A training program to reduce reader search errors for liver metastasis detection in CT

Scott S. Hsieh, Akitoshi Inoue, Mariana Yalon, David A. Cook, Jeff L. Fidler, Hao Gong, Parvathy Sudhir Pillai, Andrew J. Vercnocke, Matthew P. Johnson, Shuai Leng, Lifeng Yu, David R. Holmes, Rickey E. Carter, Cynthia H. McCollough, Joel G. Fletcher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detection of low contrast liver metastases varies between radiologists. Training may improve performance for lower-performing readers and reduce inter-radiologist variability. We recruited 31 radiologists (15 trainees, eight non-abdominal staff, and eight abdominal staff) to participate in four separate reading sessions: pre-test, search training, classification training, and post-test. In the pre-test, each radiologist interpreted 40 liver CT exams containing 91 metastases, circumscribed suspected hepatic metastases while under eye tracker observation, and rated confidence. In search training, radiologists interpreted a separate set of 30 liver CT exams while receiving eye tracker feedback and after coaching to increase use of coronal reformations, interpretation time, and use of liver windows. In classification training, radiologists interpreted up to 100 liver CT image patches, most with benign or malignant lesions, and compared their annotations to ground truth. Post-test was identical to pre-test. Between pre- and post-test, sensitivity increased by 2.8% (p = 0.01) but AUC did not change significantly. Missed metastases were classified as search errors (<2 seconds gaze time) or classification errors (>2 seconds gaze time) using the eye tracker. Out of 2775 possible detections, search errors decreased (10.8% to 8.1%; p < 0.01) but classification errors were unchanged (5.7% vs 5.7%). When stratified by difficulty, easier metastases showed larger reductions in search errors: for metastases with average sensitivity of 0-50%, 50-90%, and 90-100%, reductions in search errors were 16%, 35%, and 58%, respectively. The training program studied here may be able to improve radiologist performance by reducing errors but not classification errors.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2023
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsClaudia R. Mello-Thoms, Yan Chen
PublisherSPIE
ISBN (Electronic)9781510660397
DOIs
StatePublished - 2023
EventMedical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States
Duration: Feb 21 2023Feb 23 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12467
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment
Country/TerritoryUnited States
CitySan Diego
Period2/21/232/23/23

Keywords

  • Eye tracking
  • low contrast detectability
  • reader performance
  • reader training

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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