Estimating survival and clinical outcome in advanced non-small cell lung cancer with bone-only metastasis using molecular markers

Risk Prediction Model for Synchronous Oligometastatic Non-Small Cell Lung Cancer: Thoracic Radiotherapy May Not Prolong Survival in High-Risk patients

Background and Purpose

On the basis of the promising clinical study results, thoracic radiotherapy (TRT)¹ has become an integral part of treatment of synchronous oligometastatic non–small cell lung cancer (SOM-NSCLC). However, some of them experienced rapid disease progression after TRT and showed no significant survival benefit. How to screen out such patients is a more concerned problem at present. In this study, we developed a risk-prediction model by screening hematological and clinical data of patients with SOM-NSCLC and identified patients who would not benefit from TRT.

Materials and Methods

We investigated patients with SOM-NSCLC between 2011 and 2019. A formula named Risk-Total was constructed using factors screened by LASSO-Cox regression analysis. Stabilized inverse probability treatment weight analysis was used to match the clinical characteristics between TRT and non-TRT groups. The primary endpoint was overall survival (OS).

Results

We finally included 283 patients divided into two groups: 188 cases for the training cohort and 95 for the validation cohort. Ten prognostic factors included in the Risk-Total formula were age, N stage, T stage, adrenal metastasis, liver metastasis, sensitive mutation status, local treatment status to metastatic sites, systemic inflammatory index, CEA, and Cyfra211. Patients were divided into low- and high-risk groups based on risk scores, and TRT was found to have improved the OS of low-risk patients (46.4 vs. 31.7 months, P = 0.083; 34.1 vs. 25.9 months, P = 0.078) but not that of high-risk patients (14.9 vs. 11.7 months, P = 0.663; 19.4 vs. 18.6 months, P = 0.811) in the training and validation sets, respectively.

Conclusion

We developed a prediction model to help identify patients with SOM-NSCLC who would not benefit from TRT, and TRT could not improve the survival of high-risk patients.

Construction and Verification of Nomogram Model for Lung Adenocarcinoma With ≤ 5 Bone-Only Metastases Basing on Hematology Markers

Objectives

This retrospective study investigated prognostic factors in advanced lung adenocarcinoma (LUAD) with one to five bone-only metastasis (BOM) and developed a nomogram model to estimate patient survival.

Methods

We investigated patients with advanced LUAD with one to five bone-only metastasis at the initial diagnosis and diagnosed between 2013 and 2019 in two hospitals. A formula named Risk-H was constructed using hematological variables screened by LASSO-Cox regression analysis in the internal set and verified by the external set. Two nomogram models were developed by clinical variables selected by LASSO-Cox regression analysis with or without Risk-H in the internal set. The concordance index (C-index), calibration curves, time-dependent receiver operating characteristic (ROC) analysis, area under the curve (AUC), and decision curve analysis (DCA) were formulated to verify nomogram models. The primary endpoint was overall survival.

Results

We finally included 125 and 69 patients, respectively, in the internal and external sets for analysis. The following were significant hematology prognostic factors and were included in the Risk-H formula: alkaline phosphatase and albumin, leukocyte. Four clinical factors, including loss of weight, sensitive mutation status, T and N stage, with or without Risk-H were used to establish nomogram models. C-index, calibration curves, ROC analysis, AUC, and DCA showed the addition of hematological data improved the predictive accuracy of survival.

Conclusions

Pretreatment peripheral blood indexes may be a meaningful serum biomarker for prognosis in LUAD. The addition of Risk-H to the nomogram model could serve as a more economical, powerful, and practical method to predict survival for LUAD patients with one to five BOM.

Establishment of prognostic nomograms for predicting the progression free survival of EGFR-sensitizing mutation, advanced lung cancer patients treated with EGFR-TKIs

Background

There is a lack of clinically available predictive models for patients with epidermal growth factor receptor (EGFR) mutation positive, advanced non–small cell lung cancer (NSCLC) treated with EGFR‐tyrosine kinase inhibitors (TKIs).

Methods

The clinical data of patients at the Cancer Hospital, Chinese Academy of Medical Sciences between from January 2016 to January 2021 were retrospectively retrieved as training set. The patients from BENEFIT trial were for the validation cohort. The nomogram was built based on independent predictors identified by univariate and multivariate Cox regression analyses. The discrimination and calibration of the nomogram were evaluated by C‐index and calibration plots.

Results

A total of 502 patients with complete clinical data and follow‐up information were enrolled in this study. Five independent prognostic factors, including The Eastern Cooperative Oncology Group Performance Status scale (ECOG PS), EGFR mutation subtype, EGFR co‐mutation, liver metastasis and malignant pleural effusion (p < 0.05). The C‐indexes of the nomogram were 0.694 (95% confidence interval [CI], 0.663–0.725) for the training set and 0.653 (95% CI, 0.610–0.696) for the validation set. The calibration curves for the probabilities of 9‐, 12‐ and 18‐month progression‐free survival (PFS) revealed satisfactory consistency in both the internal and external validations. Additionally, the patients were divided into two groups according to risk (high‐risk, low‐risk), and significant differences in PFS were observed between the groups in the training and external validation cohorts (p < 0.001).

Conclusions

We constructed and validated a convenient nomogram that have the potential to become an accurate and reliable tool for patients with EGFR mutation positive, advanced NSCLC to individually predict their potential benefits from EGFR‐TKIs, and facilitate clinical decision‐making.