[Predicting response to non-small cell lung cancer immunotherapy using pre-treatment contrast-enhanced CT texture-based classification].

Objective: To explore the value of pre-treatment contrast-enhanced computed tomography (CT)-based texture analysis in predicting response to non-small cell lung cancer (NSCLC) immunotherapy. Methods: From January to July 2018, a total of 51 lesions from 42 patients with advanced non-small cell lung cancer receiving immunotherapy at Shanghai Chest Hospital were selected in this retrospective study. Pre-treatment contrast-enhanced CT-based texture features were extracted by MaZda software. Ten optimal texture features were chosen based on three different methods: Fisher coefficient, mutual information measure (MI) and minimization of classification error probability combined average correlation coefficients(POE+ ACC), respectively. According to the efficacy of the first immunotherapy, 51 lesions were divided into non-progressive disease (non-PD, n=26) and progressive disease (PD, n=25). The differences were tested in each texture feature set between the two groups. The immunotherapy effects of target lesions were analyzed by principal component analysis(PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA). The sensitivity, specificity, accuracy, positive-predictive value (PPV) and negative-predictive value (NPV) were calculated. The area under the curve (AUC) was used to quantify the predictive accuracy of the three analysis models for each texture feature set and compare them with the actual classification results. Results: In all of three texture feature sets, the texture parameter differences of Perc.50%, Perc.90%, "S(5, 5)SumEntrp" and "S(4, 4)SumEntrp" were higher in PD group than those in non-PD group (all P<0.05). The classification result of texture feature set chosen by POE+ ACC and analyzed by NDA was identified as the best model (AUC=0.802, 95%CI: 0.674-0.930), and its sensitivity, specificity, accuracy, PPV and NPV were 72%, 88.5%, 80.4%, 85.7%, 76.7%, respectively. Conclusion: Pre-treatment contrast-enhanced CT-based texture characteristics of NSCLC may function as non-invasive biomarkers for the evaluation of response to immunotherapy.

Effect of Schisandra chinensis extract and Ginkgo biloba extract on the pharmacokinetics of talinolol in healthy volunteers.

The authors investigated the effect of herbal medicine Schisandra chinensis extract (SchE) and Ginkgo biloba extract (GBE) on the oral pharmacokinetics of P-glycoprotein substrate talinolol in humans. Twelve healthy male volunteers took a single 100-mg oral dose of talinolol either alone or after pretreatment with 300 mg SchE twice daily or with 120 mg GBE three times daily for 14 days. On day 14, a single 100-mg oral dose of talinolol was administered. Plasma concentrations of talinolol from zero to 24 h were measured by high-performance liquid chromatography. SchE increased the area under the curve (AUC)(0-24) of talinolol by 47% (90% confidence interval (CI), 18-84%; p = 0.010), and GBE by 21% (90% CI = 11-32%; p = 0.002). The C(max) of talinolol increased by 51% (90% CI = 21-89%; p = 0.007) with SchE treatment and by 33% (90% CI = 18-51%; p = 0.002) with GBE treatment, respectively. The t(1/2) of talinolol increased by 7% (90% CI = -4% to 19%; p = 0.320) with SchE treatment and by 11% (90% CI = -12% to 38%; p = 0.436) with GBE treatment, respectively. The results suggest that both SchE and GBE significantly inhibited P-glycoprotein in humans. Patients receiving either SchE or GBE may require dose adjustments when treated with drugs primarily transported by P-glycoprotein.