Hyperpolarized 13C-MRI and Radiomics in Rectal Cancer

Overview

This study is testing new imaging and analysis methods to improve how doctors predict treatment response in rectal cancer patients receiving chemoradiotherapy. Current MRI scans sometimes miss complete tumor response, which can lead to unnecessary surgery. To address this, researchers will use a special MRI technique called hyperpolarized carbon-13 MRI, along with blood and tissue analysis (metabolomics) and advanced image feature analysis (radiomics). These tools can track how cancer cells use energy and how metabolism changes during treatment. The study will follow 30 rectal cancer patients at Chang Gung Memorial Hospital over 3 years. Patients will undergo standard MRI, colonoscopy, and advanced metabolic imaging before and after treatment. Results will compare patients whose tumors completely respond to therapy with those who do not. The goal is to see if metabolic changes detected by hyperpolarized MRI and metabolomics can predict treatment outcomes earlier and more accurately. This may help doctors avoid unnecessary surgery and provide more personalized care for rectal cancer patients.

Assessing rectal cancer response after neoadjuvant chemoradiotherapy (nCRT) with MRI has limitations, particularly in predicting pathological complete response (pPCR). While T2-weighted images exhibits high specificity, its low sensitivity may result in unnecessary surgeries. Diffusion-weighted imaging offers an alternative, but its reported accuracy varies with potential interpretation pitfalls. A more accurate approach for anticipating pCR in rectal cancers after nCRT is essential. Early prediction could optimize patient care by avoiding traditional MRI's treatment delays. This project is novel because it aims to investigate rectal cancer undergoing nCRT from the perspective of metabolism by integrating hyperpolarized (HP) 13C-MRI, metabolomics, and radiomics. HP 13C-MRI is a new non-invasive, real-time dynamic imaging technique used to detect the metabolic flux in vivo. Dynamic nuclear polarization (DNP) is a hyperpolarization technique that increases the signal of 13C-labeled probes by up to 50,000-fold. With DNP, \[1 13C\]pyruvate can be used to probe various metabolic pathways, including its conversion to lactate (anaerobic glycolysis), alanine (transamination), and bicarbonate (indirect marker for TCA cycle). In our preliminary colorectal MC38 cells models, a significant decrease in pyruvate-to-lactate conversion can be observed 3 days for irradiation. Additionally, NMR-based metabolomics analyses of patients' cancer tissue and blood serum will provide a more global view of metabolic changes of whole human body. Furthermore, the observed metabolic alterations can be translated into MRI radiomics features. In this single-center cross-sectional study, a prospective trial integrates novel MRI techniques to address clinical challenges and enhance clinical care. The 3 year project employs a non-randomized, two-group observational cohort study design with 30 rectal cancer patients undergoing nCRT at Chang Gung Memorial Hospital at Linkou (CGMH). Comprehensive pre-nCRT assessments include colonoscopy with biopsy, standard-of-care MRI, High-Resolution 13C Magnetic Resonance Imaging (HP 13C-MRI), and 1H-NMR metabolomics analysis. The institutional nCRT protocol comprises short-course radiotherapy (2,500 Gy in 5 fractions) followed by FOLFOX6 or TEGAFOX chemotherapy. Early response assessment at one month post-nCRT utilizes HP 13C-MRI and 1H-NMR metabolomics. Routine response evaluation at four months post-nCRT involves standard-of-care MRI and colonoscopy with biopsy. Following colonoscopic biopsy results, patients are categorized into pCR and non-pCR groups. Data from HP 13C-MRI, 1H-NMR metabolomics, and radiomics are compared, and machine learning integrates multi-omics for pCR assessment. We hypothesize that metabolic activity assessed using HP 13C-MRI can predict the response of rectal cancer to nCRT. Furthermore, the extent of metabolic alterations observed through HP 13C-MRI will be correlated with the tumor regression grade of rectal cancers, offering additional outcome categorization. Additionally, by integrating metabolic information and tissue characteristics from radiomics, dynamic changes in cancer metabolism can be monitored with quantifiable and reproducible tissue properties. Ultimately, the combination of HP 13C-MRI and metabolomics will enhance our understanding of rectal cancer pathophysiology from a metabolic perspective, potentially paving the way for new therapeutic developments.