A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis

We used a mathematical approach to investigate the quantitative spatial profile of cancer cells and stroma in lung squamous cell carcinoma tissues and its clinical relevance. The study enrolled 132 patients with 3-5 cm peripheral lung squamous cell carcinoma, resected at the National Cancer Center Hospital East. We utilized machine learning to segment cancer cells and stroma on cytokeratin AE1/3 immunohistochemistry images. Subsequently, a spatial form of Shannon's entropy was employed to precisely quantify the spatial distribution of cancer cells and stroma. This quantification index was defined as the spatial tumor-stroma distribution index (STSDI). The patients were classified as STSDI-low and -high groups for clinicopathological comparison. The STSDI showed no significant association with baseline clinicopathological features, including sex, age, pathological stage, and lymphovascular invasion. However, the STSDI-low group had significantly shorter recurrence-free survival (5-years RFS: 49.5% vs. 76.2%, p < 0.001) and disease-specific survival (5-years DSS: 53.6% vs. 81.5%, p < 0.001) than the STSDI-high group. In contrast, the application of Shannon's entropy without spatial consideration showed no correlation with patient outcomes. Moreover, low STSDI was an independent unfavorable predictor of tumor recurrence and disease-specific death (RFS; HR = 2.668, p < 0.005; DSS; HR = 3.057, p < 0.005), alongside the pathological stage. Further analysis showed a correlation between low STSDI and destructive growth patterns of cancer cells within tumors, potentially explaining the aggressive nature of STSDI-low tumors. In this study, we presented a novel approach for histological analysis of cancer tissues that revealed the prognostic significance of spatial tumor-stroma distribution in lung squamous cell carcinoma.

Prognostic Factors among Patients with Resected Non-Adenocarcinoma of the Lung

INTRODUCTION

Few studies have investigated the prognostic factors for non-adenocarcinoma of the lung. We retrospectively evaluated the prognostic factors on the basis of histological type of non-adenocarcinoma of the lung treated by pulmonary resection.

METHODS

We enrolled 266 patients with non-adenocarcinoma of the lung in this retrospective study: 196 with squamous cell carcinoma (SCC) and 70 with non-SCC.

RESULTS

Relapse-free survival (RFS) did not differ significantly between SCC and non-SCC patients (p = 0.33). For SCC patients, RFS differed significantly between patients who underwent wedge resection and non-wedge resection (p < 0.01) and between patients with Clavien-Dindo grade ≥3a and 0-2 postoperative complications (p < 0.01). For non-SCC patients, RFS rates were significantly different in the groups divided at neutrophil-to-lymphocyte ratio = 2.40 (p = 0.02), maximum standardized uptake value (SUVmax) = 8.39 (p < 0.01), between patients with pathological stage (pStage) 0-I and with pStage more than II (p < 0.01). For SCC patients, male sex (p = 0.04), wedge resection (p = 0.01), and Clavien-Dindo grade ≥3a (p = 0.02) were significant factors for RFS in multivariate analysis. For non-SCC patients, neutrophil-to-lymphocyte ratio >2.40 (p < 0.01), SUVmax >8.39 (p = 0.01), and pStage ≥II (p = 0.03) were significant factors for RFS in multivariate analysis.

CONCLUSION

RFS did not differ significantly differently between SCC and non-SCC patients. It is necessary to perform more than segmentectomy and to avoid severe postoperative complications for SCC patients. SUVmax might be an adaptation criterion of adjuvant chemotherapy for patients with non-adenocarcinoma and non-SCC of the lung.

Metastasis pattern and prognosis of large cell neuroendocrine carcinoma: a population-based study

PURPOSE

As a rare type of tumor, the metastasis pattern of large cell neuroendocrine carcinoma (LCNEC) is still unclear. Our aim was to investigate metastatic patterns and develop a predictive model of prognosis in patients with advanced LCNEC.

METHODS

Patients of LCNEC diagnosed between 2010-2015 from the Surveillance, Epidemiology and End Results (SEER) database were retrospectively included. Chi-square test was used for baseline characteristics analysis. Survival differences were assessed using Kaplan-Meier curves. Independent prognostic factors identified by multivariate Cox proportional risk model were used for the construction of nomogram.

RESULTS

557 eligible patients with metastasis LCNEC (median (IQR), 64 (56 to 72) years; 323 males) were included in this research. Among patients with isolated metastases, brain metastases had the highest incidence (29.4%), and multisite metastases had worse OS (HR: 2.020: 95% CI 1.413-2.888; P < 0.001) and LCSS (HR: 2.144, 95% CI 1.480-3.104; P < 0.001) in all age groups. Independent prognostic indicators including age, race, T stage, N stage, chemotherapy, radiotherapy and metastatic site were used for the construction of nomogram. Concordance index (C-index) and decision-curve analyses (DCAs) showed higher accuracy and net clinical benefit of nomogram compared to the 7th TNM staging system (OS: 0.692 vs 0.555; P < 0.001; LCSS: 0.693 vs 0.555; P < 0.001).

CONCLUSIONS

We firstly established a novel comprehensive nomogram to predict the prognosis of metastasis LCNEC. The prognostic model demonstrated excellent accuracy and predictive performance. Chemotherapy and metastasis pattern were the two strongest predictive variables. Close follow-up of patients with LCNEC is necessary to make individualized treatment decisions according to different metastasis patterns.

Health Equity in Patients Receiving Durvalumab for Unresectable Stage III Non-Small Cell Lung Cancer in the US Veterans Health Administration

BACKGROUND

Real-world evidence is limited regarding the relationship between race and use of durvalumab, an immunotherapy approved for use in adults with unresectable stage III non-small cell lung cancer (NSCLC) post-chemoradiotherapy (CRT). This study aimed to evaluate if durvalumab treatment patterns differed by race in patients with unresectable stage III NSCLC in a Veterans Health Administration (VHA) population.

MATERIALS AND METHODS

This was a retrospective analysis of White and Black adults with unresectable stage III NSCLC treated with durvalumab presenting to any VHA facility in the US from January 1, 2017, to June 30, 2020. Data captured included baseline characteristics and durvalumab treatment patterns, including treatment initiation delay (TID), interruption (TI), and discontinuation (TD); defined as CRT completion to durvalumab initiation greater than 42 days, greater than 28 days between durvalumab infusions, and more than 28 days from the last durvalumab dose with no new durvalumab restarts, respectively. The number of doses, duration of therapy, and adverse events were also collected.

RESULTS

A total of 924 patients were included in this study (White = 726; Black = 198). Race was not a significant factor in a multivariate logistic regression model for TID (OR, 1.39; 95% CI, 0.81-2.37), TI (OR, 1.58; 95% CI, 0.90-2.76), or TD (OR, 0.84; 95% CI, 0.50-1.38). There were also no significant differences in median (interquartile range [IQR]) number of doses (White: 15 [7-24], Black: 18 [7-25]; P = .25) or median (IQR) duration of therapy (White: 8.7 months [2.9-11.8], Black: 9.8 months [3.6-12.0]; P = .08), although Black patients were less likely to experience an immune-related adverse event (28% vs. 36%, P = .03) and less likely to experience pneumonitis (7% vs. 14%, P < .01).

CONCLUSION

Race was not found to be linked with TID, TI, or TD in this real-world study of patients with unresectable stage III NSCLC treated with durvalumab at the VHA.

A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study

Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors.

Prognostic significance of location index in resected T1-sized early-stage non-small cell lung cancer

BACKGROUND

While the central location is a known adverse prognostic factor in lung cancer, a precise definition of central lung cancer has not yet emerged.

PURPOSE

To determine the prognostic significance of central lung cancer (defined by location index) in resected T1-sized early-stage non-small cell lung cancer (NSCLC).

MATERIAL AND METHODS

Patients with resected T1-sized early-stage NSCLC between 2010 and 2015 at a single tertiary cancer center were retrospectively reviewed. Central lung cancer was defined by a location index of the second tertile or less. Kaplan-Meier analysis with log-rank test and multivariable Cox regression analysis were performed to analyze the relationship between central lung cancer and the prognosis of relapse-free survival (RFS) and overall survival (OS). Inter-observer agreement was assessed using Cohen's kappa value and intraclass correlation coefficient (ICC).

RESULTS

Overall, 289 patients (169 men; median age 65 years; interquartile range 58-70 years) were evaluated. Central lung cancer (defined by location index) was adversely associated with RFS (P = 0.005) and OS (P = 0.01). Multivariable Cox regression analysis showed that central lung cancer was independently associated with poor RFS (adjusted hazard ratio 1.91; 95% confidence interval [CI] 1.12-3.24; P = 0.017) and OS (adjusted hazard ratio 1.69; 95% CI 1.04-2.74; P = 0.033). Location index demonstrated excellent inter-observer agreement (Cohen's kappa value 0.88; 95% CI 0.82-0.93) with a high ICC (0.98; 95% CI 0.97-0.98).

CONCLUSION

Central lung cancer defined by a location index of the second tertile or lower is an independent adverse prognostic factor in resected T1-sized early-stage NSCLC.

Development and Validation of Prognostic Nomograms for Lung Squamous Cell Carcinoma With Brain Metastasis in Patients Aged 45 Years or Older: A Population-Based Study

PURPOSE

We aimed to establish nomograms to predict the survival in patients aged ≥45  years with lung squamous cell carcinoma and brain metastasis.

METHODS

We collected patients diagnosed as lung squamous cell carcinoma with brain metastasis aged ≥45 years between 2010 and 2019 from the Surveillance, Epidemiology, and End Results database. Prognostic factors were determined by the univariate and multivariate Cox regression analysis, and then the nomogram was constructed to predict cancer-specific survival and overall survival. Nomograms were evaluated by decision curve analysis, the area under the receiver operating characteristic curve, calibration plot, concordance index, and risk group stratification.

RESULTS

In total, 2437 patients were included, with 1706 and 731 in the cohorts of training and validation, respectively. The age, N stage, T stage, liver metastasis, chemotherapy, bone metastasis, along with radiotherapy were significant in predicting the survival, and adopted for the establishment of nomograms. In the training and validation sets, the concordance index were .713(95%CI:0.699-.728) & .700(95%CI:0.677-.722) in predicting cancer-specific survival and .715(95%CI:0.701-.729) & .712(95%CI:0.690-.735) in predicting overall survival, respectively. Besides, the area under the receiver operating characteristic curve for predicting cancer-specific survival and overall survival in the training set were all >.7 at 1-, 2-, and 3- years. Calibration plots proved the survival predicted by nomograms were consistent with the actual values. decision curve analysis revealed better clinical validity of the nomogram in predicting cancer-specific survival and overall survival at 1-year than TNM staging. Patients were stratified into the high-/low-risk groups according to the optimal cutoff value of 100.21 for cancer-specific survival and 91.98 for overall survival. A web-based probability calculator was constructed finally.

CONCLUSION

Two nomograms were developed for the prognostic prediction of lung squamous cell carcinoma patients with brain metastasis aged ≥45 years, providing guidance for decision-making in clinical practice.

Prognostic value of GPC5 polymorphism rs2352028 and clinical characteristics in Chinese lung cancer patients

Background: GPC5 rs2352028 is associated with the risk of lung cancer, but its relationship with lung cancer prognosis is unclear. Materials & methods: The authors collected blood samples from 888 patients with lung cancer and used a Cox proportional hazards model to analyze the association between prognosis and GPC5 polymorphism rs2352028. Results: GPC5 rs2352028 C > T was associated with a better prognosis. Patients with CT genotype had longer overall survival than those with CC genotype. Additionally, older and early-stage patients with CT + TT genotype had a lower risk of death than those with CC genotype. Conclusion: GPC5 rs2352028 C > T may play a protective role in patients with lung cancer and GPC5 rs2352028 may be a potential genetic marker for lung cancer prognosis.

Construction and validation of prognostic nomograms for elderly patients with metastatic non-small cell lung cancer

BACKGROUND

Metastatic non-small cell lung cancer (NSCLC) is mostly seen in older patients and is associated with poor prognosis. There is no reliable method to predict the prognosis of elderly patients (≥60 years old) with metastatic NSCLC. The aim of our study was to develop and validate nomograms which accurately predict survival in this group of patients.

METHODS

NSCLC patients diagnosed between 2010 and 2015 were all identified from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed by significant clinicopathological variables (p < 0.05) selected in multivariate Cox analysis regression.

RESULTS

A total of 9584 patients met the inclusion criteria and were randomly allocated in the training (n = 6712) and validation (n = 2872) cohorts. In training cohort, independent prognostic factors included age, gender, race, grade, tumor site, pathology, T stage, N stage, radiotherapy, surgery, chemotherapy, and metastatic site (p < 0.05) for lung cancer-specific survival (LCSS) and overall survival (OS) were identified by the Cox regression. Nomograms for predicting 1-, 2-, and 3-years LCSS and OS were established and showed excellent predictive performance with a higher C-index than that of the 7th TNM staging system (LCSS: training cohort: 0.712 vs. 0.534; p < 0.001; validation cohort: 0.707 vs. 0.528; p < 0.001; OS: training cohort: 0.713 vs. 0.531; p < 0.001; validation cohort: 0.710 vs. 0.528; p < 0.001). The calibration plots showed good consistency from the predicted to actual survival probabilities both in training cohort and validation cohort. Moreover, the decision curve analysis (DCA) achieved better net clinical benefit compared with TNM staging models.

CONCLUSIONS

We established and validated novel nomograms for predicting LCSS and OS in elderly patients with metastatic NSCLC with desirable discrimination and calibration ability. These nomograms could provide personalized risk assessment for these patients and assist in clinical decision.

Prognostic Impact of EGFR Amplification and Visceral Pleural Invasion in Early Stage Pulmonary Squamous Cell Carcinomas Patients after Surgical Resection of Primary Tumor

Over the last few decades, an increasing amount of information has been accumulated on biomarkers in non-small cell lung cancer (NSCLC). Despite these advances, most biomarkers have been identified in the adenocarcinoma histological subtype (AC). However, the application of molecular-targeted therapies in the prognosis and treatment of SCC in the clinical setting is very limited, becoming one of the main focus areas in research. Here, we prospectively analyzed the frequency of numerical/structural abnormalities of chromosomes 5, 7, 8, 9, 13 and 22 with FISH in 48 pulmonary SCC patients. From a total of 12 probes, only abnormalities of the 7p12 and 22q12 chromosomal regions were identified as unique genetic variables associated with the prognosis of the disease. The study for these two chromosomal regions was extended to 108 patients with SCC. Overall, chromosome losses were observed more frequently than chromosome gains, i.e., 61% versus 19% of all the chromosome abnormalities detected. The highest levels of genetic amplification were detected for the 5p15.2, 7p12, 8q24 and 22q11 chromosome bands, of which several genes are potentially involved in the pathogenesis of SCC, among others, include the EGFR gene at chromosome 7p12. Patients who displayed EGFR amplification (n = 13; 12%) were mostly older than 65 years (p = 0.07) and exclusively patients in early T-primary tumor stage (pT1−pT2; p = 0.03) with a significantly shortened overall survival (OS) (p ≤ 0.001). Regarding prognosis, the clinical, biological, and histopathologic characteristics of the disease that displayed a significant adverse influence on OS in the univariate analysis included patients older than 65 years (p = 0.02), the presence of lymph node involvement (p = 0.005), metastasis (p = 0.01) and, visceral pleural invasion (VPI) at diagnosis (p = 0.04). EGFR amplification also conferred an adverse impact on patient OS in the whole series (p = 0.02) and especially in patients in early stages (pT1−pT2; p = 0.01). A multivariate analysis of the prognostic factors for OS showed that the most informative combination of independent variables to predict an adverse outcome was the presence of VPI and/or EGFR amplification (p < 0.001). Based on these two variables, a scoring system was built to stratify patients into low- (no adverse features: score 0; n = 69), intermediate- (one adverse feature: score 1; n = 29) and high-risk (two adverse features: score 2; n = 5) groups, with significantly different (p = 0.001) OS rates at 50 months, which were as following: 32%, 28% and 0%, respectively. In the present study, we show that the presence of a high level of 7p12 (EGFR) amplification, exclusively detected in early stage SCC (pT1−pT2), is an independent adverse prognostic factor for OS. The identification of the EGFR gene copy number using FISH techniques may provide a more accurate diagnosis of high-risk populations after the complete resection of the primary tumor. When combined with VPI, three groups of pulmonary SCC were clearly identified that show the extent of the disease. This is of such importance that further prospective studies are necessary in larger series of SCC patients to be classified at the time of diagnosis. This could be achieved with the combined assessment of 7p12 amplification and VPI in primary tumor samples.

Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

Intrapulmonic Cavity or Necrosis on Baseline CT Scan Serves as an Efficacy Predictor of Anti-PD-(L)1 Inhibitor in Advanced Lung Squamous Cell Carcinoma

Background

Predictive markers for guidance and monitoring of immunotherapy in lung squamous cell carcinoma (LSCC) are an interesting topic but have yet to be fully explored. A primary characteristic of LSCC is tumor necrosis that results in extensive immune suppression in patients. We sought to assess whether tumor necrosis or cavity on baseline CT could effectively predict the efficacy of immune checkpoint inhibitors (ICIs) in advanced LSCC.

Methods

Advanced LSCC cases undergoing pre-treatment chest CT imaging and receiving ICIs were retrospectively collected. All CT images were reviewed by an independent chest radiologist blinded to any previous diagnosis to confirm morphological alterations in necrosis or cavity. We performed Logistic regression and developed Cox proportional hazards models to assess the predictive performance of baseline necrosis or cavity characteristics in advanced LSCC. Survival estimates were observed using Kaplan–Meier curves.

Results

Ninety-three patients were eligible for analysis, predominantly consisting of patients with ECOG performance status of 0 or 1 (97.8%), male patients (95.7%), and heavy smokers (92.5%). Intrapulmonic necrosis or cavity on CT scan was present in 52.7% of all patients. Generally, the objective response rate (ORR) in patients with necrosis or cavity to ICI treatment was significantly worse versus those without (30.6% vs 54.5%, p = 0.020), with the subgroup ORRs as follows: ICI monotherapy (necrosis vs non-necrosis: 10.0% vs 36.8%, p =0.047) and ICI combination therapy (44.8% vs 68.0%, p =0.088). Multivariable analysis identified intrapulmonic necrosis or cavity at baseline as a major risk factor for advanced LSCC (HR 4.042, 95% CI1.149–10.908, p = 0.006). Multivariate Cox analysis showed that baseline necrosis or cavity and ICI monotherapy were unfavorable factors for progression-free survival (HR 1.729; 95% CI1.203–2.484, p =0.003).

Conclusion

LSCC patients with intrapulmonic cavity or necrosis on baseline CT scan may respond poorly to anti-PD-(L)1-treatment, monotherapy and combination therapy alike.