Development and Validation of an Algorithm to Identify Patients with Advanced Cutaneous Squamous Cell Carcinoma from Pathology Reports.

[1]  M. Louwman,et al.  Cumulative incidence and disease-specific survival of metastatic cutaneous squamous cell carcinoma: a nationwide cancer registry study. , 2021, Journal of the American Academy of Dermatology.

[2]  H. Feng,et al.  Invasive and in situ squamous cell carcinoma of the skin: a nationwide study in Iceland , 2021, The British journal of dermatology.

[3]  M. Wehner.  Underestimation of Cutaneous Squamous Cell Carcinoma Incidence, Even in Cancer Registries. , 2020, JAMA dermatology.

[4]  T. Nijsten,et al.  Incidence of Multiple vs First Cutaneous Squamous Cell Carcinoma on a Nationwide Scale and Estimation of Future Incidences of Cutaneous Squamous Cell Carcinoma. , 2020, JAMA dermatology.

[5]  D. Whiteman,et al.  Web Application for the Automated Extraction of Diagnosis and Site From Pathology Reports for Keratinocyte Cancers , 2020, JCO clinical cancer informatics.

[6]  M. Falk,et al.  Actinic Keratosis Diagnosis and Increased Risk of Developing Skin Cancer: A 10-year Cohort Study of 17,651 Patients in Sweden , 2020, Acta dermato-venereologica.

[7]  A. Stang,et al.  Incidence and mortality for cutaneous squamous cell carcinoma: comparison across three continents , 2019, Journal of the European Academy of Dermatology and Venereology : JEADV.

[8]  I. Nagtegaal,et al.  Identification of barriers and facilitators in nationwide implementation of standardized structured reporting in pathology: a mixed method study , 2019, Virchows Archiv.

[9]  S. Langan,et al.  Epidemiology of basal and cutaneous squamous cell carcinoma in the U.K. 2013–15: a cohort study , 2019, The British journal of dermatology.

[10]  J. Meeks,et al.  Automated Extraction of Grade, Stage, and Quality Information From Transurethral Resection of Bladder Tumor Pathology Reports Using Natural Language Processing. , 2018, JCO clinical cancer informatics.

[11]  Anthony N. Nguyen,et al.  Assessing the Utility of Automatic Cancer Registry Notifications Data Extraction from Free-Text Pathology Reports , 2015, AMIA.

[12]  Goran Nenadic,et al.  Text mining of cancer-related information: Review of current status and future directions , 2014, Int. J. Medical Informatics.

[13]  Nicolette de Keizer,et al.  A survey of SNOMED CT implementations , 2012, Journal of Biomedical Informatics.

[14]  J. Leonardi-Bee,et al.  A systematic review of worldwide incidence of nonmelanoma skin cancer , 2012, The British journal of dermatology.

[15]  C. Johnson,et al.  Validation of claims data algorithms to identify nonmelanoma skin cancer , 2012, The Journal of investigative dermatology.

[16]  Fernanda Polubriaginof,et al.  The feasibility of using natural language processing to extract clinical information from breast pathology reports , 2012, Journal of pathology informatics.

[17]  A Burgun,et al.  Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer , 2011, Methods of Information in Medicine.

[18]  James W. Cooper,et al.  Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model , 2009, J. Biomed. Informatics.

[19]  David A Hanauer,et al.  The registry case finding engine: an automated tool to identify cancer cases from unstructured, free-text pathology reports and clinical notes. , 2007, Journal of the American College of Surgeons.

[20]  M. Casparie,et al.  Pathology Databanking and Biobanking in The Netherlands, a Central Role for PALGA, the Nationwide Histopathology and Cytopathology Data Network and Archive , 2007, Cellular oncology : the official journal of the International Society for Cellular Oncology.

[21]  John Liu,et al.  Automated Extraction of Free-Text from Pathology Reports , 2006, AMIA.