Background-The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and welldifferentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advancedstage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds.Methods-We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the

The DNA base excision repair gene APE1 involves in DNA damage repair pathway and overexpression in a variety of human cancers. Analyses of patients with non-small cell lung cancer (NSCLC) suggested that multiple factors associated with prognosis of NSCLC patients. Further investigation showed that APE1 expression was able to predict the progression-free survival and overall survival in patients with NSCLC and correlated with lymph node metastasis. Intriguingly, as a stratification of APE1-141 SNPs in APE1 positive expression, we also found APE1-141 GT/GG was identified as a marker for prediction of poor survival in NSCLC patients. In the in vitro experiments, the results showed that when APE1 expression was inhibited by siRNA or AT101 (an APE1 inhibitor), the migration and invasion of NSCLC cells were suppressed. Furthermore, epithelial-mesenchymal transition (EMT) markers was tested to provide evidence that APE1 promoted NSCLC EMT through interaction with SirT1. Using NSCLC xenograft models, we confirmed that AT101 shrank tumor volumes and inhibited lymph node metastasis. In conclusion, APE1 could be a potential target for patients with NSCLC metastasis and AT101 is a potent inhibitor in further treatment of NSCLC patients.

PurposeTo investigate the capacity of Superb Microvascular Imaging (SMI) to detect microvascular details and to explore the different SMI features in various focal liver lesions (FLLs) and the correlation between SMI and microvessel density (MVD).Method: Eighty-three liver lesions were enrolled in our study, including 35 hepatocellular carcinomas (HCCs) and 48 non-HCCs. All patients underwent color Doppler flow imaging (CDFI) and SMI examination and were categorized into subgroups according to Adler semiquantitative grading (grade 0–3) or the microvascular morphologic patterns (pattern a-f). The correlation between SMI blood flow signal percentage and MVD was assessed.ResultsCompared with CDFI, SMI detected more high-level blood flow signals (grade 2–3) and more hypervascular supply patterns (pattern e-f) in HCCs (p < 0.05). Furthermore, more hypervascular supply patterns and fewer hypovascular supply patterns were detected in HCC compared with non-HCC (p < 0.05). Based on Adler’s grading or microvascular morphologic patterns, the areas under the receiver operating characteristic curve were 0.696 and 0.760 for SMI, 0.583 and 0.563 for CDFI. The modality of “SMI-microvascular morphologic pattern” showed the best diagnostic performance. There was significant correlation between MVD and the SMI blood flow signal percentage (vascular index, VI) in malignant lesions (r = 0.675, p < 0.05).ConclusionSMI was superior to CDFI in detecting microvascular blood flow signals. More hypervascular supply patterns were depicted in HCC than in non-HCC, suggesting a promising diagnostic value for SMI in the differentiation between HCC and non-HCC. Meanwhile, we were the first to demonstrate that SMI blood flow signal percentage (VI) was correlated with MVD in malignant lesions.

Comparing with CDFI, SMI could effectively detect vascularity, vascular characteristics and distribution and annular blood flow in renal tumour. SMI appears more sensitive and precise in distinguishing benign renal mass from malignant tumour. Advances in knowledge: SMI seems potentially valuable in evaluating renal tumour vascularity and in differentiating benign from malignant renal tumours.

Gastric cancer remains a disease with a high mortality rate despite of multiple therapeutic strategies. So far, it is very important to develop new treatment approaches to improve current therapeutic efficacy in gastric cancer. Apurinic/apyrimidinic endonuclease (APE1) involves in DNA base excision repair (BER) during DNA damage pathway. APE1 was found to be associated with poor overall survival with gastric cancer patients. In the in vitro experiment, we tested APE1 inhibitor-AT101 could potently inhibit gastric cancer cell growth and further induce cancer cell apoptosis and autophagy through p53-dependent pathway. Downregulation of APE1 by AT101 has ability to suppress gastric cancer cell migration and renewal through inhibition of CD133, Nanog and LC3expression. Based on findings that Her-2 positive expression cases has poor prognosis from our dataset and TCGA database, we investigated the role of AT101 in synergetic efficacy with 5-FU treatment in Her-2 overexpression gastric cancer in vivo, indicating that AT101 is able to enhance 5-FU in the shrinkage of xenograft mice tumor and induction of cell apoptosis. In summary, the data obtained from our study showed APE1 is guided as a potential therapeutic target for gastric cancer. AT101 could be regarded as a potent inhibitor to promote chemotherapeutic sensitivity in patients with gastric cancer.