Liver resection remains the mainstay modality in the treatment of hepatocellular carcinoma (HCC) with a curative intent. With the advances of surgical techniques and perioperative management in recent years, cases of extended or complex liver resection are increasing (1), which makes it increasingly important to make individual evaluations to avoid insufficient remnant liver volumes and impaired liver function after the surgery, the so-called post-hepatectomy liver failure (PHLF). At present, PHLF poses a fatal threat after liver resection and is the prominent cause of perioperative mortality (2), with a reported incidence as high as 40% (3).

Precise evaluation of liver function makes it possible to predict PHLF preoperatively. Previous studies have explored blood biochemistry tests, indocyanine green (ICG) test (4), and clinical scoring systems such as Child–Pugh score (5) and the Model for End-Stage Liver Disease (MELD) score (6) and computed tomography (CT)-based remnant liver volume (7) in the prediction of PHLF. However, the overall performance of these factors has been suboptimal. A more accurate, non-invasive approach for comprehensive liver function evaluation is urgently needed.

Gadoxetic acid (Primovist®) is a T1 magnetic resonance imaging (MRI) contrast medium widely used in clinical practice for liver lesion detection and characterization. Compared with the extracellular contrast media, it is actively taken up by hepatocyte at 10–40 min after administration (the so-called hepatobiliary phase) (8). Recent studies have shown that gadoxetic acid–enhanced MRI is promising in quantitative evaluation of liver function (9, 10). Classically, the methods used are based on the measurement of signal intensity (for instance relative liver enhancement or liver-to-muscle ratio or liver-to-spleen ratio), T1 relaxometry (e.g., T1 reduction rate), or dynamic contrast–enhanced MRI parameters (including hepatic extraction fraction) (11). These gadoxetic acid–enhanced MRI-derived parameters have shown a good correlation with ICG test and clinical scoring systems (Child–Pugh grades and MELD score), indicating a potential value in prediction of PHLF (10, 12, 13). However, when measuring signal intensity or T1 relaxation time, regions of interest (ROIs) with a limited diameter are most often placed in a single selected slice, which may not fully represent the whole liver function. Furthermore, the placement of the ROI is subjective, potentially reducing the reproducibility. In addition, the measurement of T1 relaxation time or dynamic contrast–enhanced MRI often requires additional scanning sequences (14).

Radiomics is a burgeoning technique, which can extract a great number of features from clinical routine medical imaging and transform them into mineable data for quantitative analysis (15). The basic assumption of radiomics is that the delicate pathophysiological alterations at cellular or molecular levels can be reflected by signal changes on images. The quantification of these imaging features and analyzing them through advanced algorithms or deep learning techniques can aid the clinician to solve clinical issues, such as disease diagnosis, prognosis, or prediction of treatment response. In the field of hepatobiliary imaging, previous studies have demonstrated that radiomics can significantly improve diagnostic and prognostic accuracy in HCC, such as the prediction of microvascular invasion (15), tumor differentiation (16), and early recurrence after hepatectomy (17).

In this study, it was tested whether radiomics analysis of gadoxetic-enhanced MR images can be used to predict PHLF in patients undergoing surgery due to HCC. The hypothesis was that radiomics analysis can detect delicate imaging features reflecting varying levels of liver function.

To predict PHLF in patients with HCC, we developed and validated a nomogram model combining two clinicopathological variables (ALBI score and ICG-R15) and one radiomics variable (Rad-score) derived from radiomics analysis of preoperative T1-weighted gadoxetic acid–enhanced MRI. This prediction model yielded an AUC of 0.82 in the test cohort, indicating a promising tool for clinical utility.

Until now, only few studies have explored radiomics for prediction of PHLF. Zhu et al. proposed a nomogram model including ICG-R15 and radiomics signature based on hepatobiliary phase of gadoxetic acid–enhanced MRI from 101 patients (25). The model yielded an AUC of 0.89 in prediction PHLF in patients undergoing major liver resection. However, the study did not further validate the model in an independent test cohort. Chen et al. developed a combined model incorporating platelet, tumor size, and radiomics score deriving from preoperative gadoxetic acid–enhanced MRI for predicting PHLF (26). They validated the model at another medical center, obtaining an AUC of 0.84. However, in that study, they did not present their model with a formula or nomogram, which made it hard to reproduce or translate their model into clinical utility. In addition, their radiomics analysis was based on a single MRI slice per patient, which may not fully reflect the liver function. There are also two radiomics models based on CT modality for PHLF prediction (27, 28). Those studies had a rather limited sample size (112 and 186 cases), which may explain the unusual outcome with AUCs in their respective test cohorts higher than that in their training cohorts (27, 28).

The Rad-model alone showed an effective prediction efficacy, almost comparable to our combined model. A majority of the radiomics features in the Rad-model (13/16) belonged to wavelet-derived features. Those described low and high frequency signals, representing homogeneity and heterogeneity of the liver tissue (29). Unfortunately, the two previously published studies on radiomics of hepatobiliary phase of gadoxetic acid–enhanced MRI, by Zhu and Chen as mentioned above, did not adopt wavelet filter, so it is not possible to make comparisons regarding the specific radiomics features. However, wavelet-derived features do frequently appear in other gadoxetic acid–enhanced MRI radiomics models, for instance, in the prediction of microvascular invasion (30) or tumor grading for HCC (31), indicating that they capture important structural features of hepatic tumor or parenchyma (14).

Another variable in our prediction model is ICG-R15. This was consistent with Zhu’s PHLF prediction model, in which ICG-R15 was the only clinical predictor (25). Currently, ICG-R15 still serves as a reference standard in the quantitative evaluation of liver function before liver resection and plays an essential role in treatment management of HCC patients (32, 33). Nevertheless, the role of ICG test as an independent risk factor for PHLF prediction remains controversial, as it can be influenced by many factors, such as blood flow or hyperbilirubinemia (32, 34). This might explain why only approximately half of currently available studies (5/11) could successfully use ICG to predict PHLF as shown in a systematnic review (3).

Our model also consists of a predictor of ALBI grade, which is a simple and objective scoring system adopting just two common biochemistry tests (serum albumin and bilirubin) for quantitative evaluation of liver function in HCC patients (19). It was proposed to overcome the limitation of the conventional Child–Pugh scoring system and has proven to be a reliable, effective tool for liver function evaluation, applicable in several different geographic regions (19). Xiang et al. have shown that ALBI could predict PHLF with an AUC of 0.64 in the test cohort (28). In the multivariable regression analysis for Clin-model, ALBI grade demonstrated an independent risk factor for PHLF incidence (odds ratio: 3.2, Grade 2/3 vs. Grade 1). However, neither Child–Pugh nor MELD score was a significant risk factor for PHLF prediction in our cohort.

This research has some limitations to be acknowledged. First, the retrospective nature of this study bore incoherent selection biases that could have had an impact on the results. However, this issue was partially compensated via inclusion of consecutive patients. Second, our model was not validated in an external cohort. Additional validation in larger prospective multicenter cohorts is warranted to generalize our prediction model. Third, the radiomics analysis was performed based on the whole normal liver parenchyma, rather than the future liver remnant (FLR) only. A study based on FLR only might show better AUC than presented here. The large AUC observed in our study might be explained by a strong relationship in radiomics between FLR and resected part. Lastly, the resection extent was not included in our prediction model, as it was not significant during univariable logistic regression analysis. Traditionally, the hepatic resection extent is regarded as an important indicator for PHLF. However, its role may be impaired with the development of surgical concepts and skills, equipment, perioperative management, and anesthesia techniques. Currently, the occurrence of PHLF is assumed as a consequence of multiple clinicopathological factors during the perioperative period, including baseline liver/patient characteristics and intraoperative and postoperative factors (35). Interestingly, among the four existing studies that developed the radiomics models for PHLF prediction using preoperative imaging (25–28), only one study detected the resection extent significant and included it in their model (28). Furthermore, we compared the difference in the prediction performance between our proposed models with and without the variable of the resection extent, and the test showed an insignificant result ( Supplementary Table S3 ). Due to the simplicity principle, this variable was not included in our final models. Future studies can further investigate the effect of this variable on the incidence of PHLF.

In conclusion, a prediction nomogram combining clinical risk factors and radiomics signature based on preoperative gadoxetic acid–enhanced MRI was constructed, and it can potentially be an effective tool for predicting liver failure after liver resection in patients with hepatocellular carcinoma.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

The studies involving human participants were reviewed and approved by The Institutional Review Board of Southwest Hospital, Army Medical University. The ethics committee waived the requirement of written informed consent for participation.

Conceptualization: KM and QW. Data curation: CL, QW, PC, MZ, XL, KF, and LZ. Formal analysis: CL, QW, and KM. Funding acquisition: KM and QW. Investigation: CL, MZ, XL, and KF. Methodology: KM, QW, and TB. Project administration: KM and LZ. Software: PC and XL. Supervision: KM, TB, LZ, and ES. Writing—original draft: CL and QW. Writing—review and editing: all authors. All authors contributed to the article and approved the submitted version.