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DOI 10.1136/bmjresp-2023-001926

Медицинская библиотека · BMJ open respiratory research · 2024

Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.

Kojima Kensuke, Samejima Hironobu, Okishio Kyoichi, Tokunaga Toshiteru, Yoon Hyungeun, Atagi Shinji

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

BACKGROUND

The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undetermined. This study aimed to elucidate the impact of the number of dissected lymph nodes on the risk of postoperative recurrence of non-small cell lung cancer (NSCLC) using machine learning algorithms and statistical analyses.

METHODS

We retrospectively analysed 650 patients with NSCLC who underwent complete resection. Five machine learning models were trained using clinicopathological variables to predict postoperative recurrence. The relationship between the number of dissected lymph nodes and postoperative recurrence was investigated in the best-performing model using Shapley additive explanations values and partial dependence plots. Multivariable Cox proportional hazard analysis was performed to estimate the HR for postoperative recurrence based on the number of dissected nodes.

RESULTS

The random forest model demonstrated superior predictive performance (area under the receiver operating characteristic curve: 0.92, accuracy: 0.83, F1 score: 0.64). The partial dependence plot of this model revealed a non-linear dependence of the number of dissected lymph nodes on recurrence prediction within the range of 0-20 nodes, with the weakest dependence at 10 nodes. A linear increase in the dependence was observed for ≥20 dissected nodes. A multivariable analysis revealed a significantly elevated risk of recurrence in the group with ≥20 dissected nodes in comparison to those with <20 nodes (adjusted HR, 1.45; 95% CI 1.003 to 2.087).

CONCLUSIONS

The number of dissected lymph nodes was significantly associated with the risk of postoperative recurrence of NSCLC. The risk of recurrence is minimised when approximately 10 nodes are dissected but may increase when >20 nodes are removed. Limiting lymph node dissection to approximately 20 nodes may help to preserve a favourable antitumour immune environment. These findings provide novel insights into the optimisation of lymph node dissection during lung cancer surgery.

Для цитирования: Kojima Kensuke, Samejima Hironobu, Okishio Kyoichi, Tokunaga Toshiteru, Yoon Hyungeun, Atagi Shinji. Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.. BMJ open respiratory research. 2024. DOI: 10.1136/bmjresp-2023-001926.

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