Quantitative CEST MRI for GBM Early Response Prediction and Biopsy Guidance

SUMMARY Despite advances in therapy, the most aggressive form of brain tumor, glioblastoma, remains almost universally fatal. The first-line therapy for this devastating cancer, with a high rate of local failure and a median survival of only 14.6 months, is maximum feasible surgical resection, followed by radiotherapy with concurrent temozolomide chemotherapy (CRT). It is encouraging that there are multiple second-line therapies in clinical trials that can prolong survival or improve life quality, such as anti-angiogenic therapy (AAT). In this scenario, the accurate determination of whether a patient is a responder or a non-responder at an early stage following CRT has become a significant factor in clinical practice. However, the limitations in neuroimaging complicate the clinical management of patients and impede efficient testing of new therapeutics. Even with the improvements in advanced imaging modalities, distinguishing between true progression and pseudoprogression (induced by CRT) or between response and pseudoresponse (induced by an antiangiogenic therapy) remains two most formidable diagnostic dilemmas in neuro-oncology. Therefore, the current gold standard for diagnosis and response assessment is still based on pathologic appraisal of tissue samples. However, even biopsy yields variable results due to the intra-tumoral heterogeneity of treatment response. Amide proton transfer (APT) imaging is a chemical exchange saturation transfer-based molecular MRI technique that generates contrast via endogenous cellular proteins in vivo. Based on numerous clinical results by our group and others, APT-MRI is considered the only non-nuclear imaging modality to rank higher than perfusion imaging for the assessment of active tumors for brain tumors. The long-term goals of this R37 proposal were, and remain at present, to demonstrate the potential of quantitative APT-MRI to resolve two formidable diagnostic dilemmas for GBM patients and to develop an automated deep-learning framework for post-treatment surveillance and biopsy guidance. During the past three and half years of support, we have made substantial progress in achieving our goals. Despite this, several unmet clinical needs and technical challenges must further be addressed. To address these major challenges from the existing R37 study, consistent with the scope of the parent proposal, we have designed the following hypotheses and specific aims to be perform during this R37 extension period: (1) Assess the value of the residual peripheral non-enhancing tumor on APT-MRI post-surgery and post-CRT to predict tumor recurrence patterns and survival; and (2) Develop a generative transformer-based multi-parametric MRI reconstruction pipeline. If successful, our results—and particularly the deep-learning platform established—will be readily available to accurately identify early response and guide local treatment regimens, thus changing the clinical pathway. Project Number: 4R37CA248077-06 | Fiscal Year: 2026 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Shanshan Jiang | Institution: JOHNS HOPKINS UNIVERSITY, BALTIMORE, MD | Award Amount: $350,599 | Activity Code: R37 View on NIH RePORTER: https://reporter.nih.gov/project-details/11129399