Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods:We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra-and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Sun et al. Ultrasound-CNN Predicted Breast Cancer MetastasisConclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Compared with microwave ablation (MWA), percutaneous radiofrequency ablation (RFA) and laser ablation (LA) have been recommended as minimally invasive treatments for patients with symptomatic benign thyroid nodules (BTNs) because of the large number of clinical applications. This prospective multicenter study sought to evaluate the clinical outcomes of RFA and MWA for BTNs. In eight participating institutions, the total number of 1252 patients treated by RFA and MWA were 649 ones with 687 BTNs and 603 ones with 664 BTNs, respectively. The clinical outcomes including the nodular maximal diameter reduction ratio (MDRR), the nodular volume reduction ratio (VRR), and the incidence of complications were compared to evaluate the efficacy and safety of the two techniques. The results for the nodular MDRR and VRR in the RFA group were significantly better than those in the MWA group at 6 months and later follow-up, and the major complication rates of 4.78% and 6.63% in RFA and MWA groups showed no statistically significant differences. In conclusion, both RFA and MWA are safe and effective techniques for selected patients with symptomatic BTNs. The achieved MDRR and VRR in the RFA group were greater than those in the MWA group at 6 months and later follow-up.

A dual-in-dual synergistic strategy was proposed based on the self-assembly of combinatorial nanocapsules (NCs) from Janus camptothecin-floxuridine (CF) conjugate and the near-infrared absorber of 1,1'-dioctadecyl-3,3,3',3'-tetramethylindotricarbocyanine iodide (DiR) by introducing PEGylated phospholipid of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycerol)-2000] to increase the blood circulation time of NCs. Due to the use of amphiphilic CF and DiR themselves to form liposome-like nanocapsules, the obtained CF-DiR NCs owned a significantly high loading content, a stable co-delivery drug combinations, a no premature release, and an excellent photothermal conversion efficiency. The in vivo fluorescence imaging indicated that CF-DiR NCs could achieve a high tumor accumulation after an intravenous injection. The dual drugs of camptothecin and floxuridine could be coordinately released due to the hydrolysis of the ester bond by the esterase in tumor. The in vivo experiments showed that more cytotoxicity of the CF-DiR NCs-mediated chemo- and photothermal dual therapy to tumor cells could be clearly observed than the chemotherapy or photothermal therapy alone due to the synergistic effect, leading to no recurrence in the entire treatment. All of the results highlighted that CF-DiR NCs were highly effective theranostic agents that could be used for imaging-guided cancer chemophotothermal therapy to conquer an intrinsic resistance to chemotherapeutics.

Background Interstitial lung disease (ILD) is a common complication of connective tissue disease (CTD) and a leading cause of morbidity and mortality. There are various lung ultrasound (LUS) scoring systems with different lung intercostal spaces (LIS). The purpose of this meta-analysis was to find a simplified LUS method for the assessment of CTD-ILD. Methods We systematically retrieved lung ultrasound diagnostic studies on CTD-ILD in PubMed, Embase, and Web of Science databases. Summary diagnostic accuracy, including sensitivity, specificity, and area under the curve (AUC), was analyzed. Subgroup analysis was conducted according to different LIS and diseases. Results The 11 studies included in this meta-analysis comprised a total of 487 patients with CTD. The pooled sensitivity and specificity of the LUS were 0.859 (95% confidence interval (CI) 0.812–0.898) and 0.839 (95% CI 0.782–0.886), respectively, illustrating its great value for CTD-ILD diagnosis. In addition, there were six methods to evaluate LIS, including 72, 65, 50, 14, 10, and all LIS. The pooled sensitivity and specificity of 14 LIS were 0.982 (95% CI 0.904–1.000) and 0.875 (95% CI 0.710–0.965), respectively. The pooled positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odd ratio (DOR) of 14 LIS were 7.297 (95% CI 6.050–17.45), 0.029 (95% CI 0.006–0.147), and 292.30 (95% CI 35.53–2403.8), respectively. Moreover, the AUC for systemic sclerosis (SSc) and rheumatoid arthritis (RA) was 0.929 and 0.981, respectively; the DOR for SSc and RA was 42.93 (95% CI 17.75–103.79) and 80.24 (95% CI 8.107–796.09), respectively. Conclusions We found a modified and simplified method of LUS, by scanning 14 LIS in a short time, which had a very high sensitivity and specificity.

Purpose: Neoadjuvant chemotherapy (NACT) is increasingly adopted in the therapy of breast cancer (BC) patients with positive axillary nodes (cNþ), but the reliability and feasibility of sentinel lymph node biopsy (SLNB) following NACT are still controversial. The objective of the present study is to conduct an updated meta-analysis on this issue. Methods: A literature search was performed using PubMed, Cochrane, Embase, and Web of Science to identify papers published from January 1, 2000 to October 22, 2020 to research SLNB after NACT in BC patients. Studies that met the quality standard were enrolled for this meta-analysis. Results: A total of 3578 participants from 27 trials were included in this meta-analysis. The pooled estimate of the identification rate (IR) for SLNB was 91 %, and the false negative rate (FNR) was 15 %. The pooled negative prediction value (NPV), accuracy, specificity, and sensitivity were 82 %, 89 %, 97 %, and 85 %, respectively. In subgroup analysis, the application of dual mapping could clearly decrease the FNR. The FNR was significantly high in the luminal types, and it declined as more sentinel lymph nodes (SLNs) were removed. Conclusion: SLNB following NACT is now technically feasible for BC with cNþ. However, it must be emphasized that the FNR is unacceptable high.

No abstract

Garlic is generally used as a therapeutic reagent against various diseases, and numerous studies have indicated that garlic and its derivatives can reduce the risk of various types of human cancer. Diallyl trisulfide (DATS), a major member of garlic derivatives, could inhibit the cell proliferation by triggering either cell cycle arrest or apoptosis in a variety of cancer cell lines as shown in many studies. However, whether DATS has the same effect on human osteosarcoma cells remains unknown. In this study, we have attempted to analyze the effects of DATS on cell proliferation, cell cycle, induction of apoptosis, global protein expression pattern in a human osteosarcoma cell line Saos-2 cells, and the potential molecular mechanisms of the action of DATS. Saos-2 cells, a human osteosarcoma cell line, were treated with or without 25, 50, and 100 micromol/l DATS for various time intervals. The cell proliferation, cell cycle progression, and apoptosis were examined in this study. Then, after treatment with or without 50 micromol/l DATS for 48 h, protein add pattern in Saos-2 cells were systematically studied using two-dimensional electrophoresis and mass spectrometry. DATS could inhibit the proliferation of Saos-2 cells in a dose-dependent and time-dependent manner. Moreover, the percentage of apoptotic cell and cell arrest in G0/G1 phase was also dose-dependent and time-dependent upon DATS treatment. A total of 27 unique proteins in Saos-2 cells, including 18 downregulated proteins and nine upregulated proteins, were detected with significant changes in their expression levels corresponding to DATS administration. Interestingly, almost half of these proteins (13 of 27) are related to either the cell cycle or apoptosis. DATS has the ability to suppress cell proliferation of Saos-2 cells by blocking cell cycle progression and inducing apoptosis in a dose and time-dependent manner. The proteomic results presented, therefore, provide additional support to the hypothesis that DATS is a strong inducer of apoptosis in tumor cells. However, the exact molecular mechanisms, how these proteins significantly changed in the Saos-2 cell line upon DATS treatment, should be further studied.

Breast cancer has become the biggest threat to female health. Ultrasonic diagnosis of breast cancer based on artificial intelligence is basically a classification of benign and malignant tumors, which does not meet clinical demand. Besides, the current target detection method performs poorly in detecting small lesions, while it is clinically required to detect nodules below 2 mm. The objective of this study is to (a) propose a diagnostic method based on Breast Imaging Reporting and Data System (BI-RADS) and (b) increase its detectability of small lesions. We modified the framework of Faster R-CNN (Faster Region-based Convolutional Neural Network) by introducing multi-scale feature extraction and multi-resolution candidate bound extraction into the network. Then, it was trained using 852 images of BI-RADS C2, 739 images of C3, and 1662 images of malignancy (BI-RADS 4a/4b/4c/5/6). We compared our model with unmodified Faster R-CNN and YOLO v3 (You Only Look Once v3). The mean average precision (mAP) is significantly increased to 0.913, while its average detection speed is slightly declined to 4.11 FPS (frames per second). Meanwhile, its detectivity of small lesions is effectively improved. Moreover, we also tentatively applied our model on video sequences and got satisfactory results. We modified Faster R-CNN and trained it partly based on BI-RADS. Its detectability of lesions, as well as small nodules, was significantly improved. In view of wide coverage of dataset and satisfactory test results, our method can basically meet clinical needs.