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

Authors :
Hsieh, Scott S.
Inoue, Akitoshi
Yalon, Mariana
Cook, David A.
Fidler, Jeff L.
Gong, Hao
Pillai, Parvathy Sudhir
Vercnocke, Andrew J
Johnson, Matthew P.
Leng, Shuai
Yu, Lifeng
Holmes, David R.
Carter, Rickey E.
McCollough, Cynthia H.
Fletcher, Joel G.
Source :
Proc SPIE Int Soc Opt Eng
Publication Year :
2023
Publisher :
SPIE, 2023.

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, 8 non-abdominal staff, and 8 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) 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.

Subjects

Subjects :
Article

Details

Database :
OpenAIRE
Journal :
Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment
Accession number :
edsair.doi.dedup.....afb2b105ffca49042b6cc4460f263abb