International journal of surgery (London, England) · 2026
A noninvasive approach based on ultrasonography and machine learning for selective cervical lymphadenectomy in thoracic esophageal cancer.
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Li Yike, Gao Yi, Li Bin, Zhang Yang, Zhao Yue, Deng Chaoqiang, Fan Fanfan, Pan Yunjian, Huang Qingyuan, Fu Fangqiu, Yu Yongfu, Ma Yuan, Sun Yihua, Zhang Yawei, Xiang Jiaqing, Chen Haiquan, Zhang Yiliang
Аннотация
BACKGROUND: The role of cervical lymph node dissection in esophageal squamous cell carcinoma (ESCC) surgery remains controversial. This study aims to explore a machine learning (ML) approach that integrates sonographic and clinical findings for the noninvasive evaluation of cervical lymph node involvement in patients with ESCC.
MATERIALS AND METHODS: The dataset contained 887 ESCC patients who underwent surgery and subsequent pathological examination of their cervical lymph nodes. Selected ML models were established to predict metastasis using patient characteristics and ultrasound evaluations. The models were tested through fivefold cross-validation and benchmarked against a baseline nomogram. The importance of each predictor variable was quantified by the permutation score.
RESULTS: Of the patients, 32.1% had confirmed cervical nodal metastasis. The random forest model exhibited a mean accuracy of 0.68 (95% confidence interval: 0.65-0.71), area under the curve of 0.72 (0.71-0.74), and F1 score of 0.56 (0.54-0.58), comparable to other models ( p > 0.1). All ML models showed superior predictive power over the baseline nomogram ( p < 0.05). The most critical predictor was the maximum cervical nodal diameter from ultrasound. Models using comprehensive baseline features surpassed those with sonographic data alone ( p < 0.001), while intraoperative pathology did not improve predictions.
CONCLUSIONS: This study highlights the diagnostic value of ultrasonography for cervical nodal metastasis in ESCC and proposes an ML-based noninvasive method to inform decisions on lymph node dissection. The predictive model may enhance surgical planning and enable personalized treatment strategies.
MATERIALS AND METHODS: The dataset contained 887 ESCC patients who underwent surgery and subsequent pathological examination of their cervical lymph nodes. Selected ML models were established to predict metastasis using patient characteristics and ultrasound evaluations. The models were tested through fivefold cross-validation and benchmarked against a baseline nomogram. The importance of each predictor variable was quantified by the permutation score.
RESULTS: Of the patients, 32.1% had confirmed cervical nodal metastasis. The random forest model exhibited a mean accuracy of 0.68 (95% confidence interval: 0.65-0.71), area under the curve of 0.72 (0.71-0.74), and F1 score of 0.56 (0.54-0.58), comparable to other models ( p > 0.1). All ML models showed superior predictive power over the baseline nomogram ( p < 0.05). The most critical predictor was the maximum cervical nodal diameter from ultrasound. Models using comprehensive baseline features surpassed those with sonographic data alone ( p < 0.001), while intraoperative pathology did not improve predictions.
CONCLUSIONS: This study highlights the diagnostic value of ultrasonography for cervical nodal metastasis in ESCC and proposes an ML-based noninvasive method to inform decisions on lymph node dissection. The predictive model may enhance surgical planning and enable personalized treatment strategies.