Search

PurposeIn clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI).Materials and methodsA total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity.ResultsIn the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10–15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3.ConclusionsOur model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.

Ionizing radiation is one of the common environmental carcinogens. miRNAs play critical roles in the processes of tumor occurrence, development, metastasis. However, the relationship between radiation-induced carcinogenesis and miRNA rarely reported. This study is aimed to investigate the effect of miRNAs on radiation-induced carcinogenesis. In this study we established the radiation-induced thymic lymphoma mice model. By using miRNA array of RTL tissue and predicting for miRNAs target genes, a miRNA-mRNA crosstalk network was established. Based on this network, we identified a critical miRNA, miR-486, which was the most down-regulated in the radiation-induced carcinogenesis. Then the function of miR-486 was confirmed by using knockout mice and cellular experiments. As a result, miR-486 could inhibit proliferation of mouse lymphoma cells by targeting IGF2BP3 mRNA. The adenovirus over-expression miR-486 vector reduced tumorigenesis in vivo. MiR-486 knockout mice have a strong tendency of radiation-induced carcinogenesis. In conclusion, miR-486 inhibits the proliferation of lymphoma cells and tumorigenesis induced by radiation through targeting IGF2BP3.