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Application of a machine learning approach to characterization of liver function using 99mTc-GSA SPECT/CT

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To assess the utility of a machine-learning approach for predicting liver function based on technetium-99 m-galactosyl serum albumin (99mTc-GSA) single photon emission computed tomography (SPECT)/CT.

Methods

One hundred twenty-eight patients underwent a 99mTc-GSA SPECT/CT-based liver function evaluation. All were classified into the low liver-damage or high liver-damage group. Four clinical (age, sex, background liver disease and histological type) and 8 quantitative 99mTc-GSA SPECT/CT features (receptor index [LHL15], clearance index [HH15], liver-SUVmax, liver-SUVmean, heart-SUVmax, metabolic volume of liver [MVL], total lesion GSA [TL-GSA, liver-SUVmean × MVL] and SUVmax ratio [liver-SUVmax/heart-SUVmax]) were obtained. To predict high liver damage, a machine learning classification with features selection based on Gini impurity and principal component analysis (PCA) were performed using a support vector machine and a random forest (RF) with a five-fold cross-validation scheme. To overcome imbalanced data, stratified sampling was used. The ability to predict high liver damage was evaluated using a receiver operating characteristic (ROC) curve analysis.

Results

Four indices (LHL15, HH15, heart SUVmax and SUVmax ratio) yielded high areas under the ROC curves (AUCs) for predicting high liver damage (range: 0.89–0.93). In a machine learning classification, the RF with selected features (heart SUVmax, SUVmax ratio, LHL15, HH15, and background liver disease) and PCA model yielded the best performance for predicting high liver damage (AUC = 0.956, sensitivity = 96.3%, specificity = 90.0%, accuracy = 91.4%).

Conclusion

A machine-learning approach based on clinical and quantitative 99mTc-GSA SPECT/CT parameters might be useful for predicting liver function.

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Authors and Affiliations

  1. Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan

    Masatoyo Nakajo, Megumi Jinguji, Atsushi Tani, Hiroaki Nagano, Koji Takumi & Takashi Yoshiura

  2. Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan

    Daisuke Hirahara

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  1. Masatoyo Nakajo
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  2. Megumi Jinguji
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  3. Atsushi Tani
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  4. Daisuke Hirahara
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  5. Hiroaki Nagano
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Correspondence to Masatoyo Nakajo.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Nakajo, M., Jinguji, M., Tani, A. et al. Application of a machine learning approach to characterization of liver function using 99mTc-GSA SPECT/CT. Abdom Radiol 46, 3184–3192 (2021). https://doi.org/10.1007/s00261-021-02985-1

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  • DOI: https://doi.org/10.1007/s00261-021-02985-1

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