Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort

Artificial intelligence for prediction of donor liver allograft steatosis and early post-transplantation graft failure

BACKGROUND

Donor livers undergo subjective pathologist review of steatosis before transplantation to mitigate the risk for early allograft dysfunction (EAD). We developed an objective, computer vision artificial intelligence (CVAI) platform to score donor liver steatosis and compared its capability for predicting EAD against pathologist steatosis scores.

METHODS

Two pathologists scored digitized donor liver biopsy slides from 2014 to 2019. We trained four CVAI platforms with 1:99 training:prediction split. Mean intersection-over-union (IU) characterized CVAI model accuracy. We defined EAD using liver function tests within 1 week of transplantation. We calculated separate EAD logistic regression models with CVAI and pathologist steatosis and compared the models' discrimination and internal calibration.

RESULTS

From 90 liver biopsies, 25,494 images trained CVAI models yielding peak mean IU = 0.80. CVAI steatosis scores were lower than pathologist scores (median 3% vs 20%, P < 0.001). Among 41 transplanted grafts, 46% developed EAD. The median CVAI steatosis score was higher for those with EAD (2.9% vs 1.9%, P = 0.02). CVAI steatosis was independently associated with EAD after adjusting for donor age, donor diabetes, and MELD score (aOR = 1.34, 95%CI = 1.03-1.75, P = 0.03).

CONCLUSION

The CVAI steatosis EAD model demonstrated slightly better calibration than pathologist steatosis, meriting further investigation into which modality most accurately and reliably predicts post-transplantation outcomes.

CT radiomics nomogram for the preoperative prediction of severe post-hepatectomy liver failure in patients with huge (≥ 10 cm) hepatocellular carcinoma

Background

This study aimed to establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥ 10 cm) hepatocellular carcinoma (HCC).

Methods

One hundred eighty-six patients with huge HCC (training dataset, n = 131 and test dataset, n = 55) that underwent curative hepatic resection were included in this study. The least absolute shrinkage and selection operator (LASSO) approach was applied to develop a radiomics signature for grade B or C PHLF prediction using the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. Decision tree analysis was performed to stratify the risk for severe PHLF.

Results

The radiomics signature consisting of nine features predicted severe PHLF with AUCs of 0.766 and 0.745 for the training and test datasets. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and the model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination ability, with AUCs of 0.842 and 0.863 for the training and test datasets, respectively. Based on decision tree analysis, patients were divided into three risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or radiomics score ≥ − 0.247 but underwent partial resections; intermediate-risk patients with radiomics score < − 0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ − 0.247 and underwent extended resections.

Conclusions

The radiomics nomogram could predict severe PHLF in huge HCC patients. A decision tree may be useful in surgical decision-making for huge HCC hepatectomy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02459-0.

Ex vivo liver resection followed by autotransplantation in radical resection of gastric cancer liver metastases: A case report

BACKGROUND

Radical resection of gastric cancer liver metastases (GCLM) can increase the 5-year survival rate of GCLM patients. However, patients may lose the theoretical feasibility of surgery due to the critical location of liver metastasis in some cases.

CASE SUMMARY

A 29-year-old woman had a chief complaint of chronic abdominal pain for 1 year. Abdominal computed tomography and magnetic resonance imaging examinations suggested a mass of unknown pathological nature located between the first and second hila and the margin of the lower segment of the right lobe of the liver. The anterior wall of the gastric antrum was unevenly thickened. The diagnosis of (gastric antrum) intramucosal well-differentiated adenocarcinoma was histopathologically confirmed by puncture biopsy with gastroscopy guidance. She underwent radical resection (excision of both gastric tumors and ex vivo liver resection followed by autotransplantation simultaneously) followed by XELOX adjuvant chemotherapy. Without serious postoperative complications, the patient was successfully discharged on the 20^th day after the operation. Pathological examination of the excised specimen indicated that gastrectomy with D2 lymph node dissection for primary gastric tumors and R0 resection for liver metastases were achieved. The resected mass was confirmed to be poorly differentiated gastric carcinoma (hepatoid adenocarcinoma with neuroendocrine differentiation) with liver metastases in segments VIII. No recurrence or metastasis within the liver was found during a 7.5-year follow-up review that began 1 mo after surgery.

CONCLUSION

Application of ex vivo liver resection followed by autotransplantation in radical resection for GCLM can help selected patients with intrahepatic metastases located in complex sites obtain a favorable clinical outcome.

Remnant Liver-to-Standard Liver Volume Ratio Below 40% is Safe in Ex Vivo Liver Resection and Autotransplantation

BACKGROUND

The successful application of ex vivo liver resection and autotransplantation (ERAT) has gained widespread attention for the treatment of end-stage hepatic alveolar echinococcosis, which is considered to be unresectable by conventional methods due to extensive invasion of the extra- and intrahepatic vasculature. However, data on remnant liver volume (RLV) are limited, and the safe volume limit of remnant liver is still unclear.

METHODS

To determine the effect of liver volume in the technically developed era, we investigated the impact of the remnant liver-to-standard liver volume ratio (RLV/SLV) on the outcomes of ERAT.

RESULTS

From February 2014 to May 2018, 56 ERAT procedures were performed. Eleven patients with an RLV/SLV < 40% (group S) were compared with 45 patients with an RLV/SLV ≥ 40% (group L). Serial changes in postoperative serum total bilirubin, alanine aminotransferase, aspartate aminotransferase, and international normalized ratio were comparable in both groups. The incidences of postoperative complications did not significantly differ between the two groups. Three patients died of intra-abdominal bleeding, acute cerebral hemorrhage, and severe liver dysfunction. In RLV estimation analysis, the actual RLV and RLV/SLV were significantly smaller than the expected RLV and RLV/SLV as determined by preoperative three-dimensional reconstruction software in patients with hepatic venous outflow obstruction.

CONCLUSION

Patients with a smaller RLV/SLV did not have outcomes inferior to those with a larger RLV/SLV. Further studies are warranted to clarify the factors that contribute to preoperative volumetric estimation and the safe lower limits for ERAT.