Dataset Information

A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning.

ABSTRACT: Objective: To propose modifications to refine prognostication over anatomic extent of the current tumor, node, and metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) for a better distinction, and reflect survival differences of lung adenocarcinoma and squamous cell carcinoma. Study Design: Three large cohorts were included in this study. The training cohort consisted of 124,788 patients in the Surveillance, Epidemiology, and End Results (SEER) database (2006-2015). The validation cohort consisted of 4,247 patients from the Zhongshan Hospital, Fudan University (FDZSH; 2005-2014), and People's Hospital, Peking University (PKUPH; 2000-2017). The algorithm generated a hierarchical clustering model based on the unsupervised learning for survival data using Kaplan-Meier curves and log-rank test statistics for recursive partitioning and selection of the principal groupings. Results: In the modified staging system, adenocarcinoma cases are usually at a lower stage than the squamous cell carcinoma cases of the same TNM, reflecting a better outcome of adenocarcinoma than that of squamous cell carcinoma. The C-index of the modified staging system was significantly superior to that of the staging system [SEER cohort: 0.722, 95% CI, (0.721-0.723) vs. 0.643, 95% CI, (0.640-0.647); FDZSH cohort: 0.720, 95% CI, (0.709-0.731) vs. 0.519, 95% CI, (0.450-0.586); and PKUPH cohort: 0.730, 95% CI, (0.705-0.735) vs. 0.728, 95% CI, (0.703-0.753)]. Conclusion: Survival differences between lung adenocarcinoma and squamous cell carcinoma have been reflected accurately and reliably in the modified staging system based on the machine learning. It may refine prognostication over anatomic extent.

SUBMITTER: Li M

PROVIDER: S-EPMC6702456 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning.

Li Ming M Zhan Cheng C Sui Xizhao X Jiang Wei W Shi Yu Y Yang Xiaodong X Feng Mingxiang M Wang Jun J Wang Qun Q

Frontiers in oncology 20190814

<b>Objective:</b> To propose modifications to refine prognostication over anatomic extent of the current tumor, node, and metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) for a better distinction, and reflect survival differences of lung adenocarcinoma and squamous cell carcinoma. <b>Study Design:</b> Three large cohorts were included in this study. The training cohort consisted of 124,788 patients in the Surveillance, Epidemiology, and End Results (SEER) database (2006-2015 ...[more]

PMID: 31475114

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Project description:TNM stage is not enough to accurately predict the prognosis of patients with non-small cell lung cancer (NSCLC). This study aimed to establish the Immunoscore (IS) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), separately, and propose a new staging system in NSCLC. We used the multiplex fluorescent immunohistochemistry (mIHC) technology to detect 17 immune biomarkers of 304 patients with NSCLC. The LASSO-COX regression model was used to establish the ISNSCLC in the training cohorts. The ISNSCLC was then validated in the validation cohort. The constructed ISLUAD contained three immune features: CD4+CD73+ core of tumor (CT), PD-L1+ CT, and IDO+ invasive margin (IM). ISLUSC also contained two immune features: CD8+CD39-CD73- CT, CD8+Tim-3+ IM. In the training cohort, significant prognostic differences were found upon comparing low-ISNSCLC patients with high-ISNSCLC patients. For LUAD, the 5-y disease-free survival (DFS) rates were 54.7% vs. 8.1% and the 5-y overall survival (OS) rates were 82.4% vs. 36% (all P< .0001). For LUSC, the 5-y DFS rates were 74.0% vs. 14.7% and the 5-y OS rates were 78.2% vs. 17.6% (all P< .0001). Multivariate analyses indicated that ISNSCLC was an independent indicator for prognosis. Finally, we combined ISNSCLC with clinicopathological factors to establish a TN-I staging system and two nomogram models for clinical use. The TN-I stage had better prediction accuracy than TNM stage. The newly established ISLUAD and ISLUSC were completely different, and both were excellent indicators for the prognostic prediction. The TN-I stage could effectively improve prognostic accuracy and facilitate clinical application. Abbreviations NSCLC, non-small cell lung cancer; IS, Immunoscore; mIHC, multiplex fluorescent immunohistochemistry; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; CT, core of tumor; IM, invasive margin; DFS, disease-free survival; OS, overall survival; SITC, the Society for Immunotherapy of Cancer; FFPE, formalin-fixed paraffin-embedded; MWT, microwave treatment; DCA, decision curve analysis; ROC, receiver operating characteristic; AUC, area under the curve; EGFR, epidermal growth factor receptor.

Project description:Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.

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Dataset Information

A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning.

Publications

A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning.

Similar Datasets

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