A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors.

Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

[1]  R. Steenbakkers,et al.  The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.

[2]  C. Rubio-Perez,et al.  LIF regulates CXCL9 in tumor-associated macrophages and prevents CD8+ T cell tumor-infiltration impairing anti-PD1 therapy , 2019, Nature Communications.

[3]  H. Aerts,et al.  Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers , 2019, Annals of oncology : official journal of the European Society for Medical Oncology.

[4]  F. Hodi,et al.  Imaging of Cancer Immunotherapy: Current Approaches and Future Directions. , 2019, Radiology.

[5]  K. Buder-Bakhaya,et al.  Biomarkers for Clinical Benefit of Immune Checkpoint Inhibitor Treatment—A Review From the Melanoma Perspective and Beyond , 2018, Front. Immunol..

[6]  Boris Sepesi,et al.  Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer , 2018, Scientific Reports.

[7]  Vanessa M. Hubbard-Lucey,et al.  Comprehensive analysis of the clinical immuno-oncology landscape , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  D. Schadendorf,et al.  Nivolumab for Patients With Advanced Melanoma Treated Beyond Progression: Analysis of 2 Phase 3 Clinical Trials , 2017, JAMA oncology.

[9]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[10]  C. Swanton,et al.  The role of tumour heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome , 2017, BMC Medicine.

[11]  Pornpimol Charoentong,et al.  Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade , 2016, bioRxiv.

[12]  Pornpimol Charoentong,et al.  Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy , 2015, Genome Biology.

[13]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[14]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[15]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[16]  E. Eisenhauer,et al.  Clinical benefit in oncology trials: is this a patient-centred or tumour-centred end-point? , 2009, European journal of cancer.

[17]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.