Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

Qian Yan; Yubin Chen; Chunsheng Liu; Hexian Shi; Mingqian Han; Zelong Wu; Shanzhou Huang; Chuanzhao Zhang; Baohua Hou

doi:10.3389/fonc.2024.1332387

Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

Front Oncol. 2024 Apr 23:14:1332387. doi: 10.3389/fonc.2024.1332387. eCollection 2024.

Authors

Qian Yan^#^{1

2}, Yubin Chen^#^{1

2}, Chunsheng Liu^#¹, Hexian Shi¹, Mingqian Han¹, Zelong Wu¹, Shanzhou Huang^{1

2}, Chuanzhao Zhang^{1

2}, Baohua Hou^{3

1

2}

Affiliations

¹ Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
² School of Medicine, South China University of Technology, Guangzhou, China.
³ Department of General Surgery, Heyuan People's Hospital, Heyuan, China.

^# Contributed equally.

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 I² = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I² = 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 (I²= 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I² = 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.

Keywords: deep learning; machine learning; meta-analysis; pancreatic neuroendocrine tumors; radiomics.

Publication types

Systematic Review

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Natural Science Foundation of China (82102961, 82072635 and 82072637), High-level Hospital Construction Research Project of Heyuan People's Hospital (YNKT202202), the Science and Technology Program of Heyuan (23051017147335), the Science and Technology Program of Guangzhou (2024A04J10016 and 202201011642).