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Could artificial intelligence replace fine-needle aspiration in endoscopic ultrasound? 人工智能能否取代内镜超声中的细针穿刺?
Pub Date : 2021-01-01 DOI: 10.21037/gist-22-11
S. Jiang, N. Parsa, M. Byrne
© Gastrointestinal Stromal Tumor. All rights reserved. Gastrointest Stromal Tumor 2022 | https://dx.doi.org/10.21037/gist-22-11 The recent application of artificial intelligence (AI) in the field of gastroenterology has shown promising results in the diagnosis and management of digestive diseases (1-3). Solutions such as AI-powered detection and diagnosis systems are now commercially available for colorectal polyps (4). The backbone of AI systems for image classification is the convolutional neural network (CNN), a deep-learning algorithm that conducts multi-level image analysis through pattern recognition, and improves its own diagnostic ability by training with large datasets (5,6). With the ability to integrate pixel-level data, CNN is able to aid endoscopists in the rapid interpretation of seemingly ambiguous visual data. One such area of diagnostic dilemma is the evaluation of gastric subepithelial lesions (SELs). While endoscopic ultrasound (EUS) is the most accurate imaging modality, there are no definitive EUS features to differentiate gastrointestinal stromal tumors (GISTs) from the commonly encountered gastrointestinal leiomyomas (GILs) (7-9). Misdiagnosis of GISTs and GILs are thought to comprise the majority of incorrect EUS diagnoses (9). Given the malignant potential of GISTs, it is crucial to accurately diagnose these lesions and to differentiate them from GILs, which are benign. The current standard is to differentiate these two by obtaining tissue samples with fine-needle aspiration or biopsy (EUS-FNA/B). However, FNA/B is invasive and is reported to have a lower diagnostic rate for SELs smaller than 20 mm (9,10). In this issue of Endoscopy, Yang et al. report the result of their AI-powered EUS model for differentiation between GISTs and GILs (11). Using a CNN for image recognition, the AI model was trained, validated, and evaluated on a total of 10,439 EUS images from 752 patients with histologically confirmed GISTs and GILs from four endoscopic centers, collected in aggregate from 2013 to 2020. They report a significantly higher diagnostic accuracy with the AI model compared with the expert endo-sonographer (94.0% vs. 70.2%, P value <0.001). More importantly, in the prospective evaluation of 508 consecutive patients with SELs, of whom 132 underwent histologic confirmation, the diagnostic accuracy remained significantly higher with the AI-powered EUS compared with the expert endosonographer (78.8% vs. 69.7%, P value =0.01). When examining only cases of histologically-confirmed GISTS or GILs, AI-joint diagnosis also had significantly higher accuracy, specificity, and PPV at 92.2%, 95.1%, and 94.1%, respectively, compared to individual diagnosis alone at 76.6% (P value =0.01), 65.9% (P value =0.002), and 69.6% (P value <0.01), respectively. The sensitivity and NPV of AI-joint diagnosis were similar to individual diagnosis. These are promising results in applying deep learning to real-time EUS to distinguish between GISTs and GILs.
©胃肠道间质瘤。版权所有。近年来,人工智能(AI)在胃肠病学领域的应用在消化系统疾病的诊断和治疗方面显示出了良好的效果(1-3)。人工智能驱动的检测和诊断系统等解决方案现在已经商业化,用于结肠直肠息肉(4)。用于图像分类的人工智能系统的骨干是卷积神经网络(CNN),这是一种深度学习算法,通过模式识别进行多层次的图像分析,并通过大数据集的训练提高自身的诊断能力(5,6)。凭借整合像素级数据的能力,CNN能够帮助内窥镜医师快速解释看似模糊的视觉数据。一个这样的诊断困境的领域是评估胃上皮下病变(SELs)。虽然内镜超声(EUS)是最准确的成像方式,但没有明确的EUS特征来区分胃肠道间质瘤(gist)和常见的胃肠道平滑肌瘤(GILs)(7-9)。误诊胃肠道间质瘤和胃肠道间质瘤被认为是EUS错误诊断的主要原因(9)。鉴于胃肠道间质瘤的恶性潜能,准确诊断这些病变并将其与良性的胃肠道间质瘤区分开来至关重要。目前的标准是通过细针抽吸或活检获得组织样本(EUS-FNA/B)来区分这两种。然而,FNA/B是侵入性的,据报道,对于小于20 mm的SELs, FNA/B的诊断率较低(9,10)。在本期《内镜》杂志上,Yang等人报道了他们的人工智能EUS模型用于区分gist和GILs的结果(11)。使用CNN进行图像识别,AI模型在2013年至2020年期间收集的来自四个内镜中心的752例组织学证实的gist和GILs患者的总共10,439张EUS图像上进行了训练、验证和评估。他们报告说,与内窥镜专家相比,人工智能模型的诊断准确率明显更高(94.0%对70.2%,P值<0.001)。更重要的是,在508例连续的SELs患者的前瞻性评估中,其中132例进行了组织学确认,与专家超声相比,人工智能EUS的诊断准确率仍然显著高于专家超声(78.8%比69.7%,P值=0.01)。当仅检查组织学证实的gist或GILs病例时,人工智能联合诊断的准确性、特异性和PPV分别为92.2%、95.1%和94.1%,显著高于单独诊断的76.6% (P值=0.01)、65.9% (P值=0.002)和69.6% (P值<0.01)。人工智能关节诊断的敏感性和NPV与个体诊断相似。这些都是将深度学习应用于实时EUS以区分gist和gil的有希望的结果。先前的研究报告了一种改进的诊断方法
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引用次数: 0
Jejunal gastrointestinal stromal tumor (GIST): a case report presenting as life threatening emergency 空肠胃肠道间质瘤(GIST): 1例危及生命的急诊报告
Pub Date : 2020-11-01 DOI: 10.21037/gist-20-3
M. Saad, I. Hajj, E. Saikaly
Small bowel gastrointestinal stromal tumor (GIST) is an uncommon tumor, with an estimated frequency of 10–20/L million population, occurring usually in the 6 decade of life. GISTs, mainly ones larger than 4 cm have a wide range of presentations from abdominal discomfort and bloating to abdominal emergencies due to massive gastrointestinal (GI) hemorrhage, with pressure necrosis and ulceration of the overlying mucosa is the main mechanism leading to GI hemorrhage. Life-threatening hemorrhage is a rare initial presentation. Among the wide differential diagnosis for GI bleeding, jejunal GIST is one of the rarest etiologies. Due to its location and hence the inability to identify by endoscopy it is difficult to diagnose. From here, computed tomography (CT) angiography is considered essential for identification of the site of GI bleed, which aids in diagnosis of GIST. The mainstay treatment for this emergent presentation is resection, which if done in a timely manner results in a good clinical outcome. Herein, we report a case of a 46-yearold male patient presenting with massive GI bleed from jejunal GIST, leading to hemodynamic instability, requiring massive transfusion protocol managed by small bowel resection, after failure of radiologic embolization. In rare instances GISTs present as massive GI bleeding necessitating admission to the hospital and urgent interference. In our case the early multidisciplinary approach manifested by involvement of interventional radiology with the surgical team was the cornerstone in the successful management of our patient and should be considered in all cases of massive upper GI bleeding.
小肠胃肠道间质瘤(GIST)是一种罕见的肿瘤,估计发生频率为10-20/L百万人群,通常发生在生命的60年中。GIST,主要是大于4cm的GIST,有广泛的表现,从腹部不适和腹胀到胃肠道大出血引起的腹部紧急情况,上覆粘膜的压力性坏死和溃疡是导致胃肠道出血的主要机制。危及生命的出血是一种罕见的初始表现。在胃肠道出血的广泛鉴别诊断中,空肠GIST是最罕见的病因之一。由于它的位置,因此无法通过内窥镜检查进行识别,因此很难诊断。因此,计算机断层扫描(CT)血管造影术被认为是识别胃肠道出血部位的关键,有助于诊断GIST。这种紧急表现的主要治疗方法是切除,如果及时切除,会产生良好的临床结果。在此,我们报告了一例46岁男性患者,在放射学栓塞失败后,出现空肠GIST大量胃肠道出血,导致血液动力学不稳定,需要通过小肠切除进行大量输血。在极少数情况下,GIST表现为大量胃肠道出血,需要入院治疗和紧急干预。在我们的病例中,早期的多学科方法表现为介入放射学和手术团队的参与,这是成功管理我们患者的基石,在所有上消化道大出血病例中都应该考虑。
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引用次数: 0
Application of the CARE guideline as reporting standard in the Gastrointestinal Stromal Tumor CARE指南作为胃肠道间质瘤报告标准的应用
Pub Date : 2019-12-01 DOI: 10.21037/gist.2019.10.02
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引用次数: 0
Avapritinib: preclinical studies of a promising novel therapeutic option for gastrointestinal stromal tumors 阿伐替尼:一种有前景的胃肠道间质瘤新治疗选择的临床前研究
Pub Date : 2019-02-14 DOI: 10.21037/GIST.2019.01.03
M. Urbini
Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal tumor of the gastrointestinal tract (80%), with an annual incidence of 10–15 cases per million people (1). GISTs have become a paradigm for the use of molecular diagnostics and targeted therapy, since its molecular classification is able to predict treatment response. Most GISTs are driven by activating mutations in KIT (70–80% of cases) or PDGFRA (10%) receptor tyrosine kinases. This discovery represents the rationale of the efficacy of target therapies efficacy using small molecule tyrosine kinase inhibitors (TKIs) for this malignancy (1,2).
胃肠道间质瘤(gist)是最常见的胃肠道间质肿瘤(80%),年发病率为每百万人10-15例(1)。由于其分子分类能够预测治疗反应,gist已成为使用分子诊断和靶向治疗的典范。大多数gist是由KIT(70-80%的病例)或PDGFRA(10%)受体酪氨酸激酶激活突变驱动的。这一发现代表了使用小分子酪氨酸激酶抑制剂(TKIs)治疗这种恶性肿瘤的靶向治疗效果的基本原理(1,2)。
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引用次数: 0
Diagnostic Imaging of Gastrointestinal Stromal Tumor 胃肠道间质瘤的影像学诊断
Pub Date : 2019-01-01 DOI: 10.1007/978-981-13-3206-7_4
Tomohiro Yoneyama, Bae Hyeyeol, Yoshio Kitazume, M. Kishino, U. Tateishi
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引用次数: 1
Treatment of advanced gastrointestinal adenocarcinoma complicated with high-risk gastrointestinal stromal tumors (GISTs) in an elderly male patient: a case report and literature review 一例老年男性晚期胃肠腺癌合并高危胃肠道间质瘤的治疗:病例报告和文献复习
Pub Date : 2019-01-01 DOI: 10.21037/gist.2019.01.02
L. Chen, Tao Chen
Currently, coexistence of gastrointestinal adenocarcinoma and gastrointestinal stromal tumors (GISTs) is still infrequent. What’s more, there are few guidelines for the drug therapy of both types of synchronous tumors. Here, we report such a dangerous case and review the related literature for a better treatment. A 79-year-old male patient was admitted to our hospital for abdominal pain and diarrhea more than a month and stopped relieve nature nine days. He was preliminarily diagnosed as sigmoid colon adenocarcinoma and GISTs after having a Computed Tomography scan at another hospital. After gastroscopy, colonoscopy and pathological examination, we further diagnosed synchronous advanced colorectal cancer (CRC) and high-risk GISTs. Through literature review and medical records, we carefully developed the treatment protocol for this case, which is the chemotherapy plan for gastrointestinal adenocarcinoma plus imatinib. There is still insufficient evidence on the safety and efficacy of the regimen and more research and trials are needed to evaluate it.
目前,胃肠腺癌和胃肠道间质瘤(GIST)共存的情况仍然很少。更重要的是,这两种类型的同步肿瘤的药物治疗指南很少。在此,我们报告了这样一个危险的病例,并回顾了相关文献,以寻求更好的治疗方法。一位79岁的男性患者因腹痛和腹泻入院一个多月,并在9天内停止缓解自然。在另一家医院进行计算机断层扫描后,他被初步诊断为乙状结肠腺癌和GIST。经胃镜、结肠镜和病理检查,我们进一步诊断为同步晚期癌症(CRC)和高危GIST。通过文献回顾和医疗记录,我们仔细制定了该病例的治疗方案,即胃肠腺癌加伊马替尼的化疗方案。目前还没有足够的证据表明该方案的安全性和有效性,还需要更多的研究和试验来评估它。
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引用次数: 0
Risk Classification 风险分类
Pub Date : 2019-01-01 DOI: 10.1007/978-981-13-3206-7_5
H. Kikuchi, Hiroyuki Konno, Hiroya Takeuchi
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引用次数: 10
Treatment Guidelines 治疗指南
Pub Date : 2019-01-01 DOI: 10.1007/978-981-13-3206-7_6
Muranaka Tetsuhito, Y. Komatsu
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引用次数: 0
New Agents for Gastrointestinal Stromal Tumors 胃肠道间质瘤的新药物
Pub Date : 2019-01-01 DOI: 10.1007/978-981-13-3206-7_11
Y. Naito, T. Doi
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引用次数: 0
First-Line Treatment 一线治疗
Pub Date : 2019-01-01 DOI: 10.1007/978-981-13-3206-7_8
Y. Onozawa
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引用次数: 5
期刊
Gastrointestinal stromal tumor
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