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CT-Based Radiomics Predict Platinum-Sensitivity Status in Limited-Stage Small Cell Lung Cancer Patients Treated with Chemoradiotherapy

Q Wen*, J Zhu , X Meng , T Bai , Y Yin , J Yu , Shandong Cancer Hospital Affliated to Shandong University, Shandong University,Jinan,China


(Sunday, 7/29/2018) 3:30 PM - 4:00 PM

Room: Exhibit Hall | Forum 6

Purpose: Theoretically, LS-SCLC patients could be denoted as platinum refractory or platinum sensitive based on shorter or longer relapse times (90 days), respectively. The purpose of this study is to investigate the CT-based radiomics features and clinicopathological parameters to predict platinum-based sensitivity status in LS-SCLC, and explore the significance of radiomics in predicting sensitivity status.

Methods: We retrospectively enrolled 200 LS-SCLC patients who were diagnosed by cytology or histology. Furthermore, patients must receive diagnostic CT scan before anti-cancer treatment and at least one cycle of chemotherapy. Primary tumors were manually delineated on treatment planning system. Radiomics features were high-throughput extracted from regions of primary tumor by in-house software. 508 radiomics features were obtained from diagnostic CT of each tumor, and 127 of these were considered as independent features. LASSO algorithm-based logistic regression was performed for independent predictive factors identification. AUC were applied for radiomics features and clinicopathological parameters predictive ability evaluation.

Results: Among all of patients, 124 patients were platinum sensitive and 76 patients were platinum refractory. LASSO logistic regression model demonstrated that Maximum Probability (AUC=0.648), Long Run Emphasis (AUC=0.665), Maximum 2D diameter Row (AUC=0.67) and Correlation-HHH (AUC=0.738) were correlated with the sensitivity status. Multivariate analysis illustrated that pretreatment NSE level (OR=1.742,95%CI=1.057-2.746,p=0.03), NLR (OR=1.812,95% CI= 1.456-2.273,p<0.001) and response to first-line therapy (OR=0.388,95% CI=0.208-0.741,p=0.003) were independent predictors for platinum-sensitivity status in LS-SCLC. Moreover, ROC analysis implied that the combination of them had a higher AUC (AUC=0.791) than radiomics features (AUC=0.738) or clinicopathological factors (AUC=0.683) alone for assessing platinum-sensitivity status.

Conclusion: Radiomics features could be considered as independent predictive factors for platinum-sensitivity status in LS-SCLC. Compared with traditional clinicopathological factors and laboratory test parameters, features extracted from diagnostic CT performed better for prediction, which might enable a step forward individualized treatment.


CT, Radiosensitivity, Texture Analysis


IM- CT: Quantitative imaging/analysis

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