Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma

Bino Varghese; Steven Cen; Haris Zahoor; Imran Siddiqui; Manju Aron; Akash Sali; Suhn Rhie; Xiaomeng Lei; Marielena Rivas; Derek Liu; Darryl Hwang; David Quinn; Mihir Desai; Ulka Vaishampayan; Inderbir Gill; Vinay Duddalwar

doi:10.1016/j.ejro.2022.100440

Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma

Eur J Radiol Open. 2022 Sep 2:9:100440. doi: 10.1016/j.ejro.2022.100440. eCollection 2022.

Authors

Affiliations

¹ USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA.
² Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA.
³ Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA.
⁴ Homi Bhabha Cancer Hospital, Department of Pathology, Sangrur, Punjab, India.
⁵ Keck School of Medicine, Department of Molecular Medicine, University of Southern California, Los Angeles, CA, USA.
⁶ Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA.
⁷ Rogel Cancer Center, Urologic Oncology Clinic, University of Michigan, Ann Arbor, MI, USA.

Abstract

Objectives: To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC).

Methods: Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively.

Results: Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively.

Conclusions: CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.

Keywords: CD8-Positive T-Lymphocytes/immunology; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Kidney Neoplasms/pathology; Programmed Cell Death 1 Receptor/immunology.

Abstract

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