La Radiologia medica · 2026
Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Yang Daoyu, Kang Shaolei, Zhao Xunran, Xie Xiaojie, Lv Fajin, Li Guochen, Liu Jian, Han Zhiquan, Zhang Xiaoxuan, Zeng Xianchun
Аннотация
OBJECTIVES: To evaluate the potential of spectral detector computed tomography (SDCT) combined with intratumoral and peritumoral radiomics for noninvasive characterization of lung adenocarcinomas (LUAD) presenting as ground-glass nodules (GGNs).
METHODS: This retrospective multicenter study included patients from two centers. In Center 1, 241 patients with GGNs (adenocarcinoma in situ [AIS], n = 42; minimally invasive adenocarcinoma [MIA], n = 138; invasive adenocarcinoma [IAC], n = 61) were randomly split into training and test sets (7:3). Center 2 served as an external validation cohort including 87 patients (AIS, n = 35; MIA, n = 27; IAC, n = 25). All patients underwent unenhanced dual-layer SDCT examinations. Radiomics features were extracted from intratumoral and peritumoral regions (1-5 mm) based on SDCT-derived conventional polychromatic images (CPI), effective atomic number (Zeff), and electron density (ED) maps. Machine learning models were then developed and evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).
RESULTS: Among all peritumoral models, the 2 mm peritumoral multiparameter fusion model showed the best performance, with macro-average AUCs of 0.795 and 0.768 in the internal and external validation sets, respectively. Combining intratumoral and peritumoral multiparameter models further improved performance, achieving internal AUCs of 0.814, 0.863, and 0.988 and external AUCs of 0.898, 0.774, and 0.812 for AIS, MIA, and IAC, respectively.
CONCLUSIONS: The combination of intratumoral and peritumoral multiparametric radiomics derived from SDCT may enhance the noninvasive differentiation of LUAD presenting as GGNs, potentially serving as a valuable tool for supporting clinical decision-making.
METHODS: This retrospective multicenter study included patients from two centers. In Center 1, 241 patients with GGNs (adenocarcinoma in situ [AIS], n = 42; minimally invasive adenocarcinoma [MIA], n = 138; invasive adenocarcinoma [IAC], n = 61) were randomly split into training and test sets (7:3). Center 2 served as an external validation cohort including 87 patients (AIS, n = 35; MIA, n = 27; IAC, n = 25). All patients underwent unenhanced dual-layer SDCT examinations. Radiomics features were extracted from intratumoral and peritumoral regions (1-5 mm) based on SDCT-derived conventional polychromatic images (CPI), effective atomic number (Zeff), and electron density (ED) maps. Machine learning models were then developed and evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).
RESULTS: Among all peritumoral models, the 2 mm peritumoral multiparameter fusion model showed the best performance, with macro-average AUCs of 0.795 and 0.768 in the internal and external validation sets, respectively. Combining intratumoral and peritumoral multiparameter models further improved performance, achieving internal AUCs of 0.814, 0.863, and 0.988 and external AUCs of 0.898, 0.774, and 0.812 for AIS, MIA, and IAC, respectively.
CONCLUSIONS: The combination of intratumoral and peritumoral multiparametric radiomics derived from SDCT may enhance the noninvasive differentiation of LUAD presenting as GGNs, potentially serving as a valuable tool for supporting clinical decision-making.