Inter-observer variability of radiologists for Cambridge classification of chronic pancreatitis using CT and MRCP: results from a large multi-center study

Purpose: Determine inter-observer variability among radiologists in assigning Cambridge Classification (CC) of chronic pancreatitis (CP) based on magnetic resonance imaging (MRI)/magnetic resonance cholangiopancreatography (MRCP) and contrast-enhanced CT (CECT). Methods: Among 422 eligible subjects enrolled into the PROCEED study between 6/2017 and 8/2018, 39 were selected randomly for this study (chronic abdominal pain (n = 8; CC of 0), suspected CP (n = 22; CC of 0, 1 or 2) or definite CP (n = 9; CC of 3 or 4). Each imaging was scored by the local radiologist (LRs) and three of five central radiologists (CRs) at other consortium sites. The CRs were blinded to clinical data and site information of the participants. We compared the CC score assigned by the LR with the consensus CC score assigned by the CRs. The weighted kappa statistic (K) was used to estimate the inter-observer agreement. Results: For the majority of subjects (34/39), the group assignment by LR agreed with the consensus composite CT/MRCP score by the CRs (concordance ranging from 75 to 89% depending on cohort group). There was moderate agreement (63% and 67% agreed, respectively) between CRs and LRs in both the CT score (weighted Kappa [95% CI] = 0.56 [0.34, 0.78]; p-value = 0.57) and the MR score (weighted Kappa [95% CI] = 0.68 [0.49, 0.86]; p-value = 0.72). The composite CT/MR score showed moderate agreement (weighted Kappa [95% CI] = 0.62 [0.43, 0.81]; p-value = 0.80). Conclusion: There is a high degree of concordance among radiologists for assignment of CC using MRI and CT

A multidisciplinary consensus on the morphological and functional responses to immunotherapy treatment

The implementation of immunotherapy has radically changed the treatment of oncological patients. Currently, immunotherapy is indicated in the treatment of patients with head and neck tumors, melanoma, lung cancer, bladder tumors, colon cancer, cervical cancer, breast cancer, Merkel cell carcinoma, liver cancer, leukemia and lymphomas. However, its efficacy is restricted to a limited number of cases. The challenge is, therefore, to identify which subset of patients would benefit from immunotherapy. To this end, the establishment of immunotherapy response criteria and predictive and prognostic biomarkers is of paramount interest. In this report, a group of experts of the Spanish Society of Medical Oncology (SEOM), the Spanish Society of Medical Radiology (SERAM), and Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM) provide an up-to-date review and a consensus guide on these issues

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging