Peng Sun

Intra- and intertumoral heterogeneities raise fundamental biological questions and have important implications for accurate cancer diagnosis, prognosis, and treatment. These heterogeneities reflect tumor complexity, including a pronounced diversity in metabolic phenotypes and profiles. This study demonstrates that in vivo deuterium metabolic imaging (DMI) data acquired with sufficiently high spatiotemporal resolution provide a minimally invasive approach to assess these heterogeneities. A multifrequency DMI approach was used to examine tumor heterogeneities within and between colon cancer models. Regions of high and low glucose enrichment and labeled lactate accumulation could thus be detected; these were analyzed using unsupervised clustering strategies based on k -means clustering of area-under-the-curve information, and principal components analysis with Gaussian mixture modeling. Spatial alignment of these imaging-derived clusters for ² H-glucose and ² H-lactate showed good agreement with each other as well as with histological sections, validating the biological relevance of the identified subregions. Immunohistochemical analyses showed that glucose-enriched subregions were positively correlated with the expression of GLUT1 and DLAT, while lactate-enriched areas showed elevated expression of LDHA and MCT4. These results demonstrate that in vivo DMI can distinguish metabolically distinct subregions within viable tumors and between tumor models. DMI-based metabolic maps could thus provide the means to characterize intra- and intertumoral heterogeneities, paving the way for imaging-based metabolic phenotyping in precision oncology.

Background: Accurate preoperative T restaging and prediction of pathological complete response (pCR) are pivotal for organ preservation strategies in locally advanced rectal cancer (LARC). However, conventional magnetic resonance imaging (MRI) shows limited accuracy after neoadjuvant therapy (NAT) because therapy-induced necrosis, edema, inflammation, and fibrosis may obscure residual tumor. Neoadjuvant immunotherapy has shown considerable promise in LARC but introduces more complex regression patterns, making post-treatment assessment even more challenging. This study aimed to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) for T restaging and pCR prediction in LARC following neoadjuvant immunotherapy. Methods: Between July 2021 and March 2023, patients with LARC who underwent neoadjuvant immunotherapy followed by surgery were enrolled prospectively. All participants underwent DKI and conventional MRI (T2WI and DWI) at baseline and following immunotherapy (before surgery). Based on postoperative pathology, patients were categorized into ypT0−1 and ypT2−4 groups, where ypT denotes the post-NAT pathological T stage, and into pCR (ypT0N0) and non-pCR groups. Histogram parameters of DKI-derived diffusivity (D) and kurtosis (K) were extracted. Two combined models, integrating DKI histogram parameters with conventional MRI, were constructed. The diagnostic performance of these models was assessed by receiver operating characteristic (ROC) analysis. Results: Sixty-four patients [median age, 58 years (interquartile range, 52–62 years); 42 men] were included. Several histogram features derived from DKI parameters D and K differed significantly between groups (all PKurtosis) and the change ratio of the median of D (∆% D_Median) were incorporated into the combined model A. Model A achieved an AUC of 0.857 [95% confidence interval (CI): 0.762, 0.953]. For predicting pCR, the change ratio of the mean of D (∆% D_Mean) and the kurtosis of K (∆% K_Kurtosis) were included in the combined model B. Model B achieved an AUC of 0.904 (95% CI: 0.825, 0.982). Conclusions: Histogram analysis of DKI showed good diagnostic performance for T restaging and predicting pCR in LARC following neoadjuvant immunotherapy.

Reoperation for papillary thyroid carcinoma (PTC) requires precise lymph node metastasis assessment, yet ultrasound (US) alone may be insufficient in complex or high-risk cases. This study evaluated whether supplementing US with magnetic resonance imaging (MRI) improves surgical guidance and outcomes in reoperation. We retrospectively analyzed 375 patients who underwent reoperation between 2014 and 2022. Propensity score matching yielded 101 patients in the USUS-only group and 62 in the US+MRI group. Pathological and imaging data were compared to assess diagnostic performance, surgical outcomes, biochemical responses, and recurrence-free survival. The combined approach significantly increased sensitivity for detecting central lymph node metastasis from 52.5% to 90.9% and resulted in higher rates of central neck dissections (65.1% versus 45.5%) with greater lymph node yield (median: 29 versus 20) but lower lymph node ratios. More patients in the combined group achieved excellent biochemical responses (50.0% versus 27.7%). While overall recurrence-free survival (RFS) was not significantly different, the US+MRI group showed improved RFS among patients with ≥2 positive central nodes (HR = 0.24, p = 0.032). Importantly, complication rates were comparable, suggesting that improved outcomes were achieved without added surgical risk. Combined US and MRI assessment enhances diagnostic performance and may improve surgical and oncological outcomes in select high-risk patients undergoing PTC reoperation.

Background: Stack-of-spirals ultrashort echo time (spiral-UTE) has been reported to be feasible for lung imaging; however, the performance of free-breathing contrast-enhanced and unenhanced spiral-UTE (UTEe and UTEu) is still unknown. Therefore, this study aimed to evaluate their performance of lung imaging in patients with malignant tumors. Methods: A total of 76 patients with malignancies suspected of pulmonary metastatic nodules were enrolled in free-breathing UTEe, UTEu and routine contrast-enhanced T1-weighted imaging [volumetric interpolated breath-hold examination (VIBE)] for lung follow-up. Two radiologists independently assessed the image quality, and qualitative analysis was scored via a 5-point scale (4, excellent; 0, unreadable) with respect to the visibility of fissures, airways and vessels; signal homogeneity; motion artifacts; lesion conspicuity; and overall image quality. Quantitative analysis included measurements of the apparent contrast-to-noise ratio (CNR) and apparent signal-to-noise ratio (SNR). Pulmonary nodules detected via magnetic resonance (MR) images were compared with those by computed tomography (CT) as the reference standard. Results: Both UTEu and UTEe outperformed VIBE in all the qualitative metrics (P0.05) in those metrics. UTEe exhibited the best performance in depicting pulmonary vessels, achieving the highest apparent SNR and apparent CNR values. Among the 130 pulmonary nodules identified via CT, spiral-UTE had a sensitivity of 76.9% and a positive predictive value (PPV) of 99.0%, significantly outperforming VIBE (sensitivity of 47.7% and PPV of 95.4%). The detection rates for spiral-UTE were 90.0% for nodules larger than 5 mm, 98.5% for nodules larger than 7 mm and 100.0% for nodules larger than 10 mm. Conclusions: Spiral-UTE demonstrated superior image quality and greater sensitivity for pulmonary nodule detection than breath-hold VIBE did. Both unenhanced and enhanced spiral-UTE showed comparable performance in nodule detection, highlighting its potential as a reliable imaging modality for patients with malignant tumors during follow-up imaging.

Objectives: To explore the feasibility of time-dependent diffusion in clinical applications of breast MRI, as well as the capacity of quantitative microstructural mapping for characterizing the cellular properties in malignant and benign breast tumors. Methods: 38 patients with 45 lesions were enrolled. Diffusion MRI acquisition was conducted with a combination of pulsed gradient spin-echo sequences (PGSE) and oscillating gradient spin-echo (OGSE) on a 3T MRI scanner. The microstructural parameters including cellularity extracellular diffusivity (D_ex), mean cell size, intracellular volume fraction (ν_in), and the apparent diffusion coefficient (ADC) values were calculated. Each parameter was compared using the unpaired t-test between malignant and benign tumors. The area under the receiver operating characteristic curve (AUC) values was used to evaluate the diagnostic performance of different indices. Results: The mean diameter, D_ex, ADC_0Hz, ADC_25Hz, and ADC_50Hz were significantly lower in the malignant group than in the benign group (p < 0.001), while ν_in and cellularity were significantly higher in the malignant group (p < 0.001). All the microstructural parameters and time-dependent ADC values achieved high accuracy in differentiating between malignant and benign tumors of the breast. For microstructural parameters, the AUC of the cellularity was greater than others (AUC = 0.936). In an immunohistochemical subgroup comparison, the PR-positive group had significantly lower ν_in and cellularity, and significantly elevated D_ex and ADC_0Hz compared to the negative groups (p < 0.05). When combining diffusion parameters (cellularity, diameter, and ADC_25Hz), the highest diagnostic performance was obtained with an AUC of 0.969. Conclusions: DWI with a short diffusion time is capable of providing additional microstructural parameters in differentiating between benign and malignant breast tumors. The time-dependent diffusion MRI parameters have the potential to serve as a non-invasive tool to probe the differences in the internal structures of breast lesions.

In hypertrophic cardiomyopathy, myocardial microstructural alterations could be detected by in vivo cardiac diffusion-tensor imaging even before the appearance of cardiac MRI findings of hypertrophy or fibrosis.

Primary poorly differentiated liposarcoma of the spine represents an exceptionally rare clinical entity. We present a 68‐year‐old patient with a poorly differentiated liposarcoma originating in the thoracic paraspinal region that was initially misdiagnosed as spinal tuberculosis. Although surgical decompression and subsequent biopsy confirmed the diagnosis of liposarcoma, the patient declined adjuvant chemotherapy and experienced disease recurrence within 2 months. This case underscores the critical consideration that differentiated spinal sarcomas may be radiologically indistinguishable from spinal tuberculosis, necessitating histopathological confirmation for accurate diagnosis.

This work proposes a new retrospective motion correction method, termed DCGAN-MS, which employs disentangled CycleGAN based onmulti-mask k-space subsampling (DCGAN-MS) to address the image domain translation challenge. The multi-mask k-space subsampling operator is utilized to decrease the complexity of motion artifacts by randomly discarding motion-affected k-space lines. The network then disentangles the subsampled, motion-corrupted images into content and artifact features using specialized encoders, and generates motion-corrected images by decoding the content features. By utilizing multi-mask k-space subsampling, motion artifact features become more sparse compared to the original image domain, enhancing the efficiency of the DCGAN-MS network. This method effectively corrects motion artifacts in clinical gadoxetic acid-enhanced human liver MRI, human brain MRI from fastMRI, and preclinical rodent brain MRI. Quantitative improvements are demonstrated with SSIM values increasing from 0.75 to 0.86 for human liver MRI with simulated motion artifacts, and from 0.72 to 0.82 for rodent brain MRI with simulated motion artifacts. Correspondingly, PSNR values increased from 26.09 to 31.09 and from 25.10 to 31.77. The method’s performance was further validated on clinical and preclinical motion-corrupted MRI using the Kernel Inception Distance (KID) and Fréchet Inception Distance (FID) metrics. Additionally, ablation experiments were conducted to confirm the effectiveness of the multi-mask k-space subsampling approach.

Time-dependent diffusion MRI can be used to effectively evaluate liver fibrosis grading, potentially enabling noninvasive assessment of treatment efficacy and facilitating personalized clinical treatment.