Prognostic impact of the 18F-fluorodeoxyglucose positron-emission tomography/computed tomography metabolic parameters and correlation with hematological inflammatory markers in lung cancer

The association of metabolic positron emission tomography/computed tomography parameters with survival in small cell lung cancer

BACKGROUND

Small cell lung cancer (SCLC) is a lung malignancy with a poor prognosis and metastases at the time of diagnosis. There is limited experience using positron emission tomography/computed tomography (PET/CT) for SCLC diagnosis, staging, and follow-up.

OBJECTIVE

Investigate the survival effect of primary tumor standardized uptake value max (SUVmax), SUV mean, metabolic tumor volume (MTV), total lesion glucose (TLG), bone marrow SUV (BM), and bone marrow to liver ratio (BLR) in SCLC.

DESIGN

Retrospective.

SETTING

Single center in Turkey.

PATIENTS AND METHODS

Patients who were cyto/histologically diagnosed with SCLC and had PET/CT simultaneous with the diagnosis were included in the study.

MAIN OUTCOME MEASURES

The effect of PET/CT parameters on overall survival (OS) and progression-free survival (PFS).

SAMPLE SIZE

304.

RESULTS

The 5-year OS median value was 14.62 months, and the 5-year PFS was 13.01 months. In Kaplan-Meier analysis, SUVmax, MTV, and TLG were statistically significant variables in OS (P=.03; P<.001; P<.001, respectively). MTV and TLG were significant in PFS (P<.001; P=.0003, respectively). In the multivariate analysis, MTV was an independent PET/CT parameter associated with OS (P=.003), stage of disease (P=.012), SUVmax (P=.003), MTV (P=.016), and TLG (P=.005) were significant variables in PFS.

CONCLUSION

In our study, MTV was an independent parameter that can be used to predict survival in SCLC. Considering the effect of MTV, a metabolic PET/CT parameter on survival, it can be recommended for clinical use as a standard measure of evaluation in PET/CT reports, just like SUVmax.

LIMITATIONS

The first limitation was the single-center and retrospective design of the study. Due to the retrospective design of the study, weight loss, performance status, and smoking history could not be obtained from every patient. Second, inaccurate registration of PET and CT images due to patient respiratory movements may affect measurements.

Development and validation of a prognostic nomogram model in locally advanced NSCLC based on metabolic features of PET/CT and hematological inflammatory indicators

BACKGROUND

We combined the metabolic features of 18F-FDG-PET/CT and hematological inflammatory indicators to establish a predictive model of the outcomes of patients with locally advanced non-small cell lung cancer (LA-NSCLC) receiving concurrent chemoradiotherapy.

RESULTS

A predictive nomogram was developed based on sex, CEA, systemic immune-inflammation index (SII), mean SUV (SUVmean), and total lesion glycolysis (TLG). The nomogram presents nice discrimination that yielded an AUC of 0.76 (95% confidence interval: 0.66-0.86) to predict 1-year PFS, with a sensitivity of 63.6%, a specificity of 83.3%, a positive predictive value of 83.7%, and a negative predictive value of 62.9% in the training set. The calibration curves and DCA suggested that the nomogram had good calibration and fit, as well as promising clinical effectiveness in the training set. In addition, survival analysis indicated that patients in the low-risk group had a significantly longer mPFS than those in the high-risk group (16.8 months versus 8.4 months, P < 0.001). Those results were supported by the results in the internal and external test sets.

CONCLUSIONS

The newly constructed predictive nomogram model presented promising discrimination, calibration, and clinical applicability and can be used as an individualized prognostic tool to facilitate precision treatment in clinical practice.

Immunotherapy for early-stage non-small cell lung cancer: A system review

With the addition of immunotherapy, lung cancer, one of the most common cancers with high mortality rates, has broadened the treatment landscape. Immune checkpoint inhibitors have demonstrated significant efficacy in the treatment of non-small cell lung cancer (NSCLC) and are now used as the first-line therapy for metastatic disease, consolidation therapy after radiotherapy for unresectable locally advanced disease, and adjuvant therapy after surgical resection and chemotherapy for resectable disease. The use of adjuvant and neoadjuvant immunotherapy in patients with early-stage NSCLC, however, is still debatable. We will address several aspects, namely the initial efficacy of monotherapy, the efficacy of combination chemotherapy, immunotherapy-related biomarkers, adverse effects, ongoing randomized controlled trials, and current issues and future directions for immunotherapy in early-stage NSCLC will be discussed here.

A novel analytical approach for outcome prediction in newly diagnosed NSCLC based on [18F]FDG PET/CT metabolic parameters, inflammatory markers, and clinical variables

OBJECTIVES

To develop a novel analytical approach based on ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography (PET) metabolic parameters, serum inflammatory markers, and clinical variables to improve the outcome prediction in NSCLC.

METHODS

A total of 190 newly diagnosed NSCLC patients who underwent pretreatment [¹⁸F]FDG PET/CT were retrospectively enrolled and divided into a training cohort (n = 127) and a test cohort (n = 63). Cox regression analysis was used to investigate the predictive values of PET metabolic parameters, inflammation markers, and clinical variables for progression-free survival (PFS) and overall survival (OS). Based on the results of multivariate analysis, PET-based, clinical, and combined models were constructed. The predictive performance of different models was evaluated using time-dependent ROC curve analysis, Harrell concordance index (C-index), calibration curve, and decision curve analysis.

RESULTS

The combined models incorporating SULmax, MTV, NLR, and ECOG PS demonstrated significant prognostic superiority over PET-based models, clinical models, and TNM stage in terms of both PFS (C-index: 0.813 vs. 0.786 vs. 0.776 vs. 0.678, respectively) and OS (C-index: 0.856 vs. 0.792 vs. 0.781 vs. 0.674, respectively) in the training cohort. Similar results were observed in the test cohort for PFS (C-index: 0.808 vs. 0.764 vs. 0.748 vs. 0.679, respectively) and OS (C-index: 0.836 vs. 0.785 vs. 0.726 vs. 0.660, respectively) prediction. The combined model calibrated well in two cohorts. Decision curve analysis supported the clinical utility of the combined model.

CONCLUSIONS

We reported a novel analytical approach combining PET metabolic information with inflammatory biomarker and clinical characteristics, which could significantly improve outcome prediction in newly diagnosed NSCLC.

KEY POINTS

• The nomogram incorporating SULmax, MTV, NLR, and ECOG PS outperformed the TNM stage for outcome prediction in patients with newly diagnosed NSCLC. • The established nomogram could provide refined prognostic stratification.

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

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 &lt; 0.05). High inflammatory markers were also associated with high MTV and TLG in patients with SCLC (p &lt; 0.005). Moreover, the incidences of co-high metabolic parameters and inflammatory markers were higher in ED-SCLC (p &lt; 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.