A novel <scp>LASSO</scp>‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

Chen, Hong-chao; Ci, Huang; Ge, Huiqing; Chen, Qianshun; Jing, Chen; Li, Yuqiang; Chen, Haiyong; Luo, Shuang; Zhao, Lilan; Xu, Xunyu

doi:10.1002/cam4.4560

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…This study shows that with increasing age, the prognosis is worse, and the study by Chen et al et al [6] also confirmed this view. With the advances in LC screening technology and treatment drugs, [20] the survival rate of MLUAD patients has improved.…”

Section: Discussionsupporting

confidence: 74%

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Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

Chen

Zhang

et al. 2022

Medicine

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The prognosis of metastatic lung adenocarcinoma (MLUAD) varies greatly. At present, no studies have constructed a satisfactory prognostic model for MLUAD. We identified 44,878 patients with MLUAD. The patients were randomized into the training and validation cohorts. Cox regression models were performed to identify independent prognostic factors. Then, R software was employed to construct a new nomogram for predicting overall survival (OS) of patients with MLUAD. Accuracy was assessed by the concordance index (C-index), receiver operating characteristic curves and calibration plots. Finally, clinical practicability was examined via decision curve analysis. The OS time range for the included populations was 0 to 107 months, and the median OS was 7.00 months. Nineteen variables were significantly associated with the prognosis, and the top 5 prognostic factors were chemotherapy, grade, age, race and surgery. The nomogram has excellent predictive accuracy and clinical applicability compared to the TNM system (C-index: 0.723 vs 0.534). The C-index values were 0.723 (95% confidence interval: 0.719-0.726) and 0.723 (95% confidence interval: 0.718-0.729) in the training and validation cohorts, respectively. The area under the curve for 6-, 12-, and 18-month OS was 0.799, 0.764, and 0.750, respectively, in the training cohort and 0.799, 0.762, and 0.746, respectively, in the validation cohort. The calibration plots show good accuracy, and the decision curve analysis values indicate good clinical applicability and effectiveness. The nomogram model constructed with the above 19 prognostic factors is suitable for predicting the OS of MLUAD and has good predictive accuracy and clinical applicability. Abbreviations: AJCC = American Joint Committee on Cancer, AUC = area under curve, CI = confidence interval, C-index = concordance index, DCA = decision curve analysis, HR = hazard ratio, LC = lung cancer, LN = lymph node, LUAD = lung adenocarcinoma, MLUAD = metastatic lung adenocarcinoma, NSCLC = non-small-cell lung cancer, OS = overall survival, SEER = the Surveillance Epidemiology and End Results database.

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Section: Discussionsupporting

confidence: 74%

“…Interestingly, LUAD patients with different metastatic sites have different prognoses, with median OS ranging from 3 to 8 months, which suggests that MLUAD is highly heterogeneous. [ 5 , 6 ] Therefore, the prognosis of patients with MLUAD varies greatly.…”

Section: Introductionmentioning

confidence: 99%

Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

Chen

Zhang

et al. 2022

Medicine

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“…The nomogram has the advantage of being a visual tool and incorporating more clinical characteristics and are widely applied in oncology ( 15 – 17 ). In this study, information on advanced pancreatic ductal adenocarcinoma in patients aged ≥50 years was obtained from the SEER database.…”

Section: Introductionmentioning

confidence: 99%

LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research

Feng,

Yang,

Duan

et al. 2024

Front. Oncol.

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BackgroundThis study aimed to develop a prognostic model for patients with advanced ductal adenocarcinoma aged ≥50 years.MethodsPatient information was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to screen the model variables. Cases from Nanchang Central Hospital were collected for external validation. The new nomogram and the American Joint Committee on Cancer (AJCC) criteria were evaluated using integrated discrimination improvement (IDI) and net reclassification index (NRI) indicators. Survival curves presented the prognosis of the new classification system and AJCC criteria.ResultsIn total, 17,621 eligible patients were included. Lasso Cox regression selected 4 variables including age, chemotherapy, radiotherapy and AJCC stage. The C-index of the training cohort was 0.721. The C-index value of the validation cohort was 0.729. The AUCs for the training cohorts at 1, 2, and 3 years were 0.749, 0.729, and 0.715, respectively. The calibration curves showed that the predicted and actual probabilities at 1, 2, and 3 years matched. External validation confirmed the model’s outstanding predictive power. Decision curve analysis indicated that the clinical benefit of the nomogram was higher than that of the AJCC staging system. The model evaluation indices preceded the AJCC staging with NRI (1-year: 0.88, 2-year: 0.94, 3-year: 0.72) and IDI (1-year: 0.24, 2-year: 0.23, 3-year: 0.22). The Kaplan–Meier curves implied that the new classification system was more capable of distinguishing between patients at different risks.ConclusionsThis study established a prognostic nomogram and risk classification system for advanced pancreatic cancer in patients aged ≥50 years to provide a practical tool for the clinical management of patients with pancreatic ductal adenocarcinoma.

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A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

Chen

et al. 2022

Cancer Medicine

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Introduction:The current American Joint Committee on Cancer (AJCC) M1a staging of non-small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis. Methods: Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan-Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results: The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1-year and 0.727 for 2-year OS prediction; validation cohort: 0.737 for 1-year and 0.734 for 2-year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1-year: 0.327; 2-year: 0.302) and IDI (1-year: 0.138; 2-year: 0.130). Conclusion: Our study established a nomogram for the prediction of 1-and 2year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare How to cite this article: Chen H, Huang C, Ge H, et al. A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases.

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A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

Cited by 4 publications

References 39 publications

Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research

A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

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