The role of readout-segmented echo-planar imaging-based diffusion-weighted imaging in evaluating tumor response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy

Abstract

Background

Accurate assessment of tumor response in rectal cancer could help individualize treatment.

Purpose

To evaluate the role of diffusion-weighted imaging (DWI) based on readout-segmented echo-planar imaging (rs-EPI) in assessing tumor response after neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC).

Material and Methods

Sixty-three patients with LARC who received neoadjuvant CRT and surgery were enrolled retrospectively. They all underwent pre- and post-CRT magnetic resonance examinations, including DWI using rs-EPI. According to pathological results, patients were grouped as pathological complete responder (pCR, n = 16) and non-pCR (n = 47). Visual assessment of residual tumor and whole-tumor histogram analysis of pre- and post-CRT apparent diffusion coefficient (ADC) map was performed by two radiologists; tumor volume on ADC map was also recorded.

Results

Overall inter-observer agreement was good for histogram analysis (ICC = 0.543–0.999). Tumor volume reduction rate on ADC map showed no significant difference between the two groups (P = 0.468). Post-CRT mean, quantile values, and their percentage changes were higher in the pCR group (all P < 0.001). Post-CRT mean value had a good diagnostic power in selecting pCR (AUC = 0.855), with a cut-off value of 1.345 × 10⁻³ mm²/s, yielding a sensitivity of 83%, specificity of 81.3%. Post-CRT 95% quantile value had the highest AUC (AUC = 0.868) among quantile values, and a higher specificity (87.5% vs. 81.3%) than mean value with comparable overall diagnostic performance (P = 0.563). Visual assessment showed a sensitivity of 85.1%, specificity of 68.8% in selecting pCR.

Conclusion

Quantitative ADC value of rs-EPI DWI could reliably evaluate tumor response in patients with LARC. Post-CRT 95% quantile ADC value could help mean value to more accurately identify pCR.