidal pattern. This was initially built upon clinical experience in sarcoma and has demonstrated prognostic utility in a number of diseases [13], [15] including NSCLC [16], [17]. This spatial modeling technique provides an assessment of the structure of the FDG uptake distribution, and allows deriving other associated variables of interest, such as metabolic gradients [15], [18].…”

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confidence: 99%

Combining Structural and Textural Assessments of Volumetric FDG-PET Uptake in NSCLC

Wolsztynski

O’Sullivan

Hughes

et al. 2019

IEEE Trans. Radiat. Plasma Med. Sci.

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Numerous studies have reported the prognostic utility of texture analyses and the effectiveness of radiomics in PET and PET/CT assessment of nonsmall cell lung cancer (NSCLC). Here we explore the potential, relative to this methodology, of an alternative model-based approach to tumour characterization, which was successfully applied to sarcoma in previous works. The spatial distribution of 3-D FDG-PET uptake is evaluated in the spatial referential determined by the best-fitting ellipsoidal pattern, which provides a univariate uptake profile function of the radial position of intratumoral voxels. A group of structural features is extracted from this fit that include two heterogeneity variables and statistical summaries of local metabolic gradients. We demonstrate that these variables capture aspects of tumour metabolism that are separate to those described by conventional texture features. Prognostic model selection is performed in terms of a number of classifiers, including stepwise selection of logistic models, LASSO, random forests and neural networks with respect to two-year survival status. Our results for a cohort of 93 NSCLC patients show that structural variables have significant prognostic potential, and that they may be used in conjunction with texture features in a traditional radiomics sense, toward improved baseline multivariate models of patient overall survival. The statistical significance of these models also demonstrates the relevance of these machine learning classifiers for prognostic variable selection.

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Tumor heterogeneity measurement using [18F] FDG PET/CT shows prognostic value in patients with non-small cell lung cancer

Cited by 6 publications

References 13 publications

Principal component analysis of texture features derived from FDG PET images of melanoma lesions

Principal component analysis of texture features derived from FDG PET images of melanoma lesions

Brain tumour genetic network signatures of survival

Combining Structural and Textural Assessments of Volumetric FDG-PET Uptake in NSCLC

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