Objective:To observe alterations in center retinal thickness (CRT) in patients diagnosed with central retinal artery occlusion (CRAO) before and after undergoing superselective arterial thrombolysis (IAT) treatment.Methods:A retrospective clinical study. From August 2022 to September 2023, 12 patients (12 eyes) diagnosed with CRAO and treated with IAT at the ophthalmology department of Shenzhen Second People's Hospital. Among these patients, there were 8 males (8 eyes) and 4 females (4 eyes), all experiencing unilateral onset. The mean age was (47.00±15.06) years. The mean duration from onset to thrombolysis was (30.00±30.42) h. All eyes underwent best corrected visual acuity (BCVA) and optical coherence tomography (OCT) assessments; additionally, 6 eyes underwent Fluorescein fundus angiography (FFA). BCVA assessments were conducted using a standard logarithmic chart and transformed into logarithm of the minimum angle of resolution (logMAR) values for statistical analysis. The OCT measured CRT at various locations around the macular fovea (M), including upper (S1, S3), lower (I1, I3), nasal (N1, N3), and temporal (T1, T3) areas at 1 mm and 3 mm distances from the fovea. CRT was defined as the vertical distance between the inner retinal boundary membrane and the inner interface of the retinal pigment epithelial layer. Pre- and post-IAT examinations were performed using the same equipment and methodologies within a 24-hour interval. Changes in CRT at different macular points were compared and observed, while arterial imaging time changes were assessed in 6 eyes that underwent FFA. Paired t-tests were utilized to analyze logMAR BCVA, CRT at different locations, and arterial imaging time pre- and post-treatment. Results:Prior to IAT treatment, the logMAR BCVA for the affected eye was 3.48±1.42, while the arterial imaging time for the 6 eyes undergoing FFA examination was (27.50±5.47) s. After 24 hours, the logMAR BCVA had improved to 2.35±1.59 for the affected eye, with 9 eyes showing varying degrees of BCVA improvement. The arterial imaging time was (24.17±7.28) s post-treatment. The differences in logMAR BCVA and arterial imaging time before and after treatment were found to be statistically significant ( t=2.489, 3.262; P<0.05). Additionally, the comparison of CRT at S3 ( t=2.871), I1 ( t=2.325), and T3 ( t=3.446) before and after treatment yielded statistically significant differences ( P<0.05). Conversely, the comparison of CRT at S1 ( t=1.879), I3 ( t=1.915), N1 ( t=2.001), N3 ( t=1.987), T1 ( t=2.180), and M ( t=-0.490) showed no statistically significant differences ( P>0.05). Conclusions:IAT treatment for CRAO has been shown to be effective in achieving therapeutic effects by reducing CRT in the macular area. However, the short-term improvement in retinal edema in the macular area is limited.

Objective:To investigate the relationship between age-adjusted Charlson comorbidity index (aCCI) and ischemic stroke in patients with ophthalmic artery occlusion (OAO) or retinal artery occlusion (RAO).Methods:A single center retrospective cohort study. Seventy-four patients with OAO or RAO diagnosed by ophthalmology examination in Shenzhen Second People's Hospital from June 2004 to December 2020 were included in the study. The baseline information of patients were collected and aCCI was used to score the patients' comorbidity. The outcome was ischemic stroke. The median duration of follow-up was 1 796.5 days. According to the maximum likelihood ratio of the two-piecewise COX regression model and the recursive algorithm, the aCCI inflection point value was determined to be 6, and the patients were divided into low aCCI group (<6 points) and high aCCI group (≥6 points). A Cox regression model was used to quantify the association between baseline aCCI and ischemic stroke.Results:Among the 74 patients, 53 were males and 21 were females, with the mean age of (55.22±14.18) (19-84) years. There were 9 patients of OAO and 65 patients of RAO. The aCCI value ranges from 1 to 10 points, with a median of 3 points. There were 63 patients (85.14%, 63/74) in the low aCCI group and 11 patients (14.86%, 11/74) in the high aCCI group. Since 2 patients could not determine the time from baseline to the occurrence of outcome events, 72 patients were included for Cox regression analysis. The results showed that 16 patients (22.22%, 16/72) had ischemic stroke in the future. The baseline aCCI in the low aCCI group was significantly associated with ischemic stroke [hazard ratio ( HR)=1.76, 95% confidence interval ( CI) 1.21-2.56, P=0.003], and for every 1 point increase in baseline aCCI, the risk of future ischemic stroke increased by 76% on average. The baseline aCCI in the high aCCI group had no significant correlation with the ischemic stroke ( HR=0.66, 95% CI 0.33-1.33, P=0.247). Conclusions:aCCI score is an important prognostic information for patients with OAO or RAO. A higher baseline aCCI score predicts a higher risk of ischemic stroke, and the association has a saturation effect.

BACKGROUND: Management of gastric leak after sleeve gastrectomy (SG) is challenging due to its unpredictable outcomes. We aimed to summarize the characteristics of SG leaks and analyze interventions and corresponding outcomes in a real-world setting. METHODS: To retrospectively review of 15,721 SG procedures from 2010 to 2020 based on a national registry. A cumulative sum analysis was used to identify a fitting curve of gastric leak rate. The Kaplan-Meier method and log-rank tests were performed to calculate and compare the probabilities of relevant outcomes. The logistic regression analysis was conducted to determine the predictors of acute leaks. RESULTS: A total of 78 cases of SG leaks were collected with an incidence of 0.5% (78/15,721) from this registry (6 patients who had the primary SG in non-participating centers). After accumulating 260 cases in a bariatric surgery center, the leak rate decreased to a stably low value of under 1.17%. The significant differences presented in sex, waist circumference, and the proportion of hypoproteinemia and type 2 diabetes at baseline between patients with SG leak and the whole registry population ( P = 0.005, = 0.026, <0.001, and = 0.001, respectively). Moreover, 83.1% (59/71) of the leakage was near the esophagogastric junction region. Leakage healed in 64 (88.9%, 64/72) patients. The median healing time of acute and non-acute leaks was 5.93 months and 8.12 months, respectively. Acute leak (38/72, 52.8%) was the predominant type with a cumulative reoperation rate >50%, whereas the cumulative healing probability in the patients who required surgical treatment was significantly lower than those requring non-surgical treatment ( P = 0.013). Precise dissection in the His angle area was independently associated with a lower acute leak rate, whereas preservation ≥2 cm distance from the His angle area was an independent risk factor. CONCLUSIONS Male sex, elevated waist circumference, hypoproteinaemia, and type 2 diabetes are risk factors of gastric leaks after SG. Optimizing surgical techniques, including precise dissection of His angle area and preservation of smaller gastric fundus, should be suggested to prevent acute leaks.
Humans ; Male ; Retrospective Studies ; Diabetes Mellitus, Type 2/complications* ; Obesity, Morbid ; Anastomotic Leak/epidemiology* ; Gastrectomy/methods* ; Reoperation/methods* ; Registries ; Laparoscopy/methods* ; Treatment Outcome

Abdomen/diagnostic imaging* ; Artificial Intelligence ; Body Composition ; Magnetic Resonance Imaging ; Practice Guidelines as Topic

Objective:To investigate the clinicopathological and molecular features, diagnosis and differential diagnosis of TFE3-rearranged epithelioid hemangioendothelioma (EHE). Methods Two cases of TFE3-rearranged EHE arising from soft tissues, diagnosed by the Pathology Department of the First Affiliated Hospital of Nanjing Medical University from 2013 to 2020 were observed. EnVision method was used for immunophenotyping, fluorescence in situ hybridization (FISH) was used to test TFE3 gene rearrangements and WWTR1-CAMTA1 fusion gene,and next-generation sequencing (NGS) was used to delineate the fusion transcripts.Results:Details of these two cases were as follows: case 1, male, 51 years old, with tumor in the right temporal region; case 2, female, 42 years old, with tumor in the right neck. The tumors showed progressive painless enlargement. Grossly, the tumor of case 1 was multinodular with unclear boundary and grayish red cut surface, while the tumor of case 2, originating from a vein, appeared as a firm, tan mass within vessel wall. Microscopically, both tumors showed moderate cellularity and were consisted of plump, epithelioid, or histiocytoid cells with eosinophilic cytoplasm and mild-to-moderate nuclear pleomorphism. Most of the tumor cells were arranged in solid or alveolar growth patterns, while some tumor cells showed intraluminal papillary growth pattern in case 1 and anastomosing vascular channels and extramedullary hematopoiesis in case 2. Immunohistochemically, the tumor cells showed diffuse positivity for CD31, CD34, ERG, and TFE3. FISH revealed TFE3 break-apart signals in two cases, but WWTR1-CAMTA1 gene fusion was not detected. NGS identified YAP1 (exon1)-TFE3 (exon6) fusion gene in case 2. Clinical follow-up information was available in both cases for a follow-up period of 15 and 59 months respectively. Patient 1 had a relapse 22 months after surgery, and was currently alive with the tumor. Patient 2 remained disease-free.Conclusions:TFE3-rearranged EHE is a rare molecular subtype of EHE, with accompanying characteristic morphologic features. However the morphologic spectrum remains under-recognized, and more experience is needed. Immunohistochemical and molecular examinations are helpful for the diagnosis and differential diagnosis of the disease.

Objective:To establish a correlation model between MRI and CT images to generate synthetic-CT (sCT) of head and neck cancer during MRI-guided radiotherapy by using generative adversarial networks (GAN).Methods:Images and IMRT plans of 45 patients with nasopharyngeal carcinoma were collected before treatment. Firstly, the MRI (T1) and CT images were preprocessed, including rigid registration, clipping, background removal and data enhancement, etc. Secondly, the cases were trained by GAN, of which 30 cases were randomly selected and put into the network as training set images for modeling and learning, and the other 15 cases were used for testing. The image quality of predicted sCT and real CT were statistically compared, and the dose distribution recalculated upon predicted sCT was statistically compared with that of real planned dose distribution.Results:The mean absolute error of the predicted sCT of the testing set was (79.15±11.37) HU, and the SSIM value was 0.83±0.03. The MAE values of dose distribution difference at different regional levels were less than 1% compared to the prescription dose. The gamma passing rate of the sCT dose distribution was higher than 92% and 98% under the 2mm/2% and 3mm/3% criteria.Conclusions:We have successfully proposed and realized the generation of sCT for head and neck cancer using GAN, which lays a foundation for the implementation of MRI-guided radiotherapy. The comparison of image quality and dosimetry shows the feasibility and accuracy of this method.

Objective:To compare the accuracy and generalized robustness of three predictive models of knowledge-based treatment strategies for radiotherapy for optimized model selection.Methods:The clinical radiotherapy plans of 45 prostate cancer (PC) cases and 25 nasopharyngeal cancer (NPC) cases were collected, and analyzed using three models (Z, L and S model), proposed by Zhu et al, Appenzoller et al and Shiraishi et al, respectively, to predict the dose-volume histogram (DVH) of bladder and rectum on PC cases and that of left and right parotid on NPC cases. The prediction error was measured by the difference of area under the predicted DVH and the clinical DVH curves (|V (pre_DVH)-V (clin_DVH)|), where a smaller prediction error implies a greater prediction accuracy. The accuracies of these three models were compared on the single organ at risk (OAR), and the generalized robustness of models was evaluated and compared by calculating the standard deviation of the prediction accuracy on different OAR. Results:For bladder and rectum, the prediction error of L model (0.114 and 0.163, respectively) was significantly higher than those values of Z and S models (≤0.071, P<0.05); for left parotid gland, the predicted error of S model (0.033) did not present significant difference from those values of Z and L models (≤0.025, P>0.05); for right parotid gland, S model (0.033) demonstrated significantly higher prediction error than those of Z and L models (≤0.028, P<0.05). Regarding different OAR, S model showed a lower standard deviation of prediction accuracy when comparing to Z and L models (0.016, 0.018 and 0.060, respectively). Conclusions:In the prediction of DVH in bladder and rectum of PC, Z and S models were more accurate than L model. In contrast, Z and L models demonstrated higher accuracy than S model in the prediction of left and right parotid glands of NPC. In respect to different OAR, the generalized robustness of S model was superior than the other two models.

Objective: To study the clinicopathological features, diagnosis, and differential diagnosis of atypical epithelioid hemangioendothelioma (EHE). Methods: Eight cases of atypical EHEs were collected from Jiangsu Province Hospital (the First Affiliated Hospital of Nanjing Medical University) between 2010 and 2018. EnVision method and fluorescence in situ hybridization (FISH) were used to detect immunophenotype, WWTR1-CAMTA1 and TFE3 gene rearrangement, respectively. Results: There were 4 males and 4 females, ranging from 42 to 59 years (median 47.5 years). The tumors located in soft tissue (3 cases), lung (3 cases), liver (1 case) and chest wall (1 case). One soft tissue EHE involved also adjacent fibula and pleural involvement was present in all three lung cases at the diagnosis. Regional lymph node metastases were present in two cases (one involving soft tissue tumor and one involving liver). Morphologically, the tumor cells were epithelioid with abundant eosinophilic cytoplasm, moderate to marked nuclear pleomorphism, irregular nuclear membrane, unevenly chromatin, and prominent nucleoli. The cells arranged in cords, small nests or solid pattern. The mitotic rate was 4.3 mitoses/2 mm² on average (ranging 2 to 9). Tumor necrosis was seen in every case. Among all 8 cases, blister cells were found upon careful observation. Myxohyaline stroma was present in 6 cases. Immunohistochemically, tumor cells expressed CD31 (8/8), CD34 (7/8), ERG (8/8), CKpan (2/7), and CAMTA1 (4/6). None of the tested cases stained for TFE3 (0/6). WWTR1-CAMTA1 fusion gene by FISH was found in all tested 6 cases and TFE3 gene rearrangement was not detected in any. Available clinical follow-up was obtained in 7 cases and the intervals range from 6 to 55 months (average 19.6 months). Six patients had metastasis and 3 patients died of disease. One patient was alive with no evidence of disease. Conclusions Atypical EHE is a more aggressive tumor than classic EHE, with histological features including high nuclear grade, increased mitotic activity, the presence of solid growth pattern and tumor necrosis. The differential diagnoses include epithelioid angiosarcoma, carcinoma and epithelioid sarcoma.

Objective To propose a treatment planning optimization algorithm which can make full use of OAR dose distribution prediction meanwhile improving the output planning quality as much as possible.Methods We had reformulated an FMO function under the guidance of dose distribution prediction and also integrated equivalent uniform dose (gEUD) based on the consideration of prediction uncertainty,for providing optimal solution.Performance of the method was evaluated by comparing the optimized IMRT plan quality of 8 cervical cancers in the term of DVH curves,dose distribution and dosimetric endpoints with the original ones.Results The proposed method had a feasible,fast solution.Compared with original plan,its output plan had better plan quality in better dose homogeneity,less hot spot and further dose sparing for OARs.V30,V45 of rectum was decreased by (6.60±3.53)％ and (17.03±7.44)％,respectively,with the statistically significant difference (t=-4.954,-6.055,P＜0.05).V30,V45 of bladder was decreased by (14.74 ± 5.61) ％ and (14.99 ± 4.53) ％,respectively,with the statistically significant difference (t=-6.945,-8.759,P＜0.05).Conclusions We have successfully developed a predicted dose distribution and equivalent uniform dose-based planning optimization method,which is able to make good use of 3D dose prediction and ensure the output plan quality for intensity modulated radiation therapy.

Objective To establish a three-dimensional (3D) dose prediction model,which can predict multiple organs simultaneously in a single model and automatically learn the effect of the geometric anatomical structure on dose distribution.Methods Clinical radiotherapy plans of patients diagnosed with the same type of tumors were collected and retrospectively analyzed.For every plan,each organs at risk (OAR) voxel was regarded as the study sample and its deposited dose was considered as the dosimetric feature.A regularized multi-task learning method than could learn the relationship among different tasks was employed to establish the relationship matrix among tasks and the correlation between geometric structure and dose distribution among organs.In this experiment,the spinal cord,brainstem and bilateral parotids involved in the intensity-modulated radiotherapy (IMRT) plan of 15 nasopharyngeal cancer patients were utilized to establish the multi-organ prediction model.The relative percentage error between the predicted dose of voxel and the clinical planning dose was calculated to assess the feasibility of the model.Results Ten cases receiving IMRT plans were utilized as the training data,and the remaining five cases were used as the test data.The test results demonstrated a higher prediction accuracy and less data demand.And the average voxel dose errors among the spinal cord,brainstem and the left and right parotids were (2.01±0.02)％,(2.65± 0.02) ％,(2.45± 0.02) ％ and (2.55± 0.02) ％,respectively.Conclusion The proposed model can accurately predict the dose of multiple organs in a single model and avoid the establishment of multiple single-organ prediction models,laying a solid foundation for patient-specific plan quality control and knowledge-based treatment planning.