Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

Abstract

Background and purpose: Multimodality magnetic resonance imaging (MRI) can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multiparametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semiautomated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method.

Methods: Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7-T animal scanner preradiation, and at day 1, day 4, and day 8 postradiation. Two experts performed manual and semiautomated methods to extract tumor ROIs on APTw images. Multimodality MRI signals of the tumors, including structural (T₂ and T₁ ), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively.

Results: The semiautomated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the postradiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy.

Conclusions: The semiautomated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.

Keywords: APT imaging; Brain tumor; image segmentation; multiparametric MRI; response assessment.

Fig. 1

Flow-chart of the semi-automated tumor region extraction method for one sample. First, an initial ROI was extracted by the human interaction from an APTw map. If necessary, the information was co-registered to the ADC map. Second, the histogram was taken into consideration and the original mask was labeled. Next, the temporary result was de-noised by a morphologic process. Finally, the contour was highlighted.

Fig. 2

Intra-rater (A and B) and inter-rater (C and D) analysis comparison between manual and semiautomated ROI extraction. This demonstrates that manual segmentation had a wider distribution than the semi-automated method in both inter- and intra-rater experiments.

Fig. 3

(A) T₂w MRI features investigated at different time points of pre-radiotherapy and on day 1, day 4, and day 8 post-radiotherapy for three different rats. The irradiated tumors were still growing during the postradiation development. (B) Tumor cross-section areas obtained from the APTw images at different time points of pre-radiation and on day 1, day 4, and day 8 postradiation. The statistical significance of the difference compared with pre-radiation: ***p < 0.001.

Fig. 4

(A) Changes in T₂ maps (0 – 100 ms), T₁ maps (0.5 – 2 s), ADC maps (0 – 2 × 10⁻⁹ m²/s), blood flow maps (0 – 200 mL/100 g/min), APTw images (−10% to 10% of the bulk water signal intensity), and MTR maps at 2 kHz (0% – 50% of the bulk water signal intensity) at different time points of pre-radiation and on day 1, day 4, and day 8 post-radiation for one representative rat with a U87 MG glioma. The outside noise of brain tissue has been filtered out. (B) The H&E image at 8 days post-radiation, taken from the central part of the tumor.

Fig. 5

Histogram analysis of multiparametric MRI biomarkers at different time points of pre-radiation and on day 1, day 4, and day 8 postradiation for U87 MG glioma in a rat. The ADC histogram had a right shift, while blood flow signals and APTw intensity had a left shift after radiotherapy.