Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma

Background: The presence of occult lymph node metastasis (OLNM) has significant implications for the staging, treatment and prognosis of patients with tumors. This study aimed to explore the potential predictive value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters obtained via multimodal tumor tracking (MMTT) for OLNM in clinical stage 1 (c1) pure solid lung adenocarcinoma (LAC). Methods: A total of 123 patients diagnosed with c1 pure solid LAC were enrolled in this retrospective study, including 29 OLNM(+) and 94 OLNM(-) patients. MMTT was used to obtain the full volume of the tumor and extract quantitative spectral parameters. The differences in clinicopathological data and spectral quantitative parameters between OLNM(+) and OLNM(-) were compared, and the efficacy of related quantitative parameters in predicting OLNM were evaluated through receiver operating characteristic (ROC) curves. Five different machine learning (ML) methods were applied to construct a forecasting OLNM model. Results: and NIC had high diagnostic efficiency in predicting OLNM, with area under the curves (AUCs) of 0.710, 0.705 and 0.701, respectively. The multilayer perceptron (MLP) model achieved the best diagnostic performance among the five ML methods, with a higher average AUC of 0.778. Conclusions: SDCT quantitative parameters obtained via MMTT might offer new insights to help predict OLNM in c1 pure solid LAC patients. The prediction model constructed with the MLP model on the basis of clinicopathological data and spectral quantitative parameters has higher diagnostic efficiency and may further aid in clinical decision-making.