Ectopic pancreas tissue in the posterior mediastinum.

The occurrence of ectopic pancreas in the mediastinum is rare. Herein, we report a 22-year-old female who presented with right shoulder pain, dysphagia, fever and headaches. Chest computer tomography revealed a mass in the posterior mediastinum with accompanying signs of acute mediastinitis. Needle biopsy and fine-needle aspiration revealed ectopic gastral tissue and ectopic pancreas tissue, respectively. Surgical resection was attempted due to recurring acute pancreatitis episodes. However, due to chronic-inflammatory adhesions of the mass to the tracheal wall, en-bloc resection was not possible without major tracheal resection. Since then, recurring pancreatitis episodes have been treated conservatively with antibiotics. We report this case due to its differing clinical and radiological findings in comparison to previous case reports, none of which pertained a case of ectopic pancreas tissue in the posterior mediastinum with recurring acute pancreatitis and mediastinitis.

[Ethics and artificial intelligence]. / Ethik und künstliche Intelligenz.

The introduction of artificial intelligence (AI) into radiology promises to enhance efficiency and improve diagnostic accuracy, yet it also raises manifold ethical questions. These include data protection issues, the future role of radiologists, liability when using AI systems, and the avoidance of bias. To prevent data bias, the datasets need to be compiled carefully and to be representative of the target population. Accordingly, the upcoming European Union AI act sets particularly high requirements for the datasets used in training medical AI systems. Cognitive bias occurs when radiologists place too much trust in the results provided by AI systems (overreliance). So far, diagnostic AI systems are used almost exclusively as "second look" systems. If diagnostic AI systems are to be used in the future as "first look" systems or even as autonomous AI systems in order to enhance efficiency in radiology, the question of liability needs to be addressed, comparable to liability for autonomous driving. Such use of AI would also significantly change the role of radiologists.

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.

OBJECTIVE: To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. METHODS: A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. RESULTS: Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. CONCLUSIONS: Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT: An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS: • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Multiclass datasets expand neural network utility: an example on ankle radiographs.

PURPOSE: Artificial intelligence in computer vision has been increasingly adapted in clinical application since the implementation of neural networks, potentially providing incremental information beyond the mere detection of pathology. As its algorithmic approach propagates input variation, neural networks could be used to identify and evaluate relevant image features. In this study, we introduce a basic dataset structure and demonstrate a pertaining use case. METHODS: A multidimensional classification of ankle x-rays (n = 1493) rating a variety of features including fracture certainty was used to confirm its usability for separating input variations. We trained a customized neural network on the task of fracture detection using a state-of-the-art preprocessing and training protocol. By grouping the radiographs into subsets according to their image features, the influence of selected features on model performance was evaluated via selective training. RESULTS: The models trained on our dataset outperformed most comparable models of current literature with an ROC AUC of 0.943. Excluding ankle x-rays with signs of surgery improved fracture classification performance (AUC 0.955), while limiting the training set to only healthy ankles with and without fracture had no consistent effect. CONCLUSION: Using multiclass datasets and comparing model performance, we were able to demonstrate signs of surgery as a confounding factor, which, following elimination, improved our model. Also eliminating pathologies other than fracture in contrast had no effect on model performance, suggesting a beneficial influence of feature variability for robust model training. Thus, multiclass datasets allow for evaluation of distinct image features, deepening our understanding of pathology imaging.

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.

OBJECTIVE: The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. METHODS: The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. RESULTS: The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). CONCLUSIONS: Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. KEY POINTS: • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.

BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.

Structured Reporting of Solid and Cystic Pancreatic Lesions in CT and MRI: Consensus-Based Structured Report Templates of the German Society of Radiology (DRG). / Strukturierte Befundung von soliden und zystischen Pankreasläsionen im CT und MRT: Konsensus-basierte Befundvorlagen der Deutschen Röntgengesellschaft (DRG).

BACKGROUND: Radiological reports of pancreatic lesions are currently widely formulated as free texts. However, for optimal characterization, staging and operation planning, a wide range of information is required but is sometimes not captured comprehensively. Structured reporting offers the potential for improvement in terms of completeness, reproducibility and clarity of interdisciplinary communication. METHOD: Interdisciplinary consensus finding of structured report templates for solid and cystic pancreatic tumors in computed tomography (CT) and magnetic resonance imaging (MRI) with representatives of the German Society of Radiology (DRG), German Society for General and Visceral Surgery (DGAV), working group Oncological Imaging (ABO) of the German Cancer Society (DKG) and other radiologists, oncologists and surgeons. RESULTS: Among experts in the field of pancreatic imaging, oncology and pancreatic surgery, as well as in a public online survey, structured report templates were developed by consensus. These templates are available on the DRG homepage under www.befundung.drg.de and will be regularly revised to the current state of scientific knowledge by the participating specialist societies and responsible working groups. CONCLUSION: This article presents structured report templates for solid and cystic pancreatic tumors to improve clinical staging (cTNM, ycTNM) in everyday radiology. KEY POINTS: · Structured report templates offer the potential of optimized radiological reporting with regard to completeness, reproducibility and differential diagnosis.. · This article presents consensus-based, structured reports for solid and cystic pancreatic lesions in CT and MRI.. · These structured reports are available open source on the homepage of the German Society of Radiology (DRG) under www.befundung.drg.de.. CITATION FORMAT: · Persigehl T, Baumhauer M, Baeßler B et al. Structured Reporting of Solid and Cystic Pancreatic Lesions in CT and MRI: Consensus-Based Structured Report Templates of the German Society of Radiology (DRG). Fortschr Röntgenstr 2020; 192: 641 - 655.

Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.

Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression. Results The average sensitivity of radiologists improved (from 65.1% [1375 of 2112; 95% confidence interval {CI}: 62.0%, 68.1%] to 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%], P < .001) and the number of false-positive findings per radiograph declined (from 0.2 [488 of 2400; 95% CI: 0.18, 0.22] to 0.18 [422 of 2400; 95% CI: 0.16, 0.2], P < .001) when the radiologists re-reviewed radiographs with the DCNN software. For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.

Comparing the diagnostic performance of radiation dose-equivalent radiography, multi-detector computed tomography and cone beam computed tomography for finger fractures - A phantom study.

PURPOSE: To compare the diagnostic performance and raters´confidence of radiography, radiography equivalent dose multi-detector computed tomography (RED-MDCT) and radiography equivalent dose cone beam computed tomography (RED-CBCT) for finger fractures. METHODS: Fractures were inflicted artificially and randomly to 10 cadaveric hands of body donors. Radiography as well as RED-MDCT and RED-CBCT imaging were performed at dose settings equivalent to radiography. Images were de-identified and analyzed by three radiologists regarding finger fractures, joint involvement and confidence with their findings. Reference standard was consensus reading by two radiologists of the fracturing protocol and high-dose multi-detector computed tomography (MDCT) images. Sensitivity and specificity were calculated and compared with Cochrane´s Q and post hoc analysis. Rater´s confidence was calculated with Friedman Test and post hoc Nemenyi Test. RESULTS: Rater´s confidence, inter-rater correlation, specificity for fractures and joint involvement were higher in RED-MDCT and RED-CBCT compared to radiography. No differences between the modalities were found regarding sensitivity. CONCLUSION: In this phantom study, radiography equivalent dose computed tomography (RED-CT) demonstrates a partly higher diagnostic accuracy than radiography. Implementing RED-CT in the diagnostic work-up of finger fractures could improve diagnostics, support correct classification and adequate treatment. Clinical studies should be performed to confirm these preliminary results.

Bayesian pretest probability estimation for primary malignant bone tumors based on the Surveillance, Epidemiology and End Results Program (SEER) database.

PURPOSE: In the diagnostic process of primary bone tumors, patient age, tumor localization and to a lesser extent sex affect the differential diagnosis. We therefore aim to develop a pretest probability calculator for primary malignant bone tumors based on population data taking these variables into account. METHODS: We access the SEER (Surveillance, Epidemiology and End Results Program of the National Cancer Institute, 2015 release) database and analyze data of all primary malignant bone tumors diagnosed between 1973 and 2012. We record age at diagnosis, tumor localization according to the International Classification of Diseases (ICD-O-3) and sex. We take relative probability of the single tumor entity as a surrogate parameter for unadjusted pretest probability. We build a probabilistic (naïve Bayes) classifier to calculate pretest probabilities adjusted for age, tumor localization and sex. RESULTS: We analyze data from 12,931 patients (647 chondroblastic osteosarcomas, 3659 chondrosarcomas, 1080 chordomas, 185 dedifferentiated chondrosarcomas, 2006 Ewing's sarcomas, 281 fibroblastic osteosarcomas, 129 fibrosarcomas, 291 fibrous malignant histiocytomas, 289 malignant giant cell tumors, 238 myxoid chondrosarcomas, 3730 osteosarcomas, 252 parosteal osteosarcomas, 144 telangiectatic osteosarcomas). We make our probability calculator accessible at http://ebm-radiology.com/bayesbone/index.html . We provide exhaustive tables for age and localization data. Results from tenfold cross-validation show that in 79.8 % of cases the pretest probability is correctly raised. CONCLUSIONS: Our approach employs population data to calculate relative pretest probabilities for primary malignant bone tumors. The calculator is not diagnostic in nature. However, resulting probabilities might serve as an initial evaluation of probabilities of tumors on the differential diagnosis list.

Comparison of Diagnostic Accuracy of Radiation Dose-Equivalent Radiography, Multidetector Computed Tomography and Cone Beam Computed Tomography for Fractures of Adult Cadaveric Wrists.

PURPOSE: To compare the diagnostic accuracy of radiography, to radiography equivalent dose multidetector computed tomography (RED-MDCT) and to radiography equivalent dose cone beam computed tomography (RED-CBCT) for wrist fractures. METHODS: As study subjects we obtained 10 cadaveric human hands from body donors. Distal radius, distal ulna and carpal bones (n = 100) were artificially fractured in random order in a controlled experimental setting. We performed radiation dose equivalent radiography (settings as in standard clinical care), RED-MDCT in a 320 row MDCT with single shot mode and RED-CBCT in a device dedicated to musculoskeletal imaging. Three raters independently evaluated the resulting images for fractures and the level of confidence for each finding. Gold standard was evaluated by consensus reading of a high-dose MDCT. RESULTS: Pooled sensitivity was higher in RED-MDCT with 0.89 and RED-MDCT with 0.81 compared to radiography with 0.54 (P = < .004). No significant differences were detected concerning the modalities' specificities (with values between P = .98). Raters' confidence was higher in RED-MDCT and RED-CBCT compared to radiography (P < .001). CONCLUSION: The diagnostic accuracy of RED-MDCT and RED-CBCT for wrist fractures proved to be similar and in some parts even higher compared to radiography. Readers are more confident in their reporting with the cross sectional modalities. Dose equivalent cross sectional computed tomography of the wrist could replace plain radiography for fracture diagnosis in the long run.

External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

PURPOSE: Lesions detected at mammography are described with a highly standardized terminology: the breast imaging-reporting and data system (BI-RADS) lexicon. Up to now, no validated semantic computer assisted classification algorithm exists to interactively link combinations of morphological descriptors from the lexicon to a probabilistic risk estimate of malignancy. The authors therefore aim at the external validation of the mammographic mass diagnosis (MMassDx) algorithm. A classification algorithm like MMassDx must perform well in a variety of clinical circumstances and in datasets that were not used to generate the algorithm in order to ultimately become accepted in clinical routine. METHODS: The MMassDx algorithm uses a naïve Bayes network and calculates post-test probabilities of malignancy based on two distinct sets of variables, (a) BI-RADS descriptors and age ("descriptor model") and (b) BI-RADS descriptors, age, and BI-RADS assessment categories ("inclusive model"). The authors evaluate both the MMassDx (descriptor) and MMassDx (inclusive) models using two large publicly available datasets of mammographic mass lesions: the digital database for screening mammography (DDSM) dataset, which contains two subsets from the same examinations-a medio-lateral oblique (MLO) view and cranio-caudal (CC) view dataset-and the mammographic mass (MM) dataset. The DDSM contains 1220 mass lesions and the MM dataset contains 961 mass lesions. The authors evaluate discriminative performance using area under the receiver-operating-characteristic curve (AUC) and compare this to the BI-RADS assessment categories alone (i.e., the clinical performance) using the DeLong method. The authors also evaluate whether assigned probabilistic risk estimates reflect the lesions' true risk of malignancy using calibration curves. RESULTS: The authors demonstrate that the MMassDx algorithms show good discriminatory performance. AUC for the MMassDx (descriptor) model in the DDSM data is 0.876/0.895 (MLO/CC view) and AUC for the MMassDx (inclusive) model in the DDSM data is 0.891/0.900 (MLO/CC view). AUC for the MMassDx (descriptor) model in the MM data is 0.862 and AUC for the MMassDx (inclusive) model in the MM data is 0.900. In all scenarios, MMassDx performs significantly better than clinical performance, P < 0.05 each. The authors furthermore demonstrate that the MMassDx algorithm systematically underestimates the risk of malignancy in the DDSM and MM datasets, especially when low probabilities of malignancy are assigned. CONCLUSIONS: The authors' results reveal that the MMassDx algorithms have good discriminatory performance but less accurate calibration when tested on two independent validation datasets. Improvement in calibration and testing in a prospective clinical population will be important steps in the pursuit of translation of these algorithms to the clinic.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PURPOSE: To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. MATERIALS AND METHODS: We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. RESULTS: In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. CONCLUSION: We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . KEY POINTS: • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.

Provision of the DDSM mammography metadata in an accessible format.

PURPOSE: The Digital Database for Screening Mammography (DDSM) is the largest publicly available resource for mammographic image analysis research and has been used extensively in the past for computer assisted diagnosis (CADx) studies. However, the database has not been searchable for a specific kind of lesion, which rendered the case selection process in past studies often times arbitrary. Therefore, the authors want to provide the complete metadata of the DDSM in an accessible format. METHODS: The authors semiautomatically transformed the data available athttp://marathon.csee.usf.edu/Mammography/Database.html into table format. The 1769 cases (914 from cancer volumes, 855 from benign volumes) comprise 1220 mass lesions (578 benign, 642 malignant) and 859 calcifications (433 benign, 426 malignant). Additionally, 694 normal cases were processed to allow for matching according to age and breast density. RESULTS: The authors provide the entire DDSM metadata (for benign, malignant, and normal cases) as tab-delimited text files[see supplementary material at http://dx.doi.org/10.1118/1.4870379E-MPHYA6-41-006405 for DDSM metadata]. CONCLUSIONS: The data provided make the case selection for future studies using the DDSM reproducible. Furthermore, it may serve as a validation dataset for CADx approaches using the BI-RADS lexicon.

Dual-energy CT virtual noncalcium technique: detecting posttraumatic bone marrow lesions--feasibility study.

PURPOSE: To evaluate traumatized bone marrow with a dual-energy (DE) computed tomographic (CT) virtual noncalcium technique. MATERIALS AND METHODS: In this prospective institutional review board-approved study, 21 patients with an acute knee trauma underwent DE CT and magnetic resonance (MR) imaging. A software application was used to virtually subtract calcium from the images. Presence of fractures was noted, and presence of bone bruise was rated on a four-point scale for six femoral and tibial regions by two radiologists. CT numbers were obtained in the same regions. Consensus reading of independently read MR images served as the reference standard. Image ratings and CT numbers were subjected to receiver operating characteristic curve analysis. RESULTS: After exclusion of 16 regions owing to artifacts, MR imaging revealed 59 bone bruises in the remaining 236 regions (19 of 114 femoral, 40 of 122 tibial). Fractures were present in eight patients. Visual rating revealed areas under the curve of 0.886 and 0.897 in the femur and 0.974 and 0.953 in the tibia for observers 1 and 2, respectively. For CT numbers, the respective areas under the curve were 0.922 and 0.974. If scores of 1 and 2 (strong or mild bone bruise) were counted as positive, sensitivities were 86.4% and 86.4% and specificities were 94.4% and 95.5% for observers 1 and 2, respectively. The kappa statistic demonstrated good to excellent agreement (femur, kappa = 0.78; tibia, kappa = 0.87). CONCLUSION: This DE CT virtual noncalcium technique can subtract calcium from cancellous bone, allowing bone marrow assessment and potentially making posttraumatic bone bruises of the knee detectable with CT.

Continuously moving table MRI with sliding multislice for rectal cancer staging: image quality and lesion detection.

PURPOSE: To determine image quality and lesion detection of sliding multislice (SMS), a recently developed moving table MRI technique, in patients with rectal cancer. MATERIALS AND METHODS: Twenty-seven paired SMS (Avanto, Siemens Medical Solutions) and MDCT (Sensation 64, Siemens Medical Solutions) examinations of abdomen and pelvis were performed in patients with rectal cancer and compared for detection of liver, lymph node and bone metastases by two independent observers. A contrast-enhanced, fat saturated 2D gradient echo sequence (TE, 2.0 ms; TR, 102 ms; slice, 5 mm) was acquired with SMS and a standard contrast-enhanced protocol (100 ml @ 2.5 ml/s; slice, 5 mm) was used for abdominal MDCT. Standard of reference consisted of a consensus evaluation of SMS, MDCT, and all available follow-up examinations after a period of 6 months. Artifact burden and image quality of SMS was assessed in comparison to stationary gradient echo sequences obtained in an age-matched group of 27 patients. RESULTS: Whereas SMS achieved a mean quality score of 3.65 (scale, 0-4) for the liver, representing very good diagnostic properties, strong breathing artifacts in the intestinal region were observed in 19 cases by both observers. The retroperitoneum still achieved a mean quality score of 3.52, although breathing artifacts were noted in 12 and 15 cases (observers 1 and 2, respectively). The sensitivities of SMS to detect hepatic metastases were 91.2% and 94.1% for both observers, respectively, compared to 98.5%/98.5% for MDCT. The sensitivities for lymph node metastases were 87.5%/81.3% for SMS compared to 78.1%/81.3% for MDCT. The sensitivities for bone metastases were 91.7%/100% for SMS compared to 8.3%/16.7% for MDCT. CONCLUSION: With slightly reduced image quality in the intestinal region, SMS exhibits equal detection of lymph node and liver metastases compared to MDCT. SMS MRI proved to be superior to MDCT in detection of bone metastases.

Effective dose estimation in whole-body multislice CT in paediatric trauma patients.

BACKGROUND: The number of multislice CT (MSCT) scans performed in polytraumatized children has increased rapidly. There is growing concern regarding the radiation dose in MSCT and its long-term consequences, especially in children. OBJECTIVE: To determine the effective dose to polytraumatized children who undergo whole-body MSCT. MATERIALS AND METHODS: A total of 51 traumatized children aged 0-16 years underwent a polytrauma protocol CT scan between November 2004 and August 2006 at our institution. The effective dose was calculated retrospectively by a computer program (CT-Expo 1.5, Hannover, Germany). RESULTS: The mean effective dose was 20.8 mSv (range 8.6-48.9 mSv, SD +/- 7.9 mSv). There was no statistically significant difference in the effective dose between male and female patients. CONCLUSION: Whole-body MSCT is a superior diagnostic tool in polytraumatized children with 20.8 mSv per patient being a justified mean effective dose. In a potentially life-threatening situation whole-body MSCT provides the clinicians with relevant information to initiate life-saving therapy. Radiologists should use special paediatric protocols that include dose-saving mechanisms to keep the effective dose as low as possible. Further studies are needed to examine and advance dose-saving strategies in MSCT, especially in children.

Computer-navigated iliosacral screw insertion reduces malposition rate and radiation exposure.

UNLABELLED: Insertion of percutaneous iliosacral screws with fluoroscopic guidance is associated with a relatively high screw malposition rate and long radiation exposure. We asked whether radiation exposure was reduced and screw position improved in patients having percutaneous iliosacral screw insertion using computer-assisted navigation compared with patients having conventional fluoroscopic screw placement. We inserted 26 screws in 24 patients using the navigation system and 35 screws in 32 patients using the conventional fluoroscopic technique. Two subgroups were analyzed, one in which only one iliosacral screw was placed and another with additional use of an external fixator. We determined screw positions by computed tomography and compared operation time, radiation exposure, and screw position. We observed no difference in operative times. Radiation exposure was reduced for the patients and operating room personnel with computer assistance. The postoperative computed tomography scan showed better screw position and fewer malpositioned screws in the three-dimensional navigated groups. Computer navigation reduced malposition rate and radiation exposure. LEVEL OF EVIDENCE: Level II, therapeutic study.