Circulating tumour cell counts and ultrasomics signature-based nomogram for preoperative prediction of early recurrence of hepatocellular carcinoma after radical treatment

Abstract

Methods: Between December 2017 and December 2018, 153 HCC patients (134 males and 19 females; mean age, 56.0 ± 10.2 years; range, 28-78 years) treated with radical therapy were enrolled in our retrospective study and were divided into a training cohort (n = 107) and a validation cohort (n = 46). All patients underwent preoperative CTC tests and CEUS examinations before treatment. The ultrasomics signature was extracted and built from CEUS images. Univariate and multivariate logistic regression analyses were used to identify the significant variables related to ER, which were then combined to build a predictive nomogram. The performance of the nomogram was evaluated by its discrimination, calibration and clinical utility. The predictive model was further evaluated in the internal validation cohort.

Results: HBV DNA, serum AFP level, CTC status, tumour size and ultrasomics score were identified as independent predictors associated with ER (all p < 0.05). Multivariable logistic regression analysis showed that the CTC status (OR = 7.02 [95% CI, 2.07 to 28.38], p = 0.003) and ultrasomics score (OR = 148.65 [95% CI, 25.49 to 1741.72], p < 0.001) were independent risk factors for ER. The nomogram based on ultrasomics score, CTC status, serum AFP level and tumour size exhibited C-indexes of 0.933 (95% CI, 0.878 to 0.988) and 0.910 (95% CI, 0.765 to 1.055) in the training and validation cohorts, respectively, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram.

Conclusion: The nomogram incorporating CTC, ultrasomics features and independent clinical risk factors achieved satisfactory preoperative prediction of ER in HCC patients after radical treatment.

Advances in knowledge: 1. CTC status and ultrasomics score were identified as independent predictors associated with ER of HCC after radical treatment. 2. The nomogram constructed by ultrasomics score generated by 17 ultrasomics features, combined with CTCs and independent clinical risk factors such as AFP and tumour size. 3. The nomogram exhibited satisfactory discriminative power, and could be clinically useful in the preoperative prediction of ER after radical treatment in HCC patients.

Figure 1.

Illustration of original image and segmentation. Annotations of the region of interest (ROI) generated by the radiologists around the tumour outline are delineated in red.

Figure 2.

Ultrasomics feature selection using the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort.

Figure 3.

Nomogram for predicting early recurrence probabilities in HCC patients after radical treatment.

Figure 4.

Calibration curves of the nomogram in the training (a) and validation (b) cohorts; the X-axis is the nomogram-predicted probability of early recurrence. The Y-axis is the actual early recurrence, and the diagonal-dashed line indicates the ideal prediction by a perfect model.

Figure 5.

Decision curve analysis (DCA) derived from the validation cohort. The Y-axis measures the net benefit. The net benefit is determined by calculating the difference between the expected benefit and the expected harm associated with each proposed model [net benefit = true positive rate − (false positive rate×weighting factor), weighting factor = threshold probability/ (1-threshold probability)]. The grey line represents the assumption that all patients had early recurrence. If the threshold probability was >10%, using the nomogram (red curve) and ultrasomics score (green curve) added more net benefit for patients than using CTCs (blue curve). When the threshold probability was >40%, the nomogram (red curve) increased the benefit to patients over that using CTCs (blue curve) and ultrasomics score (green curve).