A non-invasive approach based on ultrasonography and machine learning for selective cervical lymphadenectomy in thoracic esophageal cancer.
Authors
Affiliations (7)
Affiliations (7)
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Departments of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China.
- Institute of Thoracic Oncology, Fudan University, Shanghai, China.
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
- Chinese Institute for Brain Research, Beijing, China.
Abstract
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 non-invasive evaluation of cervical lymph node involvement in patients with ESCC. 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 permutation score. 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. This study highlights the diagnostic value of ultrasonography for cervical nodal metastasis in ESCC and proposes an ML-based non-invasive method to inform decisions on lymph node dissection. The predictive model may enhance surgical planning and enable personalized treatment strategies.