RESUMO
Objectives: To develop and validate a CT-based radiomics nomogram that can provide individualized pretreatment prediction of the response to platinum treatment in small cell lung cancer (SCLC). Materials: A total of 134 SCLC patients who were treated with platinum as a first-line therapy were eligible for this study, including 51 patients with platinum resistance (PR) and 83 patients with platinum sensitivity (PS). The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were applied for feature selection and model construction. The selected texture features were calculated to obtain the radiomics score (Rad-score), and the predictive nomogram model was composed of the Rad-score and the clinical features selected by multivariate analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to assess the performance of the nomogram. Results: The Rad-score was calculated using 10 radiomic features, and the resulting radiomics signature demonstrated good discrimination in both the training set (area under the curve [AUC], 0.727; 95% confidence interval [CI], 0.627-0.809) and the validation set (AUC, 0.723; 95% CI, 0.562-0.799). To improve diagnostic effectiveness, the Rad-score created a novel prediction nomogram by combining CA125 and CA72-4. The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.900; 95% CI, 0.844-0.947) and the validation set (AUC, 0.838; 95% CI, 0.534-0.735). The radiomics nomogram proved to be clinically beneficial based on decision curve analysis. Conclusion: We developed and validated a radiomics nomogram model for predicting the response to platinum in SCLC patients. The outcomes of this model can provide useful suggestions for the development of tailored and customized second-line chemotherapy regimens.
RESUMO
Drug resistance reflects the evolution of tumors, which is the main cause of recurrence and death. Currently, EGFR-TKI treatment is the first-line therapy for lung adenocarcinoma (LUAD) patients. Although EGFR-TKI achieved good effects at the beginning, most of the LUAD patients eventually acquired resistance. Therefore, it's urgently need to develop a strong criterion for identifying these patients who may benefit from additional therapy. In this study, we established a three TKI resistant-related gene signature (DDIT4, OAS3, FSCN1), and determined that's an accuracy, independent and specific prognostic model for LUAD patients. Patients categorized as high-risk by this signature showed more sensitive to chemotherapy, and exhibited higher expression of common immune checkpoints such as PD-L1/B7H3/PD-L2/IDO1. Moreover, these patients were characterized by increased infiltration of M0 macrophage and activated memory CD4+ T cells. The expression and prognostic values of DDIT4, FSCN1 and OAS3 were further confirmed in clinical data. In addition, experimental data showed that FSCN1 promoted LUAD development via PI3K/AKT signaling. In conclusion, this signature is highly predictive of prognostic in LUAD patients, and may serve as a powerful prediction tool for LUAD patients to further choose chemo- and immunotherapies.
