Academic radiology · 2025
A Deep Learning Model for Preoperative Prediction of Lymph Node Metastasis in cT1-Stage Lung Adenocarcinoma: A Multicenter External Validation Study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Yan Qinqin, Wei Ying, Cheng Zenghui, Xue Zhong, Shi Feng, Yang Shan, Zhang Zhiyong, Yan Fuhua, Shan Fei
Аннотация
PURPOSE: To develop and validate a deep learning (DL) model for preoperative prediction of lymph node metastasis (LNM) in clinical T1-stage lung adenocarcinoma (LUAD), and to compare its performance with conventional semantic and radiomics signatures.
METHODS: This multicenter retrospective study enrolled 2503 patients with 2568 pathologically confirmed cT1-stage LUAD nodules from eight institutions. Data from six centers (1994 patients/2059 nodules) were randomly divided into training (1600 patients/1664 nodules) and internal test (394 patients/395 nodules) cohorts. Two independent external validation cohorts (Set-1: 283 patients/nodules; Set-2: 226 patients/nodules) were included. Three predictive models were developed as follows: 1) a semantic model incorporating spiculation, pleural traction, air bronchogram, and vacuole signs; 2) a radiomics model; and 3) ResLNM-a residual network-based DL model. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).
RESULTS: Both ResLNM and radiomics models significantly outperformed the semantic model in predicting LNM (AUC range: 0.71-0.85 and 0.72-0.84 vs. 0.58-0.74, respectively; P<0.05). While ResLNM demonstrated comparable performance to the radiomics model in the internal test set (AUC: 0.85, 0.81-0.89 vs. 0.84, 0.80-0.88; P=0.624) and external validation set-2 (0.71, 0.63-0.79 vs. 0.72, 0.64-0.80; P=0.472), it achieved superior accuracy in external validation set-1 (0.82, 0.76-0.88 vs. 0.77, 0.71-0.83; P=0.039). DCA confirmed the clinical superiority of ResLNM. Notably, integrating ResLNM with either semantic or radiomics signatures provided no incremental value (P>0.05), whereas incorporating radiologically mediastinal enlarged lymph node status (short-axis diameter ≥10mm) significantly enhanced predictive performance, achieving AUCs of 0.88 (95%CI:0.85-0.92), 0.89 (95%CI:0.84-0.94), and 0.76 (95%CI:0.68-0.83) in the test set and two validation cohorts, respectively.
CONCLUSION: The ResLNM model provides a clinically feasible tool for preoperative LNM prediction in cT1-stage LUAD, outperforming conventional semantic and radiomics approaches. Its performance can be further optimized by integrating routinely available lymph node size criteria, offering potential to refine surgical decision-making and reduce overtreatment.
METHODS: This multicenter retrospective study enrolled 2503 patients with 2568 pathologically confirmed cT1-stage LUAD nodules from eight institutions. Data from six centers (1994 patients/2059 nodules) were randomly divided into training (1600 patients/1664 nodules) and internal test (394 patients/395 nodules) cohorts. Two independent external validation cohorts (Set-1: 283 patients/nodules; Set-2: 226 patients/nodules) were included. Three predictive models were developed as follows: 1) a semantic model incorporating spiculation, pleural traction, air bronchogram, and vacuole signs; 2) a radiomics model; and 3) ResLNM-a residual network-based DL model. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).
RESULTS: Both ResLNM and radiomics models significantly outperformed the semantic model in predicting LNM (AUC range: 0.71-0.85 and 0.72-0.84 vs. 0.58-0.74, respectively; P<0.05). While ResLNM demonstrated comparable performance to the radiomics model in the internal test set (AUC: 0.85, 0.81-0.89 vs. 0.84, 0.80-0.88; P=0.624) and external validation set-2 (0.71, 0.63-0.79 vs. 0.72, 0.64-0.80; P=0.472), it achieved superior accuracy in external validation set-1 (0.82, 0.76-0.88 vs. 0.77, 0.71-0.83; P=0.039). DCA confirmed the clinical superiority of ResLNM. Notably, integrating ResLNM with either semantic or radiomics signatures provided no incremental value (P>0.05), whereas incorporating radiologically mediastinal enlarged lymph node status (short-axis diameter ≥10mm) significantly enhanced predictive performance, achieving AUCs of 0.88 (95%CI:0.85-0.92), 0.89 (95%CI:0.84-0.94), and 0.76 (95%CI:0.68-0.83) in the test set and two validation cohorts, respectively.
CONCLUSION: The ResLNM model provides a clinically feasible tool for preoperative LNM prediction in cT1-stage LUAD, outperforming conventional semantic and radiomics approaches. Its performance can be further optimized by integrating routinely available lymph node size criteria, offering potential to refine surgical decision-making and reduce overtreatment.