Multi-label recognition of cancer-related lesions with clinical priors on white-light endoscopy

Yu, Tao; Lin, Ne; Zhong, Xian; Zhang, Xiaoyan; Zhang, Xinsen; Chen, Yihe; Liu, Jiquan; Duan, Huilong; Si, Jianmin

doi:10.1016/j.compbiomed.2022.105255

Cited by 9 publications

(10 citation statements)

References 21 publications

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“…3 B). The deep learning network [ 17 , 18 , 30 , 32 , 33 , 54 ] was used to predict ovarian cancer clustering subtypes based on transcription factor activity profile. The best hyper-parameters of the deep neural network were optimized by the grid search algorithm, and this model was further evaluated via the ten-fold cross-validation [ 54 , 55 ].…”

Section: Resultsmentioning

confidence: 99%

“…Based on changes in the target gene expression levels of transcription factors, algorithms and analysis tools have been developed for evaluating transcription factor activity [ [28] , [29] , [30] , [31] ]. However, the activity levels of transcription factors in ovarian cancer are not well-understood, and neither are their prognostic nor therapeutic effects on ovarian cancer [ 7 , 8 , 32 , 33 ]. In this study, we constructed transcription factor activity profiles of ovarian cancer using the VIPER algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Xiong

Wei

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Xiong

Wei

et al. 2023

Heliyon

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“…Two studies ( 43 , 47 ) were from the same team, one of which ( 47 ) constructed and tested a CAG diagnostic model, and the other ( 43 ) performed a further test; hence, we selected the more extensive test set of data ( 43 ) included in this meta-analysis. Two other studies ( 41 , 48 ) were also from the same team; one study ( 41 ) used AI to identify AG and IM, and another study ( 48 ) added the identification of GC to the former, and we chose the first ( 41 ) to be included in this meta-analysis. Three studies were excluded because only IM was identified ( 49 – 51 ).…”

Section: Resultsmentioning

confidence: 99%

Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis

Shi¹,

Wei²,

Wang³

et al. 2023

Front. Med.

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Background and aimsThe diagnosis of chronic atrophic gastritis (CAG) under normal white-light endoscopy depends on the endoscopist's experience and is not ideal. Artificial intelligence (AI) is increasingly used to diagnose diseases with good results. This review aimed to evaluate the accuracy of AI-assisted diagnosis of CAG through a meta-analysis.MethodsWe conducted a comprehensive literature search of four databases: PubMed, Embase, Web of Science, and the Cochrane Library. Studies published by November 21, 2022, on AI diagnosis CAG with endoscopic images or videos were included. We assessed the diagnostic performance of AI using meta-analysis, explored the sources of heterogeneity through subgroup analysis and meta-regression, and compared the accuracy of AI and endoscopists in diagnosing CAG.ResultsEight studies that included a total of 25,216 patients of interest, 84,678 image training set images, and 10,937 test set images/videos were included. The results of the meta-analysis showed that the sensitivity of AI in identifying CAG was 94% (95% confidence interval [CI]: 0.88–0.97, I2 = 96.2%), the specificity was 96% (95% CI: 0.88–0.98, I2 = 98.04%), and the area under the summary receiver operating characteristic curve was 0.98 (95% CI: 0.96–0.99). The accuracy of AI in diagnosing CAG was significantly higher than that of endoscopists.ConclusionsAI-assisted diagnosis of CAG in endoscopy has high accuracy and clinical diagnostic value.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023391853.

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“…Deep learning models have high accuracy but poor interpretability [ 19 ]. For interpretation of the results, a Class Activation Map was applied for visualization.…”

Section: Resultsmentioning

confidence: 99%

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

Zhang,

Qu,

Tian

et al. 2023

BMC Cancer

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Objective To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. Methods We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. Results Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. Conclusions In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

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Multi-label recognition of cancer-related lesions with clinical priors on white-light endoscopy

Cited by 9 publications

References 21 publications

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

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