Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made ...remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.
This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method.
1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952–0·957) in the internal validation set, 0·927 (0·925–0·929) in the prospective set, and ranged from 0·915 (0·913–0·917) to 0·977 (0·977–0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 95% CI 0·924–0·957 vs 0·945 0·927–0·959; p=0·692) and superior sensitivity compared with competent (0·858 0·832–0·880, p<0·0001) and trainee (0·722 0·691–0·752, p<0·0001) endoscopists. The positive predictive value was 0·814 (95% CI 0·788–0·838) for GRAIDS, 0·932 (0·913–0·948) for the expert endoscopist, 0·974 (0·960–0·984) for the competent endoscopist, and 0·824 (0·795–0·850) for the trainee endoscopist. The negative predictive value was 0·978 (95% CI 0·971–0·984) for GRAIDS, 0·980 (0·974–0·985) for the expert endoscopist, 0·951 (0·942–0·959) for the competent endoscopist, and 0·904 (0·893–0·916) for the trainee endoscopist.
GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses.
The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such ...methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions are solid and smooth.
To overcome these problems and to provide an efficient, accurate, robust and concise solution that simplifies the whole segmentation pipeline in AI-assisted applications, we propose the BézierSeg model which outputs Bézier curves encompassing the region of interest.
Directly modeling the contour with analytic equations ensures that the segmentation is connected and continuous, and that the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of precision, the Bézier contour can be resampled and overlaid with images of any resolution. Moreover, clinicians can conveniently adjust the curve's control points to refine the result.
Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Purpose
Nasopharyngeal carcinoma (NPC) is epidemic in south China, especially in Guangdong province. Radiotherapy is the main treatment, with the non-keratinizing type accounting for more than 95% of ...cases. Metastatic lymph nodes, which should be included in the radiotherapy target volume, are detected among approximately 70–80% of cases when the disease is first diagnosed. Accurate spatial modelling of metastatic lymph nodes is important for successful treatment.
Methods
We propose a coarse-to-fine deep supervision convolutional neural network (CF-Net) to perform metastatic lymph node segmentation using a 3D residual V-Net. Contrast-enhanced axial T1-weighted (T1C) magnetic resonance images of more than 6000 patients with NPC were enrolled in this study. We used the probability map predicted at a coarse scale as the weight map for training at a fine scale. This method draws attention to a fine scale within an area already detected at a coarse scale.
Results
CF-Net achieves a median Dice score of 81.0% in the segmentation of metastatic lymph nodes with a sensitivity and specificity of 79.1% and 99.2%, respectively.
Conclusion
The results show that our method can accurately identify, locate and segment NPC lymph nodes. We compared CF-Net with popular methods: V-Net, DeepLab-v3, HR-Net, and DenseNet. Our proposed method, across all variants, consistently and statistically outperformed the other models.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions.
...In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%;
= 224) and a validation set (ca. 30%;
= 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan-Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors.
Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12-63.86) in the high-risk group and 83.0% (95%CI, 76.31-90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model.
The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Wood vinegar, a by-product of charcoal biomass pyrolysis, has been used as a biofungicide in plant disease management because of its antimicrobial properties. However, the physiological and ...biochemical mechanisms through which wood vinegar alleviates biotic stress are poorly understood. In this study, pot experiments were conducted to investigate the resistance and regulation mechanism of wood vinegar prepared from different raw materials (ZM) and from a single raw material (SM) in controlling tomato (
"Bonny Best") Fusarium wilt at different concentrations (0.3%, 0.6%, 0.9%, 1.2%, and 1.5%). The results showed that ZM and SM had significant control effects on tomato fusarium wilt under different concentrations in the same growth cycle. Under biotic stress, the two kinds of wood vinegar significantly increased the plant height, stem diameter, leaf area and yield of tomato under the concentration of 0.3%, 0.6%, 0.9% and 1.2%, and significantly reduced the content of malondialdehyde (MDA) and hydrogen peroxide (H
O
) in tomato leaves. The effect of 0.9% treatment was the most significant, ZM and SM significantly increased tomato yield by 122% and 74%, respectively, compared with CK under 0.9% treatment. However, the plant height, stem diameter and leaf area of tomato were significantly reduced under 1.5% treatment, but the content of soluble sugar, soluble protein and vitamin C in tomato fruit was the best. Compared with CK, ZM significantly increased by 14%, 193% and 67%, respectively, and SM significantly increased by 28%, 300% and 159%, respectively. Except for 0.3% treatment, both significantly increased the activities of catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) in tomato leaves. The response intensity of two kinds of wood vinegar-physiological and biochemical-to tomato disease resistance, growth and development, showed ZM > SM. The disease index of tomato showed highly significant negative correlation with plant height, stem thickness, leaf area and antioxidant physiology CAT, and highly significant positive correlation with MDA and H
O
content. In conclusion, ZM was more effective than SM in enhancing tomato disease resistance by promoting tomato growth and development, decreasing leaf MDA and H
O
content, and inducing antioxidant enzyme activity in leaves at moderate concentrations.
Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be ...challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.
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•MMRNet can automatically predict MMR/MSI status from gastric cancer H&E-stained WSIs•A mapping approach MMRMapping can efficiently interpret MMRNet•The interpretable method can aid the growth of young pathologists
Pathology; Diagnostics; Cancer; Machine learning
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The gut microbiome plays a crucial role in various diseases, and its regulation is a potential treatment option for these conditions. However, the relationship between the gut microbiome and venous ...thromboembolism (VTE) remains poorly explored.
In this study, we collected feces and serum samples from 8 VTE patients and 7 healthy controls. The gut microbiota and serum metabolites were analyzed using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Additionally, a combined analysis of microbiota and metabolome was performed.
The alpha and beta diversity between the VTE and control groups were significantly different. Patients with VTE exhibited an overgrowth of Blautia, Roseburia, Coprococcus, and Ruminococcus. Moreover, serum metabolomics analysis revealed altered levels of choline and lithocholic acid. Pathway enrichment analysis indicated a significant upregulation of bile secretion pathways. In addition, a positive correlation was observed between the levels of serum choline and lithocholic acid and the abundance of gut flora enriched in the VTE group.
This study provided novel insights into the disordered gut microbiota and serum metabolome associated with VTE, suggesting potential common pathological mechanisms between VTE and arterial thrombosis. Targeted modulation of the gut microbiome may hold promise as a preventive and therapeutic approach for VTE.
•This study is the first to demonstrate significant dysbiosis of the gut microbiota and metabolic alterations in patients with VTE.•Patients with VTE exhibit a higher richness and diversity in their gut microbiota community compared to controls. Moreover, changes in choline, LCA and anandamide metabolism were associated with microbial alterations.•It is possible that VTE and arterial thrombosis share common pathological mechanisms.•Targeted modulation of the gut microbiome may hold potential as a preventive and therapeutic approach for VTE.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A dual-task AI model was developed to detect and segment NPC in MRI automatically.•The model showed higher overall accuracy than experienced radiologists to detect NPC.•The model showed encouraging ...dice similarity coefficient in segmentation of NPC.
We aimed to develop a dual-task model to detect and segment nasopharyngeal carcinoma (NPC) automatically in magnetic resource images (MRI) based on deep learning method, since the differential diagnosis of NPC and atypical benign hyperplasia was difficult and the radiotherapy target contouring of NPC was labor-intensive.
A self-constrained 3D DenseNet (SC-DenseNet) architecture was improved using separated training and validation sets. A total of 4100 individuals were finally enrolled and split into the training, validation and test sets at a proximate ratio of 8:1:1 using simple randomization. The diagnostic metrics of the established model against experienced radiologists was compared in the test set. The dice similarity coefficient (DSC) of manual and model-defined tumor region was used to evaluate the efficacy of segmentation.
Totally, 3142 nasopharyngeal carcinoma (NPC) and 958 benign hyperplasia were included. The SC-DenseNet model showed encouraging performance in detecting NPC, attained a higher overall accuracy, sensitivity and specificity than those of the experienced radiologists (97.77% vs 95.87%, 99.68% vs 99.24% and 91.67% vs 85.21%, respectively). Moreover, the model also exhibited promising performance in automatic segmentation of tumor region in NPC, with an average DSC at 0.77 ± 0.07 in the test set.
The SC-DenseNet model showed competence in automatic detection and segmentation of NPC in MRI, indicating the promising application value as an assistant tool in clinical practice, especially in screening project.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Choosing a good crop rotation plan helps maintain soil fertility and creates a healthy soil ecosystem. However, excessive fertilization and continuous cultivation of vegetables in a greenhouse ...results in secondary salinization of the soil. It remains unclear how crop rotation affects Yunnan's main place for vegetable growing in the greenhouse. Six plant cultivation patterns were chosen to determine how different rotation patterns affect the chemical properties and the soil microbial communities with secondary salinization, including lettuce monoculture, lettuce-large leaf mustard, lettuce-red leaf beet, lettuce-cabbage, lettuce-romaine lettuce, and lettuce-cilantro (DZ, A1, A2, A3, A4, and A5). The results showed that all treatments increased the proportion of nutrients available in the soil, and the effect of the A1 treatment was the most significant compared to the monoculture mode. The high-throughput sequencing findings revealed that distinct crop rotation patterns exerted varying effects on the microbial communities. Microbial community diversity was significantly lower in the monoculture than in the other treatments. The number of microbial operational taxonomic units OTUs was significantly higher in the crop rotation modes (P < 0.05), and the A1 treatment had larger numbers and diversity of bacterial and fungal OTUs (Shannon's and Simpson's) than other treatments (P < 0.05). Prominent bacterial and fungal communities were readily observable in the soils planted with rotational crops. Proteobacteria had the highest relative abundance of bacteria, whereas Ascomycota was the most abundant fungus. The principal coordinate analysis at the OTU level separated soil bacterial and fungal growth communities under the different treatments. Among the six treatments, The first two axes (PC1 and PC2) described 46.44 % and 42.42 % of the bacterial and fungal communities, respectively. Network-based analysis showed that Bacteroidota and Gemmatimonadota members of the genus Bacteroidota were positively correlated with Proteobacteria. Members of Ascomycota and Chytridiomycota exhibited positive relationships. These results extend the theoretical understanding of how various crop rotation patterns affect soil chemical properties, microbial community diversity, and metabolic functions. They reveal the beneficial effects of crop rotation patterns on enhanced soil quality. This study provides theoretical guidance for the future enhancement of sustainable agriculture and soil management planning.
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•The microbial community structure and chemical index of degraded soil were impacted by the crop rotation mode choice.•The soil nutrient imbalance was improved by a reasonable crop rotation schedule.•A reasonable crop rotation strategy makes the soil microbial community more complex and diverse.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
