Dataset Information

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

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 (?_{AUC_CSH}) 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 ?_{AUC_CSH} during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC.

SUBMITTER: Carles M

PROVIDER: S-EPMC7919471 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Carles Montserrat M Fechter Tobias T Radicioni Gianluca G Schimek-Jasch Tanja T Adebahr Sonja S Zamboglou Constantinos C Nicolay Nils H NH Martí-Bonmatí Luis L Nestle Ursula U Grosu Anca L AL Baltas Dimos D Mix Michael M Gkika Eleni E

Cancers 20210215 4

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 vari ...[more]

PMID: 33672052

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Project description:Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.

Project description:BACKGROUND:Immunotherapy represents a new therapeutic approach in non-small cell lung carcinoma (NSCLC) with the potential for prolonged benefits. Because of the systemic nature and heterogeneity of tumoral diseases, as well as the immune restoration process induced by immunotherapy, the assessment of therapeutic efficacy is challenging, and the role of FDG PET is not well established. We evaluated the potential of FDG PET to monitor NSCLC patients treated with a checkpoint inhibitor. RESULTS:This was a retrospective analysis of 28 NSCLC patients treated with nivolumab, a programmed cell death 1 (PD-1) blocker. All patients underwent a PET scan before treatment (SCAN-1) and another scan 2 months later (SCAN-2). Disease progression was assessed by immune PET Response Criteria in Solid Tumors (iPERCIST), which was adapted from PERCIST; and the immune Response Evaluation Criteria in Solid Tumors (iRECIST). iPERCIST is a dual-time-point evaluation of "unconfirmed progressive metabolic disease" (UPMD) status at SCAN-2. UPMD at SCAN-2 was re-evaluated after 4 weeks with SCAN-3 to confirm PMD. Patients with complete/partial metabolic response (CMR or PMR) or stable metabolic disease (SMD) at SCAN-2 or -3 were considered responders. Patients with UPMD confirmed at SCAN-3 were considered non-responders. The Kaplan-Meier method was used to estimate survival. At SCAN-2, we found 9/28 cases of PMR, 4/28 cases of SMD, 2/28 cases of CMR, and 13/28 cases of UPMD. Four of the 13 UPMD patients were classified as responders at SCAN-3 (PMR n?=?1, SMD n?=?3). The remaining nine UPMD patients were classified as non-responders due to clinical degradation, and treatment was stopped. The median follow-up was 16.7 months [3.6-32.2]. Responders continued treatment for a mean of 10.7 months [3.8-26.3]. Overall survival was longer for responders than that for non-responders (19.9 vs. 3.6 months, log rank p?=?0.0003). The 1-year survival rates were 94% for responders and 11% for non-responders. A comparison with iRECIST showed reclassification in 39% (11/28) of patients with relevant additional prognostic information. CONCLUSIONS:iPERCIST dual-time-point evaluation might be a powerful tool for evaluating anti-PD-1-based immunotherapy, with the ability to identify patients who can benefit most from treatment. The prognostic value of iPERCIST criteria should be confirmed in large prospective multicentric studies.

Project description:PurposeTumor microenvironment immune types (TMITs) are closely related to the efficacy of immunotherapy. We aimed to assess the predictive ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics of TMITs in treatment-naive patients with non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was performed in 103 patients with NSCLC who underwent 18F-FDG PET/CT scans. The patients were randomly assigned into a training set (n = 71) and a validation set (n = 32). Tumor specimens were analyzed by immunohistochemistry for the expression of programmed death-ligand 1 (PD-L1), programmed death-1 (PD-1), and CD8+ tumor-infiltrating lymphocytes (TILs) and categorized into four TMITs according to their expression of PD-L1 and CD8+ TILs. LIFEx package was used to extract radiomic features. The optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm, and a radiomics signature score (rad-score) was developed. We constructed a combined model based on the clinical variables and radiomics signature and compared the predictive performance of models using receiver operating characteristic (ROC) curves.ResultsFour radiomic features (GLRLM_LRHGE, GLZLM_SZE, SUVmax, NGLDM_Contrast) were selected to build the rad-score. The rad-score showed a significant ability to discriminate between TMITs in both sets (p < 0.001, p < 0.019), with an area under the ROC curve (AUC) of 0.800 [95% CI (0.688-0.885)] in the training set and that of 0.794 [95% CI (0.615-0.916)] in the validation set, while the AUC values of clinical variables were 0.738 and 0.699, respectively. When clinical variables and radiomics signature were combined, the complex model showed better performance in predicting TMIT-I tumors, with the AUC values increased to 0.838 [95% CI (0.731-0.914)] in the training set and 0.811 [95% CI (0.634-0.927)] in the validation set.ConclusionThe FDG-PET/CT-based radiomic features showed good performance in predicting TMIT-I tumors in NSCLC, providing a promising approach for the choice of immunotherapy in a clinical setting.

Dataset Information

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

Publications

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

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