Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram

Abstract

Background: Predicting early recurrence (ER) after radical therapy for HCC patients is critical for the decision of subsequent follow-up and treatment. Radiomic features derived from the medical imaging show great potential to predict prognosis. Here we aim to develop and validate a radiomics nomogram that could predict ER after curative ablation. Methods: Total 184 HCC patients treated from August 2007 to August 2014 were included in the study and were divided into the training (n = 129) and validation(n = 55) cohorts randomly. The endpoint was recurrence free survival (RFS). A set of 647 radiomics features were extracted from the 3 phases contrast enhanced computed tomography (CECT) images. The minimum redundancy maximum relevance algorithm (MRMRA) was used for feature selection. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to build a radiomics signature. Recurrence prediction models were built using clinicopathological factors and radiomics signature, and a prognostic nomogram was developed and validated by calibration. Results: Among the four radiomics models, the portal venous phase model obtained the best performance in the validation subgroup (C-index = 0.736 (95%CI:0.726-0.856)). When adding the clinicopathological factors to the models, the portal venous phase combined model also yielded the best predictive performance for training (Cindex = 0.792(95%CI:0.727-0.857) and validation (C-index = 0.755(95%CI:0.651-0.860) subgroup. The combined model indicated a more distinct improvement of predictive power than the simple clinical model (ANOVA, P < 0. 0001). Conclusions: This study successfully built a radiomics nomogram that integrated clinicopathological and radiomics features, which can be potentially used to predict ER after curative ablation for HCC patients.

Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study

“…Inclusion of additional parameters in the prediction model could potentially further increase its performance. This could not only include clinical parameters that have already demonstrated predictive value, such as postembolization syndrome after first TACE, but also novel predictive parameters such as texture analysis . Potentially, a combination of our ANN with a convolutional neural network using pattern recognition might further enhance prediction.…”

“…Moreover, some variables were not included because they were not available for all patients, for example, most patients lacked tumour grading and status of small vessel infiltration because they were diagnosed non‐invasively. Furthermore, it may be possible that the inclusion of other more advanced parameters could further enhance prediction, for example, radiomic data including texture analysis …”

“…Furthermore, it may be possible that the inclusion of other more advanced parameters could further enhance prediction, for example, radiomic data including texture analysis. [45][46][47] Another possible reason of bias could be the large period of data recruitment comprising 13 years. Meanwhile, several technical improvements were made for TACE.…”

“…This could not only include clinical parameters that have already demonstrated predictive value, such as postembolization syndrome after first TACE, 50 but also novel predictive parameters such as texture analysis. [45][46][47] Potentially, a combination of our ANN with a convolutional neural network using pattern recognition might further enhance prediction. However, to avoid the problem of overfitting and to enhance generalizability, the network needs to be built on a broader database including clinical data from several institutions.…”

Radiomics and Deep Learning: Hepatic Applications

“…Radiomics has been applied to determine the prognosis of hepatocellular carcinoma (HCC) after radiofrequency ablation (31,32), surgical resection (13,22,25,31,33), and liver transplantation (20). Zheng et al (13) developed nomograms incorporating CT-based radiomics and clinical variables to predict recurrence-free and overall survival outcomes after surgical resection of solitary HCC and reported that these nomograms had better prognostic performance than traditional staging.…”