Authors: Hsin‐Yu Chen | Ivan de Kouchkovsky | Robert Bok | Michael A. Ohliger | Zhen J. Wang | Daniel Gebrezgiabhier | Tanner Nickles | Lucas Carvajal | Jeremy W. Gordon | Peder E. Z. Larson | John Kurhanewicz | Rahul Aggarwal | Daniel B. Vigneron
Source: Cancers (article, 2025-07-01)
Field: Chemistry — Spectroscopy

Multivariate Framework of Metabolism in Advanced Prostate Cancer Using Whole Abdominal and Pelvic Hyperpolarized 13C MRI—A Correlative Study with Clinical Outcomes <b>Background:</b> Most of the existing hyperpolarized (HP) <sup>13</sup>C MRI analyses use univariate rate maps of pyruvate-to-lactate conversion (k<sub>PL</sub>), and radiomic-style multiparametric models extracting complex, higher-order features remain unexplored. <b>Purpose:</b> To establish a multivariate framework based on whole abdomen/pelvis HP <sup>13</sup>C-pyruvate MRI and evaluate the association between multiparametric features of metabolism (MFM) and clinical outcome measures in advanced and metastatic prostate cancer. <b>Methods:</b> Retrospective statistical analysis was performed on 16 participants with metastatic or local-regionally advanced prostate cancer prospectively enrolled in a tertiary center who underwent HP-pyruvate MRI of abdomen or pelvis between November 2020 and May 2023. Five patients were hormone-sensitive and eleven were castration-resistant. GMP-grade [1-<sup>13</sup>C]pyruvate was polarized using a 5T clinical-research DNP polarizer, and HP MRI used a set of flexible vest-transmit, array-receive coils, and echo-planar imaging sequences. Three basic metabolic maps (k<sub>PL</sub>, pyruvate summed-over-time, and mean pyruvate time) were created by semi-automatic segmentation, from which 316 MFMs were extracted using an open-source, radiomic-compliant software package. Univariate risk classifier was constructed using a biologically meaningful feature (k<sub>PL,median</sub>), and the multivariate classifier used a two-step feature selection process (ranking and clustering). Both were correlated with progression-free survival (PFS) and overall survival (OS) (median follow-up = 22.0 months) using Cox proportional hazards model. <b>Results:</b> In the univariate analysis, patients harboring tumors with lower-k<sub>PL,median</sub> had longer PFS (11.2 vs. 0.5 months, <i>p</i> < 0.01) and OS (NR vs. 18.4 months, <i>p</i> < 0.05) than their higher-k<sub>PL,median</sub> counterparts. Using a hypothesis-generating, age-adjusted multivariate risk classifier, the lower-risk subgroup also had longer PFS (NR vs. 2.4 months, <i>p</i> < 0.002) and OS (NR vs. 18.4 months, <i>p</i> < 0.05). By contrast, established laboratory markers, including PSA, lactate dehydrogenase, and alkaline phosphatase, were not significantly associated with PFS or OS (<i>p</i> > 0.05). Key limitations of this study include small sample size, retrospective study design, and referral bias. <b>Conclusions:</b> Risk classifiers derived from select multiparametric HP features were significantly associated with clinically meaningful outcome measures in this small, heterogeneous patient cohort, strongly supporting further investigation into their prognostic values. Prostate cancer ; Medicine ; Univariate ; Multivariate analysis ; Univariate analysis ; Multivariate statistics ; Nuclear medicine ; Radiology ; Internal medicine ; Cancer ; Computer science ; Machine learning Advanced NMR Techniques and Applications ; Radiomics and Machine Learning in Medical Imaging ; Medical Imaging Techniques and Applications