In this study we showed that laboratory tests reflecting hepatic function are closely related with hepatic enhancement during HBP. Using both quantitative and qualitative assessments, decreased serum levels of Alb, PLT, and elevated TB, PT-INR, CPS, MELD-Na score were related to decreased hepatic enhancement on HBP. Multivariate analyses revealed that increased TB and decreased Alb were significantly associated with decreased hepatic enhancement at HBP. We also used a machine learning algorithm to develop a predictive model for insufficient hepatic enhancement of HBP using a combination of biochemical parameters as well as CPS and MELD-Na, which are well-known scoring systems for reflecting reserved hepatic function. The prediction of insufficient hepatic enhancement during the HBP using KNN with a combination of biochemical parameters showed a higher accuracy and AUC than CPS or MELD-Na.
It would be useful if a simple visual MRI finding could determine the possibility of obscuring focal hepatic lesions due to insufficient enhancement of the background liver parenchyma. Measurements of LPR showed a strong relationship with LPVC grade, which was consistent results with a previous study by Tamada et al. 12. In this regard, we could easily identify insufficient hepatic enhancement during HBP. Furthermore, our results showed that both measurements of LPR and a 5-level assessment of hepatic enhancement provided similar patterns of association as the biochemical parameters.
Some previous studies have reported the association between liver function and insufficient enhancement during the HBP 9,11. Biochemical parameters commonly known to be associated with liver function are TB, ALT, ALP, GGT, Alb, PLT, and coagulation tests such as PT-INR 11,16−18. Our results were consistent with the previous studies. Insufficient hepatic enhancement during HBP was correlated with TB, Alb, PT-INR, platelet, and the 5-level degree of hepatic enhancement during HBP was correlated with TB, Alb, PT-INR, AST, ALP, GGT, Cr, and PLT levels. Multivariate analysis showed that Tb (OR = 4.71) and Alb (OR = 0.12) were the independent factors for predicting insufficient hepatic enhancement on HBP, which showed a stronger correlation than any other single parameter examined. Because GB-EOB-DTPA shares the same transport and excretion pathways as bilirubin, elevated serum TB is thought to reduce hepatocyte uptake and bile secretion of GB-EOB-DTPA 19. The decrease in serum Alb, which is known as a critical plasma protein produced by the liver, not only reflects the chronicity of liver disease but also has potential diagnostic value for determining a prognosis 20.
As shown in a previous study, many liver function tests and hepatic enhancement have a close relationship with HBP 21,22. However, because these liver function tests were all different, the combination of these parameters would be a better indicator than a single parameter 17. Several scoring systems have been accepted for assessment of reserved hepatic function in patients with liver cirrhosis, such as CPS, MELD, and MELD-Na scores 23–25. In our study, both CPS (r = -0.482, ρ = 0.643) and MELD-Na (r = -0.427, ρ = 0.535) were included among the high correlation factors concerning the LPR and the 5-level visual assessment of hepatic enhancement on HBP.
Machine learning makes it possible to train algorithms to discover and identify complex patterns and relationships within a variety of parameters by semi-automating the extraction of knowledge and insights from complex data. 26 In medicine, predictive studies based on machine learning are emerging and developed algorithms can be directly applied to patient care to improve the accuracy of predicting diseases and subsequent outcomes 27. Insufficient enhancement during the HBP can cause poor contrast differences between background liver parenchyma and a focal hepatic lesion without uptake of Gd-EOB-DTPA 7,28. If insufficient enhancement during the HBP can be predicted in advance, image quality can be improved by a delay of the time to obtain the HBP or modification of several sequence parameters such as flip angle and K-space 29,30. Therefore, it is clinically important to predict insufficient enhancement of liver parenchyma before performing Gd-EOB-DTPA-enhanced MRI. There have been studies to predict insufficient enhancement during the HBP using a traditional classification approach 9,11. Of our predictive models based on machine learning with the KNN algorithm, the model using a combination of biochemical parameters showed a higher accuracy of 82.8% and AUC of 0.86 than the models using CPS or MELD-Na. As mentioned above, because the various parameters reflecting liver function all work differently, a combination of the various parameters would more accurately determine hepatic enhancement on HBP. While the prediction of insufficient hepatic enhancement on HBP yielded a high diagnostic performance, classification of hepatic enhancement using a 5-level visual assessment showed incomplete results because of the small data set of unbalanced data.
There were some limitations in our study. First, this study was conducted retrospectively, and thus carries the potential for selection bias. Second, the model was developed from a small population in a single center and was not validated using another set of populations. Therefore, our results should be further validated through prospective studies in multiple institutions. Finally, we did not consider the degree of liver fibrosis, which also affects hepatic enhancement on HBP. Liver fibrosis was identified with an invasive biopsy procedure, which has the inherent problems of safety and some degree of sampling error. Nevertheless, research comparing the performance of the predictive models when integrating histopathologic results should be conducted in the future.
In conclusion, radiologists can predict insufficient hepatic enhancement during HBP in advance with a machine learning-based predictive model that uses the KNN algorithm to adjust each patient's individually optimized MRI protocol.