[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

Mattonen, Sarah A.; Davidzon, Guido; Bakr, Shaimaa; Echegaray, Sebastian; Leung, Ann N.; Vasanawala, Minal; Horng, George; Napel, Sandy; Nair, Viswam S.

doi:10.18383/j.tom.2018.00026

Cited by 29 publications

(25 citation statements)

References 29 publications

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“…We collected 71 articles evaluating lung cancer [ 37 , 57 , 58 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 ,…”

Section: Resultsunclassified

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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Section: Resultsunclassified

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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“…The addition of clinicopathological risk factors to the Cox proportional hazards regression models resulted in AUCs of 0.61, 0.64, and 0.65 for the CT, PET, and PET/CT images, respectively. Mattonen et al (44) constructed a Cox proportional hazards model that included stage and an MTV plus penumbra texture 4 | that the area under the curve (AUC) at five time points was obtained on the training and validation cohorts. The AUC for predicting the prognosis from 1-5 years was >0.7.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Yang

Zhong

et al. 2020

Front. Oncol.

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In this study, we developed and validated a radiomics nomogram by combining the radiomic features extracted from 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-FDG PET/CT) images and clinicopathological factors to evaluate the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). Patients and Methods: A total of 315 consecutive patients with NSCLC (221 in the training cohort and 94 in the validation cohort) were enrolled in this study. A total of 840 radiomic features were extracted from the CT and PET images. Three radiomic scores (rad-scores) were calculated using the least absolute shrinkage and selection operator (LASSO) Cox regression based on subsets of CT, PET, and PET/CT radiomic features. A multivariate Cox regression analysis was performed for each rad-score combined with clinicopathological factors to determine the independent risk factors. The OS nomogram was constructed based on the PET/CT rad-score and independent clinicopathological factors. Validation and calibration were conducted to evaluate the performance of the model in the training and validation cohorts, respectively. Results: A total of 144 (45.71%) women and 171 (54.29%) men with NSCLC were enrolled in this study. The PET/CT rad-score combined with the clinical model had the best C-index (0.776 and 0.789 for the training and validation cohorts, respectively). Distant metastasis, stage, carcinoembryonic antigen (CEA), and targeted therapy were independent risk factors for patients with NSCLC. The validation curve Yang et al. PET/CT Radiomics to Predict the Survival of NSCLC showed that the OS nomogram had a strong predictive power in patients' survival. The calibration curve showed that the predicted survival time was significantly close to the observed one. Conclusion: A radiomic nomogram based on 18 F-FDG PET/CT rad-score and clinicopathological factors had good predictive performance for the survival outcome, offering feasible, and practical guidance for individualized management of patients with NSCLC.

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“…In this work we compiled data from four separate studies of PET-CT imaging of NSCLC patients (Furumoto et al, 2018;Mattonen et al, 2019;Chardin et al, 2020;Pérez-García et al, 2020). These studies consisted of pre-treatment PET-CT scans for 535 patients, of which 401 were adenocarcinoma and 134 were squamous cell carcinoma.…”

Section: Establishment Of Metabolic Scalingmentioning

confidence: 99%

Cancer as a Model System for Testing Metabolic Scaling Theory

Brummer

Savage

2021

Front. Ecol. Evol.

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Biological allometries, such as the scaling of metabolism to mass, are hypothesized to result from natural selection to maximize how vascular networks fill space yet minimize internal transport distances and resistance to blood flow. Metabolic scaling theory argues two guiding principles—conservation of fluid flow and space-filling fractal distributions—describe a diversity of biological networks and predict how the geometry of these networks influences organismal metabolism. Yet, mostly absent from past efforts are studies that directly, and independently, measure metabolic rate from respiration and vascular architecture for the same organ, organism, or tissue. Lack of these measures may lead to inconsistent results and conclusions about metabolism, growth, and allometric scaling. We present simultaneous and consistent measurements of metabolic scaling exponents from clinical images of lung cancer, serving as a first-of-its-kind test of metabolic scaling theory, and identifying potential quantitative imaging biomarkers indicative of tumor growth. We analyze data for 535 clinical PET-CT scans of patients with non-small cell lung carcinoma to establish the presence of metabolic scaling between tumor metabolism and tumor volume. Furthermore, we use computer vision and mathematical modeling to examine predictions of metabolic scaling based on the branching geometry of the tumor-supplying blood vessel networks in a subset of 56 patients diagnosed with stage II-IV lung cancer. Examination of the scaling of maximum standard uptake value with metabolic tumor volume, and metabolic tumor volume with gross tumor volume, yield metabolic scaling exponents of 0.64 (0.20) and 0.70 (0.17), respectively. We compare these to the value of 0.85 (0.06) derived from the geometric scaling of the tumor-supplying vasculature. These results: (1) inform energetic models of growth and development for tumor forecasting; (2) identify imaging biomarkers in vascular geometry related to blood volume and flow; and (3) highlight unique opportunities to develop and test the metabolic scaling theory of ecology in tumors transitioning from avascular to vascular geometries.

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[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

Cited by 29 publications

References 29 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Cancer as a Model System for Testing Metabolic Scaling Theory

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