Journal of International Oncology ›› 2025, Vol. 52 ›› Issue (3): 144-151.doi: 10.3760/cma.j.cn371439-20241113-00022

• Original Article • Previous Articles     Next Articles

Predictive value of a combined model for lymph node metastasis in NSCLC based on primary lesion radiomics from 18F-FDG PET/CT

Lai Ruihe1, Teng Yue1, Rong Jian2, Sheng Dandan3, Geng Yuzhi3, Chen Jianxin2, Jiang Chong4, Ding Chongyang5, Zhou Zhengyang6()   

  1. 1Department of Nuclear Medicine,Nanjing Drum Tower Hospital,Clinical Medical School of Nanjing Medical University,Nanjing 210008,China
    2Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education),School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
    3Department of Nuclear Medicine,Second Affiliated Hospital of Nanjing Medical University,Nanjing 210011,China
    4Department of Nuclear Medicine,West China Hospital of Sichuan University,Chengdu 610041,China
    5Department of Nuclear Medicine,First Affiliated Hospital with Nanjing Medical University,Jiangsu Province Hospital,Nanjing 210029,China
    6Department of Radiology,Nanjing Drum Tower Hospital,Clinical Medical School of Nanjing Medical University,Nanjing 210008,China
  • Received:2024-11-13 Revised:2025-01-03 Online:2025-03-08 Published:2025-04-02
  • Contact: Zhou Zhengyang,Email:zyzhou@nju.edu.cn
  • Supported by:
    Nanjing Health Science and Technology Development Special Fund(YKK24090)

Abstract:

Objective To evaluate the value of a combined model based on primary lesion 18F-fluorodeoxyglucose(18F-FDG) PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer(NSCLC). Methods A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set(n=142) and the validation set(n=61) at a ratio of 7∶3. A predictive model was developed in the training set, and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator(LASSO), random forest, and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score(Radscore). Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic(ROC) curves and clinical application value was assessed by calibration curves and decision curve analysis(DCA). Results Among 203 NSCLC patients, 116 had lymph node metastasis, with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening, and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that, sex(OR=0.48, 95%CI:0.24-0.95, P=0.036), tumor marker levels(OR=3.81, 95%CI:1.84-7.91, P<0.001), long diameter of tumor(OR=2.56, 95%CI:1.27-5.16, P=0.009), short diameter of tumor(OR=3.73, 95%CI:1.75-7.92, P=0.001), vacuolar sign(OR=0.32, 95%CI:0.12-0.86, P=0.024), ring-like metabolism(OR=3.67, 95%CI:1.33-10.13, P=0.012), maximum standardized uptake value(SUVmax)(OR=6.57, 95%CI:3.03-14.25, P<0.001), metabolic tumor volume(MTV)(OR=2.91, 95%CI:1.43-5.92, P=0.003), total lesion glycolysis(TLG)(OR=4.23, 95%CI:2.08-8.59, P<0.001), Radscore-PET(OR=21.93, 95%CI:9.04-53.20, P<0.001) and Radscore-CT(OR=13.72, 95%CI:6.12-30.76, P<0.001) were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that, tumor marker levels(OR=2.55, 95%CI:1.11-5.90, P=0.028), vacuolar sign(OR=0.26, 95%CI:0.08-0.83, P=0.023), SUVmaxOR=5.94, 95%CI:1.99-17.75, P=0.001), Radscore-PET(OR=25.51, 95%CI:5.92-110.22, P<0.001), and Radscore-CT(OR=8.68, 95%CI:2.73-27.61, P<0.001) were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors, models were constructed:the traditional model(tumor marker levels, vacuolar sign, SUVmax), the PET model(SUVmax, Radscore-PET), the CT model(vacuolar sign, Radscore-CT), and the combined model(tumor marker levels, vacuolar sign, SUVmax, Radscore-PET, Radscore-CT). ROC curve analysis showed that, the area under curve(AUC) of the traditional, PET, CT, and combined models in the training set were 0.75(95%CI:0.67-0.82), 0.90(95%CI:0.84-0.95), 0.85(95%CI:0.78-0.90), and 0.94(95%CI:0.88-0.97), respectively. The predictive value of the combined model was higher than that of the traditional model(Z=5.01, P<0.001), the PET model(Z=1.99, P=0.047), and the CT model(Z=3.25, P=0.001). In the validation set, the AUCs for the traditional model, PET model, CT model, and combined model were 0.65(95%CI:0.52-0.77), 0.86(95%CI:0.74-0.93), 0.85(95%CI:0.73-0.93), and 0.90(95%CI:0.80-0.96), respectively. The predictive value of the combined model was superior to that of the traditional model(Z=3.23, P=0.001). The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%, while in the validation set, the sensitivity and specificity were 82.61% and 94.74%, respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions Tumor marker levels, vacuolar sign, SUVmax, Radscore-PET, and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.

Key words: Carcinoma, non-small-cell lung, Positron emission tomography computed tomography, Radiomics, Lymph node metastasis