Clinical language search algorithm from free-text: facilitating appropriate imaging.

BACKGROUND: The comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource for evidence-based clinical imaging decision support, but it is underutilized by clinicians. To facilitate the use of imaging recommendations, we develop a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations. METHODS: We apply a hybrid model of semantic similarity from a sent2vec model trained on 223 million scientific sentences, combined with term frequency inverse document frequency features. AC documents are ranked based on their embeddings' cosine distance to query. For model testing, we compiled a dataset of simulated simple and complex indications for each AC document (n = 410) and another with clinical indications from randomly sampled radiology reports (n = 100). We compare our algorithm to a custom google search engine. RESULTS: On the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. Similarly, on the randomly sampled radiology report dataset, the algorithm ranked 86% of indications with a single match as top 3. Vague and distracting phrases present in the free-text indications were main sources of errors. Our algorithm provides more relevant results than a custom Google search engine, especially for complex queries. CONCLUSIONS: We have developed and evaluated an NLP algorithm that matches clinical indications to appropriate AC guidelines. This approach can be integrated into imaging ordering systems for automated access to guidelines.

AI MSK clinical applications: cartilage and osteoarthritis.

The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.

CT Features of Benign Intrapulmonary Lymph Nodes in Pediatric Patients With Known Extrapulmonary Solid Malignancy.

OBJECTIVE. The purpose of our study was to determine the CT features of benign intrapulmonary lymph nodes in pediatric patients with known extrapulmonary solid malignancy. MATERIALS AND METHODS. A retrospective review of surgical pathology archives was performed to identify consecutive chest CT studies of pediatric patients (≤ 18 years) with extrapulmonary solid malignancy and histologically confirmed benign intrapulmonary lymph nodes between January 1, 2004, and March 15, 2020. CT features of intrapulmonary lymph nodes-including size, shape, margin, type, associated calcification or fat, and location-were independently evaluated by two pediatric radiologist reviewers. The CT features of benign intrapulmonary lymph nodes in pediatric patients were analyzed using summary statistics. Interobserver agreement was measured with the kappa coefficient. RESULTS. There were 36 pathology-confirmed benign intrapulmonary lymph nodes in 27 pediatric patients (18 boys and nine girls; mean age, 12 years; age range, 1-18.2 years). Twenty-three (63.9%) of the benign intrapulmonary lymph nodes were biopsied from the right lung and 13 (36.1%) from the left lung (p = .03). The mean size, determined from CT studies, of benign intrapulmonary lymph nodes was 3.6 mm (SD, 1.4 mm; range, 1.3-7.8 mm). Triangular shape (25/36, 69.4%) was the most common shape of the benign intrapulmonary lymph nodes. Less commonly seen shapes of benign intrapulmonary lymph nodes were oval (6/36, 16.7%), round (3/36, 8.3%), and trapezoidal (2/36, 5.6%). All benign intrapulmonary lymph nodes were smoothly marginated and solid without associated calcification or fat. Of the 36 benign intrapulmonary lymph nodes, 15 (41.7%) were pleura-based; 11 (30.6%), perifissural; and 10 (27.8%), parenchymal. The kappa value for interobserver agreement between the two reviewers was 0.917 (95% CI, 0.825-1.000; standard error, 0.047), which corresponds to near-perfect agreement. CONCLUSION. In pediatric patients with known extrapulmonary solid malignancy, benign intrapulmonary lymph nodes are subcentimeter (mean size, 3.6 mm), smoothly marginated, and solid without containing calcification or fat on CT. In particular, triangular shape was the most commonly encountered shape of a benign intrapulmonary lymph node.

Domain specific word embeddings for natural language processing in radiology.

BACKGROUND: There has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus. PURPOSE: We examined the potential of Radiopaedia to serve as a general radiology corpus to produce radiology specific word embeddings that could be used to enhance performance on a NLP task on radiological text. MATERIALS AND METHODS: Embeddings of dimension 50, 100, 200, and 300 were trained on articles collected from Radiopaedia using a GloVe algorithm and evaluated on analogy completion. A shallow neural network using input from either our trained embeddings or pre-trained Wikipedia 2014 + Gigaword 5 (WG) embeddings was used to label the Radiopaedia articles. Labeling performance was evaluated based on exact match accuracy and Hamming loss. The McNemar's test with continuity and the Benjamini-Hochberg correction and a 5×2 cross validation paired two-tailed t-test were used to assess statistical significance. RESULTS: For accuracy in the analogy task, 50-dimensional (50-D) Radiopaedia embeddings outperformed WG embeddings on tumor origin analogies (p < 0.05) and organ adjectives (p < 0.01) whereas WG embeddings tended to outperform on inflammation location and bone vs. muscle analogies (p < 0.01). The two embeddings had comparable performance on other subcategories. In the labeling task, the Radiopaedia-based model outperformed the WG based model at 50, 100, 200, and 300-D for exact match accuracy (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively) and Hamming loss (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively). CONCLUSION: We have developed a set of word embeddings from Radiopaedia and shown that they can preserve relevant medical semantics and augment performance on a radiology NLP task. Our results suggest that the cultivation of a radiology-specific corpus can benefit radiology NLP models in the future.

High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels.

BACKGROUND: Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radiology reports to generate high precision nodule predictions. METHODS: 888 CTs from Lung Nodule Analysis were used to train a 2-dimensional (2D) object detection neural network. A pipeline of 2D object detection, 3D unsupervised clustering, false positive reduction, and axial-slice numbers were used to generate nodule candidates. 47 CTs from the National Lung Cancer Screening Trial (NLST) were used for model evaluation. RESULTS: Our nodule detector achieved a precision of 0.962 at a recall of 0.573 on the NLST test set for any nodule. When adjusting for unintended nodule predictions, we achieved a precision of 0.931 at a recall 0.561, which corresponds to 0.06 false positives per CT. Error analysis revealed better detection of nodules with soft tissue attenuation compared to ground glass and undeterminable attenuation. Nodule margins, size, location, and patient demographics did not differ between correct and incorrect predictions. CONCLUSIONS: Utilization of axial-slice numbers from radiology reports allowed for development of a lung nodule detector with a low false positive rate compared to prior feature-engineering and machine learning approaches. This high precision nodule detector can reduce time spent on reidentification of prior nodules during lung cancer screening and can rapidly develop new institutional datasets to explore novel applications of computer vision in lung cancer imaging.

Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing.

BACKGROUND: A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of accurate automated protocol assignment. We aim to develop, evaluate, and deploy an NLP model that automates protocol assignment, given the clinician indication text. METHODS: We collected 7139 spine MRI protocols (routine or contrast) and 990 head MRI protocols (routine brain, contrast brain, or other) from a single institution. Protocols were split into training (n = 4997 for spine MRI; n = 839 for head MRI), validation (n = 1071 for spine MRI, fivefold cross-validation used for head MRI), and test (n = 1071 for spine MRI; n = 151 for head MRI) sets. fastText and XGBoost were used to develop 2 NLP models to classify spine and head MRI protocols, respectively. A Flask-based web app was developed to be deployed via Heroku. RESULTS: The spine MRI model had an accuracy of 83.38% and a receiver operator characteristic area under the curve (ROC-AUC) of 0.8873. The head MRI model had an accuracy of 85.43% with a routine brain protocol ROC-AUC of 0.9463 and contrast brain protocol ROC-AUC of 0.9284. Cancer, infectious, and inflammatory related keywords were associated with contrast administration. Structural anatomic abnormalities and stroke/altered mental status were indicative of routine spine and brain MRI, respectively. Error analysis revealed increasing the sample size may improve performance for head MRI protocols. A web version of the model is provided for demonstration and deployment. CONCLUSION: We developed and web-deployed two NLP models that accurately predict spine and head MRI protocol assignment, which could improve radiology workflow efficiency.

Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.

Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis still relies on expert radiologists' readings. The potential of such a model in clinical routine should be investigated. Purpose To develop a multitask deep learning model for grading radiographic hip osteoarthritis features on radiographs and compare its performance to that of attending-level radiologists. Materials and Methods This retrospective study analyzed hip joints seen on weight-bearing anterior-posterior pelvic radiographs from participants in the Osteoarthritis Initiative (OAI). Participants were recruited from February 2004 to May 2006 for baseline measurements, and follow-up was performed 48 months later. Femoral osteophytes (FOs), acetabular osteophytes (AOs), and joint-space narrowing (JSN) were graded as absent, mild, moderate, or severe according to the Osteoarthritis Research Society International atlas. Subchondral sclerosis and subchondral cysts were graded as present or absent. The participants were split at 80% (n = 3494), 10% (n = 437), and 10% (n = 437) by using split-sample validation into training, validation, and testing sets, respectively. The multitask neural network was based on DenseNet-161, a shared convolutional features extractor trained with multitask loss function. Model performance was evaluated in the internal test set from the OAI and in an external test set by using temporal and geographic validation consisting of routine clinical radiographs. Results A total of 4368 participants (mean age, 61.0 years ± 9.2 [standard deviation]; 2538 women) were evaluated (15 364 hip joints on 7738 weight-bearing anterior-posterior pelvic radiographs). The accuracy of the model for assessing these five features was 86.7% (1333 of 1538) for FOs, 69.9% (1075 of 1538) for AOs, 81.7% (1257 of 1538) for JSN, 95.8% (1473 of 1538) for subchondral sclerosis, and 97.6% (1501 of 1538) for subchondral cysts in the internal test set, and 82.7% (86 of 104) for FOS, 65.4% (68 of 104) for AOs, 80.8% (84 of 104) for JSN, 88.5% (92 of 104) for subchondral sclerosis, and 91.3% (95 of 104) for subchondral cysts in the external test set. Conclusion A multitask deep learning model is a feasible approach to reliably assess radiographic features of hip osteoarthritis. © RSNA, 2020 Online supplemental material is available for this article.

An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.

Although machine learning (ML) has made significant improvements in radiology, few algorithms have been integrated into clinical radiology workflow. Complex radiology IT environments and Picture Archiving and Communication System (PACS) pose unique challenges in creating a practical ML schema. However, clinical integration and testing are critical to ensuring the safety and accuracy of ML algorithms. This study aims to propose, develop, and demonstrate a simple, efficient, and understandable hardware and software system for integrating ML models into the standard radiology workflow and PACS that can serve as a framework for testing ML algorithms. A Digital Imaging and Communications in Medicine/Graphics Processing Unit (DICOM/GPU) server and software pipeline was established at a metropolitan county hospital intranet to demonstrate clinical integration of ML algorithms in radiology. A clinical ML integration schema, agnostic to the hospital IT system and specific ML models/frameworks, was implemented and tested with a breast density classification algorithm and prospectively evaluated for time delays using 100 digital 2D mammograms. An open-source clinical ML integration schema was successfully implemented and demonstrated. This schema allows for simple uploading of custom ML models. With the proposed setup, the ML pipeline took an average of 26.52 s per second to process a batch of 100 studies. The most significant processing time delays were noted in model load and study stability times. The code is made available at " http://bit.ly/2Z121hX ". We demonstrated the feasibility to deploy and utilize ML models in radiology without disrupting existing radiology workflow.

Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.

Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For this retrospective study, DLAD was developed by using 43 292 chest radiographs (normal radiograph-to-nodule radiograph ratio, 34 067:9225) in 34 676 patients (healthy-to-nodule ratio, 30 784:3892; 19 230 men [mean age, 52.8 years; age range, 18-99 years]; 15 446 women [mean age, 52.3 years; age range, 18-98 years]) obtained between 2010 and 2015, which were labeled and partially annotated by 13 board-certified radiologists, in a convolutional neural network. Radiograph classification and nodule detection performances of DLAD were validated by using one internal and four external data sets from three South Korean hospitals and one U.S. hospital. For internal and external validation, radiograph classification and nodule detection performances of DLAD were evaluated by using the area under the receiver operating characteristic curve (AUROC) and jackknife alternative free-response receiver-operating characteristic (JAFROC) figure of merit (FOM), respectively. An observer performance test involving 18 physicians, including nine board-certified radiologists, was conducted by using one of the four external validation data sets. Performances of DLAD, physicians, and physicians assisted with DLAD were evaluated and compared. Results According to one internal and four external validation data sets, radiograph classification and nodule detection performances of DLAD were a range of 0.92-0.99 (AUROC) and 0.831-0.924 (JAFROC FOM), respectively. DLAD showed a higher AUROC and JAFROC FOM at the observer performance test than 17 of 18 and 15 of 18 physicians, respectively (P < .05), and all physicians showed improved nodule detection performances with DLAD (mean JAFROC FOM improvement, 0.043; range, 0.006-0.190; P < .05). Conclusion This deep learning-based automatic detection algorithm outperformed physicians in radiograph classification and nodule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians' performances when used as a second reader. © RSNA, 2018 Online supplemental material is available for this article.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

Papillary and sclerosing lesions of the breast detected and biopsied by MRI: Clinical management, upgrade rate, and association with apocrine metaplasia.

Benign papillary and sclerosing lesions of the breast (intraductal papillomas, complex sclerosing lesions, radial scars) are considered high-risk lesions due to the potential for upgrade to carcinoma on subsequent surgical excision. Optimal clinical management of such lesions remains unclear due to variable reported upgrade rates. Apocrine metaplasia is a common finding in breast tissue and its role in MRI enhancing lesions is increasingly being recognized. The purpose of this study was to investigate the MRI features of papillary and sclerosing lesions of the breast, evaluate the clinical management and upgrade rate of such lesions, and examine the contribution of apocrine metaplasia to the imaging findings. A 13-year retrospective review of MRI-guided biopsies identified 70 MRI-detected and -biopsied papillary and sclerosing lesions. Sixteen lesions without atypia underwent surgical excision; only one case (6%) was upgraded to pleomorphic lobular carcinoma in situ. The majority (64%) of biopsies contained apocrine metaplasia either within or adjacent to the targeted lesion. We found that half of MRI-detected lesions had T2 hyperintense foci (2-5 mm) or masses (>5 mm) adjacent to the lesion. Histologic correlation showed apocrine cysts were likely responsible for this imaging finding in 56% of these cases.

Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

Breast cancer is a leading cause of cancer death among women in the USA. Screening mammography is effective in reducing mortality, but has a high rate of unnecessary recalls and biopsies. While deep learning can be applied to mammography, large-scale labeled datasets, which are difficult to obtain, are required. We aim to remove many barriers of dataset development by automatically harvesting data from existing clinical records using a hybrid framework combining traditional NLP and IBM Watson. An expert reviewer manually annotated 3521 breast pathology reports with one of four outcomes: left positive, right positive, bilateral positive, negative. Traditional NLP techniques using seven different machine learning classifiers were compared to IBM Watson's automated natural language classifier. Techniques were evaluated using precision, recall, and F-measure. Logistic regression outperformed all other traditional machine learning classifiers and was used for subsequent comparisons. Both traditional NLP and Watson's NLC performed well for cases under 1024 characters with weighted average F-measures above 0.96 across all classes. Performance of traditional NLP was lower for cases over 1024 characters with an F-measure of 0.83. We demonstrate a hybrid framework using traditional NLP techniques combined with IBM Watson to annotate over 10,000 breast pathology reports for development of a large-scale database to be used for deep learning in mammography. Our work shows that traditional NLP and IBM Watson perform extremely well for cases under 1024 characters and can accelerate the rate of data annotation.

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson's Natural Language Processing Algorithm.

Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader's contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was relatively robust to spelling and grammatical errors, which were frequent. Implementation of this automated MR contrast determination system as a clinical decision support tool may save considerable time and effort of the radiologist while potentially decreasing error rates, and require no change in order entry or workflow.

Validation of the Hong Kong Liver Cancer Staging System in Determining Prognosis of the North American Patients Following Intra-arterial Therapy.

BACKGROUND & AIMS: There is debate over the best way to stage hepatocellular carcinoma (HCC). We attempted to validate the prognostic and clinical utility of the recently developed Hong Kong Liver Cancer (HKLC) staging system, a hepatitis B-based model, and compared data with that from the Barcelona Clinic Liver Cancer (BCLC) staging system in a North American population that underwent intra-arterial therapy (IAT). METHODS: We performed a retrospective analysis of data from 1009 patients with HCC who underwent IAT from 2000 through 2014. Most patients had hepatitis C or unresectable tumors; all patients underwent IAT, with or without resection, transplantation, and/or systemic chemotherapy. We calculated HCC stage for each patient using 5-stage HKLC (HKLC-5) and 9-stage HKLC (HKLC-9) system classifications, and the BCLC system. Survival information was collected up until the end of 2014 at which point living or unconfirmed patients were censored. We compared performance of the BCLC, HKLC-5, and HKLC-9 systems in predicting patient outcomes using Kaplan-Meier estimates, calibration plots, C statistic, Akaike information criterion, and the likelihood ratio test. RESULTS: Median overall survival time, calculated from first IAT until date of death or censorship, for the entire cohort (all stages) was 9.8 months. The BCLC and HKLC staging systems predicted patient survival times with significance (P < .001). HKLC-5 and HKLC-9 each demonstrated good calibration. The HKLC-5 system outperformed the BCLC system in predicting patient survival times (HKLC C = 0.71, Akaike information criterion = 6242; BCLC C = 0.64, Akaike information criterion = 6320), reducing error in predicting survival time (HKLC reduced error by 14%, BCLC reduced error by 12%), and homogeneity (HKLC chi-square = 201, P < .001; BCLC chi-square = 119, P < .001) and monotonicity (HKLC linear trend chi-square = 193, P < .001; BCLC linear trend chi-square = 111, P < .001). Small proportions of patients with HCC of stages IV or V, according to the HKLC system, survived for 6 months and 4 months, respectively. CONCLUSIONS: In a retrospective analysis of patients who underwent IAT for unresectable HCC, we found the HKLC-5 staging system to have the best combination of performances in survival separation, calibration, and discrimination; it consistently outperformed the BCLC system in predicting survival times of patients. The HKLC system identified patients with HCC of stages IV and V who are unlikely to benefit from IAT.

Imaging Biomarkers of Tumor Response in Neuroendocrine Liver Metastases Treated with Transarterial Chemoembolization: Can Enhancing Tumor Burden of the Whole Liver Help Predict Patient Survival?

Purpose To investigate whether whole-liver enhancing tumor burden [ETB] can serve as an imaging biomarker and help predict survival better than World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), modified RECIST (mRECIST), and European Association for the Study of the Liver (EASL) methods in patients with multifocal, bilobar neuroendocrine liver metastases (NELM) after the first transarterial chemoembolization (TACE) procedure. Materials and Methods This HIPAA-compliant, institutional review board-approved retrospective study included 51 patients (mean age, 57.8 years ± 13.2; range, 13.5-85.8 years) with multifocal, bilobar NELM treated with TACE. The largest area (WHO), longest diameter (RECIST), longest enhancing diameter (mRECIST), largest enhancing area (EASL), and largest enhancing volume (ETB) were measured at baseline and after the first TACE on contrast material-enhanced magnetic resonance images. With three-dimensional software, ETB was measured as more than 2 standard deviations the signal intensity of a region of interest in normal liver. Response was assessed with WHO, RECIST, mRECIST, and EASL methods according to their respective criteria. For ETB response, a decrease in enhancement of at least 30%, 50%, and 65% was analyzed by using the Akaike information criterion. Survival analysis included Kaplan-Meier curves and Cox regressions. Results Treatment response occurred in 5.9% (WHO criteria), 2.0% (RECIST), 25.5% (mRECIST), and 23.5% (EASL criteria) of patients. With 30%, 50%, and 65% cutoffs, ETB response was seen in 60.8%, 39.2%, and 21.6% of patients, respectively, and was the only biomarker associated with a survival difference between responders and nonresponders (45.0 months vs 10.0 months, 84.3 months vs 16.7 months, and 85.2 months vs 21.2 months, respectively; P < .01 for all). The 50% cutoff provided the best survival model (hazard ratio [HR]: 0.2; 95% confidence interval [CI]: 0.1, 0.4). At multivariate analysis, ETB response was an independent predictor of survival (HR: 0.2; 95% CI: 0.1, 0.6). Conclusion Volumetric ETB is an early treatment response biomarker and surrogate for survival in patients with multifocal, bilobar NELM after the first TACE procedure. © RSNA, 2016.

Prospective Comparison of Diagnostic Accuracy Between Point-of-Care and Conventional Ultrasound in a General Diagnostic Department: Implications for Resource-Limited Settings.

OBJECTIVES: To compare the diagnostic accuracy of hand-held point-of-care (POC) versus conventional sonography in a general diagnostic setting with the intention to inform medical providers or clinicians on the rational use of POC ultrasound in resource limited settings. METHODS: Over 3 months in 2010, 47 patients were prospectively enrolled at a single academic center to obtain 54 clinical conventional ultrasound examinations and 54 study-only POC ultrasound examinations. Indications were 48% abdominal, 26% retroperitoneal, and 24% obstetrical. Nine blinded readers (sonographers, residents, and attending radiologists) sequentially assigned diagnoses to POC and then conventional studies, yielding 476 interpreted study pairs. Diagnostic accuracy was obtained by comparing POC and conventional diagnoses to a reference diagnosis established by the unblinded, senior author. Analysis was stratified by study type, body mass index (BMI), diagnostic confidence, and image quality. RESULTS: The mean diagnostic accuracy of conventional sonography was 84% compared with 74% for POC (P < .001). This difference was constant regardless of reader, exam type, or BMI. The sensitivity and specificity to detect abnormalities with conventional was 85 and 83%, compared with 75 and 68% for POC. The POC sonography demonstrated greater variability in image quality and diagnostic confidence, and this accounted for lower diagnostic accuracy. When image quality and diagnostic confidence were similar between POC and conventional examinations, there was no difference in accuracy. CONCLUSIONS: Point-of-care was nearly as accurate as conventional sonography for basic, focused examinations. Observed differences in accuracy were attributed to greater variation in POC image quality.

Comparison of Existing Response Criteria in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Using a 3D Quantitative Approach.

PURPOSE: To compare currently available non-three-dimensional methods (Response Evaluation Criteria in Solid Tumors [RECIST], European Association for Study of the Liver [EASL], modified RECIST [mRECIST[) with three-dimensional (3D) quantitative methods of the index tumor as early response markers in predicting patient survival after initial transcatheter arterial chemoembolization (TACE). MATERIALS AND METHODS: This was a retrospective single-institution HIPAA-compliant and institutional review board-approved study. From November 2001 to November 2008, 491 consecutive patients underwent intraarterial therapy for liver cancer with either conventional TACE or TACE with drug-eluting beads. A diagnosis of hepatocellular carcinoma (HCC) was made in 290 of these patients. The response of the index tumor on pre- and post-TACE magnetic resonance images was assessed retrospectively in 78 treatment-naïve patients with HCC (63 male; mean age, 63 years ± 11 [standard deviation]). Each response assessment method (RECIST, mRECIST, EASL, and 3D methods of volumetric RECIST [vRECIST] and quantitative EASL [qEASL]) was used to classify patients as responders or nonresponders by following standard guidelines for the uni- and bidimensional measurements and by using the formula for a sphere for the 3D measurements. The Kaplan-Meier method with the log-rank test was performed for each method to evaluate its ability to help predict survival of responders and nonresponders. Uni- and multivariate Cox proportional hazard ratio models were used to identify covariates that had significant association with survival. RESULTS: The uni- and bidimensional measurements of RECIST (hazard ratio, 0.6; 95% confidence interval [CI]: 0.3, 1.0; P = .09), mRECIST (hazard ratio, 0.6; 95% CI: 0.6, 1.0; P = .05), and EASL (hazard ratio, 1.1; 95% CI: 0.6, 2.2; P = .75) did not show a significant difference in survival between responders and nonresponders, whereas vRECIST (hazard ratio, 0.6; 95% CI: 0.3, 1.0; P = .04), qEASL (Vol) (hazard ratio, 0.5; 95% CI: 0.3, 0.9; P = .02), and qEASL (%) (hazard ratio, 0.3; 95% CI: 0.15, 0.60; P < .001) did show a significant difference between these groups. CONCLUSION: The 3D-based imaging biomarkers qEASL and vRECIST were tumor response criteria that could be used to predict patient survival early after initial TACE and enabled clear identification of nonresponders.