A Model to Predict the Use of Surgical Resection for Advanced-Stage Non-Small Cell Lung Cancer Patients

Distinguishing optimal candidates for primary tumor resection in patients with metastatic lung adenocarcinoma: A predictive model based on propensity score matching

Background

Primary tumor resection is associated with survival benefits in patients with metastatic lung adenocarcinoma (mLUAD). However, there are no established methods to determine which individuals would benefit from surgery. Therefore, we developed a model to predict the patients who are likely to benefit from surgery in terms of survival.

Methods

Data on patients with mLUAD were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Depending on whether surgery was performed on the primary tumor, patients were categorized into two groups: cancer-directed surgery (CDS) and no-cancer-directed surgery (No-CDS). Propensity Score Matching (PSM) was utilized to address bias between the CDS and No-CDS groups. The prognostic impact of CDS was assessed using Kaplan-Meier analysis and Cox proportional hazard models. Subsequently, we constructed a nomogram to predict the potential for surgical benefits based on multivariable logistic regression analysis using preoperative factors.

Results

A total of 89,039 eligible patients were identified, including 6.4% (5705) who underwent surgery. Following PSM, the CDS group demonstrated a significantly longer median overall survival (mOS) compared with the No-CDS group (23 [21-25] vs. 7 [7-8] months; P < 0.001). The nomogram showed robust performance in both the training and validation sets (area under the curve [AUC]: 0.698 and 0.717, respectively), and the calibration curves exhibited high consistency. The nomogram proved clinically valuable according to decision curve analysis (DCA). According to this nomogram, surgical patients were categorized into two groups: no-benefit candidates and benefit candidates groups. Compared with the no-benefit candidate group, the benefit candidate group was associated with longer survival (mOS: 25 vs. 6 months, P < 0.001). Furthermore, no difference in survival was observed between the no-benefit candidates and the no-surgery groups (mOS: 6 vs. 7 months, P = 0.9).

Conclusions

A practical nomogram was developed to identify optimal CDS candidates among patients with mLUAD.

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND

Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC).

METHODS

We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients.

RESULTS

We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions.

CONCLUSION

We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

A nomogram model of postoperative prognosis for metastatic lung adenocarcinoma: A study based on the SEER database

We have observed that patients with metastatic lung adenocarcinoma can obtain survival benefits from surgical resection of the primary tumor. A model was developed to evaluate the prognosis of patients. The patients with metastatic lung adenocarcinoma were identified in the Surveillance, Epidemiology, and End Results database and divided into surgery group and non-surgical group. Through Kaplan-Meier analysis, the survival rate of the non-surgical group was found to be significantly lower no matter before or after propensity score matching. One thousand one hundred and seventy surgical patients were divided into a training group and a verification group. In the training group, univariate and multivariate Cox models were used to explore the prognostic factors, and logistic regression was used to establish a nomogram based on significant predictors. In total, 12,228 patients with metastatic lung adenocarcinoma were recognized; primary tumor surgery accounted for 9.5%. After propensity score matching, the median survival time of 2 groups was significantly different. For the training group, univariate and multivariate COX analysis was conducted, and a nomogram was constructed. Acceptable agreement has been achieved between the predicted and observed survival rates, and the nomogram can divide patients with metastatic lung adenocarcinoma into different risk groups and predict their prognostic survival rate.

Practical Management of Oligometastatic Non-Small-Cell Lung Cancer

Local ablative therapies, including surgery or stereotactic radiotherapy (SABR), are becoming an integral component in the treatment of oligometastatic disease in non-small-cell lung cancer. In this review, we summarize recent randomized evidence supporting progression-free survival and overall survival benefits of local ablation in these patients, as well as upcoming phase III data which should help us better understand the ideal treatment conditions and provide more insight into the oligometastatic state. Since practical management of oligometastatic disease in non-small-cell lung cancer can be challenging, we discuss a modern framework to identify patient, tumor, and treatment characteristics that can best guide management.

Prognostic factors of oligometastatic non-small-cell lung cancer following radical therapy: a multicentre analysis

OBJECTIVES

Patients with oligometastatic non-small-cell lung cancer (NSCLC) may benefit from therapy with curative intent. Our goal was to identify prognostic factors related to better prognosis in a multicentre analysis of patients who underwent surgery of primary tumours in combination with radical treatment of all metastatic sites.

METHODS

We retrospectively reviewed the records of oligometastatic patients who underwent resection of primary tumours at 4 centres (August 2001-February 2018). Oligometastasis was defined as ≤5 synchronous metastases in ≤2 organs. Radical metastatic treatment was surgery, radiotherapy or a combination. The Cox proportional hazards model was used for identification of prognostic factors on overall survival.

RESULTS

We treated 124 patients; 72 (58%) were men, mean age 60 ± 9.8 years, with 87 (70%) adenocarcinoma. Sixty-seven (54%) patients had positive pathologic-N stage (pN). Brain metastases were most common (n = 76; 61%) followed by adrenal (n = 13; 10%) and bone (n = 12; 10%). Systemic therapy was administered in 101 (82%) patients. Median follow-up was 60 months [95% confidence interval (CI) 41-86]. Thirty- and 90-day mortality rates were 0 and 2.4%, respectively. One-, 2-, and 5-year overall survival were 80%, 58% and 36%, respectively. Cox regression analysis showed that patients ≤60 years [hazard ratio (HR) 0.41, 95% CI 0.24, 0.69; P = 0.001] and patients with pN0 (HR 0.38, 95% CI 0.21-0.69; P = 0.002) had a significant survival benefit. The presence of bone metastases negatively affected survival (HR 2.53, 95% CI 1.05-6.09; P = 0.04).

CONCLUSIONS

Treatment with curative intent of selected oligometastatic NSCLC, including resection of the primary tumour, can be performed safely and with excellent 5-year survival rates, especially in younger patients with pN0 disease.

Identifying optimal candidates for primary tumor resection among metastatic non-small cell lung cancer patients: a population-based predictive model

Background

A survival benefit was observed in metastatic non-small cell lung cancer (NSCLC) patients that underwent surgical resection of the primary tumor. We developed a model testing the hypothesis that only certain stage IV patients would benefit from surgery and the potential benefit would vary based on primary tumor characteristics.

Methods

Patients with stage IV NSCLC were identified in the Surveillance, Epidemiology and End Results (SEER) database and then divided into surgery and non-surgery groups. A 1:1 Propensity score matching (PSM) was performed to balance characters. We assumed that patients received primary tumor surgery that lived longer than median cancer specific survival (CSS) time of those who didn't underwent surgery could benefit from the operation. Multivariable Cox model was used to explore the independent factors of CSS in two groups (beneficial and non-beneficial group). Logistic regression was used to build a nomogram based on the significant predictive factors.

Results

A total of 30,342 patients with stage IV NSCLC were identified; 8.03% (2,436) received primary tumor surgery. After PSM, surgical intervention was independently correlated with longer median CSS time (19 vs. 9 months, P<0.001). Among the surgery cohort, 1,374 (56.40%) patients lived longer than 9 months (beneficial group). Differentiated characters (beneficial and non-beneficial group) included age, gender, TNM stage, histologic type, tumor position and differentiation grade, which were integrated as predictors to build a nomogram.

Conclusions

A practical predictive model was created and might be used to identify the optimal candidates for surgical resection of the primary tumor among stage IV NSCLC patients.

Performance Comparison Between SURPAS and ACS NSQIP Surgical Risk Calculator in Pulmonary Resection

BACKGROUND

Accurate preoperative risk assessment is critical for informed decision making. The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) predict risks of common postoperative complications. This study compares observed and predicted outcomes after pulmonary resection between SURPAS and NSQIP SRC.

METHODS

Between January 2016 and December 2018, 2514 patients underwent pulmonary resection and were included. We entered the requisite patient demographics, preoperative risk factors, and procedural details into the online NSQIP SRC and SURPAS formulas. Performance of the prediction models was assessed by discrimination and calibration.

RESULTS

No statistically significant differences were found between the 2 models in discrimination performance for 30-day mortality, urinary tract infection, readmission, and discharge to a nursing or rehabilitation facility. The ability to discriminate between a patient who will develop a complication and a patient who will not was statistically indistinguishable between NSQIP and SURPAS, except for renal failure. With a C index closer to 1.0, the NSQIP performed significantly better than the SURPAS SRC in discriminating risk of renal failure (C index, 0.798 vs 0.694; P = .003). The calibration curves of predicted and observed risk for each model demonstrate similar performance with a tendency toward overestimation of risk, apart from renal failure.

CONCLUSIONS

Overall, SURPAS and NSQIP SRC performed similarly in predicting outcomes for pulmonary resections in this large, single-center validation study with moderate to good discrimination of outcomes. Notably, SURPAS uses a smaller set of input variables to generate the preoperative risk assessment. The addition of thoracic-specific input variables may improve performance.

Pulmonary resection is associated with long-term survival and should remain a therapeutic option in oligometastatic lung cancer

OBJECTIVES

Comprehensive local consolidative therapy led to improved overall survival in oligometastatic non-small cell lung cancer in a recent phase II trial, yet the role of pulmonary resection in ongoing oligometastatic trials is a matter of controversy. We sought to examine outcomes after pulmonary resection with radiotherapy used as a benchmark comparator.

METHODS

Patients treated at a single institution (2000-2017) with cT1-3N0-2M1 non-small cell lung cancer, 3 or less synchronous metastases, and performance status 0 to 1, and who received comprehensive local consolidative therapy were analyzed according to local consolidative therapy modality for the primary lesion. Progression was analyzed with death as a competing risk.

RESULTS

Of 88 patients meeting inclusion criteria, 63 (71.6%) received radiotherapy for local consolidative therapy modality for the primary lesion and 25 (28.4%) underwent surgery (lobectomy 20/25 [80.0%], pneumonectomy 3/25 [12.0%], sublobar 2/25 [8.0%]). Time from diagnosis to local consolidative therapy modality for the primary lesion was similar. Surgical patients were younger and had lower intrathoracic disease burden. Ninety-day post-treatment mortality was low (surgery 0/25 [0.0%], radiotherapy 1/63 [1.6%]). Median postoperative survival time was 55.2 months (95% confidence interval, 20.1 to not reached), with 1- and 5-year overall survivals of 95.7% and 48.0%, respectively. After radiotherapy, median postoperative survival time was 23.4 months (confidence interval, 17.2-35.9); 1- and 5-year overall survivals were 74.3% and 24.2%, respectively. No differences were observed between modalities in site of first failure, cumulative incidence of locoregional failure (P = .635), or systemic progression (P = .747).

CONCLUSIONS

Pulmonary resection is feasible and associated with long-term survival in selected patients with synchronous oligometastatic non-small cell lung cancer. Surgery should remain a local consolidative therapeutic option for patients with operable oligometastatic non-small cell lung cancer enrolled in ongoing and future randomized clinical trials.

Lung Large Cell Neuroendocrine Carcinoma: An Analysis of Patients from the Surveillance, Epidemiology, and End-Results (SEER) Database

Background

The aim of this study was to assess the incidence, clinicopathologic characteristics, prognostic factors, and treatment outcomes in lung large cell neuroendocrine carcinoma (LCNEC).

Material/Methods

Patients diagnosed with lung LCNEC between 2000 and 2013 were identified using the Surveillance, Epidemiology, and End-Results database. Kaplan–Meier methods and univariate and multivariate analyses were used for statistical analysis.

Results

A total of 2097 patients were identified. The total age-adjusted incidence rate of lung LCNEC was 0.3/100 000, with a rise in incidence over the study period. The 5-year lung cancer-specific survival (LCSS) and overall survival (OS) were 20.7% and 16.7%, respectively. Multivariate analysis indicated that age ≥65 years, male sex, advanced tumor stage, advanced nodal stage, not undergoing surgery. and not undergoing chemotherapy were independent adverse indicators for survival outcomes. After stratification by tumor stage, undergoing surgery was associated with more favorable LCSS and OS compared with those without surgery, regardless of tumor stage.

Conclusions

LCNEC is a rare lung cancer subtype with a dismal prognosis. Primary surgical treatment has significant survival benefits, even for stage IV patients. The optimal treatment strategies for lung LCNEC require further investigation.

Survival benefits associated with surgery for advanced non-small cell lung cancer

OBJECTIVE

Overall survival (OS) for advanced stage (IIIA-IV) non-small cell lung cancer (NSCLC) is highly variable, and retrospective data show a survival advantage for patients receiving therapeutic intent pulmonary resection. We hypothesized that this variability in OS can be modeled separately by stage to allow a personalized estimate of OS.

METHODS

In a cohort of patients with advanced-stage NSCLC from the National Cancer Database, we assessed the accuracy of Surgical Selection Score (SSS) to predict OS using Cox proportional hazards models and determined by stage the effect of surgery on survival among people with similarly high levels of SSS.

RESULTS

In total, 300,572 patients were identified; 18,701 (6%) had surgery. The SSS was a strong predictor of OS (C-index, 0.89; 95% confidence interval [CI], 0.89-0.90). We observed significantly greater OS (P < .001) among patients who had surgery. The hazard of death was at least 2 times greater for patients in the upper quartile of SSS who did not receive surgery compared with surgical patients even when adjusting for the SSS (stage IIIA: hazard ratio [HR], 2.1; 95% CI, 2.0-2.2, stage IIIB: HR, 2.3; 95% CI, 2.2-2.5, stage IV: HR, 2.3; 95% CI, 2.2-2.4).

CONCLUSIONS

The SSS is highly predictive of individual OS and can be used as a risk assessment tool. These findings are important for a more robust evaluation of the likely benefits of surgical resection for these patients. After further prospective validation, the SSS can be used during treatment decision-making for patients with advanced-stage NSCLC.