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

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

Individualización de Carboplatino en el anciano con cáncer de pulmón no microcítico avanzado

Non small cell lung cancer (NSCLC) is frequently diagnosed in patients older than age 65 years. Elderly patients often have comorbidities associated with the antineoplasic treatment that request individualization of the chemotherapy. Treatment options are numerous and carboplatin (CbPt) is in the first line of treatment. Conventional doses of CbPt are individually adjusted applying the Calvert formulae (standar) that demands the accurate measure of renal function. The aim of this study is to develop a pharmacokinetic model in order to individualise the dose of CbPt in elderly patients in advanced NSCLC, and to characterize its bias and precision respect to the standard. The pharmacokinetic models for the unbound fraction of CpPt were obtained from concentration-time data of ultrafiltrate plasma samples of twenty-four advanced NSCLC men patients enrolled in the study. Age was significantly related to the carboplatin clearance, although is a confusion factor. The mean dose error, in percentage, was 5% (1-9%) in adult patients (Age< 65 years) and 25% (19-30%) in elderly patients. Consequently, CbPt the dose regimen in enderly patients, established by means of Calvert’s formula is overestimated and the exposure to the antineoplastic is higher than desired. The clinical relevance of these results requires the validation of the model with a new population group.El cáncer de pulmón no microcítico (CPNM) se diagnostica mayoritariamente en pacientes mayores de 65 años. Los pacientes ancianos presentan una elevada comorbilidad asociada al tratamiento antineoplásico que demanda la individualización de las pautas posológicas. Las opciones de tratamiento son abundantes y el carboplatino (CbPt) se encuentra entre los fármacos de primera línea. La dosis de CbPt se establece con la fórmula de Calvert (estándar) que requiere la medida exacta de la función renal. El objetivo de este trabajo es aportar un modelo farmacocinético que permita individualizar las dosis de CbPt en ancianos con CPNM avanzado y evaluar su exactitud y precisión respecto al estándar. Los modelos farmacocinéticos para el CbPt no unido a las proteínas plasmáticas, obtenidos con las concentraciones plasmáticas de una población de 24 pacientes varones con CPNM, indican que la edad es la covariable biométrica más estrechamente relacionada con el aclaramiento plasmático de CbPt, sin dejar por ello de ser un factor de confusión. El error relativo medio (ERM) de la dosis ha sido para los pacientes adultos (edad < 65 años) del 5% (1-9%) y para los pacientes ancianos del 25% (19-30%). Por consiguiente, la dosificación de CbPt con la fórmula de Calvert conduce a una sobredosificación en los pacientes ancianos, produciendo mayor exposición al fármaco de la deseada. El alcance clínico de estos hallazgos requiere su validación en una nueva población de pacientes

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

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