Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study

Abstract

Objectives To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. Methods Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2–5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. Results Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. Conclusion The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. Clinical relevance statement Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. Key Points The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons’ decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

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

“…After screening titles and abstracts, 85 potentially eligible articles were identified. After full-text review, six articles were excluded because of insufficient information; thus, 26 articles were included in this systematic review ( 21 , 22 , 26 – 49 ). Among them, six studies lacked information on positive and negative diagnosis values; therefore, only 20 articles were eligible for the meta-analysis.…”

“…Clinical data such as age, gender, tumor size, tumor shape, tumor margin and CT stage are closely related to the pathogenic process of PNETs and therefore should not be ignored in diagnostic models ( 27 – 29 , 47 , 49 ).,Liang et al. ( 37 ) built a combined model which can improve the performance (0.856, [0.730–0.939] vs. 0.885 [0.765–0.957]).…”

Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers

“…Radiomics is a promising field in NEN management, offering advanced imaging analysis for enhanced tumor characterization, prognostication, and treatment response assessment. The fusion model combining radiomics signatures and radiological characteristics showed good performance in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors, with AUCs of 0.956 in the training set and 0.864 in the testing set in the paper of Zhu et al, 2024 [ 100 ]. Ye et al showed an interpretable radiomics-based random forest model that can effectively differentiate between G1 and G2/3 pancreatic NETs, demonstrating favorable interpretability [ 101 ].…”

Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer

“…These features are subsequently utilized to develop diverse tumor diagnosis and prediction models using various machine learning, deep learning, and other algorithmic approaches (10). Numerous studies have elucidated the utility of CT-based and MRI-based radiomics in diagnosing and predicting PNET, showcasing its efficacy (11,12). However, MRI is contraindicated for certain populations, including individuals with claustrophobia or metal implants.…”