Dataset Information

Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST.

ABSTRACT: 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.

SUBMITTER: Goldfarb L

PROVIDER: S-EPMC6890907 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST.

Goldfarb Lucas L Duchemann Boris B Chouahnia Kader K Zelek Laurent L Soussan Michael M

EJNMMI research 20190129 1

<h4>Background</h4>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.<h4 ...[more]

PMID: 30694399

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Project description:BackgroundRadiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset.Materials and methodsThe proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.ResultsThe number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).ConclusionWhen performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

Dataset Information

Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST.

Publications

Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST.

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