Editorial for “MRI Radiomics‐Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study”

Pancreatic cancer is the 12th most common malignancy and the seventh leading cause of cancer mortality worldwide. Majority of the cases (more than 90%) are pancreatic ductal adenocarcinoma (PDAC). Less than 20% of the cases are potentially resectable at the time of diagnosis. Even after microscopically complete surgical resection (R0), many patients develop local or systemic recurrence, with an extremely poor prognosis. Despite recent advancements in surgery and oncological treatment, few patients achieve a cure. The 5-year survival rate was approximately 10% in the United States and has remained lowest of any cancer for the past several decades. Computed Tomography (CT) is still considered as method of choice for diagnosis of PDAC because of its high spatial resolution, wide coverage, and accessibility, however, accurate diagnosis and staging of PDAC sometimes still difficult by CT alone. Magnetic resonance imaging (MRI) has been increasing its importance in this field because of excellent tissue characterization, pancreatic duct information added by magnetic resonance cholangiopancreatography (MRCP), and high detection rate for liver metastasis with hepatobiliary contrast agent. However, in spite of recent progresses in imaging techniques, problems still remain. Reportedly, up to 40% of PDAC patients who had been diagnosed to be resectable by CT or MRI, were found to have peritoneal metastasis by staging laparoscopy. Sensitivities and specficities of CT and MRI for lymph node metastasis were reported to be less than 80%. Tissue characterization ability is still insufficient in this era of genetic profiling and multidisciplinary treatment. To overcome these limitations, many researchers have developed and evaluated image-based radiomics approaches. Radiomics refers to extraction and analysis of quantitative texture features on medical images. Radiomics extract data on images, majority of which cannot be recognized by the naked eye, and converts it into myriad features, which are categorized into first-order features such as signal intensity and shape and into higher-order features that require matrix transformation by computational tools. These features are expected to contain much information invisible to the eye and assumed to enable quantitative assessments of macroand microscopic tissue structures and heterogeneities. After selection processes of features to reduce data and eliminate overlaps, diagnostic and predictive models are established. Models are needed to be established using training cohort and confirmed using other validation cohorts. In some studies, models were built using clinical and pathological data as well as radiomics one. In the pancreas, radiomics have been evaluated for various purposes and in many conditions such as assessments of genetic mutations, pathological grades, and protein expressions, and predictions of postoperative early recurrence, and effect of adjuvant chemotherapy. Capability of radiomics in peripancreatic lymph node metastasis (PLNM) estimation also has been evaluated using CT images, and excellent initial results were reported. However, no previous study using MR images for PLNM estimation has been reported. In this issue of JMRI, Shi et al assessed MR-radiomics features and developed nomograms for estimation of PLNM in PDAC patients. They retrospectively recruited 251 patients with pathologically proven PDACs from three hospitals and divided into two groups with/without PLNM, then each cohort was divided into training and validation cohorts. A total of 850 texture features were extracted from arterialdominant phase fat-suppressed T1-weighted images and