[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab

[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab

Abstract Background Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [ 18 F]FDG PET radiomics signature (iRADIOMICS) predi...

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Bibliographic Details
Published in:Radiology and oncology Vol. 54; no. 3; pp. 285 - 294
Main Authors: Valentinuzzi, Damijan, Vrankar, Martina, Boc, Nina, Ahac, Valentina, Zupancic, Ziga, Unk, Mojca, Skalic, Katja, Zagar, Ivana, Studen, Andrej, Simoncic, Urban, Eickhoff, Jens, Jeraj, Robert
Format: Journal Article
Language:English
Published: Ljubljana Sciendo 29-07-2020
De Gruyter Poland
Subjects:
[18f]fdg pet/ct
anti-PD-1
Biomarkers
Computed tomography
F]FDG PET/CT
Fluorine isotopes
Immune checkpoint inhibitors
Immunotherapy
iRADIOMICS
Lung cancer
Metastases
Metastasis
Monoclonal antibodies
Multivariate analysis
Non-small cell lung carcinoma
non-small-cell lung cancer
Patients
PD-L1 protein
Pembrolizumab
Positron emission tomography
Radiomics
radiomics analysis
Regression analysis
Small cell lung carcinoma
Survival
Targeted cancer therapy
Tumors
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Summary:Abstract Background Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [ 18 F]FDG PET radiomics signature (iRADIOMICS) predicts response of metastatic non-small-cell lung cancer (NSCLC) patients to pembrolizumab better than the current clinical standards. Patients and methods Thirty patients receiving pembrolizumab were scanned with [ 18 F]FDG PET/CT at baseline, month 1 and 4. Associations of six robust primary tumour radiomics features with overall survival were analysed with Mann-Whitney U-test (MWU), Cox proportional hazards regression analysis, and ROC curve analysis. iRADIOMICS was constructed using univariate and multivariate logistic models of the most promising feature(s). Its predictive power was compared to PD-L1 tumour proportion score (TPS) and iRECIST using ROC curve analysis. Prediction accuracies were assessed with 5-fold cross validation. Results The most predictive were baseline radiomics features, e.g. Small Run Emphasis (MWU, p = 0.001; hazard ratio = 0.46, p = 0.007; AUC = 0.85 (95% CI 0.69–1.00)). Multivariate iRADIOMICS was found superior to the current standards in terms of predictive power and timewise with the following AUC (95% CI) and accuracy (standard deviation): iRADIOMICS (baseline), 0.90 (0.78–1.00), 78% (18%); PD-L1 TPS (baseline), 0.60 (0.37–0.83), 53% (18%); iRECIST (month 1), 0.79 (0.62–0.95), 76% (16%); iRECIST (month 4), 0.86 (0.72–1.00), 76% (17%). Conclusions Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab. The predicted non-responders could be offered other treatment options to improve their overall survival.
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ISSN:1581-3207
1318-2099
1581-3207
0485-893X
DOI:10.2478/raon-2020-0042