A Nomogram to Predict Brain Metastases of Resected Non-Small Cell Lung Cancer Patients

Efficacy and safety of immune checkpoint inhibitors for brain metastases of non-small cell lung cancer: a systematic review and network meta-analysis

Background

Previous studies have demonstrated that immune checkpoint inhibitors (ICIs) significantly improve prognosis in lung cancer patients with brain metastases (BMs). This systematic review and network meta-analysis aims to evaluate the efficacy and safety of 10 ICIs recommended by the 2024 Chinese Society of Clinical Oncology guidelines for treating non-small cell lung cancer (NSCLC) without driver genes, focusing on NSCLC patients presenting with BMs.

Materials and methods

A comprehensive literature search of PubMed, Embase, and the Cochrane Library was conducted through June 2024 to identify eligible controlled trials and head-to-head randomized controlled trials investigating 10 ICIs in NSCLC patients with BMs. Pairwise and network meta-analyses were performed using hazard ratios (HRs) and relative risks (RRs) with 95% confidence intervals (CIs). Treatment efficacy was ranked hierarchically through the surface under the cumulative ranking curve (SUCRA).

Results

Sixteen trials from 11 studies, encompassing 1,274 NSCLC patients with BMs, were included. The meta-analysis demonstrated that ICIs significantly improved overall survival (OS: HR, 0.66; 95% CI, 0.52-0.85; P = 0.001) and progression-free survival (PFS: HR, 0.67; 95% CI, 0.54-0.84; P < 0.001). SUCRA ranking identified pembrolizumab as the most effective agent for OS improvement (SUCRA 71%), while camrelizumab showed superior PFS benefits (SUCRA 92%). ICIs were associated with increased objective response rates (RR: 1.52; 95% CI, 1.13-2.06; P = 0.006), but elevated risks of immune-mediated adverse events (RR: 2.50; 95% CI, 1.46-4.30; P = 0.001) and grade 3-5 immune-mediated adverse events and infusion reaction (RR: 6.39; 95% CI, 1.53-26.69; P = 0.011).

Conclusion

ICIs demonstrate superior survival benefits compared to chemotherapy in NSCLC patients with BMs, with pembrolizumab and camrelizumab emerging as optimal choices for OS and PFS improvement, respectively. However, vigilant monitoring of immune-mediated adverse events and infusion reactions remains critical in clinical practice.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer

PURPOSE

To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis.

METHODS

In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram.

RESULTS

Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram.

CONCLUSIONS

Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.

A clinical model to predict brain metastases in resected early-stage non-small cell lung cancer

BACKGROUND

Despite the rising diagnosis of early-stage non-small cell lung cancer (NSCLC), there remains a limited understanding of the risk factors associated with postoperative brain metastases in early-stage NSCLC. Our goal was to identify the risk factors and construct a predictive model for postoperative brain metastases in this population.

METHODS

This study retrospectively enrolled patients with resected stage I-II NSCLC at the Department of Thoracic Surgery, West China Hospital from January 2015 to January 2021. Risk factors were identified through univariable and multivariable Cox regression analyses, followed by the construction of a nomogram. Evaluation of the model involved metrics such as the area under the curve (AUC), C-index, and calibration curves. To ensure reliability, internal validation was performed through bootstrap resampling.

RESULTS

This study included 2106 patients, among whom 67 (3.18%) patients were diagnosed with postoperative brain metastases. Multivariable Cox regression analysis revealed that higher pT and pN stages, along with specific histological subtypes, particularly solid/micropapillary predominant adenocarcinoma, were identified as independent risk factors for brain metastases. The performance of the nomogram in the training set exhibited AUC values of 0.759, 0.788, and 0.782 for predicting 1-year, 2-year, and 3-year occurrences, respectively. Bootstrap resampling validated its reliability, with C-index values of 0.758, 0.799, and 0.792 for the respective timeframes. Calibration curves affirmed consistency of the model.

CONCLUSIONS

A nomogram was developed to predict the likelihood of postoperative brain metastases in individuals with early-stage NSCLC. The tool aids in identifying high-risk patients and facilitating timely interventions.

Clinicopathological characteristics and prognosis of synchronous brain metastases from non-small cell lung cancer compared with metachronous brain metastases

Purpose

Brain metastasis (BM) from non-small cell lung cancer (NSCLC) is a serious complication severely affecting patients' prognoses. We aimed to compare the clinicopathological features and prognosis of synchronous and metachronous BM from NSCLC.

Methods

Clinical data of 461 patients with brain metastases from NSCLC who visited the Cancer Hospital of China Medical University from 2005 to 2017 were retrospectively collected. We analyzed the pathophysiological characteristics of synchronous and metachronous BM from NSCLC and survival rates of the patients. Propensity score matching analysis was used to reduce bias between groups. In addition, we used the Kaplan-Meier method for survival analysis, log-rank test to compare survival rates, and Cox proportional hazards regression model for multivariate prognosis analysis.

Results

Among 461 patients with BM, the number of people who met the inclusion criteria was 400 cases, and after 1:2 propensity score matching,130 had synchronous BM and 260 had metachronous BM. The survival time was longer for metachronous BM in driver mutation-negative patients with squamous cell carcinoma than synchronous BM. Conversely, metachronous and synchronous BM with gene mutations and adenocarcinoma showed no differences in survival time. Multivariate analysis showed that metachronous BM was an independent prognostic factor for overall survival. Furthermore, the pathological type squamous cell carcinoma and Karnofsky Performance Status score <80 were independent risk factors affecting overall survival.

Conclusion

BM status is an independent factor influencing patient outcome. Moreover, synchronous and metachronous BM from NSCLC differ in gene mutation profile, pathological type, and disease progression and hence require different treatments.

A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis

Background

The most serious manifestation of pulmonary cryptococcosis is complicated with cryptococcal meningitis, while its clinical manifestations lack specificity with delayed diagnosis and high mortality. The early prediction of this complication can assist doctors to carry out clinical interventions in time, thus improving the cure rate. This study aimed to construct a nomogram to predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis through a scoring system.

Methods

The clinical data of 525 patients with pulmonary cryptococcosis were retrospectively analyzed, including 317 cases (60.38 %) with cryptococcal meningitis and 208 cases (39.62 %) without cryptococcal meningitis. The risk factors of cryptococcal meningitis were screened by univariate analysis, LASSO regression analysis and multivariate logistic regression analysis. Then the risk factors were incorporated into the nomogram scoring system to establish a prediction model. The model was validated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve.

Results

Fourteen risk factors for cryptococcal meningitis in patients with pulmonary cryptococcosis were screened out by statistical method, including 6 clinical manifestations (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and 8 clinical indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells and B cells). The AUC value was 0.978 (CI 96.2 %∼98.9 %), indicating the nomogram was well verified.

Conclusion

The nomogram scoring system constructed in this study can accurately predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis, which may provide a reference for clinical diagnosis and treatment of patients with cryptococcal meningitis.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model

BACKGROUND

Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model.

METHODS

This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model.

RESULTS

The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05).

CONCLUSIONS

Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma

BACKGROUND

Brain metastasis (BM) are a devastating consequence of lung cancer. This study was aimed to screen risk factors for predicting BM.

METHODS

Using an in vivo BM preclinical model, we established a series of lung adenocarcinoma (LUAD) cell subpopulations with different metastatic ability. Quantitative proteomics analysis was used to screen and identify the differential protein expressing map among subpopulation cells. Q-PCR and Western-blot were used to validate the differential proteins in vitro. The candidate proteins were measured in LUAD tissue samples (n = 81) and validated in an independent TMA cohort (n = 64). A nomogram establishment was undertaken by performing multivariate logistic regression analysis.

RESULTS

The quantitative proteomics analysis, qPCR and Western blot assay implied a five-gene signature that might be key proteins associated with BM. In multivariate analysis, the occurrence of BM was associated with age ≤ 65 years, high expressions of NES and ALDH6A1. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI, 0.881-0.988) in the training set. The validation set showed a good discrimination with an AUC of 0.719 (95% CI, 0.595-0.843).

CONCLUSIONS

We have established a tool that is able to predict occurrence of BM in LUAD patients. Our model based on both clinical information and protein biomarkers will help to screen patient in high-risk population of BM, so as to facilitate preventive intervention in this part of the population.

The Impact of Time Interval on Prognosis in Patients with Non-Small Cell Lung Cancer Brain Metastases After Metastases Surgery

BACKGROUND

Non-small cell lung cancer (NSCLC) is a prominent malignancy often linked to the development of brain metastases (BM), which commonly appear at diverse time intervals (TI) following the lung cancer diagnosis. This study endeavors to determine the prognostic significance of the time interval in patients with NSCLC who undergo BM surgery. Through this investigation, we aim to improve our understanding of the factors impacting the prognosis of BM cases originating from NSCLC.

METHODS

We analyzed data from 74 patients (2011-2021) who underwent BM surgery at our institution. The relationship between various clinical, radiological, and histopathological factors, as well as TI and overall survival (OS), was examined.

RESULTS

The median TI from initial NSCLC diagnosis to BM surgery was 19 months (range: 9-36 months). Notably, a shorter TI of less than 23 months was found to be independently associated with postoperative survival (adjusted odds ratio [aOR] 2.87, 95% confidence interval [CI] 1.03-8.02, P = 0.045). Additionally, a shorter TI was independently correlated with the absence of adjuvant chemotherapy for NSCLC (aOR 0.25, 95% CI 0.07-0.83, P = 0.023) and lack of targeted therapy (aOR 0.02, 95% CI 0.00-0.16, P < 0.001). Late-onset BM (TI ≥ 36 months) was observed in 15 cases (20.3%), in this subgroup, patients aged 60 years or older at the time of lung cancer diagnosis exhibited a significant independent correlation with late-onset BM (aOR 7.24, 95% CI 1.59-32.95, P = 0.011). NSCLC patients who underwent adjuvant chemotherapy displayed a notable correlation with late-onset BM (aOR 6.46, 95% CI 1.52-27.43, P = 0.011), while those who received targeted therapy also exhibited an independent association (aOR 2.27, 95% CI 1.70-3.03, P < 0.001).

CONCLUSIONS

Multiple factors contribute to the variability in the onset interval of BM subsequent to NSCLC diagnosis. The occurrence of BM within TI < 23 months following the initial diagnosis of NSCLC was demonstrated as an independent factor associated with an unfavorable prognosis following BM surgery. Furthermore, patients with NSCLC who did not receive adjuvant chemotherapy and lacked targeted therapy were shown to have an elevated likelihood of developing BM after a long progression-free survival.

Prognostic value of cranial radiotherapy and optimal timing stratified by lung-molGPA for NSCLC patients with brain metastases

PURPOSE

The updated Graded Prognostic Assessment for Lung Cancer Using Molecular Markers (lung-molGPA) index provide more accurate survival prediction for patients diagnose with advanced non-small cell lung cancer (NSCLC) with brain metastases (BM). Given that the value of cranial radiotherapy (CRT) is still controversial for NSCLC patients with BM, this retrospective study aimed to evaluate the value of CRT and optimal timing in NSCLC patients with initial BM after stratified with lung-molGPA index.

METHODS

This study screened NSCLC patients with initial BM in our cancer center from February 2012 to July 2018. The prognosis value of CRT and optimal timing was evaluated with Kaplan-Meier survival analysis and the patients were classified into lung-molGPA0-2 and lung-molGPA2.5-4 group. Upfront CRT was defined as received CRT within 3 months after initial diagnosis and without BM progression, other CRT was classified into deferred CRT.

RESULTS

Overall, 288 patients were enrolled in our study, 156 patients received CRT. The median follow-up time was 47 months. In the entire cohort, the median PFS and OS were 9.2 and 17.0 months, respectively. In the lung-molGPA2.5-4 group, CRT can bring significantly overall survival benefit for NSCLC patients with initial BM (HR: 0.48, 95% CI: 0.34-0.68, P < 0.0001), and the upfront CRT can further expand this survival benefits compared with deferred CRT (HR: 0.49, 95% CI: 0.27-0.89, P = 0.0026). But this phenomenon was not observed in lung-molGPA0-2 group patients.

CONCLUSION

Upfront CRT could bring significantly overall survival benefit for these patients with lung-molGPA2.5-4 but not for patients with lung-molGPA0-2.

18F-FDG PET/CT radiomics predicts brain metastasis in I-IIIA resected Non-Small cell lung cancer

OBJECTIVE

To establish ¹⁸F-FDG PET/CT radiomics model for predicting brain metastasis in non-small cell lung cancer (NSCLC) patients.

METHODS

This research comprised 203 NSCLC patients who had received surgical therapy at two institutions. To identify independent predictive factors of brain metastasis, metabolic indicators, CT features, and clinical features were investigated. A prediction model was established by incorporating radiomics signature and clinicopathological risk variables. The suggested model's performance was assessed from the perspective of discrimination, calibration, and clinical application.

RESULTS

The C-indices of the PET/CT radiomics model in the training, internal validation, and external validation cohorts were 0.911, 0.825 and 0.800, respectively. According to the multivariate analysis, neuron-specific enolase (NSE) and air bronchogram were independent risk factors for brain metastasis (BM). Furthermore, the combined model integrating radiomics and clinicopathological characteristics related to brain metastasis performed better in terms of prediction, with C-indices of 0.927, 0.861, and 0.860 in the training, internal validation, and external validation cohorts, respectively. The decision curve analysis (DCA) suggested that the PET/CT nomogram was clinically beneficial.

CONCLUSIONS

A predictive algorithm based on PET/CT imaging information and clinicopathological features may accurately predict the probability of brain metastasis in NSCLC patients following surgery. This presented doctors with a unique technique for screening NSCLC patients at high risk of brain metastasis.

Clinical characteristics and overall survival prognostic nomogram for metaplastic breast cancer

Background

Metaplastic breast cancer (MBC) is a rare breast tumor and the prognostic factors for survival in patients still remain controversial. This study aims to develop and validate a nomogram to predict the overall survival (OS) of patients with MBC.

Methods

We searched the Surveillance, Epidemiology, and End Results (SEER) database for data about patients including metaplastic breast cancer and infiltrating ductal carcinoma (IDC) from 2010 to 2018. The survival outcomes of patients between MBC and IDC were analyzed and compared with the Kaplan-Meier (KM) method. MBC patients were randomly allocated to the training set and validation I set by a ratio of eight to two. Meanwhile, the performance of this model was validated again by the validation II set, which consisted of MBC patients from the Union Hospital of Fujian Medical University between 2010 and 2018. The independent prognostic factors were selected by univariate and multivariate Cox regression analyses. The nomogram was constructed to predict individual survival outcomes for MBC patients. The discriminative power, calibration, and clinical effectiveness of the nomogram were evaluated by the concordance index (C-index), the receiver operating characteristic (ROC) curve, and the decision curve analysis (DCA).

Results

MBC had a significantly higher T stage (T2 and above accounting for 75.1% vs 39.9%), fewer infiltrated lymph nodes (N0 accounted for 76.2% vs 67.7%), a lower proportion of ER (22.2% vs 81.2%), PR (13.6% vs 71.4%), and HER-2(6.7% vs 17.7%) positive, radiotherapy(51.6% vs 58.0%) but more chemotherapy(67.5% vs 44.7%), and a higher rate of mastectomy(53.2% vs 36.8%), which was discovered when comparing the clinical baseline data between MBC and IDC. Age at diagnosis, T, N, and M stage, as well as surgery and radiation treatment, were all significant independent prognostic factors for overall survival (OS). In the validation I cohort, the nomogram's C-index (0.769 95% CI 0.710 -0.828) was indicated to be considerably higher than the standard AJCC model's (0.700 95% CI 0.644 -0.756). Nomogram's great predictive capability capacity further was supported by the comparatively high C-index of the validation II sets (0.728 95%CI 0.588-0.869).

Conclusions

Metaplastic breast cancer is more aggressive, with a worse clinical prognosis than IDC. This nomogram is recommended for patients with MBC, both American and Chinese, which can help clinicians make more accurate individualized survival analyses.

Brain parenchymal and leptomeningeal metastasis in non-small cell lung cancer

Patients with advanced non-small cell lung cancer (NSCLC) are prone to brain metastases (BM), which essentially include brain parenchymal metastases (PM) and leptomeningeal metastases (LM). We conducted a retrospective study to comprehensively assess the clinical characteristics and risk factors of patients with advanced NSCLC who develop PM and LM. Patients with advanced NSCLC were enrolled. These patients were then divided into three groups for analysis: patients without BM (No-BM), patients with PM and patients with LM. Data on clinical characteristics of each patient at the time of diagnosis advanced NSCLC were extracted and analyzed. In addition, prediction models were developed and evaluated for PM and LM. A total of 592 patients were enrolled in the study. BM was present in 287 patients (48.5%). Among them, 185 and 102 patients had PM or LM. Patients with LM had a higher proportion of EGFR exon 21point mutations (L858R) compared to patients with No-BM and PM (p < 0.0001). The median time to the onset of PM and LM from the diagnosis of advanced NSCLC was 0 months and 8.3 months, respectively. Patients with LM had a statistically shorter over survival (OS) compared to either No-BM or PM patients (p < 0.0001). Based on independent predictive variables, two nomogram models were constructed to predict the development of PM and LM in advanced NSCLC patients, and the C-indexes were 0.656 and 0.767, respectively. Although both considered as BM, PM and LM had different clinical characteristics. And the nomogram showed good performance in predicting LM development, but not PM.

A nomogram for predicting overall survival rate in patients with brain metastatic non-small cell lung cancer

The purpose was to develop a nomogram for the prediction of the 1- and 2-year overall survival (OS) rates in patients with brain metastatic non-small cell lung cancer (BMNSCLC). Patients were collected from the Surveillance Epidemiology and End Results program (SEER) and classified into the training and validation groups. Several independent prognostic factors identified by statistical methods were incorporated to establish a predictive nomogram. The concordance index (C-index), the area under the receiver operating characteristics curve (AUC), and calibration curve were applied to estimate predictive ability of the nomogram. To compare the clinical practicability of the nomogram and TNM staging system by decision curve analysis (DCA). A total of 24,164 eligible patients were collected and assigned into the training (n = 16,916) and validation groups (n = 7248). Based on the prognostic factors, we developed a nomogram with good discriminative ability. The C-indices for training and validation group were 0.727 and 0.728. The AUCs of 1- and 2-year OS rates were both 0.8, and the calibration curves also demonstrated good performance of the nomogram. DCA illustrated that the nomogram provided clinical net benefit compared with the TNM staging system. We developed a predictive nomogram for more accurate and comprehensive prediction of OS in BMNSCLC patients, which can be a useful and convenient tool for clinicians to make proper clinical decisions, and adjust follow-up management strategies.

Establishment of a Nomogram-Based Prognostic Model (LASSO-COX Regression) for Predicting Progression-Free Survival of Primary Non-Small Cell Lung Cancer Patients Treated with Adjuvant Chinese Herbal Medicines Therapy: A Retrospective Study of Case Series

Nowadays, Jin-Fu-Kang oral liquid (JFK), one of Chinese herbal medicines (CHMs) preparations, has been widely used as an adjuvant therapy for primary non-small cell lung cancer (PNSCLC) patients with the syndrome of deficiency of both Qi and Yin (Qi–Yin deficiency pattern) based on Traditional Chinese Medicine (TCM) theory. However, we found insufficient evidence of how long-term CHM treatment influence PNSCLC patients’ progression-free survival (PFS). Thus, using electronic medical records, we established a nomograph-based prognostic model for predicting PNSCLC patients’ PFS involved with JFK supplementary formulas (JFK-SFs) over 6 months, in order to preliminarily investigate potential predictors highly related to adjuvant CHMs therapies in theoretical epidemiology. In our retrospective study, a series of 197 PNSCLC cases from Long Hua Hospital were enrolled by non-probability sampling and divided into 2 datasets at the ratio of 5:4 by Kennard–Stone algorithm, as a result of 109 in training dataset and 88 in validation dataset. Besides, TNM stage, operation history, sIL-2R, and CA724 were considered as 4 highly correlated predictors for modeling based on LASSO-Cox regression. Additionally, we respectively used training dataset and validation dataset for establishment including internal validation and external validation, and the prediction performance of model was measured by concordance index (C-index), integrated discrimination improvement, and net reclassification indices (NRI). Moreover, we found that the model containing clinical characteristics and bio-features presented the best performance by pairwise comparison. Next, the result of sensitivity analysis proved its stability. Then, for preliminarily examination of its discriminative power, all eligible cases were divided into high-risk or low-risk progression by the cut-off value of 57, in the light of predicted nomogram scores. Ultimately, a completed TRIPOD checklist was used for self-assessment of normativity and integrity in modeling. In conclusion, our model might offer crude probability of uncertainly individualized PFS with long-term CHMs therapy in the real-world setting, which could discern the individuals implicated with worse prognosis from the better ones. Nevertheless, our findings were prone to unmeasured bias caused by confounding factors, owing to retrospective cases series.

rhG-CSF is associated with an increased risk of metastasis in NSCLC patients following postoperative chemotherapy

Background

Recombinant human granulocyte colony-stimulating factor (rhG-CSF) reduces neutropenia events and is widely used in cancer patients receiving chemotherapy. However, the effects of rhG-CSF on distant organ metastasis (DOM) in non-small-cell lung cancer (NSCLC) patients following postoperative chemotherapy are not clear.

Methods

A retrospective cohort study was performed on NSCLC patients who underwent complete surgical resection and postoperative systemic chemotherapy at The First Affiliated Hospital of Nanchang University between 1 January 2012 and 31 December 2017. The effect of rhG-CSF on DOM was assessed with other confounding factors using Cox regression analyses.

Results

We identified 307 NSCLC patients who received postoperative systemic chemotherapy (n = 246 in the rhG-CSF group, n = 61 in the No rhG-CSF group). The incidence of DOM in postoperative NSCLC patients with rhG-CSF treatment was observably higher than in patients without rhG-CSF treatment (48.3% vs. 27.9%, p < 0.05). Univariate regression analysis revealed that rhG-CSF and pathological stage were independent risk factors for metastasis-free survival (MFS) (p < 0.05). RhG-CSF users had a higher risk of DOM (adjusted HR: 2.33, 95% CI: 1.31–4.15) than nonusers of rhG-CSF. The association between rhG-CSF and the risk of DOM was significant only in patients presenting with myelosuppression (HR: 3.34, 95% CI: 1.86–6.02) and not in patients without myelosuppression (HR: 0.71, 95% CI: 0.17–2.94, Interaction p-value< 0.01). The risk increased with higher dose density of rhG-CSF compared to rhG-CSF versus no users (p for trend< 0.001).

Conclusion

These analyses indicate that rhG-CSF use is related to DOM following postoperative chemotherapy in NSCLC.

Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events

Objectives

The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients.

Materials and methods

Three thousand eight hundred and six cases of LCSBM were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 using SEER Stat 8.3.5. Lung cancer patients only had brain metastasis with no other organ metastasis were defined as LCSBM patients. Prognostic factors of LCSBM were analyzed with log-rank method and Cox proportional hazards model. Independent risk and protective prognostic factors were used to construct nomogram with accelerated failure time model. C-index was used to evaluate the prediction effect of nomogram.

Results and conclusion

The younger patients (18–65 years old) accounted for 54.41%, while patients aged over 65 accounted for 45.59%.The ratio of male: female was 1:1. Lung cancer in the main bronchus, upper lobe, middle lobe and lower lobe were accounted for 4.91%, 62.80%, 4.47% and 27.82% respectively; and adenocarcinoma accounted for 57.83% of all lung cancer types. The overall median survival time was 12.2 months. Survival rates for 1-, 3- and 5-years were 28.2%, 8.7% and 4.7% respectively. We found female (HR = 0.81, 95% CI 0.75–0.87), the married (HR = 0.80; 95% CI 0.75–0.86), the White (HR = 0.90, 95% CI 0.84–0.95) and primary site (HR = 0.45, 95% CI 0.39–0.52) were independent protective factors while higher age (HR = 1.51, 95% CI 1.40–1.62), advanced grade (HR = 1.19, 95% CI 1.12–1.25) and advanced T stage (HR = 1.09, 95% CI 1.05–1.13) were independent risk prognostic factors affecting the survival of LCSBM patients. We constructed the nomogram with above independent factors, and the C-index value was 0.634 (95% CI 0.622–0.646). We developed a nomogram with seven significant LCSBM independent prognostic factors to provide prognosis prediction.

[Research Progress on Risk Factors of Brain Metastasis in Non-small Cell Lung Cancer]

Brain metastasis of non-small cell lung cancer (NSCLC) is a common treatment failure mode, and the median survival time of NSCLC patients with brain metastasis is only 1 mon-2 mon. Prophylactic cranial irradiation (PCI) can delay the occurrence of brain metastasis, but the survival benefits of NSCLC patients are still controversial. It is particularly important to identify the patients who are most likely to benefit from PCI. This article reviews the high risk factors of brain metastasis in NSCLC.
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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 Nomogram for Predicting Brain Metastasis in IIIA-N2 Non-Small Cell Lung Cancer After Complete Resection: A Competing Risk Analysis

Background

Brain metastasis (BM) is one of the most common failure patterns of pIIIA-N2 non-small cell lung cancer (NSCLC) after complete resection. Prophylactic cranial irradiation (PCI) can improve intracranial control but not overall survival. Thus, it is particularly important to identify the risk factors that are associated with BM and subsequently provide instructions for selecting patients who will optimally benefit from PCI.

Methods and Materials

Between 2011 and 2014, patients with pIIIA-N2 NSCLC who underwent complete resection in our institution were reviewed and enrolled in the study. Clinical characteristics, pathological parameters, treatment mode, BM time, and overall survival were analyzed. A nomogram was built based on the corresponding parameters by Fine and Gray’s competing risk analysis to predict the 1-, 3-, and 5-year probabilities of BM. Receiver operating characteristic curves and calibration curves were chosen for validation. A statistically significant difference was set as P <0.05.

Results

A total of 517 patients were enrolled in our retrospective study. The median follow-up time for surviving patients was 53.2 months (range, 0.50–123.17 months). The median age was 57 (range, 25–80) years. Of the 517 patients, 122 (23.6%) had squamous cell carcinoma, 391 (75.6%) received adjuvant chemotherapy, and 144 (27.3%) received post-operative radiotherapy. The 1-, 3-, and 5-year survival rates were 94.0, 72.9, and 66.0%, respectively. The 1-, 3-, and 5-year BM rates were 5.4, 15.7, and 22.2%, respectively. According to the univariate analysis, female, non-smokers, patients with non-squamous cell carcinoma, bronchial invasion, perineural invasion, and patients who received adjuvant chemotherapy were more likely to develop BM. In a multivariate analysis, non-squamous cell carcinoma (subdistribution hazard ratios, SHR: 3.968; 95% confidence interval, CI: 1.743–9.040; P = 0.0010), bronchial invasion (SHR: 2.039, 95% CI: 1.325–3.139; P = 0.0012), perineural invasion (SHR: 2.514, 95% CI: 1.058–5.976; P = 0.0370), and adjuvant chemotherapy (SHR: 2.821, 95% CI: 1.424–5.589; P = 0.0030) were independent risk factors for BM. A nomogram model was established based on the final multivariable analysis result. The area under the curve was 0.767 (95% CI, 0.758–0.777).

Conclusions

For patients with IIIA-N2 NSCLC after complete resection, a nomogram was established based on clinicopathological factors and treatment patterns for predicting the BM. Based on this nomogram, patients with a high risk of BM who may benefit from PCI can be screened.

Identification of a high-risk group for brain metastases in non-small cell lung cancer patients

PURPOSE

Identification of a high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors.

METHODS

A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured with the AUC/C-statistic, Akaike information criterion, and Brier score.

RESULTS

From 570 patients with NSCLC who met the strict eligibility criteria a TD and VD were randomly assembled, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.001, OR 0.96 (95% CI 0.93-0.98)]; mutational status positive [P 0.027, OR 1.96 (95% CI 1.07-3.56); and carcinoembryonic antigen levels [P 0.016, OR 1.001 (95% CI 1.000-1.003). BM were diagnosed in 24% of the whole cohort. Stratification into a high-risk group after simplification of the model, displayed a frequency of BM of 63% (P < 0.001).

CONCLUSION

A multivariate model comprising age, carcinoembryonic antigen levels, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.

Serum neurofilament light chain or glial fibrillary acidic protein in the diagnosis and prognosis of brain metastases

INTRODUCTION

Brain metastases (BM) remains the most cumbersome disease burden in patients with lung cancer. This study aimed to investigate whether serum brain injury biomarkers can indicate BM, to further establish related diagnostic models, or to predict prognosis of BM.

MATERIALS AND METHODS

This was a prospective study of patients diagnosed with lung cancer with BM (BM group), with lung cancer without BM (NBM group), and healthy participants (control group). Serum neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were detected at baseline. We identified and integrated the risk factors of BM to establish diagnostic models.

RESULTS

A total of 158 patients were included (n = 37, 57, and 64 in the BM, NBM, and control groups, respectively). Serum biomarker levels were significantly higher in the NBM group than in the control group. Higher serum NfL and GFAP concentrations were associated with BM (odds ratios, 3.06 and 1.79, respectively). NfL (area under curve [AUC] = 0.77, p < 0.001) and GFAP (AUC = 0.64, p = 0.02) had diagnostic value for BM. The final diagnostic model included NfL level, age, Karnofsky Performance Status. The model had an AUC value of 0.83 (95% confidence interval [CI] 0.75-0.92). High NfL concentration was correlated with poor overall survival of patients with BM (hazard ratio, 3.31; 95% CI 1.22-9.04; p = 0.019).

CONCLUSION

Serum NfL and GFAP could be potential diagnostic biomarkers for BM in patients with lung cancer. We established a model that can provide individual diagnoses of BM. Higher NfL level may be associated with poor prognosis of patients with BM.

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study

BACKGROUND

The prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis.

METHODS

The 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test.

RESULTS

Univariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group.

CONCLUSIONS

We established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.

Individualized Nomogram for Predicting Survival in Patients with Brain Metastases After Stereotactic Radiosurgery Utilizing Driver Gene Mutations and Volumetric Surrogates

It is well-known that genomic mutational analysis plays a significant role in patients with NSCLC for personalized treatment. Given the increasing use of stereotactic radiosurgery (SRS) for brain metastases (BM), there is an emerging need for more precise assessment of survival outcomes after SRS. Patients with BM and treated by SRS were eligible in this study. The primary endpoint was overall survival (OS). Cox regression models were used to identify independent prognostic factors. A survival predictive nomogram was developed and evaluated by Concordance-index (C-index), area under the curve (AUC), and calibration curve. From January 2016 to December 2019, a total of 356 BM patients were eligible. The median OS was 17.7 months [95% confidence interval (CI) 15.5–19.9] and the actual OS at 1- and 2-years measured 63.2 and 37.6%, respectively. A nomogram for OS was developed by incorporating four independent prognostic factors: Karnofsky Performance Score, cumulative tumor volume, gene mutation status, and serum lactate dehydrogenase. The nomogram was validated in a separate cohort and demonstrated good calibration and good discriminative ability (C-index = 0.780, AUC = 0.784). The prognostic accuracy of the nomogram (0.792) was considerably enhanced when compared with classical prognostic indices, including the Graded Prognostic Assessment (0.708), recursive partitioning analysis (0.587), and the SRS (0.536). Kaplan–Meier curves showed significant differences in OS among the stratified low-, median- and high-risk groups (P < 0.001). In conclusion, we developed and validated an individualized prognostic nomogram by integrating physiological, volumetric, clinical chemistry, and molecular biological surrogates. Although this nomogram should be validated by independent external study, it has a potential to facilitate more precise risk-stratifications to guide personalized treatment for BM.

Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors

BACKGROUND

Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice.

OBJECTIVE

The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables.

METHODS

Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value.

RESULTS

The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646.

CONCLUSION

We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.

Clinical characteristics and overall survival prognostic nomogram for invasive cribriform carcinoma of breast: a SEER population-based analysis

BACKGROUND

The prognositc factors in patient with invasive cribriform carcinoma (ICC) of breast is still remain controversal. The study aims to establish a nomogram to predict the survival outcomes in patients with ICC based on the Surveillance, Epidemiology and End Results (SEER) database.

METHODS

We retrieved SEER database for clinical data about patients including ICC and infiltrating ductal carcinoma (IDC) from 2004 to 2015. Kaplan-Meier survival was used to compare the difference survival outcomes between ICC and IDC. ICC patients were randomly allocated to training cohort and validation cohort. A nomogram was built to predict individual patient's 3-year and 5-year survival status for ICC. The established TMN model and the newly established nomogram was further evaluated by the concordance index (C-index) and the decision curve analysis (DCA).

RESULTS

Comparing the baseline clinical data between IDC and ICC, a significant of smaller tumor mass, less infiltrated lymph nodes, lower metastases rate, better tumor differentiation degree, higher proportion of estrogen receptor (ER) and progesterone receptor (PR) positive and lower rate of chemotherapy and radiotherapy was found in ICC. Age at diagnosis, marriage status, tumor location, T stage, M stage, ER status, surgery were independent significant prognostic factors for the overall survival (OS). A significantly higher C-index was found in nomogram compared with established TNM model in validation cohort.

CONCLUSIONS

The prognosis of ICC patients is better than that of IDC patients. The nomogram is recommended for future patient with ICC to survival analysis.

Can we define any marker associated with brain failure in patients with locally advanced non-small cell lung cancer?

PURPOSE

To define the factors which may be related to brain metastasis (BM) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) who developed brain metastases after definitive treatment.

PATIENTS AND METHODS

A total of 208 patients with LA-NSCLC, without BM who received definitive radiotherapy (RT) or RT+chemotherapy (CT) between January 2005 and January 2016 were evaluated retrospectively. Platelet, neutrophil, lymphocyte counts, LDH, CRP, Hb levels, neutrophil-to-lymphocyte radio (NLR), platelet-to-lymphocyte radio (PLR), advanced lung cancer inflammation index (ALI) and FDG-PET/CT parameters (SUVmax of the primary tumor and mediastinal lymph nodes), and patient characteristics were evaluated for brain metastasis free survival (BMFS).

RESULTS

Median follow-up duration was 25 months (range: 3-130months). Cut-off values for platelet, NLR, PLR, LDH, CRP, and Hb were 290×103/μL, 2.6, 198, 468 IU/L, 2.5mg/dL, and 11.5g/dl. We defined each parameter as low or high according to the cut-off values. 56 patients (26.9%) developed brain metastases during follow-up. In univariate analysis, high NLR (P=0.001), PLR (P=0.037), LDH (P=0.028), CRP (P=0.002) values, value ≥7.5 for lymph nodes (P=0.005) and low ALI value (P=0.002) were poor prognostic factors for BMFS. In multivariate analysis, high NLR (P=0.022), PLR (P=0.017), CRP (P=0.006), stage ≥IIIB disease (P<0.001), multi-stational N2 disease (P=0.036), adenocarcinoma histology (P<0.001) and SUVmax value ≥7.5 (P=0.035) were poor prognostic factors for BMFS.

CONCLUSIONS

High NLR, PLR, LDH, CRP values, SUVmax values for lymph nodes, and low ALI which indicates high tumor burden were additional prognostic factors besides stage, histology, and lymph node status.

The construction and validation of the model for predicting the incidence and prognosis of brain metastasis in lung cancer patients

BACKGROUND

Brain metastasis (BM) causes high morbidity and mortality rates in lung cancer (LC) patients. The present study aims to develop models for predicting the development and prognosis of BM using a large LC cohort.

METHODS

A total of 266,522 LC cases diagnosed between 2010 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program cohort. Risk factors for developing BM and prognosis were calculated by univariable and multivariable logistic and Cox regression analysis, respectively, and nomograms were constructed based on risk factors. Nomogram performance was evaluated with receiver operating characteristics (ROC) curve, or C-index and calibration curve.

RESULTS

The prevalence of BM was 13.33%. Associated factors for developing BM include: advanced age; Asian or Pacific Islander race; uninsured status; primary tumor site; higher T stage; higher N stage; poorly differentiated grade; the presence of lung, liver, and bone metastases; and adenocarcinoma histology. Median overall survival (OS) was 4 months; associated prognosis factors were similar to risk factors plus female gender, unmarried status, and surgery. The calibration curve showed good agreement between predicted and actual probability, and the AUC/C-index was 73.1% (95% CI: 72.6-73.6%) and 0.88 (95% CI: 0.87-0.89) for risk and prognosis predictive models, respectively.

CONCLUSIONS

BM was highly developed in LC patients, and homogeneous and heterogeneous factors were found between risk and prognosis for BM. The nomogram showed good performance in predicting BM development and prognosis.

CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer

PURPOSE

Brain metastasis (BM) is the primary first failure pattern in patients with curatively resected locally advanced non-small cell lung cancer (LA-NSCLC). It is not yet possible to accurately predict the occurrence of BM. The purpose of the research is to develop and validate a prediction model of BM-free survival based on radiomics characterising the primary lesions combined with clinical characteristics in patients with curatively resected LA-NSCLC.

METHODS

This study consisted of 124 patients with curatively resected stage IIB-IIIB NSCLC in our institution between January 2014 and June 2018. Patients were randomly divided into training and validation cohorts using a 4:1 ratio. Radiomics features were selected from the chest CT images before surgery. A radiomics signature was constructed using the LASSO algorithm based on the training cohort. Clinical model was developed using the Cox proportional hazards model. The clinical, radiomics, and integrated nomograms were constructed. The prediction performance of the models was assessed based on its discrimination, calibration, and clinical utility.

RESULTS

The radiomics signature is significantly associated with BM-free survival in the overall cohort. The discrimination performance of the integrated nomogram, with the C-indexes 0.889 (0.872-0.906, 95 % CI) and 0.853 (0.788-0.918, 95 % CI) in the training and validation cohorts, respectively, is significantly better than the clinical nomogram (p < 0.0001 for the training cohort, p = 0.0008 for the validation cohort). Compared with the radiomics nomogram, the integrated nomogram is also improved to varying degrees, but not apparent in the validation cohort (p = 0.0007 for the training cohort, p = 0.0554 for the validation cohort). The calibration curve and decision curve analysis demonstrated that the integrated nomogram exceeded the clinical or radiomics nomograms in predicting BM-free survival.

CONCLUSIONS

Compared with the clinical or radiomics nomograms, the predictive performance of the integrated nomogram is significantly improved. The integrated nomogram is most suitable for predicting BM-free survival in patients with curatively resected LA-NSCLC.

Applicability of the adjusted graded prognostic assessment for lung cancer with brain metastases using molecular markers (Lung-molGPA) in a Chinese cohort: A retrospective study of multiple institutions

Background

In this era of precision medicine, prognostic heterogeneity is an important feature of patients with non‐small cell lung cancer (NSCLC) with brain metastases (BM). This multi‐institutional study is aimed to verify the applicability of the adjusted Lung‐molGPA model for NSCLC with BM in a Chinese cohort.

Methods

This retrospective study included 1903 patients at three hospitals in Southwest China. The performance of the Lung‐molGPA model was compared with that of the adjusted DS‐GPA model in terms of estimating the survival of NSCLC with BM.

Results

The median OS of this patient cohort was 27.0 months, and the adenocarcinoma survived longer than the non‐adenocarcinoma (28.0 months vs 18.7 months, p < 0.001). The adjusted Lung‐molGPA model was more accurate in predicting survival of adenocarcinoma patients than the adjusted DS‐GPA model (C‐index: 0.615 vs 0.571), and it was not suitable for predicting survival of non‐adenocarcinoma patients (p = 0.286, 1.5‐2.0 vs 2.5‐3.0; p = 0.410, 2.5‐3.0 vs 3.5‐4.0).

Conclusions

The adjusted Lung‐molGPA model is better than the DS‐GPA model in predicting the prognosis of adenocarcinoma patients. However, it failed to estimate the prognosis for non‐adenocarcinoma patients.

Construction of Nomograms for Predicting Pathological Complete Response and Tumor Shrinkage Size in Breast Cancer

Purpose

Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NAC) for the HER2-positive and triple-negative subtypes of breast cancer and is related to survival benefit; however, luminal breast cancer is not sensitive to NAC, and the size of tumor shrinkage is a more meaningful clinical indicator for the luminal breast cancer subtype. We wanted to use a nomogram or formula to develop and implement a series of prediction models for pCR or tumor shrinkage size.

Patients and Methods

We developed a prediction model in a primary cohort consisting of 498 patients with invasive breast cancer, and the data were gathered from July 2016 to September 2018. The endpoint was pCR and tumor shrinkage size. In the primary cohort, the HER2-positive cohort, and the triple-negative cohort, multivariate logistic regression analysis was used to screen the significant clinical features and clinicopathological features to develop nomograms. In the luminal group, multivariate linear regression analysis was used to test the risk factors that affect tumor shrinkage size. The area under the receiver operating characteristic curve (AUC) and calibration curves were adopted to evaluate and analyze the discrimination and calibration ability of nomograms. Furthermore, we also performed internal validation and independent validation in the primary cohort.

Results

ER status, KI67 status, HER2 status, number of NAC cycles, and tumor size were independent predictive factors of pCR in the primary cohort. These indicators had good discrimination and calibration in the primary and validation cohorts (AUC: 0.873, 0.820). The nomogram for HER2-positive and triple-negative breast cancer (TNBC) had an AUC of 0.820 and 0.785, respectively. Both the HER2 positive and TNBC nomogram calibration curves indicated significant agreement. Moreover, the luminal subtype prediction model was Y (tumor shrinkage size) = -0.576 × (age at diagnosis) + 2.158 × (number of NAC cycles) + 0.233 × (pre-NAC tumor size) + 51.662.

Conclusion

Utilizing this predictive model will enable us to identify patients at high probability for pCR after NAC. Clinicians can stratify these patients and make individualized and personalized recommendations for therapy.

Lung-molGPA Index Predicts Survival Outcomes of Non-Small-Cell Lung Cancer Patients with Synchronous or Metachronous Brain Metastases

Background

Graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA) for brain metastases is a powerful prognostic tool. However, it has not been validated for non-small-cell lung cancer (NSCLC) patients with synchronous or metachronous brain metastases.

Methods

A total of 1184 NSCLC patients with synchronous or metachronous brain metastases were reviewed in this study. Comparative clinicopathological variables and survival analysis for these two groups (synchronous vs metachronous), as well as complimentary analysis of prognostic factors for the entire patient cohort, were performed. Afterward, patients were stratified using Lung-molGPA to evaluate the accuracy of the survival estimates.

Results

A total of 763 patients (64.4%) had synchronous metastases while 35.6% (421 patients) had metachronous metastasis. Patients with synchronous metastases were more likely to have a smoking history, limited metastatic lesions, and absence of cerebral symptoms (P<0.05). Patients with metachronous metastatic NSCLC had an overall survival (OS) period of 16.5 (95% CI 14.5–18.6) months and were longer compared to patients with synchronous metastases (16.5 vs 13.5 [12.5–14.6] months, P=0.004). In Cox regression multivariable analysis, age (HR=1.25, P=0.008), Karnofsky performance status (HR=1.30, P=0.005), extracranial metastases (HR=1.57, P<0.001), number of brain metastases (HR=1.22, P=0.043), gene mutation (HR=1.40 [wild type vs mutation], P=0.050; HR=1.42 [unknown vs mutation], P=0.007), and treatment (including TKI, chemotherapy, and local brain treatment, P<0.05) were independent prognostic predictors of OS. Additionally, metachronous metastatic patients were at lower risk for disease-related death compared to synchronous metastatic patients (HR=0.69, P<0.001). Importantly, median OS stratified by Lung-molGPA of 0–1, 1.5–2, 2.5–3 and 3.5–4 scores were 11.0, 14.0, 24.9, and 26.3 months for synchronous brain metastases, and 13.1, 17.0, 37.2, and 66.5 months for metachronous metastases, respectively (P<0.001).

Conclusion

Lung-molGPA could estimate the prognosis of NSCLC patients with synchronous or metachronous brain metastases. Hence, patients should be carefully stratified for consideration of aggressive therapy.

Establishment of a Prognostic Nomogram for Lung Adenocarcinoma with Brain Metastases

BACKGROUND

The brain is one of the common metastatic sites of lung adenocarcinoma, and the prognosis associated with brain metastasis is not good. We performed a large data analyses to determine the prognostic factors of lung adenocarcinoma with brain metastases (LABM) and to develop a nomogram to predict its prognosis.

METHODS

We conducted a retrospective study of 2879 patients with LABM from the Surveillance, Epidemiology, and End Results database. An X-tile analysis provided the optimal age cutoff point. We used univariate and multivariate Cox regression analyses to determine the independent prognostic factors of LABM. Finally, we established and validated a nomogram to predict the prognosis of LABM.

RESULTS

A total of 2879 patients with brain metastases were included in this study. Multivariate Cox regression analysis showed that age, race, sex, T stage, N stage, surgery, chemotherapy, bone metastasis, liver metastasis, and marital status were independent prognostic factors. We constructed a nomogram to predict the prognosis of LABM with the RMS package. Through calibration curves, receiver operating characteristic curves, and decision curve analyses, we found that the nomogram, which predicted the prognosis of LABM, performed well internally.

CONCLUSIONS

The nomogram is expected to be a precise and personalized tool for predicting the prognosis of patients with LABM. This nomogram will help clinicians develop more rational and effective treatment strategies.

A nomogram to predict outcomes of lung cancer patients after pneumonectomy based on 47 indicators

Aims

We aimed to establish a nomogram for lung cancer using patients' characteristics and potential hematological biomarkers.

Methods

Principle component analysis was used to reduce the dimensions of the data, and each component was transformed into categorical variables based on cutoff values obtained using the X‐tile software. Multivariate analysis was used to determine potential prognostic biomarkers. Five components were used in the predictive nomogram. Internal validation of the model was performed by bootstrapping of samples, while external validation was performed on a separate cohort from Shandong Cancer Hospital. The predictive accuracy of the model was measured by concordance index and risk group stratification. Decision curve analysis was performed to evaluate the net benefit of the models.

Results

One hundred patients in the Discovery group and 111 patients in the Validation group were retrospectively analyzed in this study. Forty‐seven indexes were sorted into eight subgroups. Five components based on cox regression analysis were enrolled into the predictive nomogram. The nomogram prediction of the probability of 3‐ and 5‐year overall survival was in great concordance with the actual observations. Of interest, the nomogram allowed better risk stratification of patients and better accuracy in predicting patients' survival compared with pathological tumor‐node‐metastasis staging system.

Conclusion

A nomogram was established for prognosis of lung cancer, which can be used for treatment selection and clinical care management.

A nomogram to predict overall survival of patients with early stage non-small cell lung cancer

Background

Nomograms have been widely used for estimating cancer prognosis. The aim of this study was to construct a clinical nomogram that would well predict overall survival of early stage non-small cell lung cancer (NSCLC) patients after surgery resection.

Methods

A total of 443 patients diagnosed with pathologic stage I and II NSCLC who had undergone curative resection without neoadjuvant chemotherapy or radiotherapy were recruited and analyzed. The log-rank test and multivariate Cox regression analysis were used to select the most significant predictors in the final nomogram for predicting overall survival. Furthermore, the model was validated by bootstrap methods and measured by concordance index (C-index) and calibration plots.

Results

Four independent predictors for overall survival were identified and included into the delineation of the nomogram (tumor differentiation, station of sampled lymph nodes, pathologic T and pathologic N). The model showed comparatively stable discrimination (bootstrap-corrected C-index =0.622, 95% CI: 0.572-0.672) and good calibration.

Conclusions

We successfully developed a nomogram incorporating available clinicopathological variables to predict overall survival of early stage NSCLC patients after surgery resection, which might help clinician select better appropriate treatment decisions.

Survival nomogram for patients with initially diagnosed metastatic non-small-cell lung cancer: a SEER-based study

Aim: Prognosis of patients with metastatic non-small-cell lung cancer differ widely. Methods: All patients were randomly divided into training or validation cohort. Cox-regression analyses were conducted to select independent predictors. We built a nomogram by R code and evaluated the accuracy and the reliability of the model using C-index, calibration curves and decision curve analyses. We made a risk classification system based on the nomogram. Results: In the validation cohort, C-index was 0.729 and 0.738 for 1- and 2-year overall survival. Calibration plots and decision curve analyses presented great prognostic accuracy and clinical applicability. Its prognostic accuracy preceded the American Joint Committee on Cancer staging with evaluated integrated discrimination improvement. Conclusion: The model can be a practical tool in treatment decision and individual counseling.

A Nomogram to Predict the Pathologic Complete Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Based on Simple Laboratory Indicators

BACKGROUND

Triple-negative breast cancer (TNBC) patients who achieve a pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have better prognoses.

OBJECTIVE

This study aimed to develop an intuitive nomogram based on simple laboratory indexes to predict the pCR of standard NAC in TNBC patients.

METHODS

A total of 80 TNBC patients who received eight cycles of thrice-weekly standard NAC (anthracycline and cyclophosphamide followed by taxane) and subsequently underwent surgery in Zhejiang Cancer Hospital were retrospectively enrolled, and data on their pretreatment clinical features and multiple simple laboratory indexes were collected. The optimal cut-off values of the laboratory indexes were determined by the Youden index using receiver operating characteristic (ROC) curve analyses. Forward stepwise logistic regression (likelihood ratio) analysis was applied to identify predictive factors for a pCR of NAC. A nomogram was then developed according to the logistic model, and internally validated using the bootstrap resampling method.

RESULTS

pCR was achieved in 39 (48.8%) patients after NAC. Multivariate analysis identified four independent indicators: clinical tumor stage, lymphocyte to monocyte ratio, fibrinogen level, and D-dimer level. The nomogram established based on these factors showed its discriminatory ability, with an area under the curve (AUC) of 0.803 (95% confidence interval 0.706-0.899) and a bias-corrected AUC of 0.771. The calibration curve and Hosmer-Lemeshow test showed that the predictive ability of the nomogram was a good fit to actual observation.

CONCLUSIONS

The nomogram proposed in the present study exhibited a sufficient discriminatory ability for predicting pCR of NAC in TNBC patients.

Brain metastases in resected non-small cell lung cancer: The impact of different tyrosine kinase inhibitors

Objectives

The purpose of this study was to examine the impact of epidermal growth factor receptor (EGFR) mutation status and tyrosine kinase inhibitors (TKIs) on the survival of brain metastases (BM) in patients with surgically resected non-small cell lung cancer (NSCLC).

Methods

We selected the patients who had developed metastatic NSCLC; analyzed the differences between brain metastases and other sites of metastases, including patient characteristics, EGFR status, and survival; and selected the patients who had BM for further investigation. We also compared the treatment effects of first-generation TKIs with those of second-/third-generation TKIs.

Results

A total of 785 cases of stage I-IIIa NSCLC were reviewed. Thirty-six (4.6%) patients were identified as having BM. Among them, 14 patients had a mutated EGFR status. No association between EGFR mutation and the incidence of BM was observed (p = 0.199). Patients with mutated EGFRs had significantly longer overall survival and post-recurrence survival than patients with wild-type EGFR mutation (p = 0.001 for both). However, there was no survival difference between patients with exon 19 and exon 21 mutations (p = 0.426). Furthermore, patients who received the second- and/or third-generation EGFR-TKIs had better survival than patients who only received first-generation EGFR-TKIs (p = 0.031). A multivariate analysis indicated that the next-generation TKIs (HR, 0.007; 95% CI, 0.000 to 0.556; p = 0.026) and a longer interval before BM development (HR, 0.848; 95% CI, 0.733 to 0.980; p = 0.025) were significant factors in longer survival.

Conclusions

EGFR-TKIs were effective in treating NSCLC patients with BM after curative pulmonary surgery, especially in those patients harboring EGFR mutations. Furthermore, the second-/third-generation EGFR-TKIs showed more promising results than the first-generation EGFR-TKIs in treating those particular patients, though larger studies needed to further prove the results.

Developing a Nomogram for Risk Prediction of Severe Hand-Foot-and-Mouth Disease in Children

OBJECTIVE

Early recognition of children with severe Hand-Foot-and-Mouth disease (HFMD) is especially important, as severe cases are associated with poor prognosis. To accomplish this, authors designed a quantitative assessment tool to build a nomogram to assist in clinical diagnosis.

METHODS

A total of 2332 HFMD patients were enrolled in this study; 1750 cases in the mild group and 582 cases in the severe group. Analysis of all of the data was performed using R software version 3.4.3. Multivariate logistic regression was utilized to screen predictors to construct a nomogram model. Finally, predictive performance of the model was evaluated using a receiver operating characteristic (ROC) curve and classifier calibration plot.

RESULTS

A nomogram was constructed with five variables: age, peak temperature, fever duration, pathogen, and vomiting. For the nomogram, the area under the curve was 0.87, and the model prediction accuracy rate was 85.2%. Depending upon the comparison of the area under the ROC curve, the nomogram model was superior to the traditional pediatric clinical illness score (PCIS). With the help of the Hosmer-Lemeshow test and resampling model calibration curve, the fitting performance of the nomogram was stable.

CONCLUSIONS

With advantages such as simplicity, intuitiveness, and practicality, the nomogram (including age, peak temperature, fever duration, pathogen, and vomiting) is capable of predicting severe HFMD and has certain auxiliary value in clinical applications.

Natural history, clinical course and predictors of interval time from initial diagnosis to development of subsequent NSCLC brain metastases

PURPOSE

Non-small cell lung cancer (NSCLC) brain metastases are associated with substantial morbidity and mortality. During recent years, accompanying dramatic improvements in systemic disease control, NSCLC brain metastases have emerged as an increasingly relevant clinical problem. However, optimal surveillance practices remain poorly defined. This purpose of this study was to further characterize the natural history, clinical course and risk factors associated with earlier development of subsequent NSCLC brain metastases to better inform clinical practice and help guide survivorship care.

METHODS

We retrospectively reviewed all institutional NSCLC brain metastasis cases treated with radiotherapy between 1997 and 2015. Exclusion criteria included presence of brain metastases at initial NSCLC diagnosis and incomplete staging information. Interval time to brain metastases and subsequent survival were characterized using Kaplan-Meier and multivariate Cox regression analyses.

RESULTS

Among 105 patients within this cohort, median interval time to development of brain metastases was 16 months. Median interval times were 29, 19, 16 and 13 months for Stage I-IV patients, respectively (P = 0.016). Additional independent predictors for earlier development of NSCLC brain metastases included non-adenocarcinomatous histopathology (HR 3.036, P < 0.001), no prior surgical resection (HR 1.609, P = 0.036) and no prior systemic therapy (HR 3.560, P = 0.004). Median survival following intracranial progression was 16 months. Delayed development of brain metastases was associated with better prognosis (HR 0.970, P < 0.001) but not survival following intracranial disease onset.

CONCLUSIONS

Collectively, our results provide valuable insights into the natural history of NSCLC brain metastases. NSCLC stage, histology, prior surgical resection and prior systemic therapy emerged as independent predictors for interval time to brain metastases.

A Radiomics Signature in Preoperative Predicting Degree of Tumor Differentiation in Patients with Non-small Cell Lung Cancer

RATIONALE AND OBJECTIVES

Poorly differentiated non-small cell lung cancer (NSCLC) indicated a poor prognosis and well-differentiated NSCLC indicates a noninvasive nature and good prognosis. The purpose of this study was to build and validate a radiomics signature to predict the degree of tumor differentiation (DTD) for patients with NSCLC.

MATERIALS AND METHODS

A total of 487 patients with pathologically diagnosed NSCLC were retrospectively included in our study. Five hundred ninety-one radiomics features were extracted from each tumor from the contrast-enhanced computed tomography images. A minimum redundancy maximum relevance algorithm and a logistic regression model were used for dimension reduction, feature selection, and radiomics signature building. The performance of the radiomics signature was assessed using receiver operating characteristic analysis, and the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to quantify the association between a signature and DTD. An independent validation set contained 184 consecutive patients with NSCLC.

RESULTS

A nine-radiomics-feature-based signature was built and it could differentiate low and high DTDs in the training set (AUC = 0.763, sensitivity = 0.750, specificity = 0.665, and accuracy = 0.687), and the radiomics signature had good discrimination performance in the validation set (AUC = 0.782, sensitivity = 0.608, specificity = 0.752, and accuracy = 0.712).

CONCLUSIONS

A radiomics signature based on contrast-enhanced computed tomography imaging is a potentially useful imaging biomarker for differentiating low from high DTD in patients with NSCLC.

Risk factors for brain metastases in patients with non-small-cell lung cancer

Brain metastases (BM) are severe incidents in patients with non-small-cell lung cancer (NSCLC). The controversial value of prophylactic cranial irradiation (PCI) in NSCLC in terms of survival benefit prompted us to explore the possible risk factors for BM in NSCLC and identify the potential population most likely to benefit from PCI. Risk factors for brain metastases in NSCLC are reviewed in this article. Identifying patients with a higher risk of BM could possibly increase the benefit of PCI while reducing the discomfort and risks caused by unnecessary invasive procedures in the NSCLC patient population. Future studies might focus on finding a solid basis for the prediction of the occurrence of brain metastases and for the therapeutic decision on the use of PCI.

GAP43, a novel metastasis promoter in non-small cell lung cancer

BACKGROUND

Brain metastasis is an extremely serious sequela with a dismal prognosis in non-small cell lung cancer (NSCLC). The present study aimed to identify novel biomarkers and potential therapeutic targets for brain metastases of NSCLC.

METHODS

We performed high-throughput Luminex assays to profile the transcriptional levels of 36 genes in 70 operable NSCLC patients, among whom 37 developed brain metastases as the first relapse within 3 years after surgery. The Cox proportional hazards regression model was used to evaluate the association between genes and brain metastases. Wound healing assay and transwell assay was carried out to estimate the function of target gene in vitro. And left ventricular injection on nude mice was used to evaluate the effect of target gene in vivo.

RESULTS

Growth-associated protein 43 (GAP43) was found to be related to brain metastasis. Multivariate Cox regression analysis showed that NSCLC patients with elevated GAP43 had a 3.29-fold increase in the risk for brain metastasis compared with those with low levels (95% confidence interval: 1.55-7.00; P = 0.002). Kaplan-Meier survival curves revealed that GAP43 was also associated with overall survival. Analysis of a cohort of 1926 NSCLC patients showed similar results: patients with high levels of GAP43 had worse progression-free and overall survival rates. Furthermore, in vitro experiments showed that GAP43 facilitated cell migration. Animal studies demonstrated that GAP43-silenced NSCLC cells were less likely to metastasize to the brain and bone than control cells. Immunofluorescence and F-actin/G-actin in vivo assays indicated that GAP43 knockdown triggered depolymerization of the F-actin cytoskeleton. Rho GTPase activation assays showed that Rac1 was deactivated after GAP43 was silenced.

CONCLUSIONS

Our findings suggest that GAP43 is an independent predictor of NSCLC brain metastasis and that it may facilitate metastasis by regulating the Rac1/F-actin pathway.

Applicability of the lung-molGPA index in non-small cell lung cancer patients with different gene alterations and brain metastases

OBJECTIVES

The Lung-molGPA index is based on the original diagnosis-specific graded prognostic assessment (DS-GPA) and incorporates recently reported gene alteration data, predicting the outcomes of non-small cell lung cancer (NSCLC) patients with brain metastases (BM). However, the prognostic values of both DS-GPA and Lung-molGPA remain undetermined, especially for patients with different molecular types.

MATERIALS AND METHODS

A total of 1184 NSCLC patients with BM were analyzed for clinical factors and outcomes at Zhejiang Cancer Hospital, China. All prognostic factors were weighted for significance by hazard ratios. The applicability of DS-GPA and Lung-molGPA were reappraised in NSCLC patients with BM and various genetic profiles. Additionally, a modified Lung-molGPA was newly developed for NSCLC patients with gene variations.

RESULTS

NSCLC patients in the present study had a median survival time of 14.0 months from BM diagnosis. Both the DS-GPA and Lung-molGPA models could effectively predict the outcomes of NSCLC patients with BM (P < 0.001), and the Lung-molGPA model appeared to deliver more accurate predictions. Furthermore, Lung-molGPA scores demonstrated discriminatory capability in patients with gene variations (P < 0.001), and no significant difference was reached in wild-type patients (P = 0.133). Regarding oncogene-positive NSCLC patients with BM, a modified Lung-molGPA index was established based on the prognostic factors with a C-index of 0.73 (95% CI: 0.68-0.80) to accurately calculate survival probability (P < 0.001).

CONCLUSIONS

In the era of precision medicine, Lung-molGPA accurately predicted the prognosis of NSCLC patients with mutant genotypes and BM, although it did not perform well in wild-type patients. Thus, it is worthwhile to explore the prognostic model for patients with positive driving genes.

Significance of Methylation of FBP1 Gene in Non-Small Cell Lung Cancer

Because NSCLC has poor overall prognosis and is frequently diagnosed at later stage, we aimed to seek novel diagnosis biomarkers or therapy target of the disease in this study. Fructose-1,6-bisphosphatase 1 (FBP1) is a rate-limiting enzyme in gluconeogenesis, which was usually lost in NSCLC due to abnormal methylation in promoter DNA sequence. The clinical data indicated that the methylation rate in FBP1 gene promoter was negatively related to the overall survival of the NSCLC patients. DNA methylation transferase inhibitor 5-aza treatment could significantly increase both expression levels of mRNA and protein in A549 cell line. On the other hand, silence of FBP1 in H460 cell line by using specific siRNA against FBP1 dramatically improved the cell proliferation and cell migration according to the date of FACS and transwell assays. All these findings implied the important roles of FBP1 expression in lung cancer development and progression and the potential use of the methylation status detected in FBP1 promoter region as a novel predictor for prognosis and therapeutic target for NSCLC patients.

Targeted DNA sequencing of non-small cell lung cancer identifies mutations associated with brain metastases

Introduction

This study explores the hypothesis that dominant molecular oncogenes in non-small cell lung cancer (NSCLC) are associated with metastatic spread to the brain.

Methods

NSCLC patient groups with no evidence of metastasis, with metastatic disease to a non-CNS site, who developed brain metastasis after diagnosis, and patients with simultaneous diagnosis of NSCLC and metastatic brain lesions were studied using targeted sequencing.

Results

In patients with brain metastasis versus those without, only 2 variants (one each in BCL6 and NOTHC2) were identified that occurred in ≥ 4 NSCLC of patients with brain metastases but ≤ 1 of the NSCLC samples without brain metastases. At the gene level, 20 genes were found to have unique variants in more than 33% of the patients with brain metastases. When analyzed at the patient level, these 20 genes formed the basis of a predictive test to discriminate those with brain metastasis. Further analysis showed that PI3K/AKT signaling is altered in both the primary and metastases of NSCLC patients with brain lesions.

Conclusion

While no single variant was associated with brain metastasis, this study describes a potential gene panel for the identification of patients at risk and implicates PI3K/AKT signaling as a therapeutic target.

Nomograms for predicting progression and efficacy of post-operation radiotherapy in IIIA-pN2 non-small cell lung cancer patients

In this retrospective study, we developed nomograms for predicting the efficacy of post-operation radiotherapy (PORT) in IIIA-N2 non-small cell lung cancer (NSCLC) patients. In total, 334 patients received post-operational chemotherapy and were included in the analysis. Of those, 115 also received either concurrent or sequential post-operational radiotherapy (PORT). Nomograms were developed using Cox proportional hazard regression models to identify clinicopathological characteristics that predicted progression free survival (PFS) and overall survival (OS), and subgroup analyses of the effects of PORT were performed using nomogram risk scores. PFS and OS predicted using the nomogram agreed well with actual PFS and OS, and patients with high PFS/OS nomogram scores had poorer prognoses. In subgroup analyses, PORT increased survival more in patients with low PFS nomogram risk scores or high OS nomogram risk scores. Thus, our novel nomogram risk score model predicted PFS, OS, and the efficacy of PORT in IIIA-N2 NSCLC patients.

Factors associated with distant recurrence following R0 lobectomy for pN0 lung adenocarcinoma

OBJECTIVE

We investigated factors associated with distant recurrence, disease-free survival (DFS), and overall survival (OS) following R0 lobectomy for pathologic node-negative (pN0) lung adenocarcinoma.

METHODS

We performed a retrospective analysis of a prospectively maintained database of patients with pT1-3N0M0 non-small cell lung cancer. Exclusion criteria included metachronous lung cancer, sublobar/incomplete resection, nonadenocarcinoma histology, and induction/adjuvant therapy. The primary outcome was distant recurrence; secondary outcomes were DFS and OS. Associations between variables and outcomes were assessed by Fine-Gray competing-risk regression for distant recurrence and Cox proportional hazard models for DFS and OS.

RESULTS

Of 2392 patients identified with pT1-3N0M0 lung adenocarcinoma, 893 met the inclusion criteria. Median follow-up was 35.0 months (range, 0.1-202 months). Thirteen percent of patients developed recurrence (n = 115), of which 86% (n = 99) were distant. The 5-year cumulative incidence of distant recurrence was 14% (95% confidence interval [CI], 11%-17%). On multivariable analysis, pT2a (hazard ratio [HR], 2.84; 95% CI, 1.56-5.16; P = .001) and pT2b/3 (HR, 6.53; 95% CI, 3.17-13.5; P < .001) tumors were associated with distant recurrence. Recent surgery was associated with decreased distant recurrence (HR, 0.43; 95% CI, 0.20-0.91; P = .028), and lymphovascular invasion was strongly associated with distant recurrence (HR, 1.62; 95% CI, 1.00-2.63; P = .05). DFS was independently associated with pT stage (P < .001) and lymphovascular invasion (P = .004).

CONCLUSIONS

In patients undergoing R0 lobectomy with pN0 lung adenocarcinoma, pT stage and lymphovascular invasion were associated with distant recurrence and decreased DFS. These observations support the inclusion of these patients in future clinical trials investigating adjuvant targeted and immunotherapies.