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. 2021 Feb 15;13(4):814.
doi: 10.3390/cancers13040814.

FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy

Montserrat Carles  1   2   3 , Tobias Fechter  1   2 , Gianluca Radicioni  4 , Tanja Schimek-Jasch  4 , Sonja Adebahr  2   4 , Constantinos Zamboglou  2   4 , Nils H Nicolay  2   4 , Luis Martí-Bonmatí  3 , Ursula Nestle  2   4   5 , Anca L Grosu  2   4 , Dimos Baltas  1   2 , Michael Mix  6 , Eleni Gkika  2   4
Affiliations

FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy

Montserrat Carles et al. Cancers (Basel). .

Abstract

The aim of this study is to identify clinically relevant image feature (IF) changes during chemoradiation and evaluate their efficacy in predicting treatment response. Patients with non-small-cell lung cancer (NSCLC) were enrolled in two prospective trials (STRIPE, PET-Plan). We evaluated 48 patients who underwent static (3D) and retrospectively-respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Predictions of overall survival (OS), local recurrence (LR) and distant metastasis (DM) were evaluated. From 135 IFs, only 17 satisfied the required criteria of being normally distributed across 4D PET and robust between 3D and 4D images. Changes during treatment in the area-under-the-curve of the cumulative standard-uptake-value histogram (δAUCCSH) within primary tumor discriminated (AUC = 0.87, Specificity = 0.78) patients with and without LR. The resulted prognostic model was validated with a different segmentation method (AUC = 0.83) and in a different patient cohort (AUC = 0.63). The quantification of tumor FDG heterogeneity by δAUCCSH during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC.

Keywords: FDG monitoring and retrospectively gated 4D PET/CT; PET radiomics; lung cancer.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Flowchart of the three cohorts: (i) cohort 1 and 3 were employed to identify the IF satisfying the criteria of being normally distributed across 4D PET and robust between 3D and 4D images; (ii) cohort 1 with manual segmentation of the primary tumor was the training cohort to develop the radiomics model for prediction of treatment response; and (iii) cohort 1 with COA (different segmentation) and cohort 2 (different voxel size for 3D image reconstruction and different patients) to validate the radiomics model.
Figure 2
Figure 2
Scheme of the workflow for the first IF selection criteria: IF normal distributed across 4D breathing phases in 70% of the patients.
Figure 3
Figure 3
Prediction of patients with local recurrence based on AUCCSH heterogeneity quantification for patients of cohort 1 with 3D PET during RT (N = 11) and lesions segmented manually (a) and for all patients in cohort 1, that is, including patients with 3D PET after RT and lesions segmented by COA (b).
Figure 3
Figure 3
Prediction of patients with local recurrence based on AUCCSH heterogeneity quantification for patients of cohort 1 with 3D PET during RT (N = 11) and lesions segmented manually (a) and for all patients in cohort 1, that is, including patients with 3D PET after RT and lesions segmented by COA (b).
Figure 4
Figure 4
Example of 3D PET images for patient with (a) and without (b) local recurrence.
See this image and copyright information in PMC

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