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Objective To analyze the imaging manifestations of malignant transformation of ovarian mature teratoma.Methods The clinical data and CT and MRI imaging findings of four patients confirmed as malignant transformation of ovarian mature teratoma by operation and pathology were analyzed retrospectively.The following imaging features were assessed:size,shape,texture,enhancement degree and pattern,et al.Results Among the four cases,three lesions were located in the left ovary and one in the right ovary.The minimum size and maximum size were 87 mm×80 mm×87 mm and 171 mm×141 mm×215 mm,respectively.All of the lesions showed as round-like or ovoid cystic masses with fat-fluid level(4/4),and floating mixed density or signal masses(2/4).The demonstrated local thickening of the cyst wall(2/4)and/or soft mass growing across the wall(3/4),with significant inhomogeneous enhancement(2/3).Conclusion The malignant transformation of ovarian mature teratoma often presents as a cystic mass with fat-fluid level,and local thickening or soft mass of the cyst wall,with significant enhancement.It should be considered in the elderly patients with abnormal tumor markers and above imaging features.
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Objective:To explore the value of nomogram based on dual-layer detector spectral CT quantitative parameters and conventional CT feature in evaluating high-grade pattern (HGP) of pulmonary invasive non-mucinous adenocarcinoma.Methods:This study was a case-control study. A total of 71 patients with pathologically confirmed pulmonary invasive non-mucinous adenocarcinoma in the First Affiliated Hospital of Soochow University from February 2022 to May 2023 were retrospectively enrolled, which were divided into HGP and non-HGP groups according to pathological results. Conventional CT features were analyzed, including size, shape, density, internal signs, margins, and pleural retraction. The iodine concentration (IC), electron density (ED), and normalized iodine concentration (NIC) of the lesions in both the arterial phase (AP) and venous phase (VP) were measured. Differences between the two groups were analyzed using independent sample t-test, Mann-Whitney U test, or χ2 test. Multivariate logistic regression analysis was used to select the independent influencing factors of HGP in pulmonary invasive non-mucinous adenocarcinoma, and the conventional CT feature model, the spectral CT quantitative parameter model, and the combined model were constructed and expressed in a nomogram. The area under the curve (AUC) of receiver operating characteristic curve was used to assess the performance of each model, and was compared by DeLong test. Decision curves (DCA) was used to assess the clinical net benefit of the models. Results:There were significant differences between HGP group and non-HGP group in terms of density, lobulation, spiculation, IC AP, IC VP, NIC AP, ED AP and ED VP (all P<0.05). The multivariate logistic regression analysis showed that the solid nodule ( OR=15.452, 95% CI 4.246-56.235, P<0.001), lobulation ( OR=7.069, 95% CI 1.618-30.883, P=0.009), ED AP( OR=1.183, 95% CI 1.064-1.315, P=0.002) and IC VP ( OR=0.231, 95% CI 0.072-0.744, P=0.014) were independent influencing factors for predicting HGP in pulmonary invasive non-mucinous adenocarcinoma. The AUC of the conventional CT feature model, spectral CT quantitative parameter model, and the combined model were 0.835, 0.890, and 0.915, respectively. The AUC of the combined model was better than that of the conventional CT feature model ( Z=2.67, P=0.008). The DCA analysis demonstrated that the nomogram had higher clinical net benefit than the conventional CT feature model. Conclusions:The nomogram based on the quantitative parameters of dual-layer detector spectral CT and conventional CT features have favorable diagnostic efficacy in predicting HGP in pulmonary invasive non-mucinous adenocarcinoma, and can be used as a reliable tool for non-invasive diagnosis of HGP before surgery.
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Objective:To investigate the clinical value of multiparametric myocardial imaging using a dual-layer detector spectral CT in the non-invasive preoperative assessment of patients with coronary atherosclerotic heart disease (CHD) undergoing percutaneous coronary intervention (PCI).Methods:The clinical and imaging data of 90 patients who underwent coronary CT angiography (CCTA) with dual-layer spectral detector CT and invasive coronary angiography (ICA) within 30 days at the Affiliated Changshu Hospital of Nantong University from January 2021 to October 2022 were retrospectively analyzed. A total of 189 coronary arteries were included in the study cohort. The patients were divided into PCI ( n=44) and non-PCI groups ( n=46) according to whether they received PCI after evaluation with ICA. The diameter stenosis rate of the coronary arteries, myocardial iodine concentration (IC) and effective atomic number (Z eff) values were obtained from CCTA conventional and spectral images. The IC values and Z eff values of the myocardium in the areas with abnormal perfusion were compared with those in the areas with normal perfusion. The diagnostic performance of these parameters, as well as their combined model, was evaluated and compared using receiver operating characteristic (ROC) curve and area under the curve (AUC) in the pre-PCI assessment of patients with CHD. Results:Baseline patient data did not show statistically significant differences between the PCI and non-PCI groups (all P>0.05). There were statistically significant differences in IC values [(0.42±0.28) and (2.26±0.48) mg/ml] and Z eff values (7.39±0.33 and 8.50±0.25) between the myocardium areas with abnormal perfusion and the myocardium areas with normal perfusion in all patients (all P<0.001). The AUC for assessing whether patients with CHD need PCI treatment using myocardial IC and Z eff values were 0.865 and 0.853, respectively, which were significantly higher than assessment based only on lumen diameter stenosis rate (AUC=0.726, P<0.001). Conclusions:The IC and Z eff derived from myocardial spectral images can be used to diagnose myocardial perfusion abnormalities in patients with CHD. The spectral myocardial multi-parameters imaging shows promising potentials in pre-PCI assessment of patients with CHD, which can improve the efficiency of evaluation and may help to avoid unnecessarily invasive procedures.
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Objective:To investigate the value of the nomogram based on quantitative parameters of dual-layer detector spectral CT and conventional CT features in preoperatively predicting tumor deposits (TDs) in colorectal cancer.Methods:This study was a case-control study. A total of 126 patients with pathologically confirmed colorectal cancer who underwent preoperative spectral CT scan from January 2022 to March 2023 in the First Affiliated Hospital of Soochow University were enrolled retrospectively. Patients were divided into TDs-positive group ( n=38) and TDs-negative group ( n=88) based on pathological results. The following conventional CT features were assessed: cT stage, cN status, uniformity of enhancement in the venous phase, pericolorectal fat invasion (PFI), maximum tumor diameter, and tumor location. The following quantitative parameters were also measured and calculated: the normalized iodine concentration (NIC) of lesions, the normalized effective atomic number (NZ eff), and the slope of the 40-100 keV spectral curve (K) in the arterial and venous phases, and the difference in NIC between the arterial and venous phases. Multivariate logistic regression analysis was used to select independent predictors of TDs and the nomogram based on spectral CT quantitative parameters and conventional CT features was constructed. The receiver operating characteristic curve was performed to evaluate the diagnostic performance of each parameter and model. DeLong test was used to compare the differences of area under the curve (AUC). Results:Statistically significant differences were found between the TDs-positive and TDs-negative groups for the cT stage, cN status, uniformity of enhancement in the venous phase, PFI, NIC, NZ eff, K in the venous phase and the difference in NIC between the arterial and venous phases ( P<0.05). After multivariate logistic regression analysis, the conventional CT feature model incorporated two features: uniformity of enhancement in the venous phase (OR=9.602, 95% CI 3.728-24.734, P=0.001) and PFI ( OR=2.881, 95% CI 1.177-7.049, P=0.020). The combined model of conventional CT features and spectral CT quantitative parameters incorporated three features: the difference in NIC between the arterial and venous phases ( OR=37.599, 95% CI 8.320-169.912, P=0.001), uniformity of enhancement in the venous phase ( OR=14.978, 95% CI 3.848-58.295, P=0.001), and PFI ( OR=4.013, 95% CI 1.320-12.760, P=0.015), and the nomogram was constructed. The AUC, sensitivity, and specificity of the nomogram for predicting TDs were 0.919 (95% CI 0.865-0.973), 84.2%, and 86.5%, respectively. The AUC of the conventional CT feature model was 0.796 (95% CI 0.707-0.885), which was lower than that of the nomogram, and the difference was statistically significant ( Z=3.87, P=0.001). Conclusion:Dual-layer spectral detector CT can be used to predict TDs in colorectal cancer preoperatively, and the nomogram based on quantitative parameters of dual-layer detector spectral CT and conventional CT features shows good diagnostic performance.
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Objective:To evaluate the diagnostic efficacy of prostate imaging recurrence reporting (PI-RR) system for detecting local recurrence after radical prostatectomy (RP) in prostate cancer (PCa) and to assess the consistency of the PI-RR scores assigned by different seniority radiologists.Methods:This study was a cross-sectional study. A total of 176 PCa patients who underwent multi-parametric MRI (mpMRI) for biochemical recurrence (BCR) after RP from July 2015 to October 2021 at the First Affiliated Hospital of Soochow University were retrospectively collected. The mpMRI images were reviewed and the PI-RR scores of the main lesions were assigned independently by six different seniority radiologists (2 junior, 2 senior and 2 expert radiologists). Following the reference standard determined by biopsy pathologic results, follow-up imaging, or prostate specific antigen levels, the patients were divided into two groups: 54 patients with local recurrence and 122 patients without local recurrence. The intraclass correlation coefficient ( ICC) and Kappa test were used to evaluate the consistency of the PI-RR scores by different seniority radiologists. The receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic efficacy of the PI-RR scores assessed by different seniority radiologists for detecting local recurrence of PCa after RP. The DeLong test was utilized to compare the areas under the ROC curve (AUC) of different seniority radiologist PI-RR scores and a false discovery rate (FDR) was applied to correct results using the Benjamini and Hochberg method. Sensitivity and specificity were calculated according to the cutoff value of PI-RR score≥3 or 4. Results:The ICC (95% CI) of all different seniority radiologists was 0.70 (0.64-0.76). The Kappa value was 0.528, 0.325 and 0.370 respectively between expert and senior radiologists, expert and junior radiologists, senior and junior radiologists. The AUC (95% CI) of junior, senior, and expert radiologists were separately 0.73 (0.65-0.81), 0.81 (0.74-0.88), and 0.86 (0.80-0.93). The AUC of the expert radiologist PI-RR score was higher than those of senior and junior radiologist PI-RR scores ( Z=2.22, 3.21, FDR P=0.039, 0.003). The PI-RR score of senior radiologist had higher AUC than that of junior radiologist ( Z=2.22, FDR P=0.026). With the PI-RR score of 3 or greater as a cutoff value, the sensitivity of junior, senior and expert radiologists were respectively 0.59, 0.65, and 0.78 and the specificity were 0.82, 0.93, and 0.95. With the PI-RR score of 4 or greater as a cutoff value, the sensitivity of junior, senior and expert radiologists were respectively 0.50, 0.54, and 0.69 and the specificity were 0.88, 0.96 and 0.97. Conclusion:PI-RR score can accurately diagnose local recurrence of PCa after RP. PI-RR score has a moderate inter-reader consistency across different seniority radiologists. And the diagnostic performance is influenced by the experience of radiologists.
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Objective:To construct a clinical-radiomics model based on MRI, and to explore its predictive value for biochemical recurrence (BCR) after radical prostatectomy in prostate cancer patients.Methods:A total of 212 patients with prostate cancer who underwent radical prostatectomy in the First Affiliated Hospital of Soochow University from January 2015 to December 2018 and had complete follow-up data were retrospectively analyzed. The random toolkit of Python language was used to randomly sample the patients at a ratio of 7∶3 without replacement, and they were divided into a training set (149 cases) and a test set (63 cases). The endpoint of follow-up was BCR or at least 3 years. BCR occurred in 50 patients in the training group and 21 patients in the test group. The imaging features of the main lesion area in the preoperative T 2WI, diffusion-weighted imaging and apparent diffusion coefficient map of patients in the training set were extracted, and the unsupervised K means clustering algorithm was used to screen the features. The selected features were fitted by a multivariate Cox regression model, and the radiomics model was constructed. Univariate Cox regression analyses were used to screen the main clinical risk factors associated with BCR, and the clinical-radiomics model was constructed combined with RadScore. In the test set, the time-dependent receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of the radiomics model, clinical-radiomics model and prostate cancer risk assessment after radical resection (CAPRA-S) score for the occurrence of BCR. Harrell consistency index (C-index) was used to evaluate the model to predict BCR consistency. The calibration curve was used to evaluate the degree of variation of the model. The decision curve was used to evaluate the clinical application value of the prediction model. Results:A total of 26 radiomics features were screened to establish the radiomics model. The univariate Cox showed that the preoperative clinical features included preoperative prostate-specific antigen level (HR=1.006, 95%CI 1.002-1.009, P=0.001), Gleason score of biopsy (HR=1.422, 95%CI 1.153-1.753, P=0.001), clinical T stage (HR=1.501, 95%CI 1.238-1.822, P<0.001). The multivariate Cox showed that the RadScore was an independent predictor of BCR after radical prostatectomy (HR=51.214, 95%CI 18.226-143.908, P<0.001). The selected preoperative clinical features were combined with RadScore to construct a clinical-radiomics model. In the test set, the AUCs of the time (3 years)-dependent ROC curves of the radiomics model, the clinical-radiomics model, and the CAPRA-S score were 0.824 (95%CI 0.701-0.948), 0.841 (95%CI 0.714-0.968), and 0.662 (95%CI 0.518-0.806), respectively. The C-index of the radiomics model, clinical-radiomics model and CAPRA-S score were 0.784 (95%CI 0.660-0.891), 0.802 (95%CI 0.637-0.912) and 0.650 (95%CI 0.601-0.821), respectively. The calibration curve showed that the predicted probability and actual probability of BCR by radiomics model, clinical-radiomics model and CAPRA-S score were in good agreement (χ 2=7.64, 10.61, 6.37, P=0.465, 0.225, 0.498). The decision curve showed that the clinical net benefit of the clinical-radiomics model and the radiomics model was significantly higher than the CAPRA-S score. When the threshold probability was 0.20-0.30, 0.40-0.50, and >0.55, the clinical net benefit of the clinical radiomics model was higher than that of the radiomics model. Conclusions:The clinical-radiomics model can effectively predict the occurrence of BCR in patients with prostate cancer after radical prostate ctomy, and the prediction efficacy is better than the radiomics model and CAPRA-S score.
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Immune checkpoint inhibitors (ICIs) have demonstrated unparalleled clinical responses and revolutionized the paradigm of tumor treatment, while substantial patients remain unresponsive or develop resistance to ICIs as a single agent, which is traceable to cellular metabolic dysfunction. Although dysregulated metabolism has long been adjudged as a hallmark of tumor, it is now increasingly accepted that metabolic reprogramming is not exclusive to tumor cells but is also characteristic of immunocytes. Correspondingly, people used to pay more attention to the effect of tumor cell metabolism on immunocytes, but in practice immunocytes interact intimately with their own metabolic function in a way that has never been realized before during their activation and differentiation, which opens up a whole new frontier called immunometabolism. The metabolic intervention for tumor-infiltrating immunocytes could offer fresh opportunities to break the resistance and ameliorate existing ICI immunotherapy, whose crux might be to ascertain synergistic combinations of metabolic intervention with ICIs to reap synergic benefits and facilitate an adjusted anti-tumor immune response. Herein, we elaborate potential mechanisms underlying immunotherapy resistance from a novel dimension of metabolic reprogramming in diverse tumor-infiltrating immunocytes, and related metabolic intervention in the hope of offering a reference for targeting metabolic vulnerabilities to circumvent immunotherapeutic resistance.
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Humanos , Neoplasias/patología , Inmunoterapia/métodos , Inhibidores de Puntos de Control Inmunológico/uso terapéuticoRESUMEN
Objective:To investigate the predictive value of thrombus enhancement (TE) and thrombus permeability in cardioembolic thrombus with acute middle cerebral artery occlusion based on CT.Methods:The clinical and image data of 93 patients with acute middle cerebral artery occlusion who were admitted to the First Affiliated Hospital of Soochow University within 12 hours after onset from January 2020 to July 2022 were retrospectively analyzed. According to the TOAST criteria, the patients were divided into the cardioembolism (CE) group (43 cases) and the large artery atherosclerosis (LAA) group (50 cases). All patients received noncontrast CT and CT angiography, and then thrombus permeability [thrombus attenuation increase (TAI), void fraction (ε)] and TE were assessed. Independent sample t-test, Mann-Whitney U test and χ2 test were used in univariable analysis between two groups. Multivariable logistic regression analysis was used to explore the independent influencing factors for cardioembolic stroke and establish a logistic model. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive value of TAI, ε, TE and the logistic model in cardioembolic thrombus with acute middle cerebral artery occlusion. Results:There were statistically significant differences in sex, atrial fibrillation, hypertension, diabetes mellitus, smoking, baseline National Institutes of health stroke scale (NIHSS), TAI, ε and TE between the CE group and the LAA group ( P<0.05). Binary logistics regression analysis showed that TAI (OR=1.300, 95%CI 1.147-1.473, P<0.001), hypertension (OR=0.116, 95%CI 0.025-0.535, P=0.006) and baseline NIHSS (OR=1.165, 95%CI 1.040-1.304, P=0.008) were independent influencing factors for cardioembolic thrombus. The ROC curve indicated that the logistic model predicted cardioembolic thrombus with the highest AUC of 0.907 (95%CI 0.848-0.966). TE predicted cardioembolic thrombus with the highest sensitivity of 90.7%. Conclusion:TE and thrombus permeability have application value for predicting cardioembolic thrombus with acute middle cerebral artery occlusion based on CT.
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Objective:To explore the predictive value of deep learning (DL)-based coronary artery calcification score (CACS) for obstructive coronary artery disease (CAD) and noncalcified plaque/mixed plaque in type 2 diabetes mellitus (T2DM).Methods:Forty hundred and twenty-four consecutive T2DM patients who accepted CACS scan and coronary CT angiography (CCTA) from December 2012 to December 2019 were included retrospectively, with clinical risk factors and plaque features collected. Plaque composition was classified as calcified, non-calcified or mixed plaque. Obstructive CAD was defined as maximum diameter stenosis≥50%. CACS was calculated with a fully automated method based on DL. Univariate and multivariate logistic regressions were applied to select statistically significant factors and the odds ratios(ORs) were measured. Receiver operating characteristic (ROC) curve was evaluated to assess the predictive performance.Results:Increased CACS was associated with a significantly higher odds of obstructive CAD in CCTA (adjusted ORs were 2.22, 6.18 and 16.98 for CACS=1-99, 100-299, 300-999 vs. CACS=0, and P values were 0.009,<0.001,<0.001 respectively). The area under ROC curve (AUC) of CACS to predict obstructive CAD was 0.764. Compared with 0, increased CACS was associated with increased risk of non-calcified/mixed plaque (adjusted ORs were 2.75, 4.76, 5.29 for CACS=1-99, 100-299, 300-999 respectively and P values were 0.001,<0.001,<0.001 respectively). The AUC of CACS to predict non-calcified/mixed plaque was 0.688. It took 1.17 min to perform automated measurement of CACS based on DL in total, which was significantly less than manual measurement of 1.73 min ( P<0.001). Conclusion:DL-based CACS can predict obstructive CAD and non-calcified plaque/mixed plaque in T2DM, which is economical and efficient, and has important value for clinical diagnosis and treatment.
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Lung cancer is the most lethal malignancy around the world and non-small cell lung cancer (NSCLC) accounts for 80% of all cases. Most of the NSCLC patients has "driver gene mutations" and targeted therapy achieved a relatively good efficacy, but some patients progressed or relapsed after treatment. Previous studies demonstrated that immune checkpoint inhibitor could improve the prognosis of advanced-stage NSCLC and prolong the survival time. However, the efficacy of immune therapy varies in NSCLC patients with different immune and molecular features. The efficacy of immune therapy was controversial in NSCLC patients with driver gene mutation. The present review will summarize the immune characteristics of NSCLC patients with driver mutation and the directions of immunotherapy for patients with driver mutation. .
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Humanos , Carcinoma de Pulmón de Células no Pequeñas/terapia , Inmunoterapia , Neoplasias Pulmonares/terapia , Terapia Molecular Dirigida , MutaciónRESUMEN
Objective:To investigate the difference in the peri-coronary fat attenuation index (FAI) between using coronary calcium score (CCS) images and coronary CT angiography (CCTA) images, and to explore the feasibility and befitting threshold of FAI measured on CCS images.Methods:The clinical and imaging data of patients who underwent CCTA examination from August 2019 to August 2020 were retrospectively analyzed in the First Affiliated Hospital of Soochow University. According to the inclusion and exclusion criteria, there were 122 cases in non-calcified plaque group (144 coronary arteries) and 97 cases in none-plaque group (186 coronary arteries). The coronary arteries were delineated both on CCS and CCTA images with Perivascular Fat Analysis Tool; the regions of interest of peri-coronary adipose tissue were generated automatically after setting the threshold of fat tissue. Then the FAI value was calculated. The thresholds were set in four levels (-190--30, -185--25, -180--20 and -175--15 HU) for CCS images and one level (-190--30 HU) for CCTA images. The intra-class correlation coefficient (ICC) was used to evaluate the consistency of the measurements of FAI values on CCS and CCTA images between the two physicians. Paired t test was used to compare the differences of FAI values between CCS and CCTA images, and Pearson correlation coefficient was used to evaluate the correlation between CCS-FAI and CCTA-FAI. Results:(1) FAI values measured on CCS and CCTA images by 2 physicians showed good consistency; (2) At the threshold of -185--25 HU, there was no significant difference in FAI values between the CCS and CCTA images for non-calcified plaque group [(-84.15±5.99)HU vs. (-83.83±5.98)HU, t=0.79, P=0.429], as well as for the none-plaque group [(-83.41±5.75)HU vs.(-83.84±6.25)HU, t=-1.08, P=0.280]; (3) There were significant differences on FAI values between the CCS images and CCTA images at the threshold of -190--30、-180--20 and -175--15 HU (all P<0.05); (4) There were moderate correlations on FAI values between the CCS images and CCTA images under different thresholds both in non-calcified plaque group and none-plaque group. Conclusion:It is feasible to measure FAI on CCS images, and the befitting threshold is -185--25 HU.
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Objective:To investigate the predictive value of measurement parameters of left atrial appendage by coronary CTA (CCTA) for the risk of cardiogenic stroke (CS) in patients with nonvalvular atrial fibrillation (NVAF).Methods:The clinical and examination data of 179 patients with NVAF who underwent CCTA examination were retrospectively analyzed. The selected patients were grouped according to the outbreak of acute ischemic stroke and TIA within 2 years after CCTA examination. Patients who met the criteria for CS were selected as cases (87 patients), and those with neither stroke nor TIA as controls (92 patients). The diameter and area of left atrial appendage (LAA) orifice, the LAA depth, and the LAA volume were measured by using dedicated software. The parameter was corrected using the body surface area (BSA) to obtain the correction index of corresponding parameter. The independent samples t test, Mann-Whitney U test, and Chi-square test were used to compare the differences in various indicators between the two groups. Binary logistic regression was used to analyze the impacts of body mass index (BMI), hyperlipidemia, the duration years of atrial fibrillation, left atrial appendage area index (LAAOA Index), and the left atrial appendage emptying fraction (LAAEF) on the risk of CS. The ROC curve was used to evaluate the predictive value of LAAOA Index and LAAEF for the onset of CS. Results:The correction index of the left atrial appendage orifice maximum and minimum diameter, the left atrial appendage orifice area, and the maximum & minimum left atrial appendage volume and the LAAEF in the case group were (1.80±0.21) cm/m 2, (1.19±0.17) cm/m 2, (3.20±0.71) cm 2/m 2, (7.91±1.92) ml/m 2, (5.03±1.40) ml/m 2, (36.20±10.54)%, and those value in the control group were (1.64±0.24) cm/m 2, (1.06±0.19) cm/m 2, (2.65±0.64) cm 2/m 2, (6.61±1.68) ml/m 2, (3.67±1.28) ml/m 2, (45.25±10.07)%, the differences were statistically significant ( t= 4.783, 4.647, 5.481, 4.826, 6.823, and -5.875, all P<0.001). Binary logistic regression analysis showed that the increase in LAAOA Index ( P= 0.005) and the decrease in LAAEF ( P<0.001) were independent risk factors for CS in NVAF patients. The area under the ROC curve (AUC) of LAAOA Index was 0.712 (95%CI 0.639-0.777), and the best diagnostic cut-off was 3.16 cm 2/m 2; the AUC of LAAEF was 0.734 (95%CI 0.663-0.797), the cut-off was 38.71%; the AUC of LAAOA Index-LAAEF was 0.786 (95%CI 0.718-0.843). The difference of AUC value between LAAOA Index and LAAEF was not statistically significant ( Z= 0.448, P= 0.654). The difference of AUC between the LAAOA Index-LAAEF and LAAOA Index ( Z=2.667, P=0.008) and between the LAAOA Index-LAAEF and LAAEF ( Z=2.061, P=0.039) were statistically significant. Conclusions:CCTA can provide a one-stop and relatively accurate evaluation of the size and function of the left atrial appendage by post-processing the coronary vascular scan data. Left atrial appendage measurement parameters from CCTA can be used as a supplement to the CHA2DS2-VASc score, and provide quantitative indicators for the risk assessment of CS in patients with NVAF.
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Objective:To develop a radiomics nomogram model based on CT to distinguish arteriovenous malformation(AVM) intracerebral hemorrhage from primary intracerebral hemorrhage.Methods:One hundred and thirty-five patients with cerebral hemorrhage confirmed by operation in the First Affiliated Hospital of Soochow University were analyzed retrospectively, including 52 patients with AVM cerebral hemorrhage and 83 patients with primary cerebral hemorrhage. Radiomics features were extracted from baseline CT, radiomics score (Radscore) was calculated and radiomic labels were constructed. Multiple logistic regression analysis was used for clinical features combined with CT signs to establish a clinical model. And then the nomogram model was generated according to the Radscore and the clinical model. The ROC curve and decision curve analysis (DCA) were used to evaluate the discrimination performance of the model.Results:Six features were selected and used to establish radiomic labels. The clinical model consisted of age (OR: 4.739, 95%CI 1.382-16.250) and hematoma location (OR: 0.111, 95%CI 0.032-0.385), while the nomogram model consisted of age, hematoma location and Radscore. In the training group, there was a significant difference between the nomogram model [area under curve (AUC) 0.912] and the clinical model (AUC 0.816), the radiomics model (AUC 0.857) ( Z=2.776, 2.034, P=0.006, 0.042, respectively); While in the validation group, there was no significant difference between the nomogram model (AUC 0.919) and the clinical model (AUC 0.788), the radiomics model (AUC 0.810) ( Z=1.796, 1.788, P=0.073, 0.074, respectively). DCA analysis showed that the clinical value of the nomogram model was superior to the clinical model and radiomic model. Conclusion:The radiomics nomogram can effectively distinguish AVM-related cerebral hemorrhage from primary cerebral hemorrhage, which is helpful for clinical decision-making.
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Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.
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Objective:To investigate the value of pericoronary adipose tissue histogram parameters based on coronary CT angiography (CTA) images for the differentiation of acute coronary syndrome and stable coronary artery disease.Methods:The clinical data and CTA images of 93 patients with coronary CTA examination in Suzhou Kowloon Hospital from 2013 to 2018 were analyzed retrospectively. There were 39 patients with acute coronary syndrome (acute coronary syndrome group) and 54 patients with stable coronary artery disease (stable coronary artery disease group). A region of interest (ROI) was drawn around the stenosis of the coronary arteries, with CT attenuation ranging from-190 to -30 HU to exclude non-adipose tissue. The CT attenuation of ROI excluding non-adipose were measured and histogram analysis was performed. The obtained parameters included the mean value, median value and the 5th, 10th, 45th, 55th, 70th and 95th percentiles. The differences in histogram parameters between the two groups were compared, and then the value of each parameter in differentiating acute coronary syndrome and stable coronary artery disease was evaluated based on receiver operating characteristic (ROC) analysis. The stepwise regression of multivariate logistic regression analysis was used to identify the useful features and establish the final prediction model. The ROC curve of the final model was calculated and its value was analyzed.Results:The mean, median and the 5th, 10th, 45th, 55th,70th and 95th percentile differences between the acute coronary syndrome group and the stable coronary artery disease group were statistically significant (all P<0.05). The ROC curve for the median and the 95th percentile had the same area under curve (AUC) of 0.73. The sensitivity, specificity and AUC of the diagnostic model established by multiple logistic regression were 82.1%, 89.1% and 0.90 respectively. Conclusion:CT attenuation histogram of pericoronary adipose tissue is of high value in differentiating acute coronary syndrome from stable coronary artery disease.
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Objective:To evaluate the application of multiparametric MRI (mpMRI)-transrectal ultrasound (TRUS) fusion guided biopsy in the diagnosis of clinical significant prostate cancer (PCa).Methods:A prospective analysis was performed in 168 patients with suspected PCa from September 2015 to June 2017 in the First Affiliated Hospital of Soochow University. Suspicious areas on mpMRl were defined and graded using prostate imaging reporting and data system version 2 (PI-RADS V2) score. All the patients had the TRUS-guided systematic biopsy, 108 patients with PI-RAD V2 scores ≥ 3 had additional MRI-TRUS targeted biopsies. Taking pathologic results as golden standard, the detection rates were compared between the 2 methods using χ 2 test. Results:Initially, all of the 168 patients underwent TRUS biopsy. PCa was detected in 86 (101 niduses) of 168 patients (51.19%, 86/168), 82 (91 niduses) (48.81%, 82/168) were not prostate cancer. Seventy eight (46.43%, 78/168) cases of PCa were detected by TRUS biopsy, and 63 (58.33%, 63/168) cases of PCa were detected by MRI-TRUS fusion guided biopsy, the difference was statistically significant between TRUS biopsy and MRI-TRUS fusion guided biopsy (χ 2=3.73, P=0.035). The 168 patients were biopsied with a total of 2 300 cores, including TRUS biopsy 2 016 cores and MRI-TRUS fusion targeted biopsy 284 cores. Additionally, the detection rate for per cores for MRI-TRUS fusion targeted biopsy (51.76%, 147/284) was significantly higher than that for TRUS biopsy cores (19.64%, 396/2 016) (χ 2=142.38, P<0.05). Among patients with a positive biopsy for PCa, the biopsy cores for conventional TRUS biopsy was 1 032 comparing to 214 cores for MRI-TRUS biopsy. The suspicious MRI-TRUS fusion targeted biopsy (68.69%, 147/214) detected more PCa compared with TRUS biopsy (38.37%, 396/1 032) (χ 2=66.27, P<0.05). Among patients with a positive biopsy for PCa, MRI-TRUS fusion targeted biopsy [69.74% (106/152)] detected more significant cancer cores than TRUS biopsy [54.50% (351/644) ] (χ 2=11.67, P<0.05). Conclusion:MRI-TRUS fusion biopsy combined with PI-RADS V2 increases positive rate markedly and improves the detection rate of clinical significant PCa.
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Objective:To explore the value of spectral CT radiomics quantitative features on differentiating lung cancer nodule from inflammatory nodule.Methods:The spectral CT imaging data of 96 lung cancer nodules and 45 inflammatory nodules from the First Affiliated Hospital of Soochow University were analyzed retrospectively. According to a ratio of two to one, patients were randomly assigned to the training group and validation group, including 64 lung cancer nodules and 30 inflammatory nodules in the training group, 32 lung cancer nodules and 15 inflammatory nodules in the validation group. MaZda software was used for radiomic feature extraction from the 70 keV monochromatic images in arterial phase and venous phase for lung cancer nodules and inflammatory nodules in the training group. Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE+ACC) and mutual information (MI) were used to select 10 optimal features for the optimal feature subsets. The optimal feature subsets were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) to calculate the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, precise and F1 score in differentiating lung cancer nodule from inflammatory nodule. The prediction model was established using the optimal feature subsets in the training group with artificial neural network (ANN). Then the established prediction model was used to differentiate lung cancer nodule from inflammatory nodule in the validation group. Delong test was used to compare the differences in the AUC of different optimal feature subsets.Results:In arterial phase, the optimal feature subset obtained from MI-NDA had the highest AUC of 0.888 [95% confidence interval (CI) 0.806-0.943], accuracy rate of 88.3%, sensitivity of 87.5% and specificity of 90.0%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between MI-NDA and Fisher-NDA or (POE+ACC)-NDA method ( Z=1.941, P=0.052; Z=1.683, P=0.092). In venous phase, the optimal feature subset obtained from (POE+ACC)-NDA had the highest AUC of 0.846 (95%CI 0.757-0.912), accuracy rate of 87.2%, sensitivity of 92.2% and specificity of 76.7%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between(POE+ACC)-NDA and MI-NDA method ( Z=1.354, P=0.18), but significant difference between (POE+ACC)-NDA and Fisher-NDA method ( Z=2.423, P=0.015). In the validation group and training group, the optimal feature subset selected by MI-NDA method had the highest AUC of 0.888(95%CI 0.806-0.943) and 0.871(95%CI 0.741-0.951). Conclusion:Spectral CT radiomics quantitative features have great value on the differential diagnosis of lung cancer nodule and inflammatory nodule.
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Objective:To explore the value of gadolinium-ethoxybenzyl- diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI nomogram model for preoperative prediction of Ki-67 expression in hepatocellular carcinoma (HCC).Methods:Data of 85 patients of HCC confirmed by postoperative pathology, who underwent preoperative Gd-EOB-DTPA enhanced MRI between September 2016 and August 2019 in the First Affiliated Hospital of Soochow University were retrospectively evaluated. According to postoperative immunohistochemistry Ki-67 index, the 85 patients were divided into Ki-67 low expression group(Ki-67 index ≤10%, n=20) and Ki-67 high expression group (Ki-67 index >10%, n=65). Clinical data (hepatitis, cirrhosis, etc.), qualitative imaging parameters (tumor margin, capsule, etc.) were compared by χ 2 test and quantitative parameters [lesion-to-normal parenchyma ratio-arterial phase (LNR-AP), lesion-to-normal parenchyma ratio-portal phase (LNR-PP), lesion-to-normal parenchyma ratio-equilibrium phase (LNR-EP) and lesion-to-normal parenchyma ratio-hepatobiliary phase (LNR-HBP)] were compared by independent sample t test. The above statistically significant parameters were included in multivariate logistic regression to identify the independent predictors of Ki-67 high expression and then the nomogram model for predicting Ki-67 expression of HCC was established. Results:alpha-fetoprotein (AFP) tumor margin, arterial rim enhancement between the Ki-67 low expression group and the Ki-67 high expression group had significant differences (χ 2 were 8.196, 10.538 and 4.717, respectively, P<0.05). LNR-AP, LNR-PP, LNR-EP and LNR-HBP between the two groups had significant differences ( t were 2.929, 2.773, 2.890 and 3.437, respectively, P<0.05).The result of multivariate logistic regression revealed that AFP≥20 μg/L, non-smooth tumor margin and low LNR-HBP were the independent predictors of Ki-67 high expression (odds ratio were 4.090, 3.509 and 0.042, respectively, P<0.05).The Gd-EOB-DTPA enhanced MRI nomogram model for predicting Ki-67 expression of HCC was established successfully. The Area under the receiver operating characteristic curve of the nomogram was 0.837 and the corrected predictive curve fitted the ideal curve, which suggested the model had a good predictive efficiency. Conclusion:Gd-EOB-DTPA enhanced MRI nomogram model has great value in preoperative prediction of Ki-67 expression of HCC, which provided a personalized prediction method for Ki-67 expression in patient with HCC.
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Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti?parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI?transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo?diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t?test was used to compare the differences in lesion parameters between prostate cancer and BPH, low?risk (BPH+Gleason score 6 points) and medium?high?risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis.Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low?risk or high?risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia(BPH) in PZ were included, meanwhile, the cases of GS≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa?BHP group and high?low risk group in TZ (P<0.05), D*and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were－0.585 and－0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid?higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.
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Objective@#To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer.@*Methods@#A total of 55 patients who were undergoing preoperative muti-parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI-transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo-diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t-test was used to compare the differences in lesion parameters between prostate cancer and BPH, low-risk (BPH+Gleason score 6 points) and medium-high-risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis. Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low-risk or high-risk.@*Results@#27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia (BPH) in PZ were included, meanwhile, the cases of GS ≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa-BHP group and high-low risk group in TZ (P<0.05), D* and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were -0.585 and -0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS ≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05).@*Conclusions@#IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid-higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.