Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

• Published in: World journal of gastroenterology : WJG 2024-02, Vol.30 (6), p.542-555
• Main Authors: Ge, Hui-Ting, Chen, Jian-Wu, Wang, Li-Li, Zou, Tian-Xiu, Zheng, Bin, Liu, Yuan-Fen, Xue, Yun-Jing, Lin, Wei-Wen
• Format: Article
• Language: English
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v30.i6.542

Lymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients. To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately. This study used a retrospective dataset involving 257 GC patients (training cohort, = 172; validation cohort, = 85). First, several clinical indicators, including serum tumor markers, CT-TN stages and CT-detected extramural vein invasion (CT-EMVI), were extracted, as were quantitative spectral CT parameters from the delineated tumor regions. Next, a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters. A logistic regression (LR)-based nomogram model was subsequently constructed to predict LVI/PNI status, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). In both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant ( < 0.05). LR analysis of the training group showed preoperative CT-T stage, CT-EMVI, single-energy CT values of 70 keV of venous phase (VP-70 keV), and the ratio of standardized iodine concentration of equilibrium phase (EP-NIC) were independent influencing factors. The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824, respectively, which were slightly greater than those of CT-T and CT-EMVI (AUC = 0.793, 0.762). The nomogram combining CT-T stage, CT-EMVI, VP-70 keV and EP-NIC yielded AUCs of 0.918 (0.866-0.954) and 0.874 (0.784-0.936) in the training and validation cohorts, which are significantly higher than using each of single independent factors ( < 0.05). The st