Kosin Med J.  2024 Sep;39(3):186-194. 10.7180/kmj.24.103.

Preliminary data on computed tomography-based radiomics for predicting programmed death ligand 1 expression in urothelial carcinoma

• Lee CM
• Hong SB
• Lee NK
• Ha HK
• Kim KH
• Kang BJ
• Kim S
• Ku JY

• KMID: 2559406
• DOI: http://doi.org/10.7180/kmj.24.103

Abstract

Background
Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma.
Methods
Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed.
Results
Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96.
Conclusions
This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Keyword

Computed tomography; Radiomics; Programmed death ligand 1, Urothelial carcinoma; Prediction

Figure

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  Fig. 1. Flow diagram of the inclusion and exclusion criteria. CT, computed tomography; PD-L1, programmed death ligand 1.

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  Fig. 2. Three-dimensional tumor segmentation procedure for urothelial carcinoma of the kidney (A), ureter (B), and bladder (C) from three patients. The segmentation was performed by manually drawing the regions of interest on the entire mass in 90-second delayed computed tomography images.

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  Fig. 3. Radiomics heat map. Clustering of patients with urothelial cancer on the x-axis and computed tomography radiomics-based features on the y-axis.

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  Fig. 4. Receiver operating characteristic (ROC) curve of the radiomics-based model for predicting immunohistochemical positivity for programmed death ligand 1.

Reference

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