Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors

Yang, Jiejin; Chen, Zeyang; Liu, Weipeng; Wang, Xiangpeng; Ma, Shuai; Jin, Feifei; Wang, Xiaoying

doi:10.3348/kjr.2019.0851

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…The AI model yielded 100% sensitivity (95% CI: 65.6-100) and 90% accuracy (95% CI: 75.6-100) in the test cohort for the diagnosis of high mitotic count. 66 Although the test cohort included only 20 patients with a 1:1 ratio of high and low mitotic count GISTs, this study shows a potential for AI in risk assessment of GIST. 66…”

Section: Risk Stratificationmentioning

confidence: 88%

“…65 One study reported the use of a predictive model to discriminate between GISTs with high mitotic count (>5/ 50 mitoses on high-power fields) and those with low mitotic count (≤5/50 mitoses). 66 The model was based on portal venous phase CT images of 108 patients with GISTs that were used to train a deep learning algorithm. The AI model yielded 100% sensitivity (95% CI: 65.6-100) and 90% accuracy (95% CI: 75.6-100) in the test cohort for the diagnosis of high mitotic count.…”

Section: Risk Stratificationmentioning

confidence: 99%

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CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

et al. 2023

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Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c- KIT proto-oncogene antigen. Expression of the c- KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

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Section: Risk Stratificationmentioning

confidence: 88%

Section: Risk Stratificationmentioning

confidence: 99%

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

et al. 2023

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“…Previously, a DL model for predicting the mitotic index of GIST was preliminarily established by providing venous images as input into CNN. The results showed that the image-based DL model could evaluate the MI of GIST before surgery ( 40 ). However, the generalization ability of the model proposed in their study was not high, and the area under the curve (AUC) in the internal test set was only 0.771–0.800.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the multi-modal image input, the construction process of the proposed hybrid model proposed was different from that of the image-based CNN model reported in previous studies (40). While the hybrid model combined shape features and clinical indicators, in order to ensure the robustness of the model, only shape features in traditional radiomics were selected to establish the model.…”

Section: Discussionmentioning

confidence: 99%

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

Yang

Zheng

et al. 2022

Front. Oncol.

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IntroductionPreoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction.MethodsA total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier.ResultsThe hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927–0.968), 0.964 (95% CI: 0.930–0.978), and 90.8 (95% CI: 88.0–93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828–1.000), 0.941 (95% CI: 0.792–1.000), and 93.6% (95% CI: 79.3–98.2) in the test set, respectively.DiscussionThe deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.

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“…Wang et al reported that a machine learning model distinguished gastric schwannomas from GISTs with excellent accuracy (area under the curve: 0.97) [ 100 ]. Yang et al developed a binary prediction model for mitotic count (area under the curve: 0.80) [ 101 ]. Furthermore, Kang et al reported a CT-based deep-learning model that effectively predicted histological risk (low, intermediate, and high risks) with an accuracy of > 0.77 [ 102 ].…”

Section: Risk Prediction Via Sectional Imagingmentioning

confidence: 99%

Gastrointestinal stromal tumors: a comprehensive radiological review

Inoue

Ota²,

Yamasaki

et al. 2022

Jpn J Radiol

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Gastrointestinal stromal tumors (GISTs) originating from the interstitial cells of Cajal in the muscularis propria are the most common mesenchymal tumor of the gastrointestinal tract. Multiple modalities, including computed tomography (CT), magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography, ultrasonography, digital subtraction angiography, and endoscopy, have been performed to evaluate GISTs. CT is most frequently used for diagnosis, staging, surveillance, and response monitoring during molecularly targeted therapy in clinical practice. The diagnosis of GISTs is sometimes challenging because of the diverse imaging findings, such as anatomical location (esophagus, stomach, duodenum, small bowel, colorectum, appendix, and peritoneum), growth pattern, and enhancement pattern as well as the presence of necrosis, calcification, ulceration, early venous return, and metastasis. Imaging findings of GISTs treated with antineoplastic agents are quite different from those of other neoplasms (e.g. adenocarcinomas) because only subtle changes in size are seen even in responsive lesions. Furthermore, the recurrence pattern of GISTs is different from that of other neoplasms. This review discusses the advantages and disadvantages of each imaging modality, describes imaging findings obtained before and after treatment, presents a few cases of complicated GISTs, and discusses recent investigations performed using CT and MRI to predict histological risk grade, gene mutations, and patient outcomes.

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Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors

Cited by 11 publications

References 36 publications

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

Gastrointestinal stromal tumors: a comprehensive radiological review

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