Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images

Tian, Panwen; He, Bing; Mu, Wei; Liu, Kunqin; Liu, Li; Zeng, Hao; Liu, Yujie; Jiang, Lili; Zhou, Ping; Huang, Zhipei; Dong, Di; Liu, Weimin

doi:10.7150/thno.48027

Cited by 81 publications

(54 citation statements)

References 38 publications

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“…More interestingly, our results showed that Delta-radiomics models outperformed pretreatment PD-L1 expression status in predicting response to ICIs in a subset of patients, and the combined model of TL approach had the highest accuracy. So far, the effectiveness of imaging-driven biomarkers with pretreatment CT images for prediction of PD-L1 expression in advanced NSCLC has been tentatively confirmed in several retrospective populations (46,47), which enables investigators to validate the combination of PD-L1 expression signature with Delta-radiomics model for a better patient stratification and management in further prospective trials.…”

Section: Discussionmentioning

confidence: 88%

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer

Liu

Zhang

et al. 2021

Front. Oncol.

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ObjectiveWe aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy.Methods197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction.ResultsRadiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P <0.001).ConclusionEarly response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.

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Section: Discussionmentioning

confidence: 88%

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer

Liu

Zhang

et al. 2021

Front. Oncol.

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“…Some reports described that the MTA treatment induced PD-L1 expression in tumor [ 25 , 26 ]. The expression level of PD-L1 in tumor correlated with better therapeutic response to anti-PD-L1 treatment [ 27 , 28 ]. With respect to safety in the treatment of atezolizumab plus bevacizumab, there was no significant increased AEs even for the MTA-experienced cases.…”

Section: Discussionmentioning

confidence: 99%

Initial Experience of Atezolizumab Plus Bevacizumab for Unresectable Hepatocellular Carcinoma in Real-World Clinical Practice

Iwamoto

Shimose

Noda

et al. 2021

Cancers

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Background: Atezolizumab plus bevacizumab was approved for patients with hepatocellular carcinoma (HCC). Although clinical trials have revealed its efficacy, the outcomes in the real-world clinical practice are unclear. We retrospectively evaluated the efficacy and safety of atezolizumab plus bevacizumab for HCC. Materials and Methods: This is a multicenter study conducted between November 2020 and March 2021. Among the 61 patients, 51 were assessed for progression-free survival (PFS), therapeutic response, and adverse events (AEs). Results: The median PFS was 5.4 months. The objective response rate (ORR) was 35.3%. The disease control rate (DCR) was 86.3%. The incidence rates of AEs at any grade and grade >3 were 98.0% and 29.4%, respectively. The most frequent AE at any grade and grade >3 was hepatic disorder. In patients with a previous history of molecular targeted agent (MTA) or the degree of albumin-bilirubin (ALBI) grade, there were no significant differences in the PFS, ORR, DCR, and incidence rates of AEs. Conclusion: The study demonstrated that atezolizumab plus bevacizumab was effective and safe for patients with HCC even in the real-world setting including patients with a previous MTA history or other than ALBI grade 1.

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“…In patients treated with the anti-PD-1 antibody, by combining PD-L1ES with a clinical model that was constructed using age, sex, smoking history and family history of malignant tumors, the reaction to immunotherapy could be anticipated in a manner more accurate than using PD-L1ES or the clinical model alone as predictors [40]. Accordingly, Tian et al conducted analyses on PD-L1 expression in 939 consecutive stage IIIB-IV NSCLC patients with baseline CT images and found that deep learning on computed tomography images could predict a high expression of PD-L1 (PD-L1 ≥50%), with an AUC of 0.78.…”

Section: Discussionmentioning

confidence: 99%

Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients

Granata

Fusco

Costa

et al. 2021

Cancers

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Purpose: To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. Methods: Seventy-four patients (median age 63 years, range 42–86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36–86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. Results: A total of 38 patients (median age 61; range 41–78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). Conclusions: Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group.

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Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images

Cited by 81 publications

References 38 publications

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer

Initial Experience of Atezolizumab Plus Bevacizumab for Unresectable Hepatocellular Carcinoma in Real-World Clinical Practice

Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients

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