Investigating the histopathologic correlates of 18F-FDG PET heterogeneity in non-small-cell lung cancer

Bashir, Usman; Foot, Oliver; Wise, Olga; Siddique, Muhammad; McLean, Emma; Billè, Andrea; Cook, Gary

doi:10.1097/mnm.0000000000000925

Cited by 13 publications

(12 citation statements)

References 30 publications

(53 reference statements)

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“…Previous studies have confirmed the correlation between histopathological heterogeneity and intratumoral heterogeneity in FDG distribution or FDG-PET-derived indices [12][13][14]. The coefficient of variation (COV) is a simple and reliable index that has been used in some recent articles [15].…”

Section: Introductionmentioning

confidence: 89%

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

2020

European Journal of Radiology

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The aim of our study was to investigate the value of a simple metabolic heterogeneity parameter, COV (coefficient of variation), by 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the prognosis prediction of central lung cancer in early and locally advanced non-small-cell lung cancer (NSCLC). Methods: Seventy-three patients with NSCLC manifesting as central lung cancer were included retrospectively, and we used the COV to evaluate metabolic heterogeneity. Univariate and multivariate analyses were used to evaluate the predictive value in terms of overall survival (OS) and progression-free survival (PFS). Result: For all 73 patients with pathologically confirmed NSCLC, 69.9 % had SCC, and 30.1 % had ADC or other types of NSCLC. The COV was a statistically significant factor in the univariate analysis for the OS rate. The optimal cut-off value was 23.1366, with sensitivity = 0.737 and specificity = 0.771. The COV values were dichotomized by this value and included with atelectasis in the Cox multivariate analysis. Both COV and atelectasis were independent risk factors for OS as follows: for COV (HR, 3.162, P = 0.0002), the 2-year OS rate was 62.5 % and 26.9 % in the low and high COV groups, respectively. For atelectasis (HR 2.047, P = 0.041), the 2year OS rate was 30.6 % and 65.2 % in the groups with and without atelectasis, respectively (P = 0.017). For PFS, only COV (HR, 2.636, P = 0.001) was a significant predictor. The 2-year PFS rate was 29.7 % in the low COV group and 8% in the high COV group. Conclusion:The pre-treatment metabolic heterogeneity parameter COV is a simple and easy way to predict the OS and PFS of patients with NSCLC manifesting as central lung cancer. Therefore, COV plays an important role in prognostic risk classification in NSCLC. The presence of atelectasis could also be a risk factor for poor prognosis of OS.

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Section: Introductionmentioning

confidence: 89%

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

2020

European Journal of Radiology

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“…In non-small cell lung carcinoma [160], 3D-fractal dimension extracted from 18 FDG-PET/CT images was found to be an independent prognostic factor of disease-free survival, and combining glucose metabolism using SUV with morphological complexity described by fractal dimension improved diagnostic accuracy [161]. Importantly, 18 FDG-PET features, including texture heterogeneity metrics, were found to be associated with histopathological features computed on H&E images, providing a link between radiomic and phenotypic ITH [162].…”

Section: Radiomic Heterogeneitymentioning

confidence: 96%

“…Measures, such as image intensity, contrast, and texture, allow for clear delineation of internal structures, 99m Tc, 111 In and 123 I) and PET (e.g., 11 C and 18 F)], which can be integrated into any organic metabolite. 18 F is used to label glucose analog, 18 [153][154][155][156][157][158][159][160][161][162] a Abbreviations: CA, contrast agents; CNR, contrast-to-noise ratio; CT, computer tomography; DCE, dynamic contrast enhanced; DW, diffusion weighted; MRI, magnetic resonance imaging; PET, positron emission tomography; SNR, signal-to-noise ratio; SPECT, single photon emission computer tomography.…”

Section: Measuring Spatial Molecular and Microenvironmental Heterogen...mentioning

confidence: 99%

Quantification of tumor heterogeneity: from data acquisition to metric generation

Kashyap

Rapsomaniki

Barros

et al. 2022

Trends in Biotechnology

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“…Attempts have been made to correlate the underlying histological and biological features of NSCLC with radiomic features from FDG PET imaging. Correlations have been observed between histopathological mean cell density and lacunarity (large gaps between clusters of cells) and standard FDG parameters, including SUVmean and total lesion glycolysis, first-order statistical features, including kurtosis and skewness, and FDG lacunarity [42]. Another study has shown associations between a number of texture parameters and NSCLC clinical stage and Ki67 IHC analysis of proliferation using k-nearest neighbours and support vector machine (SVM) methods [43].…”

Section: Nsclc: Defining Molecular Mechanisms From Imagingmentioning

confidence: 96%

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

Cook

2019

Eur J Nucl Med Mol Imaging

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While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.

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Investigating the histopathologic correlates of 18F-FDG PET heterogeneity in non-small-cell lung cancer

Abstract: Investigating the histopathologic correlates of 18 F-FDG PET heterogeneity in non-small-cell lung cancer. Nuclear Medicine Communications.

Cited by 13 publications

References 30 publications

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

Quantification of tumor heterogeneity: from data acquisition to metric generation

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

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