In this study, a combined model based on the Radscore selected from radiomics features and the number of metastatic lesions selected from clinical features was constructed to predict the OS for ES-SCLC after chemotherapy and immunotherapy. The test results of the C-index and the KM curve were satisfactory, which indicated that the model established in this study can objectively and accurately stratify the survival of ES-SCLC patients.
Chen et al [13] reported on the value of radiomics for progression-free survival prediction for ES-SCLC after chemotherapy with etoposide and cisplatin. 5 and 6 radiomics features were extracted from the lung window and the enhanced mediastinal window, respectively. The C-index of the model constructed with 11 features was 0.7531 and the average C/D AUC was 0.8487 in the validation set, which was greater than that of the model constructed with lung window (C-index 0.6951, the mean C/D AUC was 0.7836) and enhanced mediastinal window (C-index was 0.7192, the mean C/D AUC was 0.7964). Another study [14] used radiomics to predict the efficacy of platinum-based chemotherapy and found an OS of 153 SCLC patients with lung window feature modeling. The results showed that the radiomics risk score was correlated with the OS, with a C-index of 0.72 in training set and 0.69 in validation set. The results of the two studies showed that the effectiveness of the lung window feature model was essentially identical, and the C-index of the validation set was 0.69. In this study, the radiomics features of the enhanced mediastinal window were extracted, and the C-index of the combined model established with the clinical features was 0.722 in the training set and 0.68 in the validation set, respectively. These results were similar to what was reported above.
Chen et al. showed that the predictive value of the model constructed by the radiomics features of different window positions was higher than that of the radiomics model with a single window position. According to the correlation study between pathological and radiomics features [15, 16], plain CT images reflected the uneven tissue and cell density caused by necrosis, bleeding, and degeneration inside the tumor. The enriched and deficient blood supply areas in the enhanced scan images reflect the heterogeneity of blood supply vessels in the tumor. The heterogeneity within the tumor was translated into a quantitative expression of radiomics pixel density and distribution characteristics. Therefore, the radiomics features acquired from both the plain sequence and the enhanced sequence is greater than that of a single sequence, and the combination of multiple sequences was needed for disease diagnosis in practice. It was suggested that the multi-sequence radiomics features can be extracted for modeling in subsequent studies, and the differences with the single sequence model can be analyzed statistically.
This study was the first to use radiomics to predict the prognosis for SCLC after first-line chemotherapy plus immunotherapy, which is unique from previous radiomics studies which predicted prognosis after chemotherapy was used alone. The C-index of this model was noted to be slightly different from other models examining only conventional chemotherapy.
There was also a limited amount of literature that reports on the application of radiomics in predicting the efficacy of immunotherapy. The study of melanoma indicated that radiomics has a certain value in predicting the efficacy of first-line immunotherapy [17]. By combining radiomics and clinical characteristics, a more realistic prognostic prediction model was constructed to accurately predict the survival rate of patients with ES-SCLC after first-line immunotherapy plus chemotherapy.
Traditionally, the prognosis of SCLC was mainly based on the TNM staging of AJCC. However, in this study, the T stage, N stage and M stage were not independent predictors of OS in ES-SCLC. Rather, only the number of metastases in the M1 stage was an independent factor, which was consistent with the results of previous literature [18]. In this study, the patients were divided into two subgroups based on those which had more than, or less than 5 metastatic lesions. The greater the number of metastatic lesions, the worse the prognosis, which was consistent with clinical practice.
This study did not find any evidence to suggest that smoking is an independent risk factor for OS in SCLC. In addition, the imbalance of male to female participants in this study may add a confounding variable. However, it has been well-documented that brain metastasis is a poor prognostic indicator [19]. Prophylactic whole brain irradiation (PCI) has been shown to reduce the incidence of brain metastasis and improve prognosis. In this study, some patients received PCI independently. As such, brain metastasis had no overall correlation with OS. Several studies [19, 21] have reported that thoracic radiotherapy could improve the OS of ES-SCLC. Some patients in this study cohort received chest radiotherapy after first-line immunotherapy plus chemotherapy. Therefore, the value of the combined radiomics model in this study may be affected by the treatment strategies that were used.
There are several limitations to this study. Firstly, as a single-center retrospective study, the design is susceptible to selective bias. The number of cases in this study is also relatively small for AI machine learning. A better prediction model may be necessary by expanding the cohort. Finally, the gender ratio of this study biases males due to the demographic of SCLC. A more balanced study may be required to draw definite conclusions.