Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma.

OBJECTIVE: To assess the ability of markers of inflammation to identify the solid or micropapillary components of stage IA lung adenocarcinoma and their effects on prognosis. METHODS: We performed a retrospective study of clinicopathologic data from 654 patients with stage IA lung adenocarcinoma collected between 2013 and 2019. Logistic regression analysis was used to identify independent predictors of these components, and we also evaluated the relationship between markers of inflammation and recurrence. RESULTS: Micropapillary-positive participants had high preoperative neutrophil-to-lymphocyte ratios. There were no significant differences in the levels of markers of systemic inflammation between the participants with or without a solid component. Multivariate analysis showed that preoperative neutrophil-to-lymphocyte ratio (odds ratio [OR] = 2.094; 95% confidence interval [CI], 1.668-2.628), tumor size (OR = 1.386; 95% CI, 1.044-1.842), and carcinoembryonic antigen concentration (OR = 1.067; 95% CI, 1.017-1.119) were independent predictors of a micropapillary component. There were no significant correlations between markers of systemic inflammation and the recurrence of stage IA lung adenocarcinoma. CONCLUSIONS: Preoperative neutrophil-to-lymphocyte ratio independently predicts a micropapillary component of stage IA lung adenocarcinoma. Therefore, the potential use of preoperative neutrophil-to-lymphocyte ratio in the optimization of surgical strategies for the treatment of stage IA lung adenocarcinoma should be further studied.

A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos).

BACKGROUND AND AIMS: It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. This study aimed to develop a real-time interpretable artificial intelligence (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC). METHODS: A novel interpretable AI system called MBSDeiT was developed consisting of 2 models to identify qualified images and then predict MBSs in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, and prospective testing data sets and subgroup analyses, and at the video level on the prospective data sets; these findings were compared with those of the endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the interpretability. RESULTS: MBSDeiT can first automatically select qualified DSOC images with an area under the curve (AUC) of .963 and .968 to .973 on the internal testing data set and the external testing data sets, and then identify MBSs with an AUC of .971 on the internal testing data set, an AUC of .978 to .999 on the external testing data sets, and an AUC of .976 on the prospective testing data set, respectively. MBSDeiT accurately identified 92.3% of MBSs in prospective testing videos. Subgroup analyses confirmed the stability and robustness of MBSDeiT. The AI system achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with 4 endoscopic features (nodular mass, friability, raised intraductal lesion, and abnormal vessels; P < .05) under DSOC, which is consistent with the endoscopists' predictions. CONCLUSIONS: The study findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBSs under DSOC.

A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: A retrospective, multicenter, diagnostic study.

BACKGROUND: We aimed to develop a deep learning-based segmentation system for rapid on-site cytopathology evaluation (ROSE) to improve the diagnostic efficiency of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy. METHODS: A retrospective, multicenter, diagnostic study was conducted using 5345 cytopathological slide images from 194 patients who underwent EUS-FNA. These patients were from Nanjing Drum Tower Hospital (109 patients), Wuxi People's Hospital (30 patients), Wuxi Second People's Hospital (25 patients), and The Second Affiliated Hospital of Soochow University (30 patients). A deep convolutional neural network (DCNN) system was developed to segment cell clusters and identify cancer cell clusters with cytopathological slide images. Internal testing, external testing, subgroup analysis, and human-machine competition were used to evaluate the performance of the system. FINDINGS: The DCNN system segmented stained cells from the background in cytopathological slides with an F1-score of 0·929 and 0·899-0·938 in internal and external testing, respectively. For cancer identification, the DCNN system identified images containing cancer clusters with AUCs of 0·958 and 0·948-0·976 in internal and external testing, respectively. The generalizable and robust performance of the DCNN system was validated in sensitivity analysis (AUC > 0·900) and was superior to that of trained endoscopists and comparable to cytopathologists on our testing datasets. INTERPRETATION: The DCNN system is feasible and robust for identifying sample adequacy and pancreatic cancer cell clusters. Prospective studies are warranted to evaluate the clinical significance of the system. FUNDING: Jiangsu Natural Science Foundation; Nanjing Medical Science and Technology Development Funding; National Natural Science Foundation of China.

Impact of minimal solid and micropapillary components on invasive lung adenocarcinoma recurrence.

BACKGROUND: The specific impacts of solid and micropapillary components on prognosis in lung adenocarcinoma remain unclear. Herein, we elucidated their distinct contributions to lung adenocarcinoma recurrence. MATERIALS AND METHODS: Lung adenocarcinoma was classified into solid and micropapillary absent (S-M-); solid absent, micropapillary present (S-M+); micropapillary absent, solid present (S + M-); and solid and micropapillary present (S + M+). Cumulative incidence of recurrence (CIR) was calculated using competing risk analysis. RESULTS: Of 994 adenocarcinomas, 650 (65.4%) were classified as S-M-; 152 (15.3%), S-M+; 148 (14.9%), S + M-; and 44 (4.4%), S + M+. In total, 168 (16.9%) patients had recurrence; 16 (1.6%) died from other causes. S-M- had significantly lower CIR than other groups (S-M- vs. S-M+: P < 0.001, S-M- vs. S + M-: P < 0.001, S-M- vs. S + M+: P < 0.001); S + M- had significantly higher CIR than S-M+ (P = 0.002). These differences remained significant in multivariable analysis. In stage IA, S-M- had significantly lower CIR than other groups (S-M- vs. S-M+: P = 0.006, S-M- vs. S + M-: P < 0.001, S-M- vs. S + M+: P < 0.001); S + M- and S + M+ had significantly higher CIR than S-M+ (P = 0.005, P = 0.008, respectively). These differences remained significant in multivariable analysis. CIR was not significantly different between S + M- and S-M+ subgroups. CONCLUSIONS: The presence of solid or micropapillary component (≥1%) was an independent risk factor for CIR; patients with solid component alone had a higher CIR than those with micropapillary component alone. In IA lung adenocarcinoma, patients with both solid and micropapillary components had a higher CIR than those with micropapillary component alone; the proportion of solid or micropapillary component was not associated with CIR.

Effects of Lycium barbarum polysaccharide on the photoinduced autophagy of retinal pigment epithelium cells.

AIM: To investigate the relationship between autophagy and apoptosis in photoinduced injuries in retinal pigment epithelium (RPE) cells and how Lycium barbarum polysaccharide (LBP) contributes to the increased of RPE cells to photoinduced autophagy. METHODS: In vitro cultures of human RPE strains (ARPE-19) were prepared and randomly divided into the blank control, model, low-dose LBP, middle-dose LBP, high-dose LBP, and 3-methyladenine (3MA) groups. The viability of the RPE cells and apoptosis levels in each group were tested through cell counting kit-8 (CCK8) method with a flow cytometer (Annexin V/PI double staining technique). The expression levels of LC3II, LC3I, and P62 proteins were detected with the immunofluorescence method. The expression levels of beclin1, LC3, P62, PI3K, P-mTOR, mTOR, P-Akt, and Akt proteins were tested through Western blot. RESULTS: LBP considerably strengthens cell viability and inhibits the apoptosis of RPE cells after photoinduction. The PI3K/Akt/mTOR signal pathway is activated because of the upregulation of the phosphorylation levels of Akt and mTOR proteins, and thus autophagy is inhibited. CONCLUSION: LBP can inhibit the excessive autophagy in RPE cells by activating the PI3K/Akt/mTOR signaling pathways and thereby protect RPE cells from photoinduced injuries.

Aqueous angiopoietin-like levels correlate with optical coherence tomography angiography metrics in diabetic macular edema.

AIM: To quantitatively detect aqueous levels of angiopoietin-like (ANGPTL)3, ANGPTL4, and ANGPTL6 and investigate their correlation with optical coherence tomography angiography (OCTA) findings in patients with diabetic macular edema (DME). METHODS: This cross-sectional study included 23 patients (27 eyes) with type 2 diabetes and 16 control subjects (20 eyes). All patients underwent OCTA imaging and ultra-wide field fundus photography. Diabetic patients were categorized into two groups according to the presence or absence of diabetic retinopathy (DME group, 14 patients, 16 eyes); and non-diabetic retinopathy (NDR) group, 9 patients, 11 eyes, respectively. Aqueous levels of ANGPTL3, ANGPTL4, and ANGPTL6 were assessed using suspension array technology, and foveal-centered 3×3 mm2 OCTA scans were automatically graded to determine the central, inner, and full vessel density (CVD, IVD, FVD); central, inner, and full perfusion density (CPD, IPD, FPD), foveal avascular zone (FAZ) area, FAZ perimeter, and FAZ circularity index (FAZ-CI) on superficial capillary plexuses. Additionally, central subfield thickness (CST), cube volume (CV), and cube average thickness (CAT) were measured in a model of macular cube 512×128. RESULTS: Aqueous ANGPTL3 levels were not significantly different among the three groups (P>0.05). ANGPTL4 levels were significantly higher in the DME group than the control and NDR groups (P<0.0001 and P<0.001), while ANGPTL6 levels were significantly higher in the DME group than the control group (P<0.05). In the whole cohort, the aqueous ANGPTL3 levels correlated negatively with the IVD, FVD, IPD, and FPD, and positively with the CV and CAT. The aqueous ANGPTL4 levels correlated negatively with the CVD, IVD, FVD, CPD, IPD, and FPD, and positively with the FAZ perimeter, CST, CV, and CAT. The aqueous ANGPTL6 levels correlated negatively with the IVD, FVD, IPD, FPD, FAZ-CI and positively with CST, CV, CAT. CONCLUSION: ANGPTL4 and ANGPTL6 may be associated with vascular leakage in DME and may represent good targets for DME therapy. In addition, OCTA metrics may be useful for evaluating macular ischemia in DME.

Cancer-associated fibroblasts, matrix metalloproteinase-9 and lymphatic vessel density are associated with progression from adenocarcinoma in situ to invasive adenocarcinoma of the lung.

The present study aimed to investigate the roles of cancer-associated fibroblasts (CAFs), matrix metalloproteinase-9 (MMP-9) and lymphatic vessel density (LVD) during the progression from adenocarcinoma in situ (AIS) to invasive lung adenocarcinoma (IAC). A total of 77 patients with stage 0-IA lung adenocarcinoma were enrolled. The expression levels of α-smooth muscle actin, MMP-9 and D2-40 were immunohistochemically analyzed. Survival analysis was performed using the Kaplan-Meier method. In the non-invasive component, the proportion of CAFs and the expression levels of MMP-9 increased from AIS to IAC; however, the LVD was not significantly different. CAFs were positively correlated with levels of MMP-9. The LVD had no significant correlation with CAFs and MMP-9. In the invasive component, CAFs, MMP-9 and LVD were significantly higher in IAC compared with in minimally invasive adenocarcinoma. CAFs, MMP-9 and LVD were all positively correlated with each other. The micropapillary subtype in IAC was associated with overall survival (OS). The LVD in IAC, but not MMP-9 and CAFs, was associated with OS. CAFs, MMP-9 and LVD were involved in the progression from AIS to IAC. CAFs exhibited a strong association with MMP-9 levels in the non-invasive and invasive components. The increase in the proportion of CAFs and the expression levels of MMP-9 may have been an early event before the adenocarcinoma became invasive. Once the adenocarcinoma was invasive, the LVD served an important role in tumor invasion and metastasis, and hence may be used as a prognostic marker of poor OS in stage IA IAC.

Discrete Latent Factor Model for Cross-Modal Hashing.

Due to its storage and retrieval efficiency, cross-modal hashing (CMH) has been widely used for cross-modal similarity search in many multimedia applications. According to the training strategy, existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than that of discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than that of the relaxation-based continuous methods, but the training of discrete methods is very time-consuming. In this paper, we propose a novel CMH method, called Discrete Latent Factor model-based cross-modal Hashing (DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments show that the DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of the relaxation-based continuous methods which are much faster than the existing discrete methods.

Deep Discrete Supervised Hashing.

Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.