Validation of optimal DCE-MRI perfusion threshold to classify at-risk tumor imaging voxels in heterogeneous cervical cancer for outcome prediction

Abstract

Purpose: To classify tumor imaging voxels at-risk for treatment failure within the heterogeneous cervical cancer using DCE MRI and determine optimal voxel's DCE threshold values at different treatment time points for early prediction of treatment failure.

Material and method: DCE-MRI from 102 patients with stage IB2-IVB cervical cancer was obtained at 3 different treatment time points: before (MRI 1) and during treatment (MRI 2 at 2-2.5 weeks and MRI 3 at 4-5 weeks). For each tumor voxel, the plateau signal intensity (SI) was derived from its time-SI curve from the DCE MRI. The optimal SI thresholds to classify the at-risk tumor voxels was determined by the maximal area under the curve using ROC analysis when varies SI value from 1.0 to 3.0 and correlates with treatment outcome.

Results: The optimal SI thresholds for MRI 1, 2 and 3 were 2.2, 2.2 and 2.1 for significant differentiation between local recurrence/control, respectively, and 1.8, 2.1 and 2.2 for death/survival, respectively.

Conclusion: Optimal SI thresholds are clinically validated to quantify at-risk tumor voxels which vary with time. A single universal threshold (SI=1.9) was identified for all 3 treatment time points and remained significant for the early prediction of treatment failure.

Keywords: Cervical cancer; Image analysis; Microcirculation; Perfusion imaging; Radiation therapy; Resistant tumor cell.

Conflict of interest statement

Disclosure of any potential Conflicts of Interest: No potential conflicts of interest were disclosed.

Copyright © 2014 Elsevier Inc. All rights reserved.

Figures

Figure 1. Distribution of at-risk tumor voxels

The distribution of the total number of at-risk tumor voxels of the 102 tumors, which were classified by an example using a specific SI threshold = 2.1, from a DCE MRI study obtained at 2–2.5 weeks into treatment. There were 21 distributions generated by varying the SI values from 1.0 to 3.0 with an increment of 0.1 for ROC analyses.

Figure 2. Favorable clinical outcome with high perfusion status early during treatment

Precontrast sagittal T2-weighted image (A) shows a large cervical cancer (arrows) indicating poor prognosis as judged by FIGO criteria. Corresponding DCE MRI at the plateau phase of the DCE imaging (B) shows the large tumor (arrows) with intense and heterogeneous dynamic contrast enhancement, indicating high tumor perfusion during the early part (2 weeks) of the 8-week treatment course. Tumor SI-pixel histogram (C) of the tumor is generated with the SI of the entire tumor pixels by plotting the SI of each tumor pixel along the x-axis and the number of pixels with same SI (frequency) along the y-axis. From this tumor SI histogram, tumor size (area under the DCE curve) can be calculated. The quantity and degree of low DCE subpopulation was quantified by computing SI percentiles. In this case, the 5th percentile (SI5%) (arrow, C) was 2.41, indicating that 5% of the pixels within the heterogeneous tumor fall below an SI of 2.41. This patient had excellent treatment outcome and survived for more than 10 years. Signal intensity-time DCE curves (D) again show abrupt and intense contrast enhancement of the tumor, indicating high tumor perfusion in both MRI 1 (gray-colored curve) and MRI 2 (blue-colored curve), and predicting excellent long term outcome.

Figure 3. Poor clinical outcome with low perfusion status early during treatment for a small tumor

Precontrast sagittal T2-weighted image (A) showed a much smaller cervical cancer (arrows), as compared with Figure 1, indicating a more favorable prognosis as judged by FIGO criteria. Corresponding DCE MRI at the plateau phase of the DCE imaging (B) showed the small tumor (arrows) with poor contrast enhancement indicating low tumor perfusion during early part (2 weeks) of the treatment course (8 weeks). From the tumor SI-pixel histogram (C), the low-DCE subpopulation was characterized by 5th percentile of SI (SI5%). SI5% was much lower (1.34) than that of Figure 1 (2.41). This patient had a primary tumor recurrence at 2 months after completion of therapy, and died 6 months after completion of therapy. Signal intensity-time DCE curves (D) again show sluggish and low contrast enhancement of the tumor mass indicating low tumor perfusion and poor response to treatment. In contrast to Figure 1, tumor perfusion remains low even 2 to 3 weeks after initiation of therapy (blue-colored curve) and is similar to that of the pretreatment (gray-colored curve).

Figure 4. ROC Curves at 3 treatment time points for LC only

ROC graphs only for local control (LC) at (A) pre-treatment, and (B) 2–2.5 weeks and (C) 4–4.5 weeks into treatment. To conserve space, only partial ROC curves are shown. The ROC graph of SI=1.9 (red) is also included to demonstrate its relationship and performance in comparison with other ROC curves. Overall, these ROC curves show a gross trend of gradual increase of AUC from A to B and C with the maximal AUC values of 0.66, 0.78 and 0.80, respectively, and the corresponding optimal SI thresholds values of 2.1, 2.1 and 2.0, respectively. Note that the thickest ROC curve (red) represents the universal SI=1.9, which shows an excellent overall performance (toward the left and top) compared to most of the other ROC curves.