Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors

Abstract

Background

Risk stratification of spinal tumors is a major unmet clinical need for personalized therapy.

Purpose

To explore the feasibility of pretreatment whole-lesion apparent diffusion coefficient (ADC) histogram in predicting local recurrence of aggressive spinal tumors.

Methods

119 aggressive spinal tumor patients (median age, 40; range, 13–74  years) confirmed by pathological findings with a mean follow-up of 36 months were enrolled and divided into the recurrence and non-recurrence group. The histogram metrics of whole-lesion, including the maximum, mean, kurtosis, skewness, entropy, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values, were evaluated and take the average. Fractal dimension (FD) was assessed in the three orthogonal directions and take maximum. Clinical and general imaging features were used to construct an alternative prognostic model for comparison. Variables with statistical differences would be included in stepwise logistic regression analysis.

Results

As for the clinical model, Enneking staging (odds ratio [OR]: 3.572; P = 0.04) and vertebral compression (OR: 4.302; P = 0.002) were independent predictors of recurrence. There was no statistical difference in FD between the two groups (P = 0.623). Among the ADC histogram parameters compared, skewness, maximum, and mean ADC values were independent risk factors and constructed ADC histogram prediction models. The ADC histogram model (AUC = 0.871) and the combined model (AUC = 0.884) performed better than the clinical prediction model (AUC = 0.704) with P-values of 0.004 and 0.001, respectively.

Conclusion

Prediction models based on the ADC histogram analysis might represent serviceable instruments for the aggressive spinal tumors.