Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1

Leo Joskowicz, Adi Szeskin, Shalom Rochman, Aviv Dodi, Richard Lederman, Hila Fruchtman-Brot, Yusef Azraq, Jacob Sosna*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning–based volumetric lesion changes analysis. Methods: A total of 86 abdominal CT studies from 43 patients (prior and current scans) of abdominal CT scans of patients with 1041 liver metastases (mean = 12.1, std = 11.9, range 1–49) were analyzed. Two radiologists performed readings of all pairs; conventional with RECIST 1.1 and with computer-aided assessment. For computer-aided reading, we used a novel simultaneous multi-channel 3D R2U-Net classifier trained and validated on other scans. The reference was established by having an expert radiologist validate the computed lesion detection and segmentation. The results were then verified and modified as needed by another independent radiologist. The primary outcome measure was the disease status assessment with the conventional and the computer-aided readings with respect to the reference. Results: For conventional and computer-aided reading, there was a difference in disease status classification in 40 out of 86 (46.51%) and 10 out of 86 (27.9%) CT studies with respect to the reference, respectively. Computer-aided reading improved conventional reading in 30 CT studies by 34.5% for two readers (23.2% and 46.51%) with respect to the reference standard. The main reason for the difference between the two readings was lesion volume differences (p = 0.01). Conclusions: AI-based computer-aided analysis of liver metastases may improve the accuracy of the evaluation of neoplastic liver disease status. Clinical relevance statement: AI may aid radiologists to improve the accuracy of evaluating changes over time in metastasis of the liver. Key Points: • Classification of liver metastasis changes improved significantly in one-third of the cases with an automatically generated comprehensive lesion and lesion changes report. • Simultaneous deep learning changes detection and volumetric assessment may improve the evaluation of liver metastases temporal changes potentially improving disease management.

Original languageAmerican English
Pages (from-to)9320-9327
Number of pages8
JournalEuropean Radiology
Volume33
Issue number12
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to European Society of Radiology.

Keywords

  • Follow-up studies
  • Liver metastases
  • Response evaluation criteria in solid tumors

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