ObjectiveTo detect the expression of Prox1 (prospero-related homeobox 1) gene in primary hepatocellular carcinoma (HCC), and to analyze the correlation of Prox1 gene expression with pathological grade and clinical stage of HCC. MethodsThe expressions of Prox1 gene in carcinoma tissues and adjacent cancerous tissues in HCC as well as normal liver tissues were detected by semi-quantitative RT-PCR, then the correlation of Prox1 gene expression with HCC pathological grade and clinical stage were analyzed. ResultsThe expression of Prox1 gene in carcinoma tissues (0.243±0.102) and adjacent cancerous liver tissues (0.537±0.235) was significantly lower than that in normal liver tissue (0.812±0.372), respectively （ Plt;0.01 or Plt;0.05）. Furthermore, the expression of Prox1 gene in carcinoma tissues was significantly lower than that adjacent cancerous liver tissues (Plt;0.05). The expressions of Prox1 gene in different pathological grade (F=97.950, Plt;0.001) and clinical stage were significantly different (F=228.300, Plt;0.001), and when compared with each other, the differences of pathological grade and clinical stage were also significant (Plt;0.001 or Plt;0.01). The expressions of Prox1 gene in HCC carcinoma tissue were negatively correlated with pathological grade (r=-0.930, Plt;0.01) and clinical stage (r=-0.980, Plt;0.01) of HCC. ConclusionsExpression of Prox1 gene may be related to the initiation and development of HCC, however, that whether Prox1 gene functions as tumor suppressor in HCC needs further investigation.
ObjectiveTo investigate the relationship of dynamic contrast enhanced(DCE) MRI scan of the mass type of invasive ductal breast cancer to histological grade. MethodThe imagings of DCEMRI of 92 patients confirmed with operation or biopsy pathology and its correlation with WHO histological grade were analyzed. ResultsThere were 29(31.52%) patients with the tumor long diameter≤2 cm, 53(57.61%) 2-5 cm, 10(10.87%)≥5 cm. There were 3(3.26%) patients with round of the morphological lesions, 7(7.61%) oval, 33(35.87%) lobulated shape, 49(53.26%) irregular shape. There were 11 (11.96%) patients with smooth margin of the periphery of the lesions, 47 (51.09%) irregular shape, 34(36.96%) spiculate margin. There were 15(16.30%) patients with homogeneous enhancement, 40(43.48%) heterogeneous enhancement, 37(40.22%) ring-like enhancement. WHO pathological grade:grade 1 was in 5 cases(5.43%), grade 2 in 30 cases(32.61%), grade 3 in 57 cases(61.96%). The statistical results showed that MRI dynamic enhancement characteristics of lesions in size, shape, and enhanced features were correlated with WHO pathological grade (P < 0.05), there was no correlation between the edge features of the tumor and WHO histological grade(P > 0.05). ConclusionThere is a certain correlation between the breast cancer enhanced MRI features and WHO histological grade, which can be evaluated biological behavior and prognosis according to MRI signs of lesions.
ObjectiveTo investigate value of MSCT imaging on differentiating low grade pancreatic neuroendo-crine neoplasms (pNENs) from non-low grade pNENs. MethodThe clinical and CT data of 32 patients with pNENs,who were confirmed by pathological diagnosis from January 2014 to August 2015,were collected and analyzed retrospec-tively. ResultsThere were 15 patients with grade 1 in the low grade pNENs group,there were 11 patients with grade 2 and 6 patients with grade 3 in the non-low grade pNENs group.Compared with the low grade pNENs,the non-low grade pNENs had the larger diameter of the tumor (P=0.007),irregular tumor shape (P=0.006),obscure tumor margin (P=0.003),peripancreatic tissue or vascular invasion (P=0.036),lymphadenopathy (P=0.003),distant metastasis (P=0.019),lower absolute enhancement of tumor at the arterial (P=0.003) and the relative enhancement of tumor at the arterial (P=0.013). ConclusionThe analysis of MSCT features might help for differentiating low grade pNENs from non-low grade pNENs,so that more timely selection of appropriate treatment strategies would be made.
ObjectiveTo evaluate the feasibility of susceptibility weighted imaging (SWI) in determining pathologic grade of hepatocellular carcinoma (HCC) in rat model. MethodsRat models were established first and SWI was performed before killing rats to be examined pathologically. The Edmondson-Steiner grading was used as gold reference for histological grade of HCC. The characters of intratumoral susceptibility signal intensity (ITSS) between lowand high-grade HCCs were compared. The diagnostic value of ITSS in differentiating lowfrom high-grade HCCs was evaluated. ResultsForty eight rat models of HCC were successfully established. Thirty two HCCs (18 low-grade HCCs and 14 highgrade HCCs) were included finally. The incidence of ITSS between two groups was not significant (P=0.113). Characters including component of ITSS (P=0.002) and ratio of ITSS in HCC (P < 0.001) were compared between lowand high-grade HCCs, which were both statistically significant. When score of ratio of ITSS in HCC was assessed as 2 scores, the sensitivity and specificity of ITSS in differentiating lowfrom high-grade HCCs were 85.7% (95% CI:74.9%-96.5%) and 94.4% (95% CI:83.6%-100%) respectively, and the area under roc curve (AUC) was 0.917. ConclusionSWI can evaluate characters of ITSS in HCC and can be an alternative method in grading HCC.
In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
ObjectiveTo summarize the status and progress of imaging studies of pancreatic neuroendocrine neoplasms (pNENs).MethodThe relevant literatures published recently at domestic and abroad about the imaging of pNENs were collected and reviewed.ResultsDue to poor visibility of pancreatic body and tail, the application of ultrasound (US) was limited. Compared with US, endoscopic ultrasound (EUS) and contrast-enhanced ultrasound (CEUS) could improve the detection rate of pNENs. The ability of plain CT scans to differentiate pathological grades was still controversial, but the value of enhanced scan was higher. CT texture analysis was feasible in the discrimination of nonhypervascular pNENs and pancreatic ductal adenocarcinoma (PDAC). Teta2 was the parameter with the highest diagnostic performance. The enhanced features of MRI were similar to CT. Combined with the apparent diffusion coefficient (ADC) value, the diagnostic and classification capabilities of MRI were improved, and the sensitivity and specificity of different ADC thresholds were also different. 68Ga-tetraazacyclododecane tetraacetic acid (68Ga-DOTA) peptide PET-CT had good preliminary diagnostic value for well-differentiated pNENs, and 18Fluoro-fluorodeoxyglucose (18F-FDG) PET-CT had limited diagnostic value.ConclusionsSomatostatin receptor imaging is of high diagnostic value and can guide clinical treatment and predict prognosis, but it has not been widely used in China. Conventional morphological images have advantages in the diagnosis and classification of pNENs. Therefore, it is important to choose a proper image inspection method.
Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods A retrospective analysis was conducted on clinical data of 261 patients with IAC treated at Yantaishan Hospital from October 2018 to May 2023. Among them, there were 101 males and 160 females, aged 27 to 88 years at a mean age of (61.96±9.17) years. Six patients had dual primary lesions, with each lesion analyzed as an independent sample. According to the 2021 WHO classification criteria for lung adenocarcinoma, 267 IACs were classified as gradeⅠ(48 patients), grade Ⅱ (89 patients), and grade Ⅲ (130 patients). Differences in parameters among groups were compared, and logistic regression analysis was used to evaluate the predictive value of AI quantitative parameters for grade Ⅲ IAC. LASSO regression analysis was employed to select parameters with non-zero coefficients, and three machine learning models were constructed and internally verified based on the joint parameters to predict grade Ⅲ IAC efficacy, which were visualized by the Nomogram. Results(1) There were statistical differences between the two groups in parameters such as solid component proportion, long diameter, short diameter, malignancy probability, CT average value, CT maximum value, CT minimum value, CT median value, CT standard deviation, kurtosis, skewness, and entropy (P<0.05). (2) Comparison between two groups: gradeⅠand gradeⅡwere combined for single-factor analysis against grade Ⅲ, indicating differences in all variables except age (P<0.05). Multi-factor analysis identified CTR and CT standard deviation as independent risk factors for distinguishing grade Ⅲ IAC, with a negative correlation between them. (3) Pathological comparisons: gradeⅠhad no lymph node metastasis, gradeⅡhad 2 patients of lymph node metastasis with micro-papillary components, and Grade Ⅲ had 19 patients of lymph node metastasis. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. (4) Correlation analysis: CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade Ⅱ. (5) CTR and CT median value were selected by using LASSO regression, and logistic regression, random forest, and XGBoost models were constructed and validated. Among them, the XGBoost model demonstrated the best predictive performance. Conclusions Cautious consideration should be given to grade Ⅲ IAC when CTR is more than 39.48% and CT standard deviation is less than 122.75 Hu. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.