Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result

Jiang, Mengmeng; Sun, Dazhen; Guo, Yunxue; Guo, Yansu; Xiao, Jie; Wang, Lisheng; Yao, Xiuzhong

doi:10.1016/j.acra.2019.04.016

Cited by 93 publications

(82 citation statements)

References 38 publications

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“…Chen et al observed that the textural features extracted from 18 F-FDG PET images were predictive factors of PD-L1 in 53 head and neck squamous cell carcinomas (HNSCCs) before radiotherapy. 48 Jiang et al reported that PET/CT radiomics features could be used to assess PD-L1 expression levels in NSCLC, 21 and a similar conclusion was confirmed by Yoon et al in patients with lung adenocarcinoma. 22 Our results demonstrated that radiomics-based features were superior to conventional clinical models in identifying PD-L1 expression, but the mechanism of the relationship between radiomics and underlying driving biology must be validated.…”

Section: Dovepressmentioning

confidence: 66%

“…Previous studies demonstrated that CT features distinguished epidermal growth factor receptor (EGFR), 18 anaplastic lymphoma kinase (ALK) and breast cancer susceptibility gene (BRCA) mutations from the wild type. 19,20 Jiang et al reported that PET/ CT radiomics features were able to assess PD-L1 expression levels in NSCLC, 21 and Yoon et al confirmed these results in patients with lung adenocarcinoma. 22 Liao et al developed and validated a radiomics-based biomarker (Rad score) to predict the infiltration of tumor-infiltrating CD8+TILs in hepatocellular carcinoma (HCC).…”

Section: Introductionmentioning

confidence: 89%

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<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Wen¹,

Yang²,

Zhu³

et al. 2020

OTT

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Background: The present study constructed and validated models to predict PD-L1 and CD8+TILs expression levels in esophageal squamous cell carcinoma (ESCC) patients using radiomics features and clinical factors. Patients and Methods: This retrospective study randomly assigned 220 ESCC patients to a discovery dataset (n= 160) and validation dataset (n= 60). A total of 462 radiomics features were extracted from the segmentation of regions of interest (ROIs) based on pretreatment CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. A multivariable logistic regression analysis was adopted to build radiomics signatures. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of these models. Results: There was no significant difference between the training and validation datasets for any clinical factors in patients with ESCC. The PD-L1 expression level correlated with the differentiation degree (p= 0.011) and tumor stage (p= 0.032). Smoking status (p= 0.043) and differentiation degree (p= 0.025) were associated with CD8+TILs expression levels. The radiomics signatures achieved good performance in predicting PD-L1 and CD8+TILs with AUCs= 0.784 and 0.764, respectively. The combined model showed a favorable predictive ability compared to radiomics signatures or clinical factors alone and improved the AUCs from 0.669 to 0.871 for PD-L1 and from 0.672 to 0.832 for CD8+TILs. These results were verified in the validation dataset with the AUCs of 0.817 and 0.795, respectively. Conclusion: CT-based radiomics features have a potential value for classifying patients according to PD-L1 and CD8+TILs expression levels. The combination of clinical factors and radiomics signatures significantly improved the predictive performance in ESCC.

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Section: Dovepressmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 89%

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Wen¹,

Yang²,

Zhu³

et al. 2020

OTT

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“…In the same way that radiolabeled imaging biomarkers reflect tumor hemodynamic information, perhaps the spectral CT imaging parameters may help to detect PD‐L1 expression and quantitatively reflect the dynamic changes of PD‐L1 expression before, after, or at different checkpoint of treatment, and this may be need to be explored and verified in future research. Additionally, the recent radiomic‐based predictive approach, especially CT‐derived predictive model, may be conducive to anticipate PD‐L1 expression status, particularly in NSCLC patients . Radiomic features from the tumor and its periphery features can provide information on both the tumor and its microenvironment.…”

Section: Discussionmentioning

confidence: 99%

Is there any correlation between spectral CT imaging parameters and PD‐L1 expression of lung adenocarcinoma?

Chen

Shi

et al. 2019

Thoracic Cancer

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Background: The aim of this study was to explore whether spectral computed tomography (CT) imaging parameters are associated with PD-L1 expression of lung adenocarcinoma. Methods: Spectral CT imaging parameters (iodine concentrations [IC] of lesion in arterial phase [ICLa] and venous phase [ICLv], normalized IC [NICa/NICv]normalized to the IC in the aorta, slope of the spectral HU curve [λHUa/λHUv] and enhanced monochromatic CT number [CT40keVa/v, CT70keVa/v] on 40 and 70 keV images) were analyzed in 34 prospectively enrolled lung adenocarcinoma patients with common molecular pathological markers including PD-L1 expression detected with immunohistochemistry. Patients were divided into two groups: positive PD-L1 expression and negative PD-L1 expression groups. Two-sample Mann-Whitney U test was used to test the difference of spectral CT imaging parameters between the two groups. Results: The CT40keVa (127.03 AE 37.92 vs. −54.69 AE 262.04), CT40keVv (124.39 AE 34.71 vs. −45.73 AE 238.97), CT70keVa (49.56 AE 11.76 vs. −136.51 AE 237.08) and CT70keVv (46.13 AE 15.81 vs. −133.10 AE 230.72) parameters in the positive PD-L1 expression group of lung adenocarcinoma were significantly higher than the negative PD-L1 expression group (all P < 0.05). There was no difference detected in IC, NIC and λHU of the arterial and venous phases between both groups (all P > 0.05). Conclusion: CT40keVa, CT40keVv, CT70keVa and CT70keVv were increased in positive PD-L1 expression. These parameters may be used to distinguish the PD-L1 expression state of lung adenocarcinoma.

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“…Radiomics, as an emerging image analysis tool, allows extracting quantitative features noninvasively from digital medical images that enables mineable high-dimensional data to be applied in oncological practice within histological classi cation, lymph node metastasis, treatment response, and prognosis [14][15][16]. As previous studies revealed, the presented radiomic-based signatures from CT and the positron emission tomography (PET)/CT were able to achieve signi cant and robust individualized estimation of speci c PD-L1 status in non-small cell lung cancer (NSCLC) and advanced lung adenocarcinoma, respectively [17,18].…”

Section: Introductionmentioning

confidence: 99%

CT Radiomics and Morphologic Characteristics for Predicting PD-L1 Expression on Tumour Cells and Tumour Infiltrating Lymphocytes in Gastric Cancer

Qiao

et al. 2020

Preprint

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Background To explore CT radiomics and morphologic characteristics for predicting programmed cell death ligand 1 on tumour cells (PD-L1) and tumour infiltrating lymphocytes (PD-L1-TILs) status in gastric cancer (GC). Methods From March 2019 to October 2019, 101 patients identified with GCs who underwent surgery at our hospital were enrolled in this study retrospectively. Radiomic features were extracted from regions of interest manually drawn on venous CT images. Besides, 13 morphologic characteristics were evaluated. The signatures based on radiomics and morphologic characteristics were built using multiple classifiers (Support Vector Machine [SVM], Naive Bayes [NB], Decision Trees [DT], and Random Forest [RF]). Receiver operating characteristic (ROC) curve was performed to assess diagnostic efficiency. Results The adjacent adipose tissue (P = 0.009) and numerous radiomic features (all P < 0.05) differed significantly between GCs with different PD-L1 status. Six radiomic features showed significant differences between different PD-L1-TILs status (all P < 0.05). The highest areas under the ROC curves (AUCs) of signatures generated by classifiers were 0.807 (SVM) and 0.729 (NB) for the prediction of PD-L1 and PD-L1-TILs status, respectively. Conclusions It was promising to predict PD-L1 status in GCs noninvasively using CT radiomics combined with morphologic characteristics. It might help to improve clinical decision making with regard to immunotherapy. However, the prediction for PD-L1-TILs needs to be explored further.

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Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result

Cited by 93 publications

References 38 publications

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Is there any correlation between spectral CT imaging parameters and PD‐L1 expression of lung adenocarcinoma?

CT Radiomics and Morphologic Characteristics for Predicting PD-L1 Expression on Tumour Cells and Tumour Infiltrating Lymphocytes in Gastric Cancer

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