Spleen Radiomics Signature: A Potential Biomarker for Prediction of Early and Late Recurrences of Hepatocellular Carcinoma After Resection

Abstract

ObjectivesTo explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection.MethodsThis retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Cox regression analysis was performed to identify the independent risk factors of early and late recurrences. Then, multiple models were built to predict the recurrence-free survival of HCC after resection, and the incremental value of the radiomics signature to the clinicopathologic model was assessed and validated. Kaplan–Meier survival analysis was used to assess the association of the models with RFS.ResultsThe spleen radiomics signature was independent risk factor of early recurrence of HCC. The mixed model that integrated microvascular invasion, tumor radiomics signature and spleen radiomics signature for the prediction of early recurrence achieved the highest C-index of 0.780 (95% CI: 0.728,0.831) in the primary cohort and 0.776 (95% CI: 0.716,0.836) in the validation cohort, and presented better predictive performance than clinicopathological model and combined model. In the analysis of late recurrence, the spleen radiomics signature was the only prognostic factor associated with late recurrence of HCC.ConclusionsThe identified spleen radiomics signatures are prognostic factors of both early and late recurrences of HCC patients after surgery and improve the predictive performance of model for early recurrence.

The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy

“…Research in spleen radiomics is limited. Li et al 13 investigated the value of splenic radiomic features in predicting recurrence after radical resection of hepatocellular carcinoma and found that a hybrid model of microvascular invasion, tumor radiomic features, and splenic radiomic features for predicting early recurrence achieved the highest C‐index values of 0.780 (95% CI: 0.728–0.831) and 0.776 (95% CI: 0.716–0.836) for the training and validation groups, respectively. On analyzing late recurrence, splenic radiomic features were the only prognostic factors associated with the recurrence of advanced hepatocellular carcinoma.…”

“…7,8 It is now extensively used to predict the survival prognosis and treatment efficacy of patients with EC. [9][10][11][12] Previous studies have reported that spleen radiomics predicts prognosis in patients with gastric cancer and recurrence in patients with hepatocellular carcinoma 13,14 ; however, none of them further explored its relationship with systemic immunity. We hypothesized that the spleen is a vital part of the immune system and, to some extent, reflects the systemic immunity of the organism.…”

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

“…Similarly, the integration of features extracted from multiple organs proved advantageous. Li et al successfully improved the model’s performance in predicting early and late recurrence of hepatocellular carcinoma after resection by integrating splenic and liver features [ 23 ].…”

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly

“…A recent study, for example, showed that splenic radiomics features on CT can predict the prognosis of gastric cancer patients [ 33 ]. Another study used the spleen’s radiomics signature on CT to predict the recurrence of HCC [ 34 ]. Additionally, considering lymphoma, Enke et al developed a radiomics model on CT to differentiate between spleen involvement versus controls, as well as to discriminate different subtypes of malignant lymphoma [ 35 ].…”