Synopsis

Keywords: Liver, Cancer, Hepatocellular carcinoma · Neoplasm invasion

• The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction.• An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE.• The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.

Objectives

To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE).

Methods

A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram incorporated the radiomics signature and radiological predictors was developed. The performance of radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score (m-score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE.

Results

A radiomics nomogram integratied with the radiomics signature, max-D(iameter) >5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC=0.982), with an accuracy of 93.6%, sensitivity of 94.1%, and specificity of 93.3%. Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group (P=0.006) with no significant effect in the low-risk group (P=0.270).

Conclusions

The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patients benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions.

Acknowledgements

The author (Zhi-Wei Shen) is an employee of Philips Healthcare. Part of the work on radiomics analysis was supported by Ding Chengyu, an employee of Philips Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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