Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer.

Purpose: The aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice. Methods: We retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status. Results: The Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application. Conclusion: The combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics.

OBJECTIVE: In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way. MATERIALS AND METHODS: A total of 48 BC patients, who were initially diagnosed by 18F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model. RESULTS: In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_SphericityCT (CT Sphericity from SHAPE), and GLCM_ContrastCT (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, GLCM_ContrastCT was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05. CONCLUSIONS: A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.

Transcriptomic signature predicts the distant relapse in patients with ER+ breast cancer treated with tamoxifen for five years.

Tamoxifen is the most commonly used drug to treat estrogen receptor positive (ER+) breast cancer. However, many patients with ER+ breast cancer have experienced resistance and other adverse side effects following treatment with tamoxifen. Furthermore, clinical and pathological parameters have thus far failed to predict the efficiency of tamoxifen administration. Therefore, gene signature based models for the prediction of survival time of such patients are urgently needed. In the current study, gene expression levels and follow­up information of samples from GSE17705 and GSE22219 databases were used to construct a risk score model based on Cox multivariate regression. The expression levels of 10 genes were included in the model: CCNB2, CCNA2, FOXD1, WSB2, RBPMS, CTDSP1, BIN3, SLBP, EPRS, FTO. The samples in the high­risk group had a relative early distant relapse time period (median survival time of 3.75 years) compared with the patients in the low risk group (median survival time of 6.5 years, P<0.01). For further validation, a further two independent datasets (GSE26971, GSE58644) were assessed. The overall survival time period of patients with high­risk scores in these datasets was significantly longer than those with low­risk scores (P<0.01). Furthermore, the associations between clinical parameters and risk score were investigated, and it was revealed that the risk score was significantly correlated with tumor age, tumor stage and grade. In addition, a 5­year survival nomogram was plotted in order to facilitate the utilization of risk score along with other clinical data. In summary, using the transcriptomic profile, a multi­gene expression based risk score was developed and was revealed as being able to successfully predict the outcome of patients with ER+ breast cancer treated with tamoxifen for 5 years.