Objective To analyze the CT features of primary pulmonary leiomyoma (PPL) and improve the diagnostic ability of PPL.Methods The CT findings and clinical characteristics were retrospectively analyzed in 6 patients with PPL proved by pathology,and the related literatures were reviewed.Results Six PPLs were single lesion from 3.0 to 8.5 cm in size,the shape was round or oval with extremely smooth margin.On CT plain scan,the CT values of all PPL lesions were 25-33 HU,4 lesions presented homogeneous moderate enhancement (40-60 HU) and 2 lesions presented inhomogeneous enhancement after contrast administration.A solid lesion showed obviously patchy enhancement with cystic degeneration,1 lesion presented ring enhancement.All lesions were benign histopathologically,the leiomyoma cells showed spindle shaped or in bundles with pseudocapsule,and with hyaline degeneration in 1 case.Smooth Muscle Actin (SMA) were all marked positive on immunohistochemistry.Conclusions CT findings of PPL have some characteristics,but lack of specificity,the final diagnosis still relies on pathological examination.

Objective To explore the correlation between CT perfusion imaging parameters and differentiation degree of gastric cancer.Methods Fifty patients with gastric cancer proved by surgery and pathology underwent 64-slice spiral CT perfusion imaging before surgery,and the CT perfusion parameters were obtained including blood flow(BF),blood volume(BV),mean transit time (MTT)and permeability surface(PS).The patients were divided into 3 groups (well,moderate and poor differentiation)according to the degree of cell differentiation.Statistical analysis was performed by using the SPSS 17.0 statistics software.Results The perfusion parameters of BF (mL·min-1 ·100 g-1 ),BV (mL/100 g),MTT (s)and PS (mL·min-1 ·100 g-1 )were as follows:75.28±6.81,9.01± 0.94,9.89±1.65 and 10.05±0.71 in well differentiation group with 10 patients (20%),110.01±31.90,18.18±5.62,9.81±3.69 and 40.08±1 5.82 in moderate differentiation group with 24 patients (48%),138.59±38.09,21.08±4.1 1,9.47 ±1.80 and 57.50± 13.28 in poor differentiation group with 1 6 patients (32%)respectively.Among three groups,there were statistic differences in BF, BV and PS between well differentiation group and moderate differentiation group or low differentiation group (P <0.05),however there was no statistic difference in MTT among three groups (P >0.05).Conclusion The BF,BV and PS values may be regarded as the evaluation index for malignant degree of gastric cancer.

Objective To observe the value of CE-MRA in differentiating secondary vascular tortuosity of the spinal cord caused by spinal vascular malformations (SCVM) or non-vascular malformations.Methods A retrospective analysis was performed on 30 patients suspected as SCVM.Based on the results of DSA or surgery,the patients were divided into SCVM group (n=16) or non-vascular malformation group (n=14).CE-MRA features were compared between the two groups,including the number,length,position,tortuosity degree of spinal cord tortuous vascular and whether the abnormal feeding artery existed surrounding tortuous vascular.Results Compared with non-vascular malformation group,SCVM group showed more average number of tortuous vascular [(1.38 ± 0.16) vs 1,P=0.012],longer tortuous vascular lengthy [similar lengthy of (9.68 ± 1.18) vertebral body vs(4.14±0.62) vertebral body,P＜0.001),higher score of tortuosity degree [(2.23 ± 0.22) scores vs (0.86±0.10) scores,P＜0.001] and more easier to appear abnormal feeding artery surrounding tortuous vascular [75.00％(12/16) vs28.57％(4/14),P=0.014].Furthermore,only2 cases (2/16,12.50％) of tortuous vascular located in and limited to lower thoracic vertebra (T7 T12 vertebrae) in SCVM group,while 11 cases (11/14,78.57％) in non-vascular malformation group (P=0.020).Conclusion Spinal cord secondary vascular tortuosity of SCVM can be well distinguished from that caused by non-vascular malformation with CE MRA spinal angiography.

Objective:To explore the value of nomogram based on arterial spin labeling (ASL) MRI perfusion parameters and clinicopathological features in predicting the response to chemoradiotherapy (CRT) in advanced nasopharyngeal carcinoma (ANPC, stage Ⅲ and Ⅳ).Methods:From June 2018 to January 2021, 70 patients with ANPC confirmed by pathology were prospectively enrolled in Affiliated Hospital of Jiangnan University. Nasopharyngeal MRI plain scan, ASL and contrast-enhanced scan were performed before CRT, and routine MRI re-examination was performed within 1 week after the end of CRT. The pre-CRT perfusion parameter tumor blood flow (TBF) from ASL and clinicopathological features were recorded, and the maximum diameter (MD) of the tumor on T 1WI images was measured. The patients were divided into CRT effective group (48 cases) and ineffective group (22 cases) according to the response evaluation criteria in solid tumors. The independent sample t test was used to compare the differences of TBF, age and MD between effective group and ineffective group. The χ 2 test was used to compare the differences of gender, clinical stage and pathological type between the 2 groups. Using binary logistic regression analysis, clinicopathological model and TBF combined clinicopathological model were constructed, and the nomogram of combined model was constructed. The diagnostic efficacy of the models was obtained by receiver operating characteristic (ROC) curve analysis, and the area under the ROC curves (AUC) of the 3 models were compared by DeLong method. The calibration curve for the nomogram was generated, and the concordance index (C index) was acquired. Results:The TBF of the effective group and the ineffective group were (113±9) and (97±14) ml·100 g -1·min -1, with a statistical difference ( t=5.17, P<0.001). The MD value of the effective group was smaller than that of the ineffective group, with a statistical difference ( t=-2.24, P=0.028). There were statistical differences in clinical stage and pathological type between the 2 groups (χ 2 values were 12.21 and 12.95, respectively, both P<0.001). Three independent predictors, including TBF (OR=7.749), clinical stage (OR=0.129) and pathological type (OR=5.228), were included in logistic regression analysis. The AUC, sensitivity and specificity of TBF model in predicting the response to CRT were 0.843, 87.5% and 72.7%, of clinicopathological model were 0.822, 80.2% and 59.1%, of the nomogram model were 0.893, 81.2% and 90.9%. There was no statistical difference of AUC between the nomogram model and TBF model ( Z=1.23, P=0.215). However, the AUC of the nomogram model was greater than that of the clinicopathological model ( Z=2.47, P=0.031). The calibration curve showed that there was a good concordance index (C index=0.892) between the predicted value of nomogram and the actual clinical observation value. Conclusion:TBF, clinical stage and pathological type are independent predictors of the response to CRT in ANPC patients, and the nomogram based on these three factors has a good ability in predicting the response to CRT.

Objective:To build a predictive model for symptomatic radiation pneumonitis(RP) using the pretreatment CT radiomics features, clinical and dosimetric data of lung cancer patients by using machine learning method.Methods:A retrospective analysis of 103 lung cancer patients who underwent radiotherapy in the Affiliated Hospital of Jiangnan University from November 2018 to April 2020 was performed. Total normal lung tissues were segmented as an interested volume in pretreatment CT images, and then 250 radiomics features were extracted. The correlations of RP and clinical or dosimetric features were firstly investigated with univariate analysis. Then all clinical data, dosimetric data and CT radiomics features were collected and considered as predictors for modeling of RP grade ≥ 2. Features were selected through LASSO machine learning method, and the predictive model was built. Finally, nomogram for risk of RP were obtained according to the selected features.Results:The result of univariate analysis showed that symptomatic RP was significantly correlated with lung dosimetric parameters including mean lung dose (MLD), V20 Gy and V30 Gy( t=2.20, 2.34 and 2.93, P<0.05). Four features, including lung dose volume percentage V30 Gyand three radiomics features, entropy feature of GLCM, mean and median feature of wavelet histogram were selected among all clinical, dosimetric features and radiomics features. AUC of the predicted model obtained from selected features reached 0.757. For convenient clinical use, the nomogram were obtained, and then personalized RP risk prediction and early intervention could be performed according to this nomogram. Conclusions:Pretreatment CT radiomics and dosimetric features can be used in predicting symptomatic RP, which will be useful for advanced intervention treatment.