Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study

Abstract

Purpose: To determine DWI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with radiotherapy (RT).

Methods: HNC patients in a prospective study were included. Patients had MRIs pre-, mid-, and post-RT completion. We used T2-weighted sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid- and post-RT and was defined as: complete response (CR) vs. non-complete response (non-CR). The Mann-Whitney U test was used to compare ADC between CR and non-CR. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique.

Results: Eighty-one patients were included. Median follow-up was 31 months. For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10^-3 mm²/s vs. (1.37 ± 0.22) × 10^-3 mm²/s, p < 0.0001), while patients with non-CR had no significant increase (p > 0.05). RPA identified GTV-P delta (Δ)ADC_mean < 7% at mid-RT as the most significant parameter associated with worse LC and RFS (p = 0.01). Uni- and multi-variable analysis showed that GTV-P ΔADC_mean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADC_mean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p < 0.0001 for both).

Conclusion: ΔADC_mean at mid-RT is a strong predictor of oncologic outcomes in HNC. Patients with no significant increase of primary tumor ADC at mid-RT are at high risk of disease relapse.

Keywords: DWI; Head and neck cancer; Imaging Biomarker; Oncologic outcomes; Radiation Therapy; Tumor response.

Figure 1.. Image registration and segmentation workflow

Illustration of the workflow process for image registration and segmentation in the study using an example of a patient with T4N1 tumor of the base of tongue. Panel (A) shows the GTV-P segmentation on baseline T2w MRI followed by rigid co-registration (RIR) and contour propagation to baseline DWI (B) and then ROI propagation to corresponding ADC map (C). Panel (D) shows mid-RT T2w image with partial response. The image was co-registered to baseline T2w using deformable image registration (DIR) and baseline GTV-P was propagated. Subsequently, the residual and response sub-volumes were segmented (E), then contours were propagated to mid-RT DWI after RIR (F), and finally to the corresponding mid-RT ADC map (G).

Figure 2.

BLADE vs. RESOLVE histograms. Histogram illustration of the distribution of tumor and nodal volumes’ ADC mean at baseline using the BLADE vs. RESOLVE DWI acquisition methods in the study. The RESOLVE in pink is overlaid on BLADE in light blue. There were no statistically significant differences using the Kolmogorov–Smirnov test (p=0.4).

Figure 3.

Kaplan–Meier curves calculated for patients with baseline GTV-P (n = 59) show better (A) local control (LC) and (B) recurrence-free survival (RFS) for patients with ≥7% ${\text{ΔADC}}_{\text{mean}}$ at mid-RT. Shaded colors represent 95% confidence intervals, short vertical lines represent censored data, and asterisks indicate significant log-rank p values.

Figure 4.

Relationship between $\text{Δ}$ volume and ${\text{ΔADC}}_{\text{mean}}$ for both GTV-P (A) and GTV-N (B) at mid-RT. Solid lines represent the linear fit and dotted lines represent the 95% confidence intervals.