Evaluation of a Novel Quantitative Multiparametric MR Sequence for Radiation Therapy Treatment Response Assessment.

Abstract

Purpose: To evaluate a Deep-Learning-enhanced MUlti-PArametric MR sequence (DL-MUPA) for treatment response assessment for brain metastases patients undergoing stereotactic radiosurgery (SRS) and head-and-neck (HnN) cancer patients undergoing conventionally fractionation adaptive radiation therapy.

Methods: DL-MUPA derives quantitative T1 and T2 maps from a single 4-6-minute scan denoised via DL method using dictionary fitting. Phantom benchmarking was performed on a NIST-ISMRM phantom. Longitudinal patient data were acquired on a 1.5T MR-simulator, including pre-treatment (PreTx) and every 3 months after SRS (PostTx) in brain, and PreTx, mid-treatment and 3 months PostTx in HnN. Changes of mean T1 and T2 values were calculated within gross tumor volumes (GTVs), residual disease (RD, HnN), parotids, and submandibular glands (HnN) for treatment response assessment. Uninvolved normal tissues (normal appearing white matter in brain, masseter in HnN) were evaluated to as control.

Results: Phantom benchmarking showed excellent inter-session repeatability (coefficient of variance <1% for T1, <7% for T2). Uninvolved normal tissue suggested acceptable in-vivo repeatability (brain |$\Delta$|<5%, HnN |$\Delta$T1|<7%, |$\Delta$T2|<18% (4ms)). Remarkable changes were noted in resolved brain metastasis ($\Delta$T1=14%) and necrotic settings ($\Delta$T1=18-40%, $\Delta$T2=9-41%). In HnN, two primary tumors showed T2 increase (PostTx GTV $\Delta$T2>13%, RD $\Delta$T2>18%). A nodal disease resolved PostTx (GTV $\Delta$T1=-40%, $\Delta$T2=-33%, RD $\Delta$T1=-29%, $\Delta$T2=-35%). Enhancement was found in involved parotids (PostTx $\Delta$T1>12%, $\Delta$T2>13%) and submandibular glands (PostTx $\Delta$T1>15%, $\Delta$T2>35%) while the uninvolved organs remained stable.

Conclusions: DL-MUPA shows promise for treatment response assessment and identifying potential endpoints for functional sparing.