Increasing AFP represents doubling of AFP, increase on two consecutive tests, or ≥ 20 ng/ml. 2 Can return to US q6 months if lesion stable on two exams. 3 CT/MRI may be preferred particularly in patients with obesity, alcohol or NASH-related cirrhosis, or Child Pugh class B or C cirrhosis. 4 Significantly elevated AFP: although no clear threshold has been established, AFP ≥ 200 ng/ml or ≥ 400 ng/ml may be considered significant elevations depending on clinical context. 5 Can perform chest and pelvic imaging in addition to alternative modality. If these are negative, other workup, including PET, can be considered.1930

Background and Aims There are limited data on hepatocellular carcinoma (HCC) growth patterns, particularly in Western cohorts, despite implications for surveillance, prognosis, and treatment. Our study’s aim was to quantify tumor doubling time (TDT) and identify correlates associated with indolent and rapid growth. Approach and Results We performed a retrospective multicenter cohort study of patients with cirrhosis diagnosed with HCC from 2008 to 2017 at six US and European health systems with two or more contrast‐enhanced imaging studies performed ≥ 30 days apart prior to HCC treatment. Radiologists independently measured tumors in three dimensions to calculate TDT and specific growth rate (SGR). We used multivariable ordinal logistic regression to identify factors associated with indolent (TDT > 365 days) and rapid (TDT < 90 days) tumor growth. In the primary cohort (n = 242 patients from four centers), median TDT was 229 days (interquartile range [IQR], 89‐627) and median SGR was 0.3% per day (IQR, 0.1%‐0.8%). Over one‐third (38%) of HCCs had indolent growth, 36.8% intermediate growth, and 25.2% rapid growth. In multivariable analysis, indolent growth was associated with larger tumor diameter (odds ratio [OR], 1.15, 95% confidence interval [CI], 1.03–1.30) and alpha‐fetoprotein < 20 ng/mL (OR, 1.90; 95% CI, 1.12‐3.21). Indolent growth was more common in nonviral than viral cirrhosis (50.9% versus 32.1%), particularly in patients with T1 HCC (OR, 3.41; 95% CI, 1.08‐10.80). Median TDT (169 days; IQR 74‐408 days) and SGR (0.4% per day) were similar in an independent cohort (n = 176 patients from two centers). Conclusions In a large Western cohort of patients with HCC, we found heterogeneous tumor growth patterns, with one‐fourth exhibiting rapid growth and over one‐third having indolent growth. Better understanding different tumor growth patterns may facilitate a precision approach to prognostication and treatment.

With increasing deployment, complexity, and sophistication of equipment and related processes within the clinical imaging environment, system failures are more likely to occur. These failures may have varying effects on the patient, ranging from no harm to devastating harm. Failure mode and effect analysis (FMEA) is a tool that permits the proactive identification of possible failures in complex processes and provides a basis for continuous improvement. This overview of the basic principles and methodology of FMEA provides an explanation of how FMEA can be applied to clinical operations in a radiology department to reduce, predict, or prevent errors. The six sequential steps in the FMEA process are explained, and clinical magnetic resonance imaging services are used as an example for which FMEA is particularly applicable. A modified version of traditional FMEA called Healthcare Failure Mode and Effect Analysis, which was introduced by the U.S. Department of Veterans Affairs National Center for Patient Safety, is briefly reviewed. In conclusion, FMEA is an effective and reliable method to proactively examine complex processes in the radiology department. FMEA can be used to highlight the high-risk subprocesses and allows these to be targeted to minimize the future occurrence of failures, thus improving patient safety and streamlining the efficiency of the radiology department.

Academic radiology is poised to play an important role in the development and implementation of quantitative imaging (QI) tools. This manuscript, drafted by the Association of University Radiologists (AUR) Radiology Research Alliance (RRA) Quantitative Imaging Task Force, reviews current issues in QI biomarker research. We discuss motivations for advancing QI, define key terms, present a framework for QI biomarker research, and outline challenges in QI biomarker development. We conclude by describing where QI research and development is currently taking place and discussing the paramount role of academic radiology in this rapidly evolving field.

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