Impact of a Contralateral Tumor Nodule on Survival in Non-Small-Cell Lung Cancer

A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases

Introduction

The current American Joint Committee on Cancer (AJCC) M1a staging of non‐small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis.

Methods

Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan–Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1‐year and 0.727 for 2‐year OS prediction; validation cohort: 0.737 for 1‐year and 0.734 for 2‐year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1‐year: 0.327; 2‐year: 0.302) and IDI (1‐year: 0.138; 2‐year: 0.130).

Conclusion

Our study established a nomogram for the prediction of 1‐ and 2‐year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare workers to accurately evaluate the individual survival of M1a NSCLC patients. The accuracy and clinical applicability of this nomogram were validated.

Prediction model for hyperprogressive disease in non-small cell lung cancer treated with immune checkpoint inhibitors

BACKGROUND

Hyperprogressive disease (HPD) is a paradoxical acceleration of tumor growth after immune checkpoint inhibitor (ICI) treatment. This study aimed to identify the risk factors and to present a predictive model for HPD in patients treated with ICIs.

METHODS

A total of 78 non-small cell lung cancer (NSCLC) cases, treated with at least two cycles of ICIs who underwent computed tomography (CT) for response assessment were recruited into the study from January 2016 to August 2019. HPD was defined by the following criteria: (i) time-to-treatment failure <2 months; (ii) a 50% increase in the sum of target lesion diameters; (iii) new development of at least two lesions in an already involved organ; (iv) appearance of a new organ lesion; and (v) a decrease in ECOG PS 2.

RESULTS

Of the 78 total patients, 15 (19.2%) had HPD. The risk factors of HPD were age; primary lesion size; and metastases in the contralateral lung, pleura, liver, and bone in multivariable logistic regression (odds ratio [OR]; 0.9038, 1.6619, 28.5913, 23.8264, 14.5711, and 20.1533, respectively, all P-values < 0.05). By using these risk factors, we developed a prediction model for HPD and the area under the receiver operating characteristic curve of the model was 0.9556 (95% confidence interval [CI]: 0.9133-0.9978).

CONCLUSIONS

HPD is relatively common and associated with a grave clinical outcome, requiring a careful monitoring in lung cancer patients treated with ICIs. Moreover, risk factors such as age, size of tumor and number of various metastatic lesions should be taken into consideration before ICI administration.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Age, primary lesion size, and number of metastases are risk factors of HPD. HPD is strongly associated with poor prognosis. HPD during ICI use needs comprehensive monitoring.

WHAT THIS STUDY ADDS

This is the first study to develop a prediction model. The area under the curve of the prediction model for HPD was 0.9556.

Does Lymph Node Metastasis Have a Negative Prognostic Impact in Patients with NSCLC and M1a Disease?

OBJECTIVES

Patients with NSCLC with M1a disease regardless of lymph node status were categorized as stage IV. This study aims to investigate whether the N descriptors in M1a patients could provide clinical information.

METHODS

Overall, 39,731 patients with NSCLC with M1a disease were identified from the Surveillance, Epidemiology, and End Results database during 2005-2012. Lung cancer-specific survival (LCSS) was compared among M1a patients stratified by N stage. A Cox proportional hazards regression model was applied to evaluate the prognostic factors. Statistical analyses were performed in all subgroups.

RESULTS

M1a patients without lymph node involvement had the best LCSS, followed by patients with N1 disease; no difference in LCSS was observed between N2 and N3 disease (N0 versus N1, p < 0.001; N1 versus N2, p < 0.001; and N2 versus N3, p = 0.478). Similarly, this trend was observed when patients were subdivided into two temporal cohorts (2005-2008 and 2009-2012) and also when M1a disease was subdivided into contralateral pulmonary nodules and pleural dissemination (malignant pleural effusion [or pericardial effusion] and pleural nodules). In addition, a difference in LCSS between N2 and N3 disease was observed in patients with malignant pleural nodules (p = 0.003). Multivariate analysis showed that lymph node involvement was an independent prognostic factor for M1a patients, and this result was also noticed in all subgroups.

CONCLUSIONS

These results provide preliminary evidence that lymph node stage may have clinical significance among patients with NSCLC with M1a disease, adding prognostic information.