Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image

Jiang, Fei; Guo, Yi; Jin, Zhendong; Wang, Yuanyuan

doi:10.1007/s11548-019-01993-3

Cited by 15 publications

(21 citation statements)

References 12 publications

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“…In this study, 905 patients went through biopsy and gastroscopy to detect the GIST from 2008 to 2018 in 19 different hospitals [2]. G-EUS images were collected from one of following ultrasound machines, Olympus EU-ME1, Olympus EU-ME2, Fuji SU9000, Fuji SU8000 and Olympus Alpha 10.…”

Section: Materials and Experiments A Materialsmentioning

confidence: 99%

“…G-EUS images were collected from one of following ultrasound machines, Olympus EU-ME1, Olympus EU-ME2, Fuji SU9000, Fuji SU8000 and Olympus Alpha 10. All patients had provided written informed consent for imaging and clinical data to be donated for the research [2].…”

Section: Materials and Experiments A Materialsmentioning

confidence: 99%

“…This part illustrates the influence of the segmentation method on clinical diagnosis. 439 high-throughput GISTs features are designed to demonstrate characteristics of GISTs in EUS images [2]. The tumor boundaries are defined and then the features are extracted.…”

Section: Clinical Gists Classification Experimentsmentioning

confidence: 99%

“…ultrasound (EUS). Due to its real-time nature, non-invasive, inexpensive, and non-radiation, endoscopic ultrasound is used to assess patient specific gastrointestinal structure and function [2]. The risk level plays a significant role in the preoperative treatment.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, HRG patients should take the targeted therapy before surgery, while LRG patients do not need. Recently, a computer-aided diagnosis model has been proposed to classify the risk level [2]. While this model depends on segmenting GISTs manually the radiologists, consuming a heap of time.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

Guo

Jiang

et al. 2020

IEEE Access

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The diagnosis of risk level of gastrointestinal stromal tumor (GIST) is of great clinical significance. The morphology of GIST in endoscopic ultrasound (EUS) images has been normally used by radiologists to diagnosis the risk level of GISTs. Hence, accurate segmentation of GISTs in EUS images is a crucial factor to influence the diagnosis. U-net, an elegant network, has been commonly used in medical images. However, due to the plain architecture and complicated up-sampling path of U-net, classical U-net does not perform well in segmenting GISTs in EUS images with diverse size, heavy shadow and ambiguous boundary. Hence, this paper proposes a novel multi-task refined boundary-supervision U-net (MRBSU-net) for GIST segmentation in EUS images. In our network, multi-task refined U-net (RU-net) is set to deal with heavy shadow and diverse size. Boundary cross entropy in loss function of multi-task RU-net boosts the influence of small size tumors and the refinement avoid the noise information in EUS images propagating to the higher resolution layers. Then we design a refined boundary-supervision U-net (RBSU-net) to solve the ambiguous problem. The boundary supervision in RBSU-net leads the network focus on finding boundary in the down-sampling part and segmenting region on the up-sampling path. At last, we put multi-task RU-net in front of the RBSU-net to increase the stability of the network, what is called MRBSU-net. Extensive experiments have been designed to evaluate the performance of the proposed network. The comparison experiments include the results from traditional U-net, generative adversarial network (GAN) and Deep Attentional Features (DAF). The results of our proposed method perform best among all the comparison methods, which proves that the proposed network could be potentially used in clinic. INDEX TERMS Gastrointestinal stromal tumor, endoscopic ultrasound, tumor segmentation, multi-task, boundary-supervision.

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Section: Materials and Experiments A Materialsmentioning

confidence: 99%

Section: Materials and Experiments A Materialsmentioning

confidence: 99%

Section: Clinical Gists Classification Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

Guo

Jiang

et al. 2020

IEEE Access

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TN‐USMA Net: Triple normalization‐based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound‐specific pretraining and meta attention

et al. 2021

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Purpose Accurate quantification of gastrointestinal stromal tumors’ (GISTs) risk stratification on multicenter endoscopic ultrasound (EUS) images plays a pivotal role in aiding the surgical decision‐making process. This study focuses on automatically classifying higher‐risk and lower‐risk GISTs in the presence of a multicenter setting and limited data. Methods In this study, we retrospectively enrolled 914 patients with GISTs (1824 EUS images in total) from 18 hospitals in China. We propose a triple normalization‐based deep learning framework with ultrasound‐specific pretraining and meta attention, namely, TN‐USMA model. The triple normalization module consists of the intensity normalization, size normalization, and spatial resolution normalization. First, the image intensity is standardized and same‐size regions of interest (ROIs) and same‐resolution tumor masks are generated in parallel. Then, the transfer learning strategy is utilized to mitigate the data scarcity problem. The same‐size ROIs are fed into a deep architecture with ultrasound‐specific pretrained weights, which are obtained from self‐supervised learning using a large volume of unlabeled ultrasound images. Meanwhile, tumors’ size features are calculated from the same‐resolution masks individually. Afterward, the size features together with two demographic features are integrated to the model before the final classification layer using a meta attention mechanism to further enhance feature representations. The diagnostic performance of the proposed method was compared with one radiomics‐based method and two state‐of‐the‐art deep learning methods. Four evaluation metrics, namely, the accuracy, the area under the receiver operator curve, the sensitivity, and the specificity were used to evaluate the model performance. Results The proposed TN‐USMA model achieves an overall accuracy of 0.834 (95% confidence interval [CI]: 0.772, 0.885), an area under the receiver operator curve of 0.881 (95% CI: 0.825, 0.924), a sensitivity of 0.844 (95% CI: 0.672, 0.947), and a specificity of 0.832 (95% CI: 0.762, 0.888). The AUC significantly outperforms other two deep learning approaches (p < 0.05, DeLong et al). Moreover, the performance is stable under different variations of multicenter dataset partitions. Conclusions The proposed TN‐USMA model can successfully differentiate higher‐risk GISTs from lower‐risk ones. It is accurate, robust, generalizable, and efficient for potential clinical applications.

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Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis

Zhang

Zhu

et al. 2022

Surg Endosc

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Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image

Cited by 15 publications

References 12 publications

Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

TN‐USMA Net: Triple normalization‐based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound‐specific pretraining and meta attention

Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis

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