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

doi:10.3877/cma.j.issn.1674-0793.2025.06.013

Abstract

Abstract:

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.

Key words: Pancreatic neuroendocrine tumors, Radiomics, Machine learning, Deep learning

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

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References 50

［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.

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

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

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