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重庆医科大学附属第一医院超声科,重庆 400016
刘 阳,Email:15882903383@163.com,研究方向:胃肠超声影像。
贺雪梅,Email:406900598@qq.com。
收稿:2025-01-04,
修回:2025-02-26,
录用:2025-03-19,
纸质出版:2025-07-28
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刘阳,贺雪梅.超声结合影像组学与AI在胃肠间质瘤诊断及预后评估中的应用进展[J].重庆医科大学学报,2025,50(7):898-904.
Liu Yang,He Xuemei.Advances in the application of ultrasound combined with radiomics and artificial intelligence in the diagnosis and prognostic evaluation of gastrointestinal stromal tumors[J].Journal of Chongqing Medical University,2025,50(7):898-904.
刘阳,贺雪梅.超声结合影像组学与AI在胃肠间质瘤诊断及预后评估中的应用进展[J].重庆医科大学学报,2025,50(7):898-904. DOI: 10.13406/j.cnki.cyxb.003841.
Liu Yang,He Xuemei.Advances in the application of ultrasound combined with radiomics and artificial intelligence in the diagnosis and prognostic evaluation of gastrointestinal stromal tumors[J].Journal of Chongqing Medical University,2025,50(7):898-904. DOI: 10.13406/j.cnki.cyxb.003841.
胃肠道间质瘤(gastrointestinal stromal tumors,GISTs)作为最常见的胃肠道间叶源性肿瘤,其早期诊断和精准评估对临床治疗至关重要。超声检查在GISTs的初步检测、血流动力学监测及预后评估中发挥着重要作用,但其表现复杂多样,增加了诊断难度。影像组学与人工智能(artificial intelligence,AI)技术显著提升了医学影像分析效率,有望提高诊断精度并为预后评估提供科学依据。本综述系统回顾了超声影像组学与AI技术在GISTs诊断及预后评估中的最新研究进展,重点探讨了其在早期诊断和危险度评估中的应用价值,希望能为优化临床决策提供理论支持。
Gastrointestinal stromal tumors(GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract,and early diagnosis and accurate evaluation are of great significance for clinical treatment. Ultrasound examination plays an important role in the preliminary detection,hemodynamic monitoring,and prognostic assessment of GISTs,but the complex and diverse manifestations of GISTs have increased the difficulties in diagnosis. Radiomics and artificial intelligence(AI) have significantly improved the efficiency of medical imaging analysis,and they are expected to enhance diagnostic accuracy and provide a scientific basis for prognostic evaluation. This article systematically reviews the latest research advances in ultrasound-based radiomics and AI in the diagnosis and prognostic evaluation of GISTs,with a focus on their application value in early diagnosis and risk assessment,in order to provide theoretical support for optimizing clinical decision-making.
Blay JY , Kang YK , Nishida T , et al . Gastrointestinal stromal tumours [J ] . Nat Rev Dis Primers , 2021 , 7 ( 1 ): 22 .
Dermawan JK , Rubin BP . Molecular pathogenesis of gastrointestinal stromal tumor:a paradigm for personalized medicine [J ] . Annu Rev Pathol , 2022 , 17 : 323 - 344 .
Wen H , Huang Y , Huang S , et al . The long-term efficacy of imatinib with hepatic resection or other local treatment for gastrointestinal stromal tumours liver metastases:a retrospective cohort study [J ] . Int J Surg , 2024 , 110 ( 4 ): 2151 - 2161 .
Joensuu H . Risk stratification of patients diagnosed with gastrointestinal stromal tumor [J ] . Hum Pathol , 2008 , 39 ( 10 ): 1411 - 1419 .
Galluzzo A , Boccioli S , Danti G , et al . Radiomics in gastrointestinal stromal tumours:an up-to-date review [J ] . Jpn J Radiol , 2023 , 41 ( 10 ): 1051 - 1061 .
Guo JJ , Tang XB , Qian QF , et al . Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors [J ] . Scand J Gastroenterol , 2022 , 57 ( 3 ): 352 - 358 .
Li TT , Li J , Hu ZY , et al . An ultrasound based method for predicting the malignant potential of primary gastrointestinal stromal tumors preoperatively [J ] . Abdom Radiol , 2024 , 49 ( 12 ): 4189 - 4197 .
Yang F , Liu F , Wen L . Multimode ultrasound imaging of an anal canal gastrointestinal stromal tumour(GIST) [J ] . Tech Coloproctol , 2024 , 28 ( 1 ): 61 .
Dong RX , Wang C , Zhou H , et al . Rare rectal gastrointestinal stromal tumor case:a case report and review of the literature [J ] . World J Clin Cases , 2023 , 11 ( 28 ): 6797 - 6805 .
von Mehren M , Kane JM , Bui MM , et al . NCCN guidelines insights:soft tissue sarcoma,version 1.2021 [J ] . J Natl Compr Canc Netw , 2020 , 18 ( 12 ): 1604 - 1612 .
Casali PG , Blay JY , Abecassis N , et al . Gastrointestinal stromal tumours:ESMO-EURACAN-GENTURIS Clinical Practice Guidelines for diagnosis,treatment and follow-up [J ] . Ann Oncol , 2022 , 33 ( 1 ): 20 - 33 .
Jacobson BC , Bhatt A , Greer KB , et al . ACG clinical guideline:diagnosis and management of gastrointestinal subepithelial lesions [J ] . Am J Gastroenterol , 2023 , 118 ( 1 ): 46 - 58 .
Goto O , Kaise M , Iwakiri K . Advancements in the diagnosis of gastric subepithelial tumors [J ] . Gut Liver , 2022 , 16 ( 3 ): 321 - 330 .
Zhang D , Wu C , Yang Z , et al . The application of artificial intelligence in EUS [J ] . Endosc Ultrasound , 2024 , 13 ( 2 ): 65 - 75 .
Wu Y , Ramai D , Smith ER , et al . Applications of artificial intelligence in gastrointestinal endoscopic ultrasound:current developments,limitations and future directions [J ] . Cancers(Basel) , 2024 , 16 ( 24 ): 4196 .
Wu S , Yue M , Zhang J , et al . The role of artificial intelligence in accurate interpretation of HER2 immunohistochemical scores 0 and 1+ in breast cancer [J ] . Mod Pathol , 2023 , 36 ( 3 ): 100054 .
Foersch S , Glasner C , Woerl AC , et al . Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer [J ] . Nat Med , 2023 , 29 ( 2 ): 430 - 439 .
Guiot J , Vaidyanathan A , Deprez L , et al . A review in radiomics:Making personalized medicine a reality via routine imaging [J ] . Med Res Rev , 2022 , 42 ( 1 ): 426 - 440 .
Vicini S , Bortolotto C , Rengo M , et al . A narrative review on current imaging applications of artificial intelligence and radiomics in oncology:focus on the three most common cancers [J ] . La Radiol Med , 2022 , 127 ( 8 ): 819 - 836 .
McCague C , Ramlee S , Reinius M , et al . Introduction to radiomics for a clinical audience [J ] . Clin Radiol , 2023 , 78 ( 2 ): 83 - 98 .
Zhang XD , Zhang L , Gong TT , et al . A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images [J ] . J Appl Clin Med Phys , 2023 , 24 ( 7 ): e14023 .
王卓然 , 张贤达 , 曹羽成 , 等 . 基于超声内镜影像组学和机器学习的胃肠道间质瘤与非胃肠道间质瘤鉴别方法 [J ] . 海军军医大学学报 , 2024 , 45 ( 1 ): 21 - 28 .
Wang ZR , Zhang XD , Cao YC , et al . Differentiating gastrointestinal stromal tumors from non-gastrointestinal stromal tumors based on endoscopic ultrasound radiomics and machine learning [J ] . Acad J Naval Med Univ , 2024 , 45 ( 1 ): 21 - 28 .
Hu B , Wang C , Jiang K , et al . Development and validation of a novel diagnostic model for initially clinical diagnosed gastrointestinal stromal tumors using an extreme gradient-boosting machine [J ] . BMC Gastroenterol , 2021 , 21 ( 1 ): 481 .
Demetri GD , von Mehren M , Antonescu CR , et al . NCCN Task Force report:update on the management of patients with gastrointestinal stromal tumors [J ] . J Natl Compr Canc Netw , 2010 , 8 ( Suppl 2 ): S1 - 41 ;quizS42-4.
Xiao X , Han X , Sun Y , et al . Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor [J ] . NPJ Precis Oncol , 2024 , 8 ( 1 ): 157 .
Lin JX , Wang FH , Wang ZK , et al . Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features [J ] . La Radiol Med , 2023 , 128 ( 6 ): 644 - 654 .
Zhuo M , Guo J , Tang Y , et al . Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors [J ] . Front Oncol , 2022 , 12 : 905036 .
杨 凡 , 刘春伟 , 张 岱 , 等 . 基于胃充盈超声造影下的影像组学构建胃间质瘤NIH危险度分级预测模型 [J ] . 中华超声影像学杂志 , 2023 , 32 ( 12 ): 1062 - 1069 .
Yang F , Liu CW , Zhang D . Prediction model of NIH risk stratification for gastrointestinal stromal tumor based on ultrasonographic radiomics by oral contrast enhanced ultrasonography [J ] . Chin. J. Ultrasonogr , 2023 , 32 ( 12 ): 1062 - 1069 .
Cai MY , Song BH , Deng YH , et al . Automatically optimized radiomics modeling system for small gastric submucosal tumor(<2 Cm) discrimination based on EUS images [J ] . Gastrointest Endosc , 2024 , 99 ( 4 ): 537 - 547 .
Li XY , Jiang F , Guo Y , et al . Computer-aided diagnosis of gastrointestinal stromal tumors:a radiomics method on endoscopic ultrasound image [J ] . Int J Comput Assist Radiol Surg , 2019 , 14 ( 10 ): 1635 - 1645 .
Zhuo ML , Tang Y , Guo JJ , et al . Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics [J ] . J Med Ultrason , 2024 , 51 ( 1 ): 71 - 82 .
Qi Y , Liu Y , Luo J . Recent application of Raman spectroscopy in tumor diagnosis:from conventional methods to artificial intelligence fusion [J ] . PhotoniX , 2023 , 4 ( 1 ): 22 .
Khalighi S , Reddy K , Midya A , et al . Artificial intelligence in neuro-oncology:advances and challenges in brain tumor diagnosis,prognosis,and precision treatment [J ] . NPJ Precis Oncol , 2024 , 8 ( 1 ): 80 .
Hossain S , Chakrabarty A , Gadekallu TR , et al . Vision transformers,ensemble model,and transfer learning leveraging explainable AI for brain tumor detection and classification [J ] . IEEE J Biomed Health Inform , 2024 , 28 ( 3 ): 1261 - 1272 .
Yang X , Wang H , Dong Q , et al . An artificial intelligence system for distinguishing between gastrointestinal stromal tumors and leiomyomas using endoscopic ultrasonography [J ] . Endoscopy , 2022 , 54 ( 3 ): 251 - 261 .
Minoda Y , Ihara E , Fujimori N , et al . Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors [J ] . Sci Rep , 2022 , 12 ( 1 ): 16640 .
Lu Y , Wu J , Hu M , et al . Artificial intelligence in the prediction of gastrointestinal stromal tumors on endoscopic ultrasonography images:development,validation and comparison with endosonographers [J ] . Gut Liver , 2023 , 17 ( 6 ): 874 - 883 .
Seven G , Silahtaroglu G , Seven OO , et al . Differentiating gastrointestinal stromal tumors from leiomyomas using a neural network trained on endoscopic ultrasonography images [J ] . Dig Dis , 2022 , 40 ( 4 ): 427 - 435 .
Kim YH , Kim GH , Kim KB , et al . Application of A convolutional neural network in the diagnosis of gastric mesenchymal tumors on endoscopic ultrasonography images [J ] . J Clin Med , 2020 , 9 ( 10 ): E3162 .
Liu J , Huang J , Song Y , et al . Differentiating gastrointestinal stromal tumors from leiomyomas of upper digestive tract using convolutional neural network model by endoscopic ultrasonography [J ] . J Clin Gastroenterol , 2024 , 58 ( 6 ): 574 - 579 .
Zhu C , Hua Y , Zhang M , et al . A multimodal multipath artificial intelligence system for diagnosing gastric protruded lesions on endoscopy and endoscopic ultrasonography images [J ] . Clin Transl Gastroenterol , 2023 , 14 ( 10 ): e00551 .
Dong ZX , Zhao XY , Zheng HB , et al . Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas:a multicenter diagnostic study [J ] . eClinicalMedicine , 2024 , 73 : 102656 .
Zhuo M , Chen X , Guo J , et al . Deep learning-based segmentation and risk stratification for gastrointestinal stromal tumors in transabdominal ultrasound imaging [J ] . J Ultrasound Med , 2024 , 43 ( 9 ): 1661 - 1672 .
Liu C , Qiao M , Jiang F , et al . TN-USMA Net:Triple normaliza-tion-based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound-specific pretraining and meta attention [J ] . Med Phys , 2021 , 48 ( 11 ): 7199 - 7214 .
Lü C , Chen H , Huang P , et al . Application of computer-assisted endoscopic ultrasonography based on texture features in differentiating gastrointestinal stromal tumors from benign gastric mesenchymal tumors [J ] . Turk J Gastroenterol , 2024 , 35 ( 5 ): 366 - 373 .
Iwai T , Kida M , Okuwaki K , et al . Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors [J ] . Scand J Gastroenterol , 2024 , 59 ( 8 ): 925 - 932 .
Hirai K , Kuwahara T , Furukawa K , et al . Artificial intelligence-based diagnosis of upper gastrointestinal subepithelial lesions on endoscopic ultrasonography images [J ] . Gastric Cancer , 2022 , 25 ( 2 ): 382 - 391 .
Tanaka H , Kamata K , Ishihara R , et al . Value of artificial intelligence with novel tumor tracking technology in the diagnosis of gastric submucosal tumors by contrast-enhanced harmonic endoscopic ultrasonography [J ] . J Gastroenterol Hepatol , 2022 , 37 ( 5 ): 841 - 846 .
Zhang BL , Zhu FP , Li P , et al . Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors:a meta-analysis [J ] . Surg Endosc , 2023 , 37 ( 3 ): 1649 - 1657 .
Ye XH , Zhao LL , Wang L . Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors:a meta-analysis [J ] . J Dig Dis , 2022 , 23 ( 5/6 ): 253 - 261 .
Gichoya JW , Thomas K , Celi LA , et al . AI pitfalls and what not to do:mitigating bias in AI [J ] . Br J Radiol , 2023 , 96 ( 1150 ): 20230023 .
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