Abstract
ObjectSTAS is associated with poor differentiation, KRAS mutation and poor recurrence-free survival. The aims of this study are to evaluate the ability of intra- and perinodular radiomic features to distinguish STAS at non-contrast CT.Patients and MethodsThis retrospective study included 216 patients with pathologically confirmed lung adenocarcinoma (STAS+, n = 56; STAS−, n = 160). Texture-based features were extracted from intra- and perinodular regions of 2, 4, 6, 8, 10, and 20 mm distances from the tumor edge using an erosion and expansion algorithm. Traditional radiologic features were also analyzed including size, consolidation tumor ratio (CTR), density, shape, vascular change, cystic airspaces, tumor–lung interface, lobulation, spiculation, and satellite sign. Nine radiomic models were established by using the eight separate models and a total of the eight VOIs (eight-VOI model). Then the prediction efficiencies of the nine radiomic models were compared to predict STAS of lung adenocarcinomas.ResultsAmong the traditional radiologic features, CTR, unclear tumor–lung interface, and satellite sign were found to be associated with STAS significantly, and the AUCs were 0.796, 0.677, and 0.606, respectively. Radiomic model of combined tumor bodies and all the distances of perinodular areas (eight-VOI model) had better predictive efficiency for predicting STAS+ lung adenocarcinoma. The AUCs of the eight-VOI model in the training and verification sets were 0.907 (95%CI, 0.862–0.947) in the training set, and 0.897 (95%CI, 0.784–0.985) in the testing set, and 0.909 (95%CI, 0.863–0.949) in the external validation set, and the diagnostic accuracy in the external validation set was 0.849.ConclusionRadiomic features from intra- and perinodular regions of nodules can best distinguish STAS of lung adenocarcinoma.
Highlights
In 2015, the World Health Organization (WHO) formally proposed a new mode of invasion of lung cancer: spread through air spaces (STAS), defined as “micropapillary clusters, solid nests or single cells spreading within air paces beyond the edge of the main tumor” [1]
There was a significant difference in the histological subtypes of lung adenocarcinoma between the STAS+ and STAS− groups (p < 0.0001): papillary, micropapillary, and solid types were predominant in STAS+ adenocarcinoma, while lepidic and acinar subtypes were more common in the STAS− group
This study analyzed the predictive value of traditional imaging features, radiomic features of the tumor body and the surrounding areas in the differential diagnosis of STAS+ lung adenocarcinoma
Summary
In 2015, the World Health Organization (WHO) formally proposed a new mode of invasion of lung cancer: spread through air spaces (STAS), defined as “micropapillary clusters, solid nests or single cells spreading within air paces beyond the edge of the main tumor” [1]. STAS can be considered the same as other more common invasive growth patterns, such as vessels or pleural infiltration [2]. It is associated with poor differentiation, K-RAS mutation, and poor recurrence-free survival (RFS). Studies have confirmed [3,4,5] that STAS is an important prognostic indicator for early lung adenocarcinoma, and the way to reduce the recurrence rate of early lung cancer after STAS is to change localized resection to a lobectomy and postoperative adjuvant chemotherapy. If STAS can be detected before surgery, it can provide important information for the choice of resection method and whether adjuvant chemotherapy is given to patients with early lung adenocarcinoma after surgery. Current studies [6, 7] have shown that frozen pathological sections before surgery cannot diagnose STAS, and there is a lack of reliable CT signs for preoperative diagnosis
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