Is There a Prognostic Difference Between Stage IIIA Subgroups in Lung Cancer?

Can stage-IIB lung cancer be divided into subgroups in terms of prognosis? A modelling study. Acta Chir Belg 2024;124:191-199. [PMID: 37615953 DOI: 10.1080/00015458.2023.2251802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

INTRODUCTION

Whether changes should be made to the TNM classification of non-small cell lung cancer (NSCLC) according to the newly proposed nodal classification is unclear. We aim to compare the survival between stage-IIB subsets using a modelling study performed using the newly proposed nodal classification.

PATIENTS AND METHODS

A total of 682 patients with stage-IIB NSCLC based on the 8th TNM classification were analysed. Hazard ratio (HR) values calculated from survival comparisons between stage-IIB subgroups were used to create a model for patients with stage-IIB NSCLC, and modelling was performed according to the HR values that were close to each other.

RESULTS

Patients with T1N1a cancer had the best survival rate (58.2%), whereas the worst prognosis was observed in those with T2bN1b cancer (39.2%). The models were created using the following HR results: Model A (T1N1a, n = 85; 12.4%), Model B (T2a/T2bN1a and T3N0, n = 438; 64.2%), and Model C (T1/T2a/T2bN1b, n = 159; 23.4%). There was a significant difference between the models in terms of overall survival (p = 0.03). The median survival time was 69 months in Model A, 56 months in Model B, and 47 months in Model C (Model A vs. Model B, p = 0.224; Model A vs. Model C, p = 0.01; and Model B vs. Model C, p = 0.04). Multivariate analysis showed that age (p < 0.001), pleural invasion (p < 0.001), and the developed modelling system (p = 0.02) were independently negative prognostic factors.

CONCLUSION

There was a prognostic difference between stage-IIB subsets in NSCLC patients. The model created for stage-IIB lung cancer showed a high discriminatory power for prognosis.

Online decision tools for personalized survival prediction and treatment optimization in elderly patients with lung squamous cell carcinoma: a retrospective cohort study

BACKGROUND

Despite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualized survival prediction and treatment optimization in elderly patients with LSCC.

METHODS

Clinical data of 4564 patients (stageIB-IIIB) diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for prognostic nomograms development. The proposed models were externally validated using a separate group consisting of 1299 patients (stage IB-IIIB) diagnosed from 2012-2015 in China. The prognostic performance was measured using the concordance index (C-index), calibration curves, the average time-dependent area under the receiver operator characteristic curves (AUC), and decision curve analysis.

RESULTS

Eleven candidate prognostic variables were identified by the univariable and multivariable Cox regression analysis. The calibration curves showed satisfactory agreement between the actual and nomogram-estimated Lung Cancer-Specific Survival (LCSS) rates. By calculating the c-indices and average AUC, our nomograms presented a higher prognostic accuracy than the current staging system. Clinical usefulness was revealed by the decision curve analysis. User-friendly online decision tools integrating proposed nomograms were created to estimate survival for patients with different treatment regimens.

CONCLUSIONS

The decision tools for individualized survival prediction and treatment optimization might facilitate clinicians with decision-making, medical teaching, and experimental design. Online tools are expected to be integrated into clinical practice by using the freely available website ( https://loyal-brand-611803.framer.app/ ).

The prognosis of clinical stage IIIa non-small cell lung cancer in Taiwan

Lung cancer is the leading cause of cancer death. The treatment of stage IIIa remained the most controversial of all stages of non-small cell lung cancer (NSCLC). We reported on the heterogenicity and current treatment strategies of stage IIIa NSCLC in Taiwan. This study is a retrospective analysis using data from the Taiwan Society of Cancer Registry between January 2010 and December 2018. 4232 patients with stage IIIa NSCLC were included. Based on cell type, the best 5-year OS (40.40%) occurred among adenocarcinoma victims. The heterogenicity of T1N2 had the best 5-year OS (47.62%), followed by T4N0 (39.82%), and the others. Patients who underwent operations had better 5-year OS (over 50%) than those who did not (less than 30%). Segmentectomy (75.28%) and lobectomy (54.06%) showed better 5-year OS than other surgical methods (less than 50%). In multivariable analysis, young age, female, lower Charlson Comorbidity Index score, adenocarcinoma cell type, well differentiated, T1N2/T4N0 heterogenicity, treatment with operation, and segmentectomy/lobectomy/bilobectomy were significant factors. In conclusions, the heterogenicity of T1N2 had the best outcomes followed by T4N0. Patients received surgical treatment revealed much better outcomes than those did not. As always, multimodal therapies with individualized treatment tend to provide better survival outcomes.

Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma

Background

High levels of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of ¹⁸F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.

Results

Using an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In ¹⁸F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower ¹⁸F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of ¹⁸F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).

Conclusions

A high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00908-9.

A Prediction Model to Optimize Invasive Mediastinal Staging Procedures for Non-Small Cell Lung Cancer in Patients With a Radiologically Normal Mediastinum: The Quebec Prediction Model

BACKGROUND

Current guideline-recommended criteria for invasive mediastinal staging in patients with a radiologically normal mediastinum fail to identify a significant proportion of patients with occult mediastinal disease (OMD), despite it leading to a large number of invasive staging procedures.

RESEARCH QUESTION

Which variables available before surgery predict the probability of OMD in patients with a radiologically normal mediastinum?

STUDY DESIGN AND METHODS

We identified all cTxN0/N1M0 non-small cell lung cancer tumors staged by CT imaging and PET with CT imaging in our institution between 2014 and 2018 who underwent gold standard surgical lymph node dissection or were demonstrated to have OMD before surgery by invasive mediastinal staging techniques and divided them into a derivation and an independent validation cohort to create the Quebec Prediction Model (QPM), which allows calculation of the probability of OMD.

RESULTS

Eight hundred three patients were identified (development set, n = 502; validation set, n = 301) with a prevalence of OMD of 9.1%. The developed prediction model included largest mediastinal lymph node size (P < .001), tumor centrality (P = .23), presence of cN1 disease (P = .29), and lesion standardized uptake value (P = .09). Using a calculated probability of more than 10% as a threshold to identify OMD, this model had a sensitivity, specificity, positive predictive value, and negative predictive value in the derivation cohort of 73.9% (95% CI, 58.9%-85.7%), 81.1% (95% CI, 77.2%-84.6%), 28.3% (95% CI, 23.4%-33.8%), and 96.8% (95% CI, 95.0%-98.1%), respectively. It performed similarly in the validation cohort (P = .77, Hosmer-Lemeshow test; P = .5163, Pearson χ² and unweighted sum-of-squares statistics; and P = .0750, Stukel score test) and outperformed current guideline-recommended criteria in identifying patients with OMD (area under the receiver operating characteristic curve [AUC] for American College of Chest Physicians guidelines criteria, 0.65 [95% CI, 0.59-0.71]; AUC for European Society of Thoracic Surgeons guidelines criteria, 0.60 [95% CI, 0.54-0.67]; and AUC for the QPM, 0.85 [95% CI, 0.80-0.90]).

INTERPRETATION

The QPM allows the clinician to integrate available information from CT and PET imaging to minimize invasive staging procedures that will not modify management, while also minimizing the risk of unforeseen mediastinal disease found at surgery.