Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm

Eduardo Pontes Reis; Louis Blankemeier; Juan Manuel Zambrano Chaves; Malte Engmann Kjeldskov Jensen; Sally Yao; Cesar Augusto Madid Truyts; Marc H Willis; Scott Adams; Edson Amaro Jr; Robert D Boutin; Akshay S Chaudhari

doi:10.1007/s00330-024-10769-6

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm

Eur Radiol. 2024 Apr 29. doi: 10.1007/s00330-024-10769-6. Online ahead of print.

Authors

Affiliations

¹ Department of Radiology, Stanford University, Stanford, CA, USA. eduardo.reis@einstein.br.
² Center for Artificial Intelligence in Medicine & Imaging (AIMI), Stanford University, Stanford, CA, USA. eduardo.reis@einstein.br.
³ Hospital Israelita Albert Einstein, Sao Paulo, Brazil. eduardo.reis@einstein.br.
⁴ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
⁵ Department of Radiology, Stanford University, Stanford, CA, USA.
⁶ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
⁷ Hospital Israelita Albert Einstein, Sao Paulo, Brazil.

PMID: 38683384
DOI: 10.1007/s00330-024-10769-6

Abstract

Objectives: To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans.

Materials and methods: Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT".

Results: The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification.

Conclusion: An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability.

Clinical relevance statement: Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging.

Key points: Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.

Keywords: Abdomen; Artificial intelligence; Contrast media; Machine learning; Radiomics.