Contextual structured annotations on PACS: a futuristic vision for reporting routine oncologic imaging studies and its potential to transform clinical work and research.

Journal: Abdominal radiology (New York)
Published Date: Jul 26, 2025

Abstract

Radiologists currently have very limited and time-consuming options to annotate findings on the images and are mostly limited to arrows, calipers and lines to annotate any type of findings on most PACS systems. We propose a framework placing encoded, transferable, highly contextual structured text annotations directly on PACS images indicating the type of lesion, level of suspicion, location, lesion measurement, and TNM status for malignant lesions, along with automated integration of this information into the radiology report. This approach offers a one-stop solution to generate radiology reports that are easily understood by other radiologists, patient care providers, patients, and machines while reducing the effort needed to dictate a detailed radiology report and minimizing speech recognition errors. It also provides a framework for automated generation of large volume high quality annotated data sets for machine learning algorithms from daily work of radiologists. Enabling voice dictation of these contextual annotations directly into PACS similar to voice enabled Google search will further enhance the user experience. Wider adaptation of contextualized structured annotations in the future can facilitate studies understanding the temporal evolution of different tumor lesions across multiple lines of treatment and early detection of asynchronous response/areas of treatment failure. We present a futuristic vision, and solution with the potential to transform clinical work and research in oncologic imaging.

Authors

  • Vincenzo K Wong
    From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S., V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University Medical Center, Durham, NC (E.S.).
  • Mindy X Wang
    Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, 1515 Holcomb Blvd., Unit 1473, Houston, TX, 77030, USA.
  • Eesha Bethi
    College of Arts and Sciences, Community Health and Biology Major, Tufts University, 419 Boston Avenue, Medford, 02155, USA.
  • Sindhu Nagarakanti
    Honors Program, 2025, College of Liberal Arts, Neuroscience Major, Temple University, 1801 N. Broad St., Philadelphia, PA, 19122, USA.
  • Ajaykumar C Morani
    Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009.
  • Leonardo P Marcal
    Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, 1515 Holcomb Blvd., Unit 1473, Houston, TX, 77030, USA.
  • Gaiane M Rauch
    Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. gmrauch@mdanderson.org.
  • Jeffrey J Brown
    Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, 1515 Holcomb Blvd., Unit 1473, Houston, TX, 77030, USA.
  • Sireesha Yedururi
    From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals