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Evaluation of a novel quantitative multiparametric MR sequence for radiation therapy treatment response assessment
Evaluation of a novel quantitative multiparametric MR sequence for radiation therapy treatment response assessment
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Evaluation of a novel quantitative multiparametric MR sequence for radiation therapy treatment response assessment
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Evaluation of a novel quantitative multiparametric MR sequence for radiation therapy treatment response assessment
Evaluation of a novel quantitative multiparametric MR sequence for radiation therapy treatment response assessment
Journal Article

Evaluation of a novel quantitative multiparametric MR sequence for radiation therapy treatment response assessment

2025
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Overview
Background Multiparametric MRI has shown great promise to derive multiple quantitative imaging biomarkers for treatment response assessment. Purpose To evaluate a novel deep‐learning‐enhanced MUlti‐PArametric MR sequence (DL‐MUPA) for treatment response assessment for brain metastases patients treated with stereotactic radiosurgery (SRS) and head‐and‐neck (HN) cancer patients undergoing conventionally fractionation adaptive radiation therapy. Methods DL‐MUPA derives quantitative T1 and T2 relaxation time maps from a single 4–6‐min scan denoised via DL method using least‐squares dictionary fitting. Longitudinal phantom benchmarking was performed on a NIST‐ISMRM phantom over 1 year. In patients, longitudinal DL‐MUPA data were acquired on a 1.5T MR‐simulator, including pretreatment (PreTx) and every ∼3 months after SRS (PostTx) in brain, and PreTx, mid‐treatment and 3 months PostTx in HN. Delta analysis was performed calculating changes of mean T1 and T2 values within gross tumor volumes (GTVs), residual disease (RD, HN), parotids, and submandibular glands (HN) for treatment response assessment. Uninvolved normal tissues (normal appearing white matter in brain, masseter in HN) were evaluated for within‐subject repeatability. Results Phantom benchmarking revealed excellent inter‐session repeatability (coefficient of variation < 0.9% for T1, < 6.6% for T2), suggesting reliability for longitudinal studies with systematic bias adjustment. Uninvolved normal tissue suggested acceptable within‐subject repeatability in the brain |ΔT1mean| < 36 ms (4.9%), |ΔT2mean| < 2 ms (6.1%) and HN |ΔT1mean| < 69 ms (7.0%), |ΔT2mean| < 4 ms (17.8%) with few outliers. In brain, remarkable changes were noted in a resolved metastasis (4‐month PostTx ΔT1mean = 155 ms (13.7%)) and necrotic settings (ΔT1mean = 214‐502 ms (17.6‐39.7%), ΔT2mean = 7‐41 ms (8.7‐41.4%), 6‐month to 3‐month PostTx). In HN, two base of tongue tumors exhibited T2 enhancement (PostTx GTV ΔT2mean > 7 ms (12.8%), RD ΔT2mean > 10 ms (18.1%)). A case with nodal disease resolved PostTx (GTV ΔT1mean = ‐541 ms (‐39.5%), ΔT2mean = ‐24 ms (‐32.7%), RD ΔT1mean = ‐400 ms (‐29.2%), ΔT2mean = ‐25 ms (‐35.3%)). Parotids (PostTx ΔT1mean > 82 ms (12.4%), ΔT2mean > 6 ms (13.4%)) and submandibular glands (PostTx ΔT1mean > 135 ms (14.6%), ΔT2mean > 17 ms (34.5%)) adjacent to gross disease exhibited enhancement while distant organs remained stable. Conclusions Preliminary results suggest promise of DL‐MUPA for treatment response assessment and highlight potential endpoints for functional sparing.