Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network

Zheng, Longbo; Zhang, Xunying; Hu, Jilin; Gao, Yuan; Zhang, Xianxiang; Zhang, Maoshen; Li, Shuai; Zhou, Xiaoming; Niu, Tianye; Lu, Yi; Wang, Dongsheng

doi:10.3389/fonc.2020.01238

Cited by 16 publications

(9 citation statements)

References 18 publications

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“…Compared with the tongue classification model constructed by the classical CNN, Faster R-CNN as a highly integrated and end-to-end model is still the mainstream object detection neural network at present [54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Jiang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Background. Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods. A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results. The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion. The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Jiang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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“…Currently, radiomics has been reported to play an important role in the diagnosis and treatment of cancer, as well as in predicting the prognosis of cancer patients (11,13). The current radiomics studies on gastric cancer T staging have been focused on the application of different imaging techniques, such as: CT (15), dual-energy CT (16) (27) etc. These studies have been able to predict the patients with T1, T2, T3, and T4a stage, however, currently there are no studies reporting the use of radiomics methods to identify pT4b stage of gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

“…The current radiomics studies on gastric cancer T staging have been focused on the application of different imaging techniques, such as: CT ( 15 ), dual-energy CT ( 16 ) and CE-CT ( 26 ), etc. It involves the extraction of different types of image features such as deep Learning Features ( 27 ); texture features ( 16 ); and spleen radiomic features ( 15 ), using different computer algorithms for modeling, such as: SVM algorithm ( 14 ); Random Forest algorithm ( 26 ); and Convolutional Neural Network algorithm ( 27 ), etc. These studies have been able to predict the patients with T1, T2, T3, and T4a stage, however, currently there are no studies reporting the use of radiomics methods to identify pT4b stage of gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer

Liu¹,

Zhang²,

Wang³

et al. 2022

Quant Imaging Med Surg

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Background: The accuracy of preoperative staging is crucial for cT4 stage gastric cancer patients. The aim of this study was to develop the radiomics model and evaluate its predictive potential for differentiating preoperative cT4 stage gastric cancer patients into pT4b and no-pT4b patients. Methods: A multicenter retrospective analysis of 704 gastric cancer patients with preoperative contrastenhanced computed tomography (CE-CT) staging cT4 between January 2008 and December 2021. These patients were divided into the training cohort (478 patients, the Affiliated Hospital of Qingdao University) and validation cohort (226 patients, the Weihai Wendeng District People's Hospital). According to the pathological stage of the tumors, the patients were divided into pT4b or no-pT4b stage. In the training cohort, the clinical and radiomics features were analyzed to construct the clinical model, tri-phase radiomics signatures and nomogram. Two kinds of methods were employed to achieve dimensionality reduction: (I) the least absolute shrinkage and selection operator (LASSO); and (II) the minimum redundancy maximum relevance (mRMR) algorithms. We utilized Logistic regression, support vector machine (SVM), Decision tree and Adaptive boosted tree (AdaBoost) algorithms as the machine learning classifiers. The nomogram was constructed on the clinical characteristics and the Rad-score. The performance of the models was evaluated by receiver operating characteristic (ROC) area under the curve (AUC), Decision Curve Analysis (DCA) curve and calibration curve. Results: The 345 pT4b and 359 no-pT4b stage patients were included in this study. In the validation cohort, the AUC of the clinical model was 0.793 (95% CI: 0.732-0.855). The tri-phase radiomics features combined with the SVM algorithm was the best radiomics signature with an AUC of 0.862 (95% CI: 0.812-0.912). The nomogram was the best predictive model of all with an AUC of 0.893 (95% CI: 0.834-0.927). In the training and validation cohorts, the calibration curves and DCA curves of the nomogram showed satisfactory result. Conclusions: CE-CT-based radiomics nomogram offers good accuracy and stability in differentiating preoperative cT4 stage gastric cancer patients into pT4b and non-pT4b stages, which has a great clinical relevance for selecting the course of treatment for cT4 stage gastric cancer patients.

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“…Despite the lack of further validation, this innovative research launched a sharp increase in related studies. Many of them aimed to detect primary lesions in CT images[ 43 - 47 ]. For example, a CNN-based model was trained with 288 CT images for polyp detection and achieved a sensitivity of 97%[ 47 ].…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

“…Studies published in earlier years used multiple types of ANNs, including BPNNs, massive-training ANNs, and BNNs[ 45 , 48 - 50 ]. In recent years, investigators have collectively turned to using CNNs[ 43 , 51 , 52 ]. This tacit alteration suggests that CNNs have fewer complications in automatic CT interpretations.…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network

Cited by 16 publications

References 18 publications

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

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