Can full-volume dual-energy CT quantitative parameter nomogram predict the new IASLC grade of pure solid invasive pulmonary adenocarcinoma

OBJECTIVES: This study aimed to develop and construct a predictive model based on the quantitative parameters of full-volume dual-energy computed tomography (DECT) to forecast the International Association for the Study of Lung Cancer classification of non-mucinous invasive pulmonary adenocarcinoma (IPA). METHODS: The preoperative clinical and imaging data of 161 patients with pure solid type non-mucinous IPA from September 2021 to May 2024 were retrospectively analysed. The semiautomated software was used to perform full-volume segmentation of the lesions and the associated DECT quantitative parameters were recorded. Through univariate and multivariate logistic regression analyses, we identified independent characteristic variables that distinguished high-grade from low-grade non-mucinous IPA. We subsequently used these characteristic variables to construct a multiparameter model. RESULTS: Volume, slope of the spectral curve (λ40keV-100keV) and normalized iodine concentration (NIC) were identified as independent feature variables to distinguish low-grade and high-grade non-mucinous IPA. By utilizing these 3 variables, we constructed a quantitative visualization nomogram to distinguish the new IASLC grade of non-mucinous IPA. The model exhibited excellent performance in both the training and testing groups, with area under the curve (AUC) values of 0.884 (95% CI: 0.826-0.943) and 0.848 (95% CI: 0.738-0.958), respectively. CONCLUSION: This study successfully established and validated a nomogram based on DECT quantitative parameters, which can effectively differentiate high-grade and low-grade non-mucinous IPA and provides potential value for clinical decision-making. ADVANCES IN KNOWLEDGE: This study is the first attempt to apply a nomogram based on DECT to assess the invasiveness of non-mucous IPA.