CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).

Cholangiocarcinomas (CCAs) are hepatobiliary cancers with features of cholangiocyte differentiation; they can be classified anatomically as intrahepatic CCA (iCCA), perihilar CCA (pCCA), or distal CCA. These subtypes differ not only in their anatomic location, but in epidemiology, origin, etiology, pathogenesis, and treatment. The incidence and mortality of iCCA has been increasing over the past 3 decades, and only a low percentage of patients survive until 5 years after diagnosis. Geographic variations in the incidence of CCA are related to variations in risk factors. Changes in oncogene and inflammatory signaling pathways, as well as genetic and epigenetic alterations and chromosome aberrations, have been shown to contribute to the development of CCA. Furthermore, CCAs are surrounded by a dense stroma that contains many cancer-associated fibroblasts, which promotes their progression. We have gained a better understanding of the imaging characteristics of iCCAs and have developed advanced cytologic techniques to detect pCCAs. Patients with iCCAs usually are treated surgically, whereas liver transplantation after neoadjuvant chemoradiation is an option for a subset of patients with pCCAs. We review recent developments in our understanding of the epidemiology and pathogenesis of CCA, along with advances in classification, diagnosis, and treatment. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.