Pathology notification for a Cancer Registry is regarded as the most valid information for the confirmation of a diagnosis of cancer. The development of a clinical decision support system to unlock information from medical free-text can significantly reduce costs arising from manual processes and enable improved decision support, enhancing efficiency and timeliness of cancer information for Cancer Registries.

The Challenge

Medtex aids Cancer Registry tasks with the notification of cancer reports and the coding of notifications data. The system automatically scans HL7 messages and analyses the free-text reports for terms and concepts relevant to cancer.

Our Solution

Our automated classification of pathology reports that are notifiable cancers is highly effective: sensitivity of 98% and specificity of 96% [1]. The coding of specific cancer notification items such as basis of diagnosis, histological type and grade, primary site and laterality can also be accurately extracted (80% accuracy [2-3]). In the case of lung cancer staging, positive results were achieved after a formal trial on lung cancer cases comparing the stages it assigned with those given by expert pathologists [4-5]. Medtex also allows for detailed tumour stream synoptic and stage reporting [6].

This software has been developed in conjunction with the Queensland Cancer Control Analysis Team, Queensland Health. For more information, contact Dr. Anthony Nguyen.

The Medtex software processes narrative reports and generates structured data to aid clinical staff in abstraction tasks.

The Medtex software processes narrative reports and generates structured data to aid clinical staff in abstraction tasks.

 

  1. Nguyen A, Moore J, Zuccon G, Lawley M, Colquist S, “Classification of Pathology Reports for Cancer Notifications,” Studies in health technology and informatics, 2012; 150-156.
  2. Nguyen A, Moore J, Lawley M, Hansen D, Colquist S. Automatic Extraction of Cancer Characteristics from Free-Text Pathology Reports for Cancer Notifications. Studies in health technology and informatics, 2011; 117-124.
  3. Nguyen A, Moore , O’Dwyer J, Philpot Assessing the Utility of Automatic Cancer Registry Notifications Data Extraction from Free-Text Pathology Reports. AMIA 2015 Annual Symposium, 2015.
  4. Nguyen A, Lawley M, Hansen D, et al. Symbolic rule-based classification of lung cancer stages from free-text pathology reports. J Am Med Inform Assoc. 2010;17(4):440-445.
  5. McCowan I, Moore D, Nguyen A, Bowman R, Clarke B, Duhig E, et al. Collection of cancer stage data by classifying free-text medical reports. J Am Med Inform Assoc. 2007 Nov/Dec;14(6):736–745.
  6. Nguyen A, Lawley M, Hansen D, Colquist S, “Structured Pathology Reporting for Cancer from Free Text: Lung Cancer Case Study”, eJHI, 7(1): e8, 2012