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Editorial for “Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors”

Pancreatic neuroendocrine neoplasia (PanNEN) is a tumor of endocrine cells of the pancreas. They account for less than 3% of primary pancreatic tumors. In contrast to pancreatic ductal adenocarcinoma, which has a poor prognosis, that of PanNEN depends mainly on the pathological grade of PanNEN, which ranges from low to high. According to the recently revised WHO classification (2017/2019), pNENs are classified into the well-differentiated neuroendocrine tumor (NET) and poorly-differentiated neuroendocrine carcinoma (NEC); NET is further classified into Grades 1, 2, and 3, whereas NEC is classified into small-cell type and large-cell type. Pancreatic NETs (PanNET or pNET) are either functional, that is, secreting hormones, or nonfunctional. Although functionality is related to prognosis, the biological behavior of both types of tumors depends on the pathological diagnosis. Magnetic resonance imaging (MRI) is useful in diagnosing the localization of pNETs and is reported to have higher sensitivity than computed tomography (CT) in detecting liver metastasis. Pathological diagnosis is crucial for the definitive diagnosis of pNET, and endoscopic ultrasound fine-needle aspiration (EUS-FNA) is performed for this purpose. Although EUS-FNA is a useful test, it is relatively invasive. The heterogeneity of the tumor may complicate accurate diagnosis by the small size and a limited number of samples obtained by EUS-FNA; therefore, highly confident diagnosis by a noninvasive modality, including MRI and CT, is warranted before EUS-FNA to raise a pretest probability of PanNEN. Recently, radiomics has been attracting much attention in the field of medical imaging. It is a high-throughput method of extracting a large number of features from the image data based on particular algorithms. It is possible to obtain more information from the image data than solely diagnosing it by human eyes. It is expected to improve the quality of diagnosis and treatment, thus leading to better patient outcomes. A combination with other diagnostic technologies, such as genomic analysis and biomarkers, is thought to be the basis for precision medicine. In this issue of JMRI, Bian et al report the results of a radiomics analysis of 139 nonfunctional pNETs, which will hopefully advance the diagnosis of pNET by MRI. As pNET is relatively rare, this is among the radiomics studies with the largest number of patients with pNET, especially using MRI. They first used pyradiomics, an open-source python package, to extract radiomics features, such as morphology and texture (spatial arrangement of color and intensities). After selecting 14 features by variable reduction, they developed a linear discriminant analysis (LDA) model, which predicts the histopathological grade of nonfunctional pNETs (G1 vs. G2/3). By temporarily splitting the patients into the training (2012–2018) and validation cohorts (2018–2019), they not only developed the model but also internally validated it. The area under the curve (AUC) of the training cohort (n = 97) and the validation cohort (n = 42) was satisfactory and was 0.851 and 0.736, respectively. The usefulness of the model was also shown by decision curve analysis (DCA). These results are comparable to those of the previously-reported prediction model of pNET grade developed by radiomics analysis of CT and the MRI’s texture analysis (these studies do not necessarily differentiate functionality of pNET). The study of Bian et al appears to have significantly contributed to the noninvasive grading of pNET by MRI. On the other hand, further studies are necessary before the results of this study can be applied to clinical practice. For example, because the segmentation of the region of interest (ROI) affects the results of radiomics features, there is a concern about intraobserver and interobserver variability when it is performed manually. Besides, the MRI signal intensity is different from that of the CT value in that it is a relative value that varies depending on the measurement conditions. Since it is affected by the vendor and the settings of the device, it is normalized before extracting the radiomics features. However, there are various methods of normalization, and a thorough examination would be necessary to identify what is the optimal method. Also, the robustness of MRI