The combined role of PET/CT metabolic parameters and inflammatory markers in detecting extensive disease in small cell lung cancer

Abstract

The combined role of inflammatory markers [including neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR), and systemic immune-inflammation index (SII)] and PET/CT metabolic parameters [including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and TLG (total lesion glycolysis)] at baseline in evaluating the binary stage [extensive-stage disease (ED) and limited-stage disease (LD)] of small cell lung cancer (SCLC) is unclear. In this study, we verified that high metabolic parameters and inflammatory markers were related to the binary stage of SCLC patients, respectively (p < 0.05). High inflammatory markers were also associated with high MTV and TLG in patients with SCLC (p < 0.005). Moreover, the incidences of co-high metabolic parameters and inflammatory markers were higher in ED-SCLC (p < 0.05) than those in LD-SCLC. Univariate logistic regression analysis demonstrated that ^Co-high MTV/NLR, ^Co-high MTV/MLR, ^Co-high MTV/SII, ^Co-high TLG/NLR, ^Co-high TLG/MLR, and ^Co-high TLG/SII were significantly related to the binary stage of SCLC patients (p = 0.00). However, only ^Co-high MTV/MLR was identified as an independent predictor for ED-SCLC (odds ratio: 8.67, 95% confidence interval CI: 3.51-21.42, p = 0.000). Our results suggest that co-high metabolic parameters and inflammatory markers could be of help for predicting ED-SCLC at baseline. Together, these preliminary findings may provide new ideas for more accurate staging of SCLC.

Keywords: MTV/MLR; PET/CT; SCLC; inflammatory markers; metabolic parameters.

Figure 1

Receiver operating characteristic (ROC) curves of inflammatory markers for predicting binary stage of SCLC. NLR, PLR, MLR, and SII could predict the binary stage of SCLC. The ROC curve analysis of the NLR to predict ED-SCLC. With an NLR of 2.64 as the threshold, the sensitivity and specificity in the prediction of ED-SCLC were 72.34% and 65.28%, respectively. The AUC was 0.672 (95% confidence interval [CI]: 0.580–0.756; p = 0.0006). The ROC curve analysis of the PLR to predict ED-SCLC. With an PLR of 170.67 as the threshold, the sensitivity and specificity for the prediction of ED-SCLC were 44.68% and 80.56%, respectively. The AUC was 0.628 (95% CI: 0.535–0.715; p = 0.0178). The ROC curve analysis of the MLR to predict ED-SCLC. With an MLR of 0.31 as the threshold, the sensitivity and specificity for the prediction of ED-SCLC were 65.96% and 70.83%, respectively. The AUC was 0.669 (95% CI: 0.577–0.753; p = 0.0010). The ROC curve analysis of the SII to predict ED-SCLC. With an SII of 583.1 as the threshold, the sensitivity and specificity for the prediction of ED-SCLC were 63.83% and 66.67%, respectively. The AUC was 0.646 (95% CI: 0.553–0.731; p = 0.0055).

Figure 2

Receiver operating characteristic (ROC) curves of SUVmean, MTV, and TLG for predicting binary stage of SCLC. The SUVmean, MTV, and TLG could predict tumor stage. The ROC curve analysis of the SUVmean to predict ED-SCLC. With an SUVmean of 7.69 as the threshold, the sensitivity and specificity in the prediction of ED-SCLC were 78.72% and 47.22%, respectively. The AUC was 0.628 (95% CI: 0.535–0.715; p = 0.0166). The ROC curve analysis of the MTV to predict ED-SCLC. With an MTV of 61.36 as the threshold, the sensitivity and specificity in the prediction of ED-SCLC were 82.98% and 66.67%, respectively. The AUC was 0.823 (95% CI: 0.742–0.887; p < 0.0001). The ROC curve analysis of the TLG to predict ED-SCLC. With a TLG of 405.85 as the threshold, the sensitivity and specificity for the prediction of ED-SCLC were 80.85% and 63.89%, respectively. The AUC was 0.779 (95% CI: 0.694–0.850; p < 0.0001).