Primary tumour resection in non-small-cell lung cancer patients with ipsilateral pleural dissemination (M1a): a population-based study

Li, Hao; Sun, Zewen; Yang, Fan; Sui, Xizhao; Liu, Taorui; Wang, Jun

doi:10.1093/ejcts/ezy439

Cited by 17 publications

(27 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, two studies using the SEER database also suggested that surgery was associated with improved OS for patients with M1a patients. 8,9 Our study also demonstrated that patients who underwent PTR had significantly better OS than biopsy alone. After PSM, the PTR group tended to have longer OS but there was no significant difference.…”

Section: Discussionsupporting

confidence: 56%

“…This result was opposite to that compared with our previous SEER-based study. 9 It may be because of the limited sample size of our retrospective study. On the other hand, differences such as the stage of pleural nodules and systemic therapy between the two groups may also bring bias to survival (Table S1).…”

Section: Discussionmentioning

confidence: 92%

“…Recently, our study based on the SEER database also supported a favorable prognostic effect of PTR for NSCLC patients with pleural dissemination. 9 However, the data from the SEER database does not include adjuvant therapeutic information or perioperative outcome.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Primary tumor resection of non‐small cell lung cancer patients with ipsilateral pleural dissemination (M1a) in the era of targeted therapy

Liu

Sun

et al. 2020

Thoracic Cancer

Self Cite

View full text Add to dashboard Cite

Background Non‐small cell lung cancer (NSCLC) patients with ipsilateral pleural dissemination (M1a) are generally contraindicated for surgery. Recently, several studies have demonstrated that these patients might benefit from primary tumor resection (PTR). However, whether PTR is beneficial for driver oncogene‐positive patients treated with targeted therapy, remains unclear. Here, we investigated the effects of PTR on survival in the era of targeted therapy. Methods In total, 105 NSCLC patients with ipsilateral pleural dissemination were identified. The mode of systemic treatment was assessed in this study. Survival analysis was performed with the Kaplan‐Meier method and Cox proportional hazards regression. The overall survival (OS) of patients with or without PTR was compared between propensity score‐matched groups (caliper: 0.02). Results In the entire cohort, PTR was associated with improved OS in both unmatched (median survival time [MST]: 50.0 vs. 29.6 months, P = 0.019) and matched (MST: 50.0 vs. 34.4 months, P = 0.052) cohorts. Multivariate regression models showed that surgery was an independent favorable prognostic factor for OS. A total of 70 patients underwent genetic testing, and targeted therapies, such as EGFR‐TKIs or ALK‐TKIs, were used in the driver oncogene‐positive patients. Subgroup analysis showed that PTR did not improve OS in the targeted therapy group (MST: 57.1 months vs. 50.4 months, P = 0.840). However, surgery significantly prolonged survival in the nontargeted therapy group (MST: 39.8 vs. 14.2 months, P = 0.002). Conclusions The results of this study indicated that PTR could prolong OS in stage IV NSCLC patients with ipsilateral pleural dissemination, especially in patients who are not candidates for targeted therapy. Key points Non‐small cell lung cancer patients with ipsilateral pleural dissemination can benefit from primary tumor resection. Primary tumor resection could prolong overall survival (OS) in non‐small cell lung cancer patients with ipsilateral pleural dissemination who are not candidates for targeted therapy.

show abstract

Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 92%

See 1 more Smart Citation

Primary tumor resection of non‐small cell lung cancer patients with ipsilateral pleural dissemination (M1a) in the era of targeted therapy

Liu

Sun

et al. 2020

Thoracic Cancer

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to our study, tumor size is a significant factor affecting the patient prognosis, which has not been recognized previously [ 11 – 13 ]. T stage roughly classifies tumor size or depth of invasion, but it cannot reflect the specific character of NSCLC patients with BM and accurately predict the prognosis, because the tumor size of the same period varies greatly [ 23 – 25 ]. Age, tumor size, race, sex, histological types, grade, T stage, N stage, surgery, chemotherapy, liver metastases, and radiotherapy were related to prognosis, which was similar to the previous research [ 26 – 30 ].…”

Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Huang

Chi

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n=4183) and validation (n=1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.

show abstract

“…There are different views regarding the ability of TNM stage to reflect accurately the prognosis of patients. For example, three retrospective studies from Asia and Europe suggested that patients with different AJCC stages had no statistical difference in prognosis, therefore it was not advisable to rely solely on AJCC stages to determine prognosis, as there are numerous remaining factors affecting the survival of patients with NSCLC (27–29). A previous study confirmed that gene mutation is one of the possible reasons for the difference in prognosis between lung SCC and ADC (30).…”

Section: Discussionmentioning

confidence: 99%

A nomogram for the prediction of overall survival in patients with stage II and III non‑small cell lung cancer using a population‑based study

et al. 2019

View full text Add to dashboard Cite

As a malignant tumor with poor prognosis, accurate and effective treatment of non-small cell lung cancer (NSCLC) is crucial. To predict overall survival in patients with stage II and III NSCLC, a nomogram was constructed using data from the Surveillance, Epidemiology and End Results database. Eligible patients with NSCLC with available clinical information diagnosed between January 1, 2010 and November 31, 2015 were selected from the database, and the data were randomly divided into a training set and a validation set. Univariate and multivariate Cox regression analyses were used to identify prognostic factors with a threshold of P<0.05, and a nomogram was constructed. Harrell's concordance indexes and calibration plots were used to verify the predictive power of the model. Risk group stratification by stage was also performed. A total of 15,344 patients with stage II and III NSCLC were included in the study. The 3- and 5-year survival rates were 0.382 and 0.278, respectively. The training and validation sets comprised 10,744 and 4,600 patients, respectively. Age, sex, race, marital status, histology, grade, Tumor-Node-Metastasis T and N stage, surgery type, extent of lymph node dissection, radiation therapy and chemotherapy were identified as prognostic factors for the construction of the nomogram. The nomogram exhibited a clinical predictive ability of 0.719 (95% CI, 0.718–0.719) in the training set and 0.721 (95% CI, 0.720–0.722) in the validation set. The predicted calibration curve was similar to the standard curve. In addition, the nomogram was able to divide the patients into groups according to stage IIA, IIB, IIIA, and IIIB NSCLC. Thus, the nomogram provided predictive results for stage II and III NSCLC patients and accurately determined the 3- and 5-year overall survival of patients.

show abstract

Primary tumour resection in non-small-cell lung cancer patients with ipsilateral pleural dissemination (M1a): a population-based study

Cited by 17 publications

References 19 publications

Primary tumor resection of non‐small cell lung cancer patients with ipsilateral pleural dissemination (M1a) in the era of targeted therapy

Primary tumor resection of non‐small cell lung cancer patients with ipsilateral pleural dissemination (M1a) in the era of targeted therapy

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

A nomogram for the prediction of overall survival in patients with stage II and III non‑small cell lung cancer using a population‑based study

Contact Info

Product

Resources

About