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A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with

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Summary

A deep learning algorithm (DLA) demonstrated comparable performance to radiologists in detecting liver metastases (LM) in colorectal cancer (CRC) patients. This AI tool shows consistent accuracy across various lesion sizes and locations, aiding in oncology diagnostics.

Keywords:
AbdomenComparative StudiesDeep LearningDiagnosisGastrointestinalImaging ModalityLiverOncologySegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Colorectal cancer (CRC) frequently metastasizes to the liver, necessitating accurate detection for effective treatment.
  • Radiologist interpretation of liver metastases (LM) on CT scans can be challenging due to subtle lesions and inter-reader variability.
  • Deep learning algorithms (DLAs) show promise in medical image analysis, but their performance in detecting LM in diverse clinical settings requires thorough evaluation.

Purpose of the Study:

  • To assess the diagnostic performance of a DLA for detecting liver metastases (LM) in colorectal cancer (CRC) patients.
  • To compare the DLA's accuracy against senior and in-training radiologists.
  • To evaluate the DLA's consistency across different lesion sizes and locations.

Main Methods:

  • Retrospective, bicentric study of 181 CRC patients undergoing contrast-enhanced abdominal CT.
  • DLA performance evaluated at per-nodule and per-patient levels.
  • Comparison with two blinded radiologists (senior and in-training) regarding detection and false detection rates.

Main Results:

  • DLA achieved an 81% LM detection rate, comparable to senior (79%) and in-training (76%) radiologists.
  • Detection rates improved with lesion size (55% for <10mm, 91% for 10-19mm, 99% for ≥20mm).
  • False detection rates were low and comparable between DLA and radiologists, with common mimics identified.

Conclusions:

  • The DLA performs comparably to radiologists in detecting liver metastases in CRC patients.
  • The algorithm shows robust performance across varying lesion sizes and locations.
  • DLAs represent a valuable tool for augmenting radiologist capabilities in liver metastasis detection.