Predicting Histologic Grades for Pancreatic Neuroendocrine Tumors by Radiologic Image-based Artificial Intelligence: a Systematic Review and Meta-analysis

Overview

Journal Front Oncol

Specialty Oncology

Date 2024 May 10

PMID 38725633

Authors

Qian Yan

Yubin Chen

Chunsheng Liu

Hexian Shi

Mingqian Han

Zelong Wu

Shanzhou Huang

Chuanzhao Zhang

Baohua Hou

Affiliations

Soon will be listed here.

Abstract

Background: Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.

Method: A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.

Results: A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I= 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I = 82.28%.

Conclusion: AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.

Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

References

Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H . Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep. 2015; 5:13087. PMC: 4538374. DOI: 10.1038/srep13087. View

Yao J, Hassan M, Phan A, Dagohoy C, Leary C, Mares J . One hundred years after "carcinoid": epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol. 2008; 26(18):3063-72. DOI: 10.1200/JCO.2007.15.4377. View

Mapelli P, Bezzi C, Palumbo D, Canevari C, Ghezzo S, Samanes Gajate A . Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours. Eur J Nucl Med Mol Imaging. 2022; 49(7):2352-2363. DOI: 10.1007/s00259-022-05677-0. View

Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A . Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging. 2021; 48(12):4002-4015. DOI: 10.1007/s00259-021-05338-8. View

Fan J, Zhang Y, Shi S, Chen Y, Yuan X, Jiang L . A nation-wide retrospective epidemiological study of gastroenteropancreatic neuroendocrine neoplasms in china. Oncotarget. 2017; 8(42):71699-71708. PMC: 5641082. DOI: 10.18632/oncotarget.17599. View

Sadula A, Li G, Xiu D, Ye C, Ren S, Guo X . Clinicopathological Characteristics of Nonfunctional Pancreatic Neuroendocrine Neoplasms and the Effect of Surgical Treatment on the Prognosis of Patients with Liver Metastases: A Study Based on the SEER Database. Comput Math Methods Med. 2022; 2022:3689895. PMC: 9200579. DOI: 10.1155/2022/3689895. View

Yang Z, Tang L, Klimstra D . Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol. 2011; 35(6):853-60. DOI: 10.1097/PAS.0b013e31821a0696. View

Zhou R, Ji H, Liu Q, Zhu C, Liu R . Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers. World J Clin Cases. 2019; 7(13):1611-1622. PMC: 6658377. DOI: 10.12998/wjcc.v7.i13.1611. View

Luo Y, Chen X, Chen J, Song C, Shen J, Xiao H . Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network. Neuroendocrinology. 2019; 110(5):338-350. DOI: 10.1159/000503291. View

10.

Mori M, Palumbo D, Muffatti F, Partelli S, Mushtaq J, Andreasi V . Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features. Eur Radiol. 2022; 33(6):4412-4421. DOI: 10.1007/s00330-022-09351-9. View

11.

DOnofrio M, Ciaravino V, Cardobi N, De Robertis R, Cingarlini S, Landoni L . CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms. Sci Rep. 2019; 9(1):2176. PMC: 6379382. DOI: 10.1038/s41598-018-38459-6. View

12.

Canakis A, Lee L . Current updates and future directions in diagnosis and management of gastroenteropancreatic neuroendocrine neoplasms. World J Gastrointest Endosc. 2022; 14(5):267-290. PMC: 9157694. DOI: 10.4253/wjge.v14.i5.267. View

13.

Nagtegaal I, Odze R, Klimstra D, Paradis V, Rugge M, Schirmacher P . The 2019 WHO classification of tumours of the digestive system. Histopathology. 2019; 76(2):182-188. PMC: 7003895. DOI: 10.1111/his.13975. View

14.

Ma Z, Gong Y, Zhuang H, Zhou Z, Huang S, Zou Y . Pancreatic neuroendocrine tumors: A review of serum biomarkers, staging, and management. World J Gastroenterol. 2020; 26(19):2305-2322. PMC: 7243647. DOI: 10.3748/wjg.v26.i19.2305. View

15.

Ricci C, Mosconi C, Ingaldi C, Vara G, Verna M, Pettinari I . The 3-Dimensional-Computed Tomography Texture Is Useful to Predict Pancreatic Neuroendocrine Tumor Grading. Pancreas. 2022; 50(10):1392-1399. DOI: 10.1097/MPA.0000000000001927. View

16.

Choi T, Kim J, Yu M, Park S, Han J . Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol. 2017; 59(4):383-392. DOI: 10.1177/0284185117725367. View

17.

Pulvirenti A, Marchegiani G, Pea A, Allegrini V, Esposito A, Casetti L . Clinical Implications of the 2016 International Study Group on Pancreatic Surgery Definition and Grading of Postoperative Pancreatic Fistula on 775 Consecutive Pancreatic Resections. Ann Surg. 2017; 268(6):1069-1075. DOI: 10.1097/SLA.0000000000002362. View

18.

Beam A, Kohane I . Big Data and Machine Learning in Health Care. JAMA. 2018; 319(13):1317-1318. DOI: 10.1001/jama.2017.18391. View

19.

Greener J, Kandathil S, Moffat L, Jones D . A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2021; 23(1):40-55. DOI: 10.1038/s41580-021-00407-0. View

20.

Marchegiani G, Landoni L, Andrianello S, Masini G, Cingarlini S, DOnofrio M . Patterns of Recurrence after Resection for Pancreatic Neuroendocrine Tumors: Who, When, and Where?. Neuroendocrinology. 2018; 108(3):161-171. DOI: 10.1159/000495774. View