Purpose: To establish magnetic resonance (MR)–based molecular imaging paradigms for the noninvasive monitoring of extracellular pH (pHe) as a functional surrogate biomarker for metabolic changes induced by locoregional therapy of liver cancer. Experimental Design: Thirty-two VX2 tumor-bearing New Zealand white rabbits underwent longitudinal imaging on clinical 3T-MRI and CT scanners before and up to 2 weeks after complete conventional transarterial chemoembolization (cTACE) using ethiodized oil (lipiodol) and doxorubicin. MR-spectroscopic imaging (MRSI) was employed for pHe mapping. Multiparametric MRI and CT were performed to quantify tumor enhancement, diffusion, and lipiodol coverage of the tumor posttherapy. In addition, incomplete cTACE with reduced chemoembolic doses was applied to mimic undertreatment and exploit pHe mapping to detect viable tumor residuals. Imaging findings were correlated with histopathologic markers indicative of metabolic state (HIF-1α, GLUT-1, and LAMP-2) and viability (proliferating cell nuclear antigen and terminal deoxynucleotidyl-transferase dUTP nick-end labeling). Results: Untreated VX2 tumors demonstrated a significantly lower pHe (6.80 ± 0.09) than liver parenchyma (7.19 ± 0.03, P < 0.001). Upregulation of HIF-1α, GLUT-1, and LAMP-2 confirmed a hyperglycolytic tumor phenotype and acidosis. A gradual tumor pHe increase toward normalization similar to parenchyma was revealed within 2 weeks after complete cTACE, which correlated with decreasing detectability of metabolic markers. In contrast, pHe mapping after incomplete cTACE indicated both acidic viable residuals and increased tumor pHe of treated regions. Multimodal imaging revealed durable tumor devascularization immediately after complete cTACE, gradually increasing necrosis, and sustained lipiodol coverage of the tumor. Conclusions: MRSI-based pHe mapping can serve as a longitudinal monitoring tool for viable tumors. As most liver tumors are hyperglycolytic creating microenvironmental acidosis, therapy-induced normalization of tumor pHe may be used as a functional biomarker for positive therapeutic outcome.

Noninvasive molecular MRI can be used to visualize resistance mechanisms mediated by the tumor metabolism and immune evasion for strategic targeting by local-regional and immunotherapy in liver cancer. Background The immuno-metabolic interplay has gained interest for determining and targeting immunosuppressive tumor micro-environments that remain a barrier to current immuno-oncologic therapies in...

To identify baseline imaging features in patients with liver cancer that correlate with Yttrium-90 (⁹⁰Y) distribution on post-procedural single photon emission computed tomography (SPECT) and predict tumor response to transarterial radioembolization (TARE). Methods: This retrospective study was approved by the institutional review board and included 38 patients with hepatocellular carcinoma (HCC) (n = 23, 18/23 males, mean age 62.39±8.62 years, 34 dominant tumors) and non-HCC hepatic malignancies (n = 15, 9/15 males, mean age 61.13±11.51 years, 24 dominant tumors) who underwent 40 resin-based TARE treatments (08/2012-01/2018). Multi-phasic contrast-enhanced MRI or CT was obtained prior to and Bremsstrahlung SPECT within two hours after TARE. Total (TTV, cm³) and enhancing tumor volume (ETV, cm³ and % of TTV), and total and enhancing tumor burden (%) were volumetrically assessed on baseline imaging. Up to two dominant tumors per treated lobe were analyzed. After multimodal image registration of baseline imaging and SPECT/CT (MIM Software Inc., Cleveland, OH), ⁹⁰Y distribution was quantified on SPECT as tumor-to-normal-liver-ratio (TNR). Response was assessed according to RECIST1.1 and quantitative European Association for the Study of the Liver (qEASL) criteria. Clinical parameters were also assessed. Statistical tests included Mann-Whitney-U, Pearson correlation, and linear regression. Results: In HCC patients, high baseline ETV% significantly correlated with high TNR on SPECT, demonstrating greater ⁹⁰Y uptake in the tumor relative to the liver parenchyma (P < 0.001). In non-HCC patients, a correlation between ETV% and TNR was observed as well (P = 0.039). Follow-up imaging for response assessments within one to four months post-TARE was available for 23 patients with 25 treatments. The change of ETV% significantly correlated with TNR in HCC (P = 0.039) but not in non-HCC patients (P = 0.886). Additionally, Child-Pugh class B patients demonstrated significantly more ⁹⁰Y deposition in non-tumorous liver compared to Child-Pugh A patients (P = 0.021). Conclusion: This study identified ETV% as a quantifiable imaging biomarker on pre-procedural MRI and CT to predict ⁹⁰Y distribution on post-procedural SPECT in HCC and non-HCC. However, the relationship between the preferential uptake of ⁹⁰Y to the tumor with tumor response after radioembolization could only be validated for HCC.

Radiology reports can change during workflows, especially when residents draft preliminary versions that attending physicians finalize. We explored how large language models (LLMs) and embedding techniques can categorize these changes into textual, semantic, or clinically actionable types. We evaluated 400 adult CT reports drafted by residents against finalized versions by attending physicians. Changes were rated on a five-point scale from no changes to critical ones. We examined open-source LLMs alongside traditional metrics like normalized word differences, Levenshtein and Jaccard similarity, and text embedding similarity. Model performance was assessed using quadratic weighted Cohen's kappa (κ), (balanced) accuracy, F₁, precision, and recall. Inter-rater reliability among evaluators was excellent (κ = 0.990). Of the reports analyzed, 1.3 % contained critical changes. The tested methods showed significant performance differences (P < 0.001). The Qwen3-235B-A22B model using a zero-shot prompt, most closely aligned with human assessments of changes in clinical reports, achieving a κ of 0.822 (SD 0.031). The best conventional metric, word difference, had a κ of 0.732 (SD 0.048), the difference between the two showed statistical significance in unadjusted post-hoc tests (P = 0.038) but lost significance after adjusting for multiple testing (P = 0.064). Embedding models underperformed compared to LLMs and classical methods, showing statistical significance in most cases. Large language models like Qwen3-235B-A22B demonstrated moderate to strong alignment with expert evaluations of the clinical significance of changes in radiology reports. LLMs outperformed embedding methods and traditional string and word approaches, achieving statistical significance in most instances. This demonstrates their potential as tools to support peer review.