113 research outputs found

    DiagnĂłstico precoz de la osteoporosis transitoria de la cadera versus necrosis isquĂŠmica de la cabeza femoral: Âżexisten realmente signos diferenciales?

    Get PDF
    —Se han estudiado de forma retrospectiva las posibles diferencias clínicas y en pruebas de imagen de 2 procesos patológicos: la osteoporosis transitoria de la cadera (OTC) y la necrosis isquémica de la cabeza femoral (NICF). El estudio se ha llevado a cabo sobre 7 pacientes diagnosticados de OTC y se han comparado los hallazgos obtenidos con 12 casos de NICF en fase inicial de evolución. Se debate si la OTC es un síndrome distinto a la NICF o si es la manifestación de un estadio precoz y reversible de la misma. Las pruebas de imagen en que se ha basado el estudio han sido: la radiografía simple, el rastreo óseo isotópico y, principalmente, la resonancia magnética (RM). Se han encontrado signos diferenciales entre ambos procesos, pero éstos no nos permiten concluir que se trata de 2 entidades patológicas totalmente diferentes. Se discute por último la actitud terapéutica a llevar a cabo ante un caso de OTC.We report a retrospective study about the differences in clinical signs and in imaging techniques between transient osteoporosis of the hip and avascular necrosis. The study was done on 7 patients of transient osteoporosis and the results obtained were compared with 12 cases of avascular necrosis. We discuss if transient osteoporosis is an individual syndrome or it is an early and reversible stage of avascular necrosis. The imaging techniques studies were: standard X-ray, radionuclide, bone-scanning and MRI. We found differential signs between there two process, but this don't allow us to conclude that the two illness are different. Finally, we discuss the treatment lo carry out in case of transient osteoporosis of the hi

    Translating radiological research into practice — from discovery to clinical impact

    Get PDF
    At the European Society of Radiology (ESR), we strive to provide evidence for radiological practices that improve patient outcomes and have a societal impact. Successful translation of radiological research into clinical practice requires multiple factors including tailored methodology, a multidisciplinary approach aiming beyond technical validation, and a focus on unmet clinical needs. Low levels of evidence are a threat to radiology, resulting in low visibility and credibility. Here, we provide the background and rationale for the thematic series Translating radiological research into practice—from discovery to clinical impact, inviting authors to describe their processes of achieving clinically impactful radiological research. We describe the challenges unique to radiological research. Additionally, a survey was sent to non-radiological clinical societies. The majority of respondents (6/11) were in the field of gastrointestinal/abdominal medicine. The implementation of CT/MRI techniques for disease characterisation, detection and staging of cancer, and treatment planning and radiological interventions were mentioned as the most important radiological developments in the past years. The perception was that patients are substantially unaware of the impact of these developments. Unmet clinical needs were mostly early diagnosis and staging of cancer, microstructural/functional assessment of tissues and organs, and implant assessment. All but one respondent considered radiology important for research in their discipline, but five indicated that radiology is currently not involved in their research. Radiology research holds the potential for being transformative to medical practice. It is our responsibility to take the lead in studies including radiology and strive towards the highest levels of evidence. Critical relevance statement For radiological research to make a clinical and societal impact, radiologists should take the lead in radiological studies, go beyond the assessment of technical feasibility and diagnostic accuracy, and—in a multidisciplinary approach—address clinical unmet needs. Key points • Multiple factors are essential for radiological research to make a clinical and societal impact. • Radiological research needs to go beyond diagnostic accuracy and address unmet clinical needs. • Radiologists should take the lead in radiological studies with a multidisciplinary approach.</p

    Translating radiological research into practice—from discovery to clinical impact

    Get PDF
    At the European Society of Radiology (ESR), we strive to provide evidence for radiological practices that improve patient outcomes and have a societal impact. Successful translation of radiological research into clinical practice requires multiple factors including tailored methodology, a multidisciplinary approach aiming beyond technical validation, and a focus on unmet clinical needs. Low levels of evidence are a threat to radiology, resulting in low visibility and credibility. Here, we provide the background and rationale for the thematic series Translating radiological research into practice—from discovery to clinical impact, inviting authors to describe their processes of achieving clinically impactful radiological research. We describe the challenges unique to radiological research. Additionally, a survey was sent to non-radiological clinical societies. The majority of respondents (6/11) were in the field of gastrointestinal/abdominal medicine. The implementation of CT/MRI techniques for disease characterisation, detection and staging of cancer, and treatment planning and radiological interventions were mentioned as the most important radiological developments in the past years. The perception was that patients are substantially unaware of the impact of these developments. Unmet clinical needs were mostly early diagnosis and staging of cancer, microstructural/functional assessment of tissues and organs, and implant assessment. All but one respondent considered radiology important for research in their discipline, but five indicated that radiology is currently not involved in their research. Radiology research holds the potential for being transformative to medical practice. It is our responsibility to take the lead in studies including radiology and strive towards the highest levels of evidence. Critical relevance statement For radiological research to make a clinical and societal impact, radiologists should take the lead in radiological studies, go beyond the assessment of technical feasibility and diagnostic accuracy, and—in a multidisciplinary approach—address clinical unmet needs. Key points Multiple factors are essential for radiological research to make a clinical and societal impact. Radiological research needs to go beyond diagnostic accuracy and address unmet clinical needs. Radiologists should take the lead in radiological studies with a multidisciplinary approach

    Translating radiological research into practice — from discovery to clinical impact

    Get PDF
    At the European Society of Radiology (ESR), we strive to provide evidence for radiological practices that improve patient outcomes and have a societal impact. Successful translation of radiological research into clinical practice requires multiple factors including tailored methodology, a multidisciplinary approach aiming beyond technical validation, and a focus on unmet clinical needs. Low levels of evidence are a threat to radiology, resulting in low visibility and credibility. Here, we provide the background and rationale for the thematic series Translating radiological research into practice—from discovery to clinical impact, inviting authors to describe their processes of achieving clinically impactful radiological research. We describe the challenges unique to radiological research. Additionally, a survey was sent to non-radiological clinical societies. The majority of respondents (6/11) were in the field of gastrointestinal/abdominal medicine. The implementation of CT/MRI techniques for disease characterisation, detection and staging of cancer, and treatment planning and radiological interventions were mentioned as the most important radiological developments in the past years. The perception was that patients are substantially unaware of the impact of these developments. Unmet clinical needs were mostly early diagnosis and staging of cancer, microstructural/functional assessment of tissues and organs, and implant assessment. All but one respondent considered radiology important for research in their discipline, but five indicated that radiology is currently not involved in their research. Radiology research holds the potential for being transformative to medical practice. It is our responsibility to take the lead in studies including radiology and strive towards the highest levels of evidence. Critical relevance statement For radiological research to make a clinical and societal impact, radiologists should take the lead in radiological studies, go beyond the assessment of technical feasibility and diagnostic accuracy, and—in a multidisciplinary approach—address clinical unmet needs. Key points • Multiple factors are essential for radiological research to make a clinical and societal impact. • Radiological research needs to go beyond diagnostic accuracy and address unmet clinical needs. • Radiologists should take the lead in radiological studies with a multidisciplinary approach.</p

    Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports

    Full text link
    Background: Radiology reports are commonly written on free-text using voice recognition devices. Structured reports (SR) have a high potential but they are usually considered more difficult to fill-in so their adoption in clinical practice leads to a lower efficiency. However, some studies have demonstrated that in some cases, producing SRs may require shorter time than plain-text ones. This work focuses on the definition and demonstration of a methodology to evaluate the productivity of software tools for producing radiology reports. A set of SRs for breast cancer diagnosis based on BI-RADS have been developed using this method. An analysis of their efficiency with respect to free-text reports has been performed. Material and Methods: The methodology proposed compares the Elapsed Time (ET) on a set of radiological reports. Free-text reports are produced with the speech recognition devices used in the clinical practice. Structured reports are generated using a web application generated with TRENCADIS framework. A team of six radiologists with three different levels of experience in the breast cancer diagnosis was recruited. These radiologists performed the evaluation, each one introducing 50 reports for mammography, 50 for ultrasound scan and 50 for MRI using both approaches. Also, the Relative Efficiency (REF) was computed for each report, dividing the ET of both methods. We applied the T-Student (T-S) test to compare the ETs and the ANOVA test to compare the REFs. Both tests were computed using the SPSS software. Results: The study produced three DICOM-SR templates for Breast Cancer Diagnosis on mammography, ultrasound and MRI, using RADLEX terms based on BIRADs 5th edition. The T-S test on radiologists with high or intermediate profile, showed that the difference between the ET was only statistically significant for mammography and ultrasound. The ANOVA test performed grouping the REF by modalities, indicated that there were no significant differences between mammograms and ultrasound scans, but both have significant statistical differences with MRI. The ANOVA test of the REF for each modality, indicated that there were only significant differences in Mammography (ANOVA p&#8201;= 0.024) and Ultrasound (ANOVA p&#8201;=&#8201;0.008). The ANOVA test for each radiologist profile, indicated that there were significant differences on the high profile (ANOVA p&#8201;=&#8201;0.028) and medium (ANOVA p&#8201;=&#8201;0.045). Conclusions: In this work, we have defined and demonstrated a methodology to evaluate the productivity of software tools for producing radiology reports in Breast Cancer. We have evaluated that adopting Structured Reporting in mammography and ultrasound studies in breast cancer diagnosis improves the performance in producing reports.INDIGO - DataCloud receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement RIA 653549.Segrelles Quilis, JD.; Medina, R.; Blanquer Espert, I.; Marti Bonmati, L. (2017). Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports. Methods of Information in Medicine. 56:1-13. https://doi.org/10.3414/ME16-01-0091S1135

    Magnetic resonance spectroscopy and brain volumetry in mild cognitive impairment. A prospective study

    Get PDF
    Objective To assess the accuracy of magnetic resonance spectroscopy (1H-MRS) and brain volumetry in mild cognitive impairment (MCI) to predict conversion to probable Alzheimer''s disease (AD). Methods Forty-eight patients fulfilling the criteria of amnestic MCI who underwent a conventional magnetic resonance imaging (MRI) followed by MRS, and T1-3D on 1.5 Tesla MR unit. At baseline the patients underwent neuropsychological examination. 1H-MRS of the brain was carried out by exploring the left medial occipital lobe and ventral posterior cingulated cortex (vPCC) using the LCModel software. A high resolution T1-3D sequence was acquired to carry out the volumetric measurement. A cortical and subcortical parcellation strategy was used to obtain the volumes of each area within the brain. The patients were followed up to detect conversion to probable AD. Results After a 3-year follow-up, 15 (31.2%) patients converted to AD. The myo-inositol in the occipital cortex and glutamate + glutamine (Glx) in the posterior cingulate cortex predicted conversion to probable AD at 46.1% sensitivity and 90.6% specificity. The positive predictive value was 66.7%, and the negative predictive value was 80.6%, with an overall cross-validated classification accuracy of 77.8%. The volume of the third ventricle, the total white matter and entorhinal cortex predict conversion to probable AD at 46.7% sensitivity and 90.9% specificity. The positive predictive value was 70%, and the negative predictive value was 78.9%, with an overall cross-validated classification accuracy of 77.1%. Combining volumetric measures in addition to the MRS measures the prediction to probable AD has a 38.5% sensitivity and 87.5% specificity, with a positive predictive value of 55.6%, a negative predictive value of 77.8% and an overall accuracy of 73.3%. Conclusion Either MRS or brain volumetric measures are markers separately of cognitive decline and may serve as a noninvasive tool to monitor cognitive changes and progression to dementia in patients with amnestic MCI, but the results do not support the routine use in the clinical settings

    Optimisation of ultrasound liver perfusion through a digital reference object and analysis tool

    Full text link
    [EN] Background Conventional ultrasound (US) provides important qualitative information, although there is a need to evaluate the influence of the input parameters on the output signal and standardise the acquisition for an adequate quantitative perfusion assessment. The present study analyses how the variation in the input parameters influences the measurement of the perfusion parameters. Methods A software tool with simulator of the conventional US signal was created, and the influence of the different input variables on the derived biomarkers was analysed by varying the image acquisition configuration. The input parameters considered were the dynamic range, gain, and frequency of the transducer. Their influence on mean transit time (MTT), the area under the curve (AUC), maximum intensity (MI), and time to peak (TTP) parameters as outputs of the quantitative perfusion analysis was evaluated. A group of 13 patients with hepatocarcinoma was analysed with both a commercial tool and an in-house developed software. Results The optimal calculated inputs which minimise errors while preserving images¿ readability consisted of gain of 15¿dB, dynamic range of 60¿dB, and frequency of 1.5¿MHz. The comparison between the in-house developed software and the commercial software provided different values for MTT and AUC, while MI and TTP were highly similar. Conclusion Input parameter selection introduces variability and errors in US perfusion parameter estimation. Our results may add relevant insight into the current knowledge of conventional US perfusion and its use in lesions characterisation, playing in favour of optimised standardised parameter configuration to minimise variability.Alberich-Bayarri, Á.; Tomás-Cucarella, J.; Torregrosa-Lloret, A.; Saiz Rodríguez, FJ.; Martí-Bonmatí, L. (2019). Optimisation of ultrasound liver perfusion through a digital reference object and analysis tool. European Radiology Experimental. 3:1-10. https://doi.org/10.1186/s41747-019-0086-5S1103Parker JM, Weller MW, Feinstein LM et al (2013) Safety of ultrasound contrast agents in patients with known or suspected cardiac shunts. Am J Cardiol 112:1039–1045.Dhamija E, Paul SB (2014) Role of contrast enhanced ultrasound in hepatic imaging. Trop Gastroenterol 35:141–151.Wang XY, Kang LK, Lan CY (2014) Contrast-enhanced ultrasonography in diagnosis of benign and malignant breast lesions. Eur J Gynaecol Oncol 35:415–420.Wang S, Yang W, Zhang H, Xu Q, Yan K (2015) The role of contrast-enhanced ultrasound in selection indication and improveing diagnosis for transthoracic biopsy in peripheral pulmonary and mediastinal lesions. Biomed Res Int 2015:231782.Green MA, Mathias CJ, Willis LR, et al (2007) Assessment of Cu-ETS as a PET radiopharmaceutical for evaluation of regional renal perfusion. Nucl Med Biol 34:247–255.Daghini E, Primak AN, Chade AR, et al (2007) Assessment of renal hemodynamics and function in pigs with 64-section multidetector CT: comparison with electron-beam CT. Radiology 243:405–412.Martin DR, Sharma P, Salman K, et al (2008) Individual kidney blood flow measured with contrast-enhanced first-pass perfusion MR imaging. Radiology 246:241–248.Tang MX, Mulvana H, Gauthier T, et al (2011) Quantitative contrast-enhanced ultrasound imaging: a review of sources of variability. Interface Focus 1:520–539.Gauthier TP, Averkiou MA, Leen EL (2011) Perfusion quantification using dynamic contrast-enhanced ultrasound: the impact of dynamic range and gain on time-intensity curves. Ultrasonics 51:102–106.Möller I, Janta I, Backhaus M, et al (2017) The 2017 EULAR standardised procedures for ultrasound imaging in rheumatology. Ann Rheum Dis. 76:1974–1979.Pitre-Champagnat S, Coiffier B, Jourdain L, Benatsou B, Leguerney I, Lassau N (2017) Toward a standardization of ultrasound scanners for dynamic contrast-enhanced ultrasonography: methodology and phantoms. Ultrasound Med Biol. https://doi.org/10.1016/j.ultrasmedbio.2017.06.032Shunichi S, Hiroko I, Fuminori M, Waki H (2009) Definition of contrast enhancement phases of the liver using a perfluoro-based microbubble agent, perflubutane microbubbles. Ultrasound Med Biol 35:1819–1827. doiFairbank WM Jr, Scully MO (1977) A new noninvasive technique for cardiac pressure measurement: resonant scattering of ultrasound from bubbles. IEEE Trans Biomed Eng 24:107–110.Malm S, Frigstad S, Helland F, Oye K, Slordahl S, Skjarpe T (2005) Quantification of resting myocardial blood flow velocity in normal humans using real-time contrast echocardiography. A feasibility study. Cardiovasc Ultrasound 3:16.Arditi M, Frinking PJ, Zhou X, Rognin NG (2006) A new formalism for the quantification of tissue perfusion by the destruction-replenishment method in contrast ultrasound imaging. IEEE Trans Ultrason Ferroelectr Freq Control 53:1118–1129.Savic RM, Jonker DM, Kerbusch T, Karlsson MO (2007) Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies. J Pharmacokinet Pharmacodyn 34:711–726.Averkiou M, Lampaskis M, Kyriakopoulou K, et al (2010) Quantification of tumor microvascularity with respiratory gated contrast enhanced ultrasound for monitoring therapy. Ultrasound Med Biol 36:68–77.Kuenen MP, Mischi M, Wijkstra H (2011) Contrast-ultrasound diffusion imaging for localization of prostate cancer. IEEE Trans Med Imaging 30:1493–1502.Garcia D, Le Tarnec L, Muth S, Montagnon E, Porée J, Cloutier G(2013) Stolt’s f-k migration for plane wave ultrasound imaging. IEEE Trans Ultrason Ferroelectr Freq Control 60:1853–1867.Brands J, Vink H, Van Teeffelen JW (2011) Comparison of four mathematical models to analyze indicator-dilution curves in the coronary circulation. Med Biol Eng Comput 49:1471–1479.Zhou JH, Cao LH, Zheng W, Liu M, Han F, Li AH (2011) Contrast-enhanced gray-scale ultrasound for quantitative evaluation of tumor response to chemotherapy: preliminary results with a mouse hepatoma model. AJR Am J Roentgenol 196:W13-17.Wei K, Jayaweera AR, Firoozan S, Linka A, Skyba DM, Kaul S (1998) Quantification of myocardial blood flow with ultrasoundinduced destruction of microbubbles administered as a constant venous infusion. Circulation 97:473–483Riascos P, Velasco-Medina J (2005) Efectos Biológicos y Consideraciones de Seguridad en Ultrasonido. Available via https://www.yumpu.com/es/document/view/15350629/efectos-biologicos-y-consideraciones-de-seguridad-en-ultrasonidode Jong N, ten Cate FJ, Vletter WB, Roelandt JR (1993) Quantification of transpulmonary echocontrast effects. Ultrasound Med Biol 19:279–288Quaia E (2011) Assessment of tissue perfusion by contrast-enhanced ultrasound. Eur Radiol 21:604–615

    What do biomarkers add: Mapping quantitative imaging biomarkers research

    Full text link
    [EN] Purpose: To understand the contribution of the concept of "biomarker" to quantitative imaging research. Method: The study consists of a bibliometric and a network analysis of quantitative imaging biomarkers research based on publication data retrieved from the Web of Science (WoS) for the period 1976-2017. Co-authorship is used as a proxy for scientific collaboration among research groups. Research groups are disambiguated and assigned to an institutional sector and to a medical specialty or academic discipline. Co-occurrence maps of specialties are built to delineate the collaborative network structure of this emerging field. Results: Two very distinct growth patterns emerged from the 5432 publications retrieved from WoS. Scientific production on 'quantitative imaging biomarkers >> (QIB) began 20 years after the first publications on 'quantitative imaging >> (QI). The field of QIB has exhibited rapid growth becoming the most used term since 2011. Among the 12,882 institutions identified, 56% include the term QIB and 44% include the term QI; among the 14,734 different research groups identified, 60% include the term QIB and 40% the term QI. QIB is characterized by a well-established community of researchers whose largest contributors are in medical specialties (radiology 17%, neurology 16%, mental 10%, oncology 10%), while QI shows a more fragmented and diverse community (radiology 13%, engineering 13%, physics 10%, oncology 9%, neurology 6%, biology 4%, nuclear 3%, computing 3%). This suggests a qualitative difference between QIB and QI networks. Conclusions: Adding biomarkers to quantitative imaging suggests that medical imaging is rapidly evolving, driven by the efforts to translate quantitative imaging research into clinical practice.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Adrian A. DĂ­az-Faes has received support from a Juan de la Cierva Incorporacion postdoctoral grant from the Spanish Ministry of Economy and Competitiveness (IJCI-2017-31454).Meseguer-Castillo, E.; BarberĂĄ TomĂĄs, JD.; Benito Amat, C.; Arias-Diaz-Faes, A.; MartĂ­-BonmatĂ­, L. (2022). What do biomarkers add: Mapping quantitative imaging biomarkers research. European Journal of Radiology. 146:1-8. https://doi.org/10.1016/j.ejrad.2021.1100521814

    A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer

    Full text link
    The final publication is available at Springer via http://dx.doi.org/10.1007/s10278-014-9728-6.This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.We thank the subject matter experts for sharing their insights through this study. We are especially appreciative of the efforts of the Radiology Unit and Medical Oncology Unit teams at the University Hospital Dr. Peset. This work was partially supported by the Vicerectorat d'Investigacio de la Universitat Politecnica de Valencia (UPVLC) to develop the project "Mejora del proceso diagnostico del cancer de mama" with reference UPV-FE-2013-8.Medina, R.; Torres Serrano, E.; Segrelles Quilis, JD.; Blanquer Espert, I.; Martí Bonmatí, L.; Almenar-Cubells, D. (2015). A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer. Journal of Digital Imaging. 28(2):132-145. doi:10.1007/s10278-014-9728-6S132145282Ratib O: Imaging informatics: From image management to image navigation. Yearb Med Inform 2009; 167–172Oakley J. Digital Imaging: A Primer for Radiographers, Radiologists and Health Care Professionals. Cambridge University Press, 2003.Prokosch HU, Dudeck J: Hospital information systems: Design and development characteristics, impact and future architecture. Elsevier health sciences, 1995Foster I, Kesselman C, Tuecke S. The anatomy of the grid: Enabling scalable virtual organizations. Int J High Perform Comput Appl 2001; 15(3):200–222.Oram A: Peer-to-Peer: Harnessing the power of disruptive technologies. O’Reilly Media, 2001National Institute of Standards and Technology. The NIST Definition of Cloud Computing. 2011. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (accessed 29 Jan 2013)Oster S, Langella S, Hastings S, Ervin D, Madduri R, Phillips J, Kurc T, Siebenlist F, Covitz P, Shanbhag K, Foster I, Saltz J. caGrid 1.0: An enterprise grid infrastructure for biomedical research. J Am Med Inform Assoc 2008; 15:138–149.Natter MD, Quan J, Ortiz DM, et al. An i2b2-based, generalizable, open source, self-scaling chronic disease registry. J Am Med Inform Assoc 2013; 20:172–179.Ohno-Machado L, Bafna V, Boxwala AA, et al. iDASH: Integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc 2012; 19:196–201.Channin DS, Mongkolwat P, Kleper V, Rubin DL. Computing human image annotation. Conf Proc IEEE Eng Med Biol Soc 2009; 1:7065–8.Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41(2):387–392.Wagholikar KB, Sundararajan V, Deshpande AW. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J Med Syst 2012; 36(5):3029–3049.Rubin DL. Creating and curating a terminology for radiology: Ontology modeling and analysis. J Digit Imaging 2008; 21(4):355–362.Kahn CE, Jr., Langlotz CP, Burnside ES, Carrino JA, Channin DS, Hovsepian DM, et al. Toward best practices in radiology reporting. Radiology 2009; 252(3):852–856.Taira PK, Soderlang SG, JAbovits RM. Automatic structuring of radiology free-text reports. Radiographics 2001; 21(1); 237–245.Fujii H, Yamagishi H, Ando Y, Tsukamoto N, Kawaguchi O, Kasamatsu T, et al. Structuring of free-text diagnostic report. Stud. Health Technol. Inform. 2007; 129: 669–673.Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011; 306(8):848–855.Clunie DA: DICOM structured reporting. PixelMed Publishing, 2000D’Avolio LW, Nguyen TM, Farwell WR, Chen Y, Fitzmeyer F, Harris OM, Fiore LD. Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). J Am Med Inform Assoc 2012; 17:375–382.Napel SA, Beaulieu CF, Redriguez C, Cui J, Xu J, Grupta A, et al. Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology 2010; 256(1): 243–252.Langlotz CP. RadLex: A new method for indexing online educational materials. Radiographics 2006; 26(6):1595–1597.Crestania F, Vegas J, de la Fuente P. A graphical user interface for the retrieval of hierarchically structured documents. Inf Process Manag 2004; 40(2):269–289.Weiss DL, Langlotz CP. Structured reporting: Patient care enhancement or productivity nightmare? Radiology 2008. 249(3):739–747.Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012; 19(3):413–422.Patrick R, Julien G, Christian L, Antoine G. Automatic medical encoding with SNOMED categories. BMC Med Inform Decis Mak 2008; 8(Suppl 1): S1–S6.Lopez-Garcia P, Boeker M, Illarramendi A, Schulz S. Usability-driven pruning of large ontologies: The case of SNOMED CT, J Am Med Inform Assoc 2012; 19:e102-e109.World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision. http://apps.who.int/classifications/apps/icd/icd10online/ (accessed 29 Jan 2013)American College of Radiology (ACR) Breast Imaging Reporting and Data System Atlas (BI-RADS® Atlas)World Health Organization. International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3). http://www.who.int/classifications/icd/adaptations/oncology/en/index.html (accessed 29 Jan 2013)Greene FL. TNM: Our language of cancer. CA Cancer J Clin 2004; 54(3):129–130.American Joint Committee of Cancer (AJCC). AJCC Cancer Staging Manual. Seventh Edition. Springer, 2010Hussein R, Engelmann U, Schroeter A, Meinzer HP. DICOM structured reporting: Part 1. Overview and characteristics, Radiographics 2004; 24(3):891–896.Sluis D, Lee KP, Mankovich N. DICOM SR - integrating structured data into clinical information systems. Medicamundi 2002; 46(2):31–36.Percha B, Nassif H, Lipson J, Burnside E, Rubin D. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc 2012; 19(5):913–916.Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, Catarzi S, Lamberini MP, Marcelli G, Pellizzoni R, Pesce B, Risso G, Russo F, Scorsolini A. Reader variability in reporting breast imaging according to BI-RADS assessment categories (the Florence experience). Breast 2006; 15(1):44–51.National Electrical Manufacturers Association (NEMA). Digital Imaging and Communications in Medicine (DICOM). Part 16: Content Mapping Resource. http://medical.nema.org/dicom/2004/04_16PU.PDF (accessed 29 Jan 2013)Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shvo AS. HL7 clinical document architecture, release 2. J Am Med Inform Assoc 2006; 13:30–39.Blanquer I, Hernández V, Meseguer JE, Segrelles D. Content-based organisation of virtual repositories of DICOM objects. Future Gener Comput Syst 2009; 25(6):627–637.Blanquer I, Hernández V, Segrelles D, Torres E. Enhancing privacy and authorization control scalability in the grid through ontologies. IEEE Trans Inf Technol Biomed 2009; 12(1):16–24.Salavert J, Maestre C, Segrelles D, Blanquer I, Hernández V, Medina R, Martí L: Grid prototype to support cancer of breast diagnostics in clinic practice. Proc of the 4th. Iberian Grid Infrastructure Conf. Netbiblo, 2010Segrelles D, Franco JM, Medina R, Blanquer I, Salavert J, Hernandez V, Martí L, Díaz G, Ramos R, Guevara MA, González N, Loureiro J, Ramos I. Exchanging data for breast cancer diagnosis on heterogeneous grid platforms. Computing and Informatics 2012; 31(1):3–15.Ali MS, Consens M, Lalmas M. Extended structural relevance framework: A framework for evaluating structured document retrieval. Inf Retrieval 2012; 15:558–590.Welter P, Riesmeier J, Fischer B, Grouls C, Kuhl C, Deserno, TM. Bridging the integration gap between imaging and information systems: A uniform data concept for content-based image retrieval in computer-aided diagnosis. J Am Med Inform Assoc 2011; 18:506–510.Jenkins CW. Application prototyping: A case study. Perform Eval Rev 1981; 10(1):21–27.Generalitat Valenciana. Conselleria de Sanitat. Oncoguía de Cáncer de Mama Comunidad Valenciana. http://publicaciones.san.gva.es/publicaciones/documentos/V.2478-2006.pdf (accessed 29 Jan 2013)Maestre C, Segrelles-Quilis JD, Torres E, Blanquer I, Medina R, Hernández V, Martí L. Assessing the usability of a science gateway for medical knowledge bases with TRENCADIS. J Grid Computing 2012; 10:665–688.Lewis J. IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. Int J Hum-Comput Interact 1995; 7(1):57–78.Lewis JR. Psychometric evaluation of the PSSUQ using data from five years of usability studies. Int J Hum-Comput Interact 2002; 14(3–4):463–488.Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika 1965; 52(3–4):591–611.Chhatwal J, Alagoz O, Lindstrom MJ, Kahn Jr CE, Shaffer KA, Burnside ES. A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. AJR Am J Roentgenol 2009; 192:1117–1127
    • …
    corecore