Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/).

rtificial intelligence (AI) algorithms have existed for decades and have recently been propelled to the forefront of medical imaging research. To a large extent, this is related to improvements in computing power, availability of a large amount of training data, and innovative and improved neural network architectures, with the recognition that certain types of algorithms are well suited to image analysis. The latter discovery was accelerated by the ImageNet competition and represents a fundamental transformation in research mechanics and methods in computer vision.Currently, in most studies, researchers collect data, perform analysis, and publish results. The same researchers may continue to augment and expand the data set and perform subsequent analysis with resulting publications. The data for each study are held quite closely and are rarely shared among institutions outside of multicenter trials. Competitions represent a different model of research: Research data are made available to the public, usually with a baseline performance metric. Groups around the world are invited to analyze the data and create algorithms to beat the performance of the prior generation. For example, the baseline performance metric for this challenge was set by the previous skeletal age model developed by Larson et al (1).The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to evaluate the performance of computer algorithms in executing a common image analysis activity that is familiar to many pediatric radiologists: estimating the bone age of pediatric patients based on radiographs of their hand (1-5). This challenge used a data set of pediatric

There has been a proliferation and divergence of imaging-based tumor-specific response criteria over the past 3 decades whose purpose is to achieve objective assessment of treatment response in oncologic clinical trials. The World Health Organization (WHO) criteria, published in 1981, were the first response criteria and made use of bidimensional measurements of tumors. The Response Evaluation Criteria in Solid Tumors (RECIST) were created in 2000 and revised in 2009. The RECIST criteria made use of unidimensional measurements and addressed several pitfalls and limitations of the original WHO criteria. Both the WHO and RECIST criteria were developed during the era of cytotoxic chemotherapeutic agents and are still widely used. However, treatment strategies changed over the past decade, and the limitations of using tumor size alone in patients undergoing targeted therapy (including arbitrarily determined cutoff values to categorize tumor response and progression, lack of information about changes in tumor attenuation, inability to help distinguish viable tumor from nonviable components, and inconsistency of size measurements) necessitated revision of these criteria. More recent criteria that are used for targeted therapies include the Choi response criteria for gastrointestinal stromal tumor, modified RECIST criteria for hepatocellular carcinoma, and Immune-related Response Criteria for melanoma. The Cheson criteria and Positron Emission Tomography Response Criteria in Solid Tumors make use of positron emission tomography to provide functional information and thereby help determine tumor viability. As newer therapeutic agents and approaches become available, it may be necessary to further modify existing anatomy-based response-assessment methodologies, verify promising functional imaging methods in large prospective trials, and investigate new quantitative imaging technologies.

Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

BackgroundBy the traditional definition of unintended weight loss, cachexia develops in ~80% of patients with pancreatic ductal adenocarcinoma (PDAC). Here, we measure the longitudinal body composition changes in patients with advanced PDAC undergoing 5‐fluorouracil, leucovorin, irinotecan, and oxaliplatin therapy.MethodsWe performed a retrospective review of 53 patients with advanced PDAC on 5‐fluorouracil, leucovorin, irinotecan, and oxaliplatin as first line therapy at Indiana University Hospital from July 2010 to August 2015. Demographic, clinical, and survival data were collected. Body composition measurement by computed tomography (CT), trend, univariate, and multivariate analysis were performed.ResultsAmong all patients, three cachexia phenotypes were identified. The majority of patients, 64%, had Muscle and Fat Wasting (MFW), while 17% had Fat‐Only Wasting (FW) and 19% had No Wasting (NW). NW had significantly improved overall median survival (OMS) of 22.6 months vs. 13.0 months for FW and 12.2 months for MFW (P = 0.02). FW (HR = 5.2; 95% confidence interval = 1.5–17.3) and MFW (HR = 1.8; 95% confidence interval = 1.1–2.9) were associated with an increased risk of mortality compared with NW. OMS and risk of mortality did not differ between FW and MFW. Progression of disease, sarcopenic obesity at diagnosis, and primary tail tumours were also associated with decreased OMS. On multivariate analysis, cachexia phenotype and chemotherapy response were independently associated with survival. Notably, CT‐based body composition analysis detected tissue loss of >5% in 81% of patients, while the traditional definition of >5% body weight loss identified 56.6%.ConclusionsDistinct cachexia phenotypes were observed in this homogeneous population of patients with equivalent stage, diagnosis, and first‐line treatment. This suggests cellular, molecular, or genetic heterogeneity of host or tumour. Survival among patients with FW was as poor as for MFW, indicating adipose tissue plays a crucial role in cachexia and PDAC mortality. Adipose tissue should be studied for its mechanistic contributions to cachexia.