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Updated: Sep 19, 2025
Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer
Published on: May 20, 2016
Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict
Kaifang Liu1, Yang Cao2, Yiyang Gao2
1Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.
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:
The tumor volume (3.868 vs. 1.236 cm3) and proportion of patients with carcinoembryonic antigen (CEA) >5 ng/mL (41.4% vs. 10.6%) in OLNM(+) patients were significantly greater than those in OLNM(-) patients (P<0.001). Among the 17 SDCT quantitative parameters, except for the tumor-to-aortal virtual plain scan ratio (SARVNC), tumor-to-aortal enhancement ratio (SAR)70keV and SAR100keV (P=0.25, 0.11, 0.98), the remaining 14 quantitative parameters [SAR40keV, Δ40keV, Δ70keV, Δ100keV, contrast enhancement ratio (CER)40keV, CER70keV, CER100keV, normalized enhancement fraction (NEF)40keV, NEF70keV, NEF100keV, λ40-70keV, λ40-100keV, normalized iodine concentration (NIC), normalized effective atomic number (NZeff)] in the OLNM(+) group were significantly lower than those in the OLNM(-) group (P<0.05). NEF40keV, NEF70keV 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.
