Objective:To develop and explore the clinical value of a nomogram model for the preoperative diagnosis of proliferative hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI).Methods:The preoperative Gd-EOB-DTPA-enhanced MRI data and clinical pathological data of patients with pathologically confirmed proliferative (178 cases) and non-proliferative type HCC (378 cases) from September 2017 to November 2022 were retrospectively collected. The MRI features and clinicopathological features of proliferative and non-proliferative type HCC were evaluated. Multivariate logistic regression analysis was used to determine the independent predictive factors of proliferative-type HCC. The nomogram prediction model was constructed using R software. The receiver operating characteristic curve (ROC) was used to evaluate its diagnostic efficacy. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal threshold for distinguishing high-risk from low-risk was determined using the Youden index. The survival prognosis of proliferative and non-proliferative type HCC was analyzed and compared using the Kaplan-Meier survival curve and the log-rank test. The measurement data were analyzed using the independent sample t-test or the Mann-Whitney U test. The count data were compared using the χ2 test. Results:There were statistically significant differences in alpha-fetoprotein (AFP) levels ( χ2=17.244, P<0.001), tumor morphology ( χ2=13.669, P<0.001), intratumoral fatty degeneration ( χ2=10.495, P=0.001), abnormal enhancement of peritumoral abnormalities during arterial phase ( χ2=37.662, P<0.001), tumor capsule ( χ2=23.961, P<0.001), intratumoral necrosis ( χ2=77.184, P<0.001), intratumoral hemorrhage ( χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase ( χ2=47.675, P<0.001), rim arterial phase hyperenhancement ( χ2=115.976, P<0.001), intratumoral artery ( χ2=15.528, P<0.001) and intravenous tumor thrombus ( χ2=10.532, P=0.001) between proliferative and non-proliferative type HCC groups. Multivariate logistic regression analysis showed that AFP>200 μg/L ( OR=1.561, P=0.044), no intratumoral fatty degeneration ( OR=1.947, P=0.033), intratumoral necrosis ( OR=2.084, P=0.003), peritumoral hypointensity in the hepatobiliary phase ( OR=2.314, P=0.001), and annular hyperenhancement in the arterial phase ( OR=5.557, P<0.001) were independent predictors for preoperative diagnosis of proliferative-type HCC. A nomogram model for preoperative prediction of proliferative type HCC was constructed based on the independent predictors. The area under the ROC curve model for predicting proliferative-type HCC was 0.772 (95% CI: 0.735-0.807), with a sensitivity of 69.1% and a specificity of 75.4%. The calibration curve and DCA curve showed superior calibration performance and clinical applicability of the nomogram model. The Kaplan-Meier curve showed that the recurrence free survival rate after liver resection was significantly lower in patients with proliferative-type HCC than that of non-proliferative-type HCC ( P<0.001), and the high-risk group was significantly lower than the low-risk group ( P<0.001). Conclusions:The construction of a nomogram model based on Gd-EOB-DTPA-enhanced MRI features combined with AFP >200μg/L can accurately diagnose proliferative-type HCC and predict its preoperative prognosis.

Objective:To develop and explore the clinical value of a nomogram model for the preoperative diagnosis of proliferative hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI).Methods:The preoperative Gd-EOB-DTPA-enhanced MRI data and clinical pathological data of patients with pathologically confirmed proliferative (178 cases) and non-proliferative type HCC (378 cases) from September 2017 to November 2022 were retrospectively collected. The MRI features and clinicopathological features of proliferative and non-proliferative type HCC were evaluated. Multivariate logistic regression analysis was used to determine the independent predictive factors of proliferative-type HCC. The nomogram prediction model was constructed using R software. The receiver operating characteristic curve (ROC) was used to evaluate its diagnostic efficacy. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal threshold for distinguishing high-risk from low-risk was determined using the Youden index. The survival prognosis of proliferative and non-proliferative type HCC was analyzed and compared using the Kaplan-Meier survival curve and the log-rank test. The measurement data were analyzed using the independent sample t-test or the Mann-Whitney U test. The count data were compared using the χ2 test. Results:There were statistically significant differences in alpha-fetoprotein (AFP) levels ( χ2=17.244, P<0.001), tumor morphology ( χ2=13.669, P<0.001), intratumoral fatty degeneration ( χ2=10.495, P=0.001), abnormal enhancement of peritumoral abnormalities during arterial phase ( χ2=37.662, P<0.001), tumor capsule ( χ2=23.961, P<0.001), intratumoral necrosis ( χ2=77.184, P<0.001), intratumoral hemorrhage ( χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase ( χ2=47.675, P<0.001), rim arterial phase hyperenhancement ( χ2=115.976, P<0.001), intratumoral artery ( χ2=15.528, P<0.001) and intravenous tumor thrombus ( χ2=10.532, P=0.001) between proliferative and non-proliferative type HCC groups. Multivariate logistic regression analysis showed that AFP>200 μg/L ( OR=1.561, P=0.044), no intratumoral fatty degeneration ( OR=1.947, P=0.033), intratumoral necrosis ( OR=2.084, P=0.003), peritumoral hypointensity in the hepatobiliary phase ( OR=2.314, P=0.001), and annular hyperenhancement in the arterial phase ( OR=5.557, P<0.001) were independent predictors for preoperative diagnosis of proliferative-type HCC. A nomogram model for preoperative prediction of proliferative type HCC was constructed based on the independent predictors. The area under the ROC curve model for predicting proliferative-type HCC was 0.772 (95% CI: 0.735-0.807), with a sensitivity of 69.1% and a specificity of 75.4%. The calibration curve and DCA curve showed superior calibration performance and clinical applicability of the nomogram model. The Kaplan-Meier curve showed that the recurrence free survival rate after liver resection was significantly lower in patients with proliferative-type HCC than that of non-proliferative-type HCC ( P<0.001), and the high-risk group was significantly lower than the low-risk group ( P<0.001). Conclusions:The construction of a nomogram model based on Gd-EOB-DTPA-enhanced MRI features combined with AFP >200μg/L can accurately diagnose proliferative-type HCC and predict its preoperative prognosis.

Hepatocellular carcinoma (HCC) is a highly heterogeneous kind of malignant tumor with a high recurrence rate and low five-year survival rate, which has become one of the major public health issues in China. Currently, HCC is the only solid tumor that can be solely diagnosed based on epidemiological history and typical imaging features without preoperative pathological confirmation. The paradigm for HCC imaging diagnosis has shifted in recent years from anatomy to function, from macroscopic to microscopic, and from diagnosis to prediction in the context of precision medicine, making it possible to study the microscopic processes such as HCC genes and their metabolic laws from the perspective of qualitative and quantitative imaging, thereby providing more accurate biological and imaging information for elucidating the occurrence, development, and clinical treatment decisions of HCC.This paper reviews the research progress of HCC imaging in recent years, demonstrating the rapid horizontal development and enormous potential of imaging in the vertical follow-up of HCC precision diagnosis and treatment. Simultaneously, it also puts forward the shortcomings of current HCC imaging research and looks forward to future development directions in order to be more accurately used in clinical decision support systems.

Objective:To investigate the feasibility and effectiveness of the blended teaching model for diagnostic radiology based on BOPPPS classroom reconstruction, i.e., bridge-in, objective, pre-assessment, participatory-learning, post-assessment, and summary.Methods:The undergraduate students in the classes of 2017 and 2018 in Department of Medical Imaging were selected as research subjects. The students in the class of 2018 were established as observation group and received the innovative blended teaching model based on BOPPPS classroom reconstruction, and those in the class of 2017 were established as control group and received teaching with traditional theoretical lectures. At the end of the course, 80 students were randomly selected from the observation group and the control group for performance analysis and teaching evaluation. SPSS 26.0 was used to perform the t-test. Results:The observation group had a total score of (82.66±6.18), while the control group had a total score of (76.47±5.42), and compared with the control group, the observation group had significantly higher scores of homework score, course discussion, and final examination ( P<0.05). Compared with the control group, the observation group had significantly higher scores of "understanding of the basic knowledge of imaging", "improvement of comprehensive diagnostic thinking ability", "stimulating the interest in learning and expanding horizons", and "cultivating clinical competence" in the self-evaluation survey ( P<0.05). Conclusion:The blended teaching model based on BOPPPS classroom reconstruction is suitable for the teaching of radiology diagnostics. It not only enriches teaching means and methods and enhances classroom participation and interaction, but also expands teaching space and teaching content.

Objective:To explore the impact of applying the imaging cloud platform to medical imaging practice teaching on diagnostic thinking ability and learning experience of students.Methods:Eighty-eight students of Batch 2016 from the Department of Medical Imaging of Chongqing Medical University were randomly divided into two groups, with 44 students in each group. The experimental group was taught by the imaging cloud platform, and the control group was taught by traditional practice. Differences in diagnostic thinking ability and learning experience were compared between the two groups after 4 weeks. SPSS 22.0 was used for t-test. Results:The total score of the diagnostic thinking ability test in the experimental group was higher than that in the control group [(80.63±6.10) vs. (70.36±8.09)], and the difference was statistically significant ( P<0.05). There were statistically significant differences in the scores of three items: description of signs, differential diagnosis and key points, and new progresses and recommendations ( P<0.05). For the five aspects of the learning experience in the questionnaire survey, the scores of the experimental group were higher than those of the control group, and all the differences were statistically significant ( P<0.05). Conclusion:The application of imaging cloud platform in imaging practice teaching has important value in improving diagnostic thinking ability and learning experience of students, and it is worthy of practice and promotion.

Objective: To improve the N biomarker in the amyloid/taueurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively.The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

Objective:To explore the risk factors for early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH), and construct a clinical-radiomics combined model to predict HE after sICH.Methods:From April 2014 to September 2020, 339 patients with sICH who underwent plain CT scans in Radiology Department of our hospital were recruited. Patients were divided into HE group and non-HE group according to whether HE occurred (HE was defined as an increase in hematoma volume>33% or 6 mL on the follow-up CT within 24 h). The clinical data of non-HE group and HE group were compared, and multivariate Logistic regression analysis was used to detect independent risk factors for HE. The radiomics features were extracted from the regions of interest of the hematoma in the first CT scan images; the optimal radiomics features were selected using least absolute shrinkage and selection operator (LASSO) regression model and 10-fold cross-validation method, and then, the radiomics scores (R-score) were calculated; the risk factors for HE (clinical data) and R-score (radiomics data) were used to construct the clinical model, R-score model, and clinical-radiomics combined model; receiver operating characteristic (ROC) curve was performed to evaluate the prediction performance of clinical model, R-score model, and clinical-radiomics combined model; the best model was visualized as a nomogram and a calibration curve was drawn to evaluate the prediction accuracy of this model.Results:As compared with patients in the non-HE group, patients in the HE group had shorter time from sICH onset to first CT, higher percentage of patients with diabetes, lower platelet count, lower Glasgow Coma Scale (GCS) scores, and larger baseline hematoma volume in CT image, with significant differences ( P<0.05). Multivariate Logistic regression analysis showed that baseline hematoma volume ( OR=1.015, 95%CI: 1.000-1.030, P=0.046), GCS scores ( OR=0.914, 95%CI: 0.839-0.995, P=0.039), time from sICH onset to first CT ( OR=0.855, 95%CI: 0.741-0.987, P=0.032), and diabetes ( OR=0.522, 95%CI: 0.311-0.875, P=0.014) were independent risk factors for HE. By using LASSO regression and 10-fold cross-validation method, 20 optimal radiomics features were finally selected. The area under ROC curve of clinical model, R-score model, and clinical-radiomics combined model were 0.650, 0.860, and 0.870, respectively. The calibration curve showed that the prediction accuracy of clinical-radiomics combined model in early HE had good consistency with the actual occurrence probability. Conclusion:The clinical-radiomics combined model could effectively predict early HE with good calibration, which is helpful in individualized clinical assessment of risk of early HE in SICH patients.

Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8–100%), 91.3% (CI: 69.6–100%), and 91.8% (CI: 23.0–98.4%), respectively. Conclusion CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

Objective To investigate the differential diagnostic value of computed tomography (CT) combined with serum tumor markers in borderline ovarian tumors (BOT) and benign epithelial ovarian tumors (BET).Methods The CT data in 28 patients with BOT and 41 patients with BET,both confirmed by surgery and pathological,were analyzed retrospectively.Their preoperative serum carbohydrate antigen 125 (CA125),human epididymis secretory protein 4 (HE4) and carcinoembryonic antigen (CEA) detection results were collected.The CT images features and serum tumor markers levels were compared between the two groups.Results The difference in the appearance rate of tumor solid composition,thick septum and wall nodule between the two groups had statistical significance (x2 =25.135,5.240,5.066,P＜0.05).The serum CA125 level had statistical difference between the two groups (Z=3.202,P＜0.05),while serum HE4 and CEA levels had no statistically significant difference between the two groups(Z=0.330,1.122,P＞0.05).The optimal critical value,sensitivity and specificity of serum CA125 level in differential diagnosis of two kinds of tumor was 42.45 U/mL,53.6％ and 85.4％.The overall diagnostic rate of solid composition and thick septum for diagnosing the two kinds of tumor was 78.5 ％.The overall diagnostic rate of solid composition,thick septum and CA125 level for diagnosing the two kinds of tumor was 81.2％.Conclusion The appearance of solid composition,thick septum and serum CA125 level increase in epithelial ovarian tumor may help to identify BOT and BET.