The validity of neoadjuvant chemotherapy with paclitaxel plus S-1 is not inferior to that of SOX regimen for locally advanced gastric cancer: an observational study.

BACKGROUND: Paclitaxel plus S-1(PTXS) has shown definite efficacy for advanced gastric cancer. However, the efficacy and safety of this regimen in neoadjuvant setting for locally advanced gastric cancer (LAGC) are unclear. This study aimed to compare the efficacy of neoadjuvant chemotherapy (NAC) PTXS and oxaliplatin plus S-1 (SOX) regime for patients with LAGC. METHODS: A total of 103 patients with LAGC (cT3/4NanyM0/x) who were treated with three cycles of neoadjuvant SOX regimen (n = 77) or PTXS regimen (n = 26) between 2011 and 2017 were enrolled in this study. NAC-related clinical response, pathological response, postoperative complication, and overall survival were analyzed between the groups. RESULTS: The baseline data did not differ significantly between both groups. After NAC, the disease control rate of the SOX group (94.8%) was comparable with that of the PTXS group (92.3%) (p = 0.641). Twenty-three cases (29.9%) in the SOX group and 10 cases (38.5%) in the PTX group got the descending stage with no statistical difference (p = 0.417). No significant differences were observed in the overall pathological response rate and the overall postoperative complication rate between the two groups (p > 0.05). There were also no differences between groups in terms of 5-year overall and disease-free survival (p > 0.05). CONCLUSIONS: The validity of NAC PTXS was not inferior to that of SOX regimen for locally advanced gastric cancer in terms of treatment response and overall survival. PTXS regimen could be expected to be ideal neoadjuvant chemotherapy for patients with LAGC and should be adopted for the test arm of a large randomized controlled trial.

A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.

Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

Robust Collaborative Learning of Patch-Level and Image-Level Annotations for Diabetic Retinopathy Grading From Fundus Image.

Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in both academic and industrial communities. Most convolutional neural network-based algorithms treat DR grading as a classification task via image-level annotations. However, these algorithms have not fully explored the valuable information in the DR-related lesions. In this article, we present a robust framework, which collaboratively utilizes patch-level and image-level annotations, for DR severity grading. By an end-to-end optimization, this framework can bidirectionally exchange the fine-grained lesion and image-level grade information. As a result, it exploits more discriminative features for DR grading. The proposed framework shows better performance than the recent state-of-the-art algorithms and three clinical ophthalmologists with over nine years of experience. By testing on datasets of different distributions (such as label and camera), we prove that our algorithm is robust when facing image quality and distribution variations that commonly exist in real-world practice. We inspect the proposed framework through extensive ablation studies to indicate the effectiveness and necessity of each motivation. The code and some valuable annotations are now publicly available.

Invisibility Cloak Printed on a Photonic Chip.

Invisibility cloak capable of hiding an object can be achieved by properly manipulating electromagnetic field. Such a remarkable ability has been shown in transformation and ray optics. Alternatively, it may be realistic to create a spatial cloak by means of confining electromagnetic field in three-dimensional arrayed waveguides and introducing appropriate collective curvature surrounding an object. We realize the artificial structure in borosilicate by femtosecond laser direct writing, where we prototype up to 5,000 waveguides to conceal millimeter-scale volume. We characterize the performance of the cloak by normalized cross correlation, tomography analysis and continuous three-dimensional viewing angle scan. Our results show invisibility cloak can be achieved in waveguide optics. Furthermore, directly printed invisibility cloak on a photonic chip may enable controllable study and novel applications in classical and quantum integrated photonics, such as invisualising a coupling or swapping operation with on-chip circuits of their own.