切换至 "中华医学电子期刊资源库"

中华普通外科学文献(电子版) ›› 2025, Vol. 19 ›› Issue (06) : 426 -432. doi: 10.3877/cma.j.issn.1674-0793.2025.06.013

综述

影像组学在胰腺神经内分泌瘤诊疗中的研究进展
杨雯林, 吴元魁()   
  1. 501515 广州,南方医科大学南方医院影像诊断科
  • 收稿日期:2025-09-17 出版日期:2025-12-01
  • 通信作者: 吴元魁
  • 基金资助:
    广东省自然科学基金项目(2023A1515011453)

Research progress in radiomics for the diagnosis and treatment of pancreatic neuroendocrine tumors

Wenlin Yang, Yuankui Wu()   

  1. Department of Imaging Diagnosis, Nanfang Hospital, Southern Medical University, Guangzhou 501515, China
  • Received:2025-09-17 Published:2025-12-01
  • Corresponding author: Yuankui Wu
引用本文:

杨雯林, 吴元魁. 影像组学在胰腺神经内分泌瘤诊疗中的研究进展[J/OL]. 中华普通外科学文献(电子版), 2025, 19(06): 426-432.

Wenlin Yang, Yuankui Wu. Research progress in radiomics for the diagnosis and treatment of pancreatic neuroendocrine tumors[J/OL]. Chinese Archives of General Surgery(Electronic Edition), 2025, 19(06): 426-432.

胰腺神经内分泌瘤(PanNETs)占胰腺肿瘤的2%~5%,其高度异质性导致治疗策略难以标准化、难达最佳疗效,临床治疗高度依赖疾病分期与分级。​传统影像学检查在PanNETs病理分级及鉴别诊断中存在明显局限性。影像组学技术近年得到快速发展,研究者围绕其在PanNETs病理侵袭性评估、鉴别诊断、治疗反应监测及预后预测等方面的应用价值开展了广泛研究。本文​通过梳理近10年相关文献,系统总结了影像组学在PanNETs临床诊疗领域的最新进展,探讨当前研究的局限性与挑战,为后续相关研究提供参考,并展望未来发展方向。

Pancreatic neuroendocrine tumors (PanNETs) account for 2% to 5% of pancreatic tumors. The high heterogeneity of PanNETs makes it difficult to standardize treatment strategies and achieve the best therapeutic effect. Clinical treatment is highly dependent on disease staging and grading. Traditional imaging examinations have obvious limitations in the pathological grading and differential diagnosis of PanNETs. In recent years, radiomics technology has developed rapidly, and researchers have conducted extensive studies on its application value in the assessment of pathological invasiveness, differential diagnosis, treatment response monitoring, and prognosis prediction of PanNETs. This article reviews the relevant literature in the past 10 years, systematically summarizes the latest progress of radiomics in the clinical diagnosis and treatment of PanNETs, discusses the current research limitations and challenges, provides references for subsequent related research, and looks forward to future development directions.

表1 本综述纳入的影像组学在PanNETs不同临床任务下的最佳预测模型和局限性分析
预测任务 图像 最佳模型 AUC 样本量 主要局限性
小PanNETs检测5 CT 影像组学特征(LGBM) 测试集: 0.87 135例PanNETs和 135例健康对照 ①PanNETs中≤2 cm肿瘤少,可能影响小肿瘤检测准确性;②自动化分割局限性;③模型敏感度高,但特异度较低
与PDACs鉴别诊断9 CT 影像组学特征(梯度提升决策树+随机森林) 训练集: 0.971
验证集: 0.930
238例患者(156例PDACs, 82例PanNETs) ①样本不平衡(PanNETs组样本相对较小);②单中心数据收集;③过度拟合风险;④外部验证缺乏;⑤深度学习模型(如梯度提升决策树)作为黑箱,缺乏可解释性
与SPNs鉴别诊断15 MRI 影像组学特征(Logistic 回归分析) 训练集: 0.97
验证集: 0.86
66例患者(31例PanNETs, 35例SPNs) ①样本量小;②单中心数据收集;③缺乏外部验证;④依赖影像科医师手动分割ROI;⑤两种肿瘤的病理亚型未细分,可能忽略内在异质性
淋巴结转移21 CT 影像组学深度学习特征 训练集: 0.88
验证集: 0.91
320例患者(140例淋巴结转移,  180例非淋巴结转移) ①样本量小,验证集仅84例;②自动化分割不完美;③未包括所有潜在预测因子
肝转移24 CT 列线图(病理组学评分+深度学习-影像组学) 训练集: 0.985
验证集: 0.961
163例患者(37例肝转移, 126例非肝转移) ①样本量小,肝转移组仅37例;②单中心数据,存在回顾性偏差;③病理特征不完整;④列线图整合多特征致模型复杂化、缺乏外部验证
病理分级36 PET/CT 列线图(临床特征+影像组学评分) 0.953 41例患者(14例G1级, 27例G2/3级) ①样本量小,且G3病例少(3例);②单中心研究,存在选择偏差;③特征选择可能过拟合;④外部验证缺失;⑤未整合多模态数据
术后复发38 CT 动脉期影像组学+深度学习-影像组学 训练集: 0.80
验证集: 0.77
74例患者(19例5年内复发或远处转移, 55例5年内未复发或远处转移) ①样本量小;②外部验证薄弱; ③依赖影像科医师手动分割 ROI;④未考虑所有临床因素
靶向治疗预后(肿瘤缩小>10%)44 CT 影像组学特征(LASSO回归) 训练集: 0.915
验证集: 0.770
38例患者, 171个病灶 ①样本量小(38例),需更大样本验证;②CT扫描仪差异;③回顾性设计可能引入偏倚
PRRT的预后(OS)47 SSTR-PET/CT 纹理特征(如熵)、Kaplan-Meier分析 0.71 31例 ①样本量小(31例);②多中心成像协议差异;③未进行多重比较校正;④回顾性设计限制因果推断
[1]
Dasari A, Shen C, Halperin D, et al. Trends in the incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States[J]. JAMA Oncol, 2017, 3(10): 1335.
[2]
Pavel M, Öberg K, Falconi M, et al. Gastroenteropancreatic neuroendocrine neoplasms: ESMO clinical practice guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2020, 31(7): 844–860.
[3]
Sugimoto M. Efficacy of endoscopic ultrasonography-guided fine needle aspiration for pancreatic neuroendocrine tumor grading[J]. World J Gastroenterol, 2015, 21(26): 8118.
[4]
Kuo EJ, Salem RR. Population-level analysis of pancreatic neuroendocrine tumors 2 cm or less in size[J]. Ann Surg Oncol, 2013, 20(9): 2815–2821.
[5]
Lopez-Ramirez F, Soleimani S, Azadi JR, et al. Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT[J]. Diagn Interv Imaging, 2025, 106(1): 28–40.
[6]
Li J, Lu J, Liang P, et al. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole tumor CT texture analysis as quantitative biomarkers[J]. Cancer Med, 2018, 7(10): 4924–4931.
[7]
Reinert CP, Baumgartner K, Hepp T, et al. Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase[J]. Abdom Radiol (NY), 2020, 45(3): 750–758.
[8]
Yu H, Huang Z, Li M, et al. Differential diagnosis of nonhypervascular pancreatic neuroendocrine neoplasms from pancreatic ductal adenocarcinomas, based on computed tomography radiological features and texture analysis[J]. Acad Radiol, 2020, 27(3): 332–341.
[9]
Zhang T, Xiang Y, Wang H, et al. Radiomics combined with multiple machine learning algorithms in differentiating pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumor: more hands produce a stronger flame[J]. J Clin Med, 2022, 11(22): 6789.
[10]
Shen K, Su W, Liang C, et al. Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features[J]. Eur Radiol, 2024, 34(12): 7553–7563.
[11]
Shi YJ, Zhu HT, Li XT, et al. Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms[J]. Clin Imaging, 2023, 96: 15–22.
[12]
Patel N, Barbieri A, Gibson J. Neuroendocrine tumors of the gastrointestinal tract and pancreas[J]. Surg Pathol Clin, 2019, 12(4): 1021–1044.
[13]
Li X, Zhu H, Qian X, et al. MRI texture analysis for differentiating nonfunctional pancreatic neuroendocrine neoplasms from solid pseudopapillary neoplasms of the pancreas[J]. Acad Radiol, 2020, 27(6): 815–823.
[14]
Song T, Zhang QW, Duan SF, et al. MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas[J]. BMC Med Imaging, 2021, 21(1): 36.
[15]
Shi YJ, Zhu HT, Liu YL, et al. Radiomics analysis based on diffusion kurtosis imaging and T2 weighted imaging for differentiation of pancreatic neuroendocrine tumors from solid pseudopapillary tumors[J]. Front Oncol, 2020, 10: 1624.
[16]
Chen J, Yang Y, Liu Y, et al. Prognosis analysis of patients with pancreatic neuroendocrine tumors after surgical resection and the application of enucleation[J]. World J Surg Oncol, 2021, 19(1): 11.
[17]
Falconi M, Eriksson B, Kaltsas G, et al. ENETS consensus guidelines update for the management of patients with functional pancreatic neuroendocrine tumors and non-Functional pancreatic neuroendocrine tumors[J]. Neuroendocrinology, 2016, 103(2): 153–171.
[18]
Mapelli P, Bezzi C, Palumbo D, et al. 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours[J]. Eur J Nucl Med Mol Imaging, 2022, 49(7): 2352–2363.
[19]
Benedetti G, Mori M, Panzeri MM, et al. CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors[J]. Radiol Med, 2021, 126(6): 745–760.
[20]
Ahmed TM, Zhu Z, Yasrab M, et al. Preoperative prediction of lymph node metastases in nonfunctional pancreatic neuroendocrine tumors using a combined CT radiomics–clinical model[J]. Ann Surg Oncol, 2024, 31(12): 8136–8145.
[21]
Gu W, Chen Y, Zhu H, et al. Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: A multicohort study[J]. eClinicalMedicine, 2023, 65: 102269.
[22]
Li J, Huang L, Liao C, et al. Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: A multicenter study[J]. BMC Cancer, 2023, 23(1): 529.
[23]
Pan M, Yang Y, Teng T, et al. Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: A large cohort study[J]. BMC Gastroenterol, 2021, 21(1): 101.
[24]
Ma M, Gu W, Liang Y, et al. A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics[J]. J Transl Med, 2024, 22(1): 768.
[25]
Takumi K, Fukukura Y, Higashi M, et al. Pancreatic neuroendocrine tumors: correlation between the contrast-enhanced computed tomography features and the pathological tumor grade[J]. Eur J Radiol, 2015, 84(8): 1436–1443.
[26]
Canellas R, Burk KS, Parakh A, et al. Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis[J]. AJR Am J Roentgenol, 2018, 210(2): 341–346.
[27]
Liang W, Yang P, Huang R, et al. A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors[J]. Clin Cancer Res, 2019, 25(2): 584–594.
[28]
Gu D, Hu Y, Ding H, et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: A multicenter study[J]. Eur Radiol, 2019, 29(12): 6880–6890.
[29]
Bian Y, Jiang H, Ma C, et al. CT-based radiomics score for distinguishing between grade 1 and grade 2 nonfunctioning pancreatic neuroendocrine tumors[J]. AJR Am J Roentgenol, 2020, 215(4): 852–863.
[30]
Zhao Z, Bian Y, Jiang H, et al. CT-radiomic approach to predict G1/2 nonfunctional pancreatic neuroendocrine tumor[J]. Acad Radiol, 2020, 27(12): e272-e281.
[31]
Zhang T, Zhang Y, Liu X, et al. Application of radiomics analysis based on CT combined with machine learning in diagnostic of pancreatic neuroendocrine tumors patient’s pathological grades[J]. Front Oncol, 2021, 10: 521831.
[32]
Ye JY, Fang P, Peng ZP, et al. A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors[J]. Eur Radiol, 2023, 34(3): 1994–2005.
[33]
Bian Y, Li J, Cao K, et al. Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs[J]. Abdom Radiol (NY), 2021, 46(2): 667–680.
[34]
Zhu HB, Zhu HT, Jiang L, et al. Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: A multicenter study[J]. Eur Radiol, 2023, 34(1): 90–102.
[35]
Liu C, Bian Y, Meng Y, et al. Preoperative prediction of G1 and G2/3 grades in patients with nonfunctional pancreatic neuroendocrine tumors using multimodality imaging[J]. Acad Radiol, 2022, 29(4): e49-e60.
[36]
Ma J, Wang X, Tang M, et al. Preoperative prediction of pancreatic neuroendocrine tumor grade based on 68Ga-DOTATATE PET/CT[J]. Endocrine, 2023, 83(2): 502–510.
[37]
Yan Q, Chen Y, Liu C, et al. Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: A systematic review and meta-analysis[J]. Front Oncol, 2024, 14: 1332387.
[38]
Song C, Wang M, Luo Y, et al. Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images[J]. Ann Transl Med, 2021, 9(10): 833–833.
[39]
Homps M, Soyer P, Coriat R, et al. A preoperative computed tomography radiomics model to predict disease-free survival in patients with pancreatic neuroendocrine tumors[J]. Eur J Endocrinol, 2023, 189(4): 476–484.
[40]
Yao JC, Pavel M, Lombard-Bohas C, et al. Everolimus for the treatment of advanced pancreatic neuroendocrine tumors: overall survival and circulating biomarkers from the randomized, phase Ⅲ RADIANT-3 study[J]. J Clin Oncol, 2016, 34(32): 3906–3913.
[41]
Faivre S, Niccoli P, Castellano D, et al. Sunitinib in pancreatic neuroendocrine tumors: updated progression-free survival and final overall survival from a phase Ⅲ randomized study[J]. Ann Oncol, 2017, 28(2): 339–343.
[42]
Caruso D, Polici M, Rinzivillo M, et al. CT-based radiomics for prediction of therapeutic response to everolimus in metastatic neuroendocrine tumors[J]. Radiol Med, 2022, 127(7): 691–701.
[43]
Lamarca A, Barriuso J, Kulke M, et al. Determination of an optimal response cut-off able to predict progression-free survival in patients with well-differentiated advanced pancreatic neuroendocrine tumours treated with sunitinib: An alternative to the current RECIST-defined response[J]. Br J Cancer, 2018, 118(2): 181–188.
[44]
Chen L, Wang W, Jin K, et al. Special issue “The advance of solid tumor research in China”: prediction of sunitinib efficacy using computed tomography in patients with pancreatic neuroendocrine tumors[J]. Int J Cancer, 2023, 152(1): 90–99.
[45]
Öksüz M Ö, Winter L, Pfannenberg C, et al. Peptide receptor radionuclide therapy of neuroendocrine tumors with 90Y-DOTATOC: is treatment response predictable by pre-therapeutic uptake of 68Ga-DOTATOC?[J]. Diagn Interv Imaging, 2014, 95(3): 289–300.
[46]
Cook GJR, O’Brien ME, Siddique M, et al. Non-small cell lung cancer treated with erlotinib: heterogeneity of 18F-FDG uptake at PET-association with treatment response and prognosis[J]. Radiology, 2015, 276(3): 883–893.
[47]
Werner RA, Ilhan H, Lehner S, et al. Pre-therapy somatostatin receptor-based heterogeneity predicts overall survival in pancreatic neuroendocrine tumor patients undergoing peptide receptor radionuclide therapy[J]. Mol Imaging Biol, 2019, 21(3): 582–590.
[48]
Önner H, Abdülrezzak Ü, Tutuş A. Could the skewness and kurtosis texture parameters of lesions obtained from pretreatment Ga-68 DOTA-TATE PET/CT images predict receptor radionuclide therapy response in patients with gastroenteropancreatic neuroendocrine tumors?[J]. Nucl Med Commun, 2020, 41(10): 1034–1039.
[49]
Weber M, Kessler L, Schaarschmidt B, et al. Treatment-related changes in neuroendocrine tumors as assessed by textural features derived from 68Ga-DOTATOC PET/MRI with simultaneous acquisition of apparent diffusion coefficient[J]. BMC Cancer, 2020, 20(1): 326.
[50]
Kocak B, Bulut E, Bayrak ON, et al. Negative results in radiomics research (NEVER): A meta-research study of publication bias in leading radiology journals[J]. Eur J Radiol, 2023, 163: 110830.
[1] 江瑶, 蒋程, 余翔, 谭莹, 温昕, 温慧莹, 彭桂艳, 李胜利. 基于注意力机制改进的子宫解剖结构检测与分割多任务模型的性能评估[J/OL]. 中华医学超声杂志(电子版), 2025, 22(08): 703-710.
[2] 戴辉水, 吕嵩, 张劲松, 巴根, 石齐芳. 基于机器学习算法构建药物中毒患者ICU住院时间延长的预测模型[J/OL]. 中华危重症医学杂志(电子版), 2025, 18(04): 274-281.
[3] 张克诚, 王瑞, 易磊, 周增丁. 烧烫伤创面深度评估模型HFNet 的构建及测试效果[J/OL]. 中华损伤与修复杂志(电子版), 2025, 20(03): 192-198.
[4] 钱何布, 朱林, 姚月平, 姚峰, 李玉卓, 马家驹, 晏倩, 倪晓艳. 应用机器学习建立脓毒性休克患者住院28天死亡预测模型及验证[J/OL]. 中华实验和临床感染病杂志(电子版), 2025, 19(05): 288-297.
[5] 汪锐, 陈自武, 杨朴强, 田静, 陈莹, 林成, 汪伟. 基于血清标志物机器学习模型对慢性阻塞性肺疾病急性加重期机械通气风险的预测分析[J/OL]. 中华肺部疾病杂志(电子版), 2025, 18(04): 615-619.
[6] 希龙夫, 薛荣泉. 人工智能在肝胆胰肿瘤诊治中应用与进展[J/OL]. 中华腔镜外科杂志(电子版), 2025, 18(03): 166-171.
[7] 黄少坚, 梁汉标, 李清平, 唐善华, 李青妍, 李芷西, 黄灿, 王小振, 陈灿辉, 王恺, 李川江. 基于影像组学和临床特征构建肝癌新辅助/转化治疗后病理学完全缓解预测模型[J/OL]. 中华肝脏外科手术学电子杂志, 2025, 14(06): 860-867.
[8] 唐善华, 赖展鸿, 刘海晴, 王小振, 王恺, 周杰. 基于XGBoost算法构建肝癌肝切除术后肝衰竭早期识别预测模型[J/OL]. 中华肝脏外科手术学电子杂志, 2025, 14(05): 725-731.
[9] 鲁莽, 马晓璐, 沈浮, 王颢, 邵成伟, 张卫, 陆建平, 陆海迪. 基于磁共振的深度学习重建方法在直肠癌术前评估中的应用研究[J/OL]. 中华结直肠疾病电子杂志, 2025, 14(05): 445-456.
[10] 张娴, 王彬瞻, 王馨媛, 罗再, 王庆国, 程云章, 黄陈. 基于增强CT的二维、三维影像组学和联合模型对术前预测结直肠癌脉管侵犯价值研究[J/OL]. 中华结直肠疾病电子杂志, 2025, 14(05): 457-467.
[11] 郭寒川, 王乾宇, 吴斌. 人工智能在神经识别的研究进展及直肠癌自主神经保护的应用[J/OL]. 中华结直肠疾病电子杂志, 2025, 14(03): 273-276.
[12] 李媛媛, 李荣山. 机器学习:肾脏疾病研究与诊疗的新前沿[J/OL]. 中华肾病研究电子杂志, 2025, 14(04): 181-187.
[13] 王柯云, 孙雅佳, 李甜, 张钰哲, 郑颖, 张伟光, 王倩, 董哲毅. 糖尿病肾脏疾病早期发生风险预测模型的研究进展[J/OL]. 中华肾病研究电子杂志, 2025, 14(04): 218-225.
[14] 夏炎, 朱帅帅, 张岩松. 基于生物信息学和机器学习探究前庭神经鞘瘤生物标志物在免疫微环境中的相关功能[J/OL]. 中华脑科疾病与康复杂志(电子版), 2025, 15(03): 171-179.
[15] 李曰平, 鞠倩, 张汝梦, 韩博. 基于CT影像组学预测胃癌根治术复发风险的临床研究[J/OL]. 中华消化病与影像杂志(电子版), 2025, 15(05): 460-466.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?