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Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer.

ABSTRACT: BACKGROUND:We characterized the performance characteristics of guideline-recommended invasive mediastinal staging (IMS) for lung cancer and developed a prediction model for nodal disease as a potential alternative approach to staging. METHODS:We conducted a prospective cohort study of adults with suspected/confirmed non-small cell lung cancer without evidence of distant metastatic disease (by computed tomography/positron emission tomography) who underwent nodal evaluation by IMS and/or at the time of resection. The true-positive rate was the proportion of patients with true nodal disease selected to undergo IMS based on guideline recommendations, and the false-positive rate was the proportion of patients without true nodal disease selected to undergo IMS. Logistic regression was used to predict nodal disease using radiographic predictors. RESULTS:Among 123 eligible subjects, 31 (25%) had pathologically confirmed nodal disease. A guideline-recommended invasive staging strategy had a true-positive rate and false-positive rate of 100% and 65%, respectively. The prediction model fit the data well (goodness-of-fit test, p = 0.55) and had excellent discrimination (optimism corrected c-statistic, 0.78; 95% confidence interval, 0.72 to 0.89). Exploratory analysis revealed that use of the prediction model could achieve a false-positive rate of 44% and a true-positive rate of 97%. CONCLUSIONS:A guideline-recommended strategy for IMS selects all patients with true nodal disease and most patients without nodal disease for IMS. Our prediction model appears to maintain (within a margin of error) the sensitivity of a guideline-recommended invasive staging strategy and has the potential to reduce the use of invasive procedures.

SUBMITTER: Verdial FC

PROVIDER: S-EPMC6535349 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer.

Verdial Francys C FC Madtes David K DK Hwang Billanna B Mulligan Michael S MS Odem-Davis Katherine K Waworuntu Rachel R Wood Douglas E DE Farjah Farhood F

The Annals of thoracic surgery 20190130 6

<h4>Background</h4>We characterized the performance characteristics of guideline-recommended invasive mediastinal staging (IMS) for lung cancer and developed a prediction model for nodal disease as a potential alternative approach to staging.<h4>Methods</h4>We conducted a prospective cohort study of adults with suspected/confirmed non-small cell lung cancer without evidence of distant metastatic disease (by computed tomography/positron emission tomography) who underwent nodal evaluation by IMS a ...[more]

PMID: 30710518

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Project description:BackgroundThe incidence of non-small cell lung cancer ranks second among malignant tumors, while the mortality rate ranks first. We established a prediction model for the long-term prognosis of lung cancer patients to accurately identify patients with a high risk of postoperative death and provide a theoretical basis for improving the prognosis of patients with non-small cell lung cancer.MethodsThe data of 277 non-small cell lung cancer patients who underwent radical lung cancer resection at Shanghai Fengxian District Central Hospital between January 2016 and December 2017 were retrospectively collected. The patients, who were followed up for 5 years, were divided into a deceased group (n=127) and survival group (n=150) according to whether the patients had died 5 years after surgery or not. The clinical characteristics of the two groups were observed, and the risk factors of death within 5 years of surgery in lung cancer patients were analyzed. A nomogram predictive model was then established to analyze the value of the model in predicting the death within 5 years of surgery in patients with non-small cell lung cancer.ResultsMultivariate logistics regression analysis showed that carcinoembryonic antigen (CEA) >193.5 ng/mL, stage III lung cancer, peritumor invasion, and vascular tumor thrombus were independent risk factors of tumor-specific death after surgery in patients with non-small cell lung cancer (P<0.05). R 4.0.3 statistical software was used to randomly divide the dataset into a training set and validation set. The sample size of the training set was 194, and the sample size of the validation set was 83. The area under the receiver operating characteristic (ROC) curve was 0.850 [95% confidence interval (CI): 0.796-0.905] in the training set, and it was 0.779 (95% CI: 0.678-0.880) in the validation set. In the validation set, the model was assessed using the Hosmer-Lemeshow goodness-of-fit test, with a chi-square value of 9.270 and a P value of 0.320.ConclusionsOur model could accurately identify high risk of death within 5 years of surgery in non-small cell lung cancer patients. Strengthening the management of high-risk patients may help improve the prognosis of these patients.

Dataset Information

Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer.

Publications

Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer.

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