Formation of Zwitterionic and Self-Healable Hydrogels via Amino-yne Click Chemistry for Development of Cellular Scaffold and Tumor Spheroid Phantom for MRI.

In situ-forming biocompatible hydrogels have great potential in various medical applications. Here, we introduce a pH-responsive, self-healable, and biocompatible hydrogel for cell scaffolds and the development of a tumor spheroid phantom for magnetic resonance imaging. The hydrogel (pMAD) was synthesized via amino-yne click chemistry between poly(2-methacryloyloxyethyl phosphorylcholine-co-2-aminoethylmethacrylamide) and dialkyne polyethylene glycol. Rheology analysis, compressive mechanical testing, and gravimetric analysis were employed to investigate the gelation time, mechanical properties, equilibrium swelling, and degradability of pMAD hydrogels. The reversible enamine and imine bond mechanisms leading to the sol-to-gel transition in acidic conditions (pH ≤ 5) were observed. The pMAD hydrogel demonstrated potential as a cellular scaffold, exhibiting high viability and NIH-3T3 fibroblast cell encapsulation under mild conditions (37 °C, pH 7.4). Additionally, the pMAD hydrogel also demonstrated the capability for in vitro magnetic resonance imaging of glioblastoma tumor spheroids based on the chemical exchange saturation transfer effect. Given its advantages, the pMAD hydrogel emerges as a promising material for diverse biomedical applications, including cell carriers, bioimaging, and therapeutic agent delivery.

DCE-MRI for Pre-Treatment Prediction and Post-Treatment Assessment of Treatment Response in Sites of Squamous Cell Carcinoma in the Head and Neck.

BACKGROUND AND PURPOSE: It is important to identify patients with head and neck squamous cell carcinoma (SCC) who fail to respond to chemoradiotherapy so that they can undergo post-treatment salvage surgery while the disease is still operable. This study aimed to determine the diagnostic performance of dynamic contrast enhanced (DCE)-MRI using a pharmacokinetic model for pre-treatment predictive imaging, as well as post-treatment diagnosis, of residual SCC at primary and nodal sites in the head and neck. MATERIAL AND METHODS: Forty-nine patients with 83 SCC sites (primary and/or nodal) underwent pre-treatment DCE-MRI, and 43 patients underwent post-treatment DCE-MRI, of which 33 SCC sites had a residual mass amenable to analysis. Pre-treatment, post-treatment and % change in the mean Ktrans, kep, ve and AUGC were obtained from SCC sites. Logistic regression was used to correlate DCE parameters at each SCC site with treatment response at the same site, based on clinical outcome at that site at a minimum of two years. RESULTS: None of the pre-treatment DCE-MRI parameters showed significant correlations with SCC site failure (SF) (29/83 sites) or site control (SC) (54/83 sites). Post-treatment residual masses with SF (14/33) had significantly higher kep (p = 0.05), higher AUGC (p = 0.02), and lower % reduction in AUGC (p = 0.02), than residual masses with SC (19/33), with the % change in AUGC remaining significant on multivariate analysis. CONCLUSION: Pre-treatment DCE-MRI did not predict which SCC sites would fail treatment, but post-treatment DCE-MRI showed potential for identifying residual masses that had failed treatment.

The use of dynamic tracer concentration in veins for quantitative DCE-MRI kinetic analysis in head and neck.

BACKGROUND: Head and neck Magnetic Resonance (MR) Images are vulnerable to the arterial blood in-flow effect. To compensate for this effect and enhance accuracy and reproducibility, dynamic tracer concentration in veins was proposed and investigated for quantitative dynamic contrast-enhanced (DCE) MRI analysis in head and neck. METHODOLOGY: 21 patients with head and neck tumors underwent DCE-MRI at 3T. An automated method was developed for blood vessel selection and separation. Dynamic concentration-time-curves (CTCs) in arteries and veins were used for the Tofts model parameter estimations. The estimation differences by using CTCs in arteries and veins were compared. Artery and vein voxels were accurately separated by the automated method. Remarkable inter-slice tracer concentration differences were found in arteries while the inter-slice concentration differences in veins were moderate. Tofts model fitting by using the CTCs in arteries and veins produced significantly different parameter estimations. The individual artery CTCs resulted in large (>50% generally) inter-slice parameter estimation variations. Better inter-slice consistency was achieved by using the vein CTCs. CONCLUSIONS: The use of vein CTCs helps to compensate for arterial in-flow effect and reduce kinetic parameter estimation error and inconsistency for head and neck DCE-MRI.

Quantitative evaluation of dual-flip-angle T1 mapping on DCE-MRI kinetic parameter estimation in head and neck.

PURPOSE: To quantitatively evaluate the kinetic parameter estimation for head and neck (HN) dynamic contrast-enhanced (DCE) MRI with dual-flip-angle (DFA) T1 mapping. MATERIALS AND METHODS: Clinical DCE-MRI datasets of 23 patients with HN tumors were included in this study. T1 maps were generated based on multiple-flip-angle (MFA) method and different DFA combinations. Tofts model parameter maps of k(ep), K(trans) and v(p) based on MFA and DFAs were calculated and compared. Fitted parameter by MFA and DFAs were quantitatively evaluated in primary tumor, salivary gland and muscle. RESULTS: T1 mapping deviations by DFAs produced remarkable kinetic parameter estimation deviations in head and neck tissues. In particular, the DFA of [2º, 7º] overestimated, while [7º, 12º] and [7º, 15º] underestimated K(trans) and v(p), significantly (P<0.01). [2º, 15º] achieved the smallest but still statistically significant overestimation for K(trans) and v(p) in primary tumors, 32.1% and 16.2% respectively. k(ep) fitting results by DFAs were relatively close to the MFA reference compared to K(trans) and v(p). CONCLUSIONS: T1 deviations induced by DFA could result in significant errors in kinetic parameter estimation, particularly K(trans) and v(p), through Tofts model fitting. MFA method should be more reliable and robust for accurate quantitative pharmacokinetic analysis in head and neck.