Cancer medicine · 2024
Development and validation of a nomogram to predict leptomeningeal metastases in lung adenocarcinoma: Cervical lymph node metastasis is an important association factor.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Hua Xiaoyu, Feng Weifeng, Ye Minting, Lai Mingyao, Yu Xiaojun, Sun Mengnan, Li Juan, Ai Ruyu, He Yanlin, Cai Linbo, Shi Changzheng, Liu Xiangning
Аннотация
BACKGROUND: The goal of this study was to create a nomogram using routine parameters to predict leptomeningeal metastases (LMs) in advanced lung adenocarcinoma (LAC) patients to prevent needless exams or lumbar punctures and to assist in accurately diagnosing LMs.
METHODS: Two hundred and seventy-three patients with LMs and brain metastases were retrospectively reviewed and divided into derivation (n = 191) and validation (n = 82) cohorts using a 3:7 random allocation. All LAC patients with LMs had positive cerebrospinal fluid cytology results and brain metastases confirmed by magnetic resonance imaging. Binary logistic regression with backward stepwise selection was used to identify significant characteristics. A predictive nomogram based on the logistic model was assessed through receiver operating characteristic curves. The validation cohort and Hosmer-Lemeshow test were used for internal validation of the nomogram.
RESULTS: Five clinicopathological parameters, namely, gene mutations, surgery at the primary lung cancer site, clinical symptoms of the head, N stage, and therapeutic strategy, were used as predictors of LMs. The area under the curve was 0.946 (95% CI 0.912-0.979) for the training cohort and 0.861 (95% CI 0.761-0.961) for the internal validation cohort. There was no significant difference in performance between the two cohorts (p = 0.116). In the internal validation, calibration plots revealed that the nomogram predictions were well suited to the actual outcomes.
CONCLUSIONS: We created a user-friendly nomogram to predict LMs in advanced lung cancer patients, which could help guide treatment decisions and reduce unnecessary lumbar punctures.
METHODS: Two hundred and seventy-three patients with LMs and brain metastases were retrospectively reviewed and divided into derivation (n = 191) and validation (n = 82) cohorts using a 3:7 random allocation. All LAC patients with LMs had positive cerebrospinal fluid cytology results and brain metastases confirmed by magnetic resonance imaging. Binary logistic regression with backward stepwise selection was used to identify significant characteristics. A predictive nomogram based on the logistic model was assessed through receiver operating characteristic curves. The validation cohort and Hosmer-Lemeshow test were used for internal validation of the nomogram.
RESULTS: Five clinicopathological parameters, namely, gene mutations, surgery at the primary lung cancer site, clinical symptoms of the head, N stage, and therapeutic strategy, were used as predictors of LMs. The area under the curve was 0.946 (95% CI 0.912-0.979) for the training cohort and 0.861 (95% CI 0.761-0.961) for the internal validation cohort. There was no significant difference in performance between the two cohorts (p = 0.116). In the internal validation, calibration plots revealed that the nomogram predictions were well suited to the actual outcomes.
CONCLUSIONS: We created a user-friendly nomogram to predict LMs in advanced lung cancer patients, which could help guide treatment decisions and reduce unnecessary lumbar punctures.