CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.

Background: The occurrence of bone metastasis (BM) will seriously shorten the survival time of lung adenocarcinoma patients and aggravate the suffering of patients. Computed tomography (CT)-based clinical radiomics nomogram may help clinicians stratify the risk of BM in lung adenocarcinoma patients, thereby enabling personalized individualized clinical decision making. Methods: A total of 501 patients with lung adenocarcinoma from March 2017 to March 2019 were enrolled in the study. Based on plain chest CT images, 1130 radiomics features were extracted from each lesion. One-way analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO) algorithm were used for radiomics features selection. Univariate and multivariate analyses were used to screen for clinical characteristics and identify independent predictors of BM. Three models (radiomics model, clinical model and combined model) were constructed to predict BM in lung adenocarcinoma patients. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the three models. The DeLong test was used to compare the performance of the models. Results: Finally, the clinical model for predicting BM in lung adenocarcinoma patients was constructed based on 5 independent predictors: cytokeratin 19-fragments (CYFRA21-1), stage, Ki-67, edge, and lobulation. The radiomics model was constructed based on 5 radiomics features. The combined model incorporating clinical independent predictors and radiomics was constructed. In the validation cohort, the area under the curve (AUC) of the clinical model, radiomics model and combined model was 0.824, 0.842 and 0.866, respectively. Delong test showed that in the training cohort, the AUC values of the radiomics model and the combined model were statistically different (P=0.03), and the AUC values of the other models were not statistically different. DCA showed that the nomogram had a highest net clinical benefit. Conclusions: The CT-based clinical radiomics nomogram can be used as a non-invasive and quantitative method to help clinicians stratify the risk of BM in patients with lung adenocarcinoma, thereby enabling personalized clinical decision making.

Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study.

PURPOSE: To evaluate the performance of CT-based intratumoral, peritumoral and combined radiomics signatures in predicting prognosis in patients with osteosarcoma. METHODS: The data of 202 patients (training cohort:102, testing cohort:100) with osteosarcoma admitted to the two hospitals from August 2008 to February 2022 were retrospectively analyzed. Progression free survival (PFS) and overall survival (OS) were used as the end points. The radiomics features were extracted from CT images, three radiomics signatures（RSintratumoral, RSperitumoral, RScombined）were constructed based on intratumoral, peritumoral and combined radiomics features, respectively, and the radiomics score (Rad-score) were calculated. Kaplan-Meier survival analysis was used to evaluate the relationship between the Rad-score with PFS and OS, the Harrell's concordance index (C-index) was used to evaluate the predictive performance of the radiomics signatures. RESULTS: Finally, 8, 6, and 21 features were selected for the establishment of RSintratumoral, RSperitumoral, and RScombined, respectively. Kaplan-Meier survival analysis confirmed that the Rad-scores of the three RSs were significantly correlated with the PFS and OS of patients with osteosarcoma. Among the three radiomics signatures, RScombined had better predictive performance, the C-index of PSF prediction was 0.833 in the training cohort and 0.814 in the testing cohort, the C-index of OS prediction was 0.796 in the training cohort and 0.764 in the testing cohort. CONCLUSIONS: CT-based intratumoral, peritumoral and combined radiomics signatures can predict the prognosis of patients with osteosarcoma, which may assist in individualized treatment and improving the prognosis of osteosarcoma patients.

Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study.

OBJECTIVE: To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS). METHODS: In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index). RESULTS: Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764-0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681-0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS. CONCLUSION: The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients. CRITICAL RELEVANCE STATEMENT: Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS. KEY POINTS: • Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma.