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

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

doi:10.1016/j.ejro.2022.100440

Cited by 5 publications

(2 citation statements)

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“…These 438 robust features were selected based on validations in prior phantom studies [13,14]. This radiomic pipeline have been reported and were effective in identifying malignant renal mass [15], predicting tumor histological grade and cancer stage [16], and assessing CD8+ cell infiltration and PD-L1 expression [7].…”

Section: Ct Image Acquisition and Radiomics Pipelinementioning

confidence: 99%

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Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

Shieh,

Cen,

Varghese

et al. 2023

Oncology

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Introduction: Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering. Results: The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. Conclusion: Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.

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Section: Ct Image Acquisition and Radiomics Pipelinementioning

confidence: 99%

“…However, this has not been investigated in detail for ccRCC. Prior work attempted to correlate ccRCC CT images to PD-L1 expression and CD8+ T cell infiltration using radiomic features [7]. However, these two markers are not sufficient to evaluate the whole complex landscape of the TIME in ccRCC.…”

Section: Introductionmentioning

confidence: 99%