Purpose: To investigate whether a combination of radiomics and automatic machine learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of primary breast cancer can non-invasively predict axillary sentinel lymph node (SLN) metastasis.Methods: 62 patients who received a DCE-MRI breast scan were enrolled. Tumor resection and sentinel lymph node (SLN) biopsy were performed within 1 week after the DCE-MRI examination. According to the time signal intensity curve, the volumes of interest (VOIs) were delineated on the whole tumor in the images with the strongest enhanced phase. Datasets were randomly divided into two sets including a training set (~80%) and a validation set (~20%). A total of 1,409 quantitative imaging features were extracted from each VOI. The select K best and least absolute shrinkage and selection operator (Lasso) were used to obtain the optimal features. Three classification models based on the logistic regression (LR), XGboost, and support vector machine (SVM) classifiers were constructed. Receiver Operating Curve (ROC) analysis was used to analyze the prediction performance of the models. Both feature selection and models construction were firstly performed in the training set, then were further tested in the validation set by the same thresholds.Results: There is no significant difference between all clinical and pathological variables in breast cancer patients with and without SLN metastasis (P > 0.05), except histological grade (P = 0.03). Six features were obtained as optimal features for models construction. In the validation set, with respect to the accuracy and MSE, the SVM demonstrated the highest performance, with an accuracy, AUC, sensitivity (for positive SLN), specificity (for positive SLN) and Mean Squared Error (MSE) of 0.85, 0.83, 0.71, 1, 0.26, respectively.Conclusions: We demonstrated the feasibility of combining artificial intelligence and radiomics from DCE-MRI of primary tumors to predict axillary SLN metastasis in breast cancer. This non-invasive approach could be very promising in application.
Long noncoding RNA DNAJC3-AS1 (lncRNA DNAJC3-AS1) has been probed in many studies, while the regulatory mechanism of DNAJC3-AS1 on papillary thyroid carcinoma (PTC) via regulating microRNA (miR)-27a-3p remains inadequate. This research aims to depict the role of DNAJC3-AS1, miR-27a-3p, collagen, and calcium-binding EGF domain-containing protein 1 (CCBE1) on PTC development. DNAJC3-AS1, miR-27a-3p, and CCBE1 expression levels in PTC tissues and adjacent normal tissues were tested. The relation of DNAJC3-AS1, miR-27a-3p, and CCBE1 was analyzed. DNAJC3-AS1 and miR-27a-3p and CCBE1-related oligonucleotides were transfected into IHH-4 cells to investigate their role in PTC development. Cell tumorigenicity was detected by in vivo assay. DNAJC3-AS1 and CCBE1 expressed highly and miR-27a-3p expressed lowly in PTC. Downregulation of DNAJC3-AS1, upregulating miR-27a-3p or downregulating CCBE1 impaired the malignant behaviors of IHH-4 cells. Depletion of miR-27a-3p reversed the DNAJC3-AS1 suppression-induced phenotypic inhibition of IHH-4 cells. DNAJC3-AS1 bound to miR-27a-3p and CCBE1 as a target of miR-27a-3p. Our study highlights that DNAJC3-AS1 inhibits miR-27a-3p to promote CCBE1 expression, thereby facilitating PTC development. This study affords distinguished therapeutic strategies and novel research directions for PTC treatment.
KEYWORDScolon cancer, lymphocyte to monocyte ratio, prognostic nutritional index, prognosis, systemic inflammation 2 Abstract Background: Systemic inflammation plays an important part in tumorigenesis and progression. The predictive values of the preoperative lymphocyte to monocyte ratio (LMR) and prognostic nutritional index (PNI) in colon cancer remained unclear.Methods: A total of 308 patients with colon cancer undergoing radical resection were enrolled and analyzed. The receiver operating curves were applied to identify the thresholds for these biomarkers.Kaplan-Meier method and multivariate analysis were used to identify independent prognostic factors.Results: The univariate analysis showed that elevated LMR and PNI were significantly correlated with better overall survival and progression-free survival. The multivariate analysis showed that LMR and PNI were the independent prognostic factors for overall survival.Conclusions: Preoperative LMR and PNI could serve as useful prognostic factor in patients with colon cancer undergoing radical resection.
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