Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging.

RATIONALE AND OBJECTIVES: Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM). MATERIALS AND METHODS: From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated. RESULTS: Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P < .01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P < .05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P < .05) and D (OR, 0.293; 95% CI,0.089-0.964; P < .05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve. CONCLUSION: The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Radiotherapy modulates tumor cell fate decisions: a review.

Cancer has always been a worldwide problem, and the application of radiotherapy has greatly improved the survival rate of cancer patients. Radiotherapy can modulate multiple cell fate decisions to kill tumor cells and achieve its therapeutic effect. With the development of radiotherapy technology, how to increase the killing effect of tumor cells and reduce the side effects on normal cells has become a new problem. In this review, we summarize the mechanisms by which radiotherapy induces tumor cell apoptosis, necrosis, necroptosis, pyroptosis, ferroptosis, autophagy, senescence, mitotic catastrophe, and cuproptosis. An in-depth understanding of these radiotherapy-related cell fate decisions can greatly improve the efficiency of radiotherapy for cancer.

Combination of three-dimensional arterial spin labeling and stretched-exponential model in grading of gliomas.

To evaluate the diagnostic value of combining 3D arterial spin labeling (ASL) and stretched-exponential diffusion model in grading of gliomas.A total of 72 patients with histo-pathology proved gliomas (34 low-grade, 38 high-grade) were included in this study. 3D ASL and multi-b diffusion weighted imaging (DWI) images were retrospectively analyzed. The ASL and DWI parameters-tumor blood flow (TBF), distributed diffusion coefficient (DDC), and diffusion heterogeneity α were compared between high-grade and low-grade groups and P < .05 was regarded as statistically significant. TBF was also normalized to the corresponding values in contralateral mirror regions of interest (ROI) (M-TBF), normal grey matter (G-TBF), and white matter (W-TBF) and were compared between high and low-grade tumors.TBF values were significantly higher in high-grade gliomas (P < .001). In stretched-exponential model, the α value of low-grade gliomas showed significant higher than high-grade gliomas group (P < .001), but there was no difference of DDC (P > .05). When TBF values were normalized to contralateral mirror ROI, normal grey matter and white matter, G-TBF showed the highest sensitivity and specificity for differentiation high-grade and low-grade gliomas. The area under area under curve (AUC) of G-TBF and α for glioma grading were 0.926 and 0.892, respectively. The area under AUC of the G-TBF combination with α was 0.960 and corresponding sensitivity and specificity were 94.1% and 98.7%.The combination of 3D ASL and stretched-exponential model parameters can be used to differentiate high-grade and low-grade gliomas. Combination G-TBF and α value can obtain best diagnostic performance.