Objective:To investigate the effects of preoperative mammalian target of rapamycin(mTOR )inhibitor on partial nephrectomy for renal angiomyolipoma associated with tuberous sclerosis complex (TSC-RAML).Methods:A retrospective analysis was conducted on clinical data from 13 patients who were diagnosed with TSC-RAML and treated at Peking Union Medical College Hospital between August 2019 and July 2022. This cohort included 4 males and 9 females, with ages ranging from 22 to 66 years. All patients underwent partial nephrectomy, with 2 patients requiring two-stage surgeries due to bilateral RAMLs, resulting in a total of 15 surgeries being performed. Preoperative mTOR inhibitors, specifically everolimus (10 mg/d) or sirolimus (2 mg/d), were administered orally for at least 3 months prior to 7 of the surgeries. The effects of mTOR inhibitors on tumor size, tumor computed tomography attenuation value (CT value), and tumor CT enhancement were evaluated. The comparison of surgery-related clinical parameters was conducted between patients who received preoperative mTOR inhibitors and those who did not to assess the influences of mTOR inhibitors on surgery.Results:Compared to the baseline tumor, there was a significant reduction in tumor diameter after mTOR treatment [(6.4±3.1) cm vs. (8.7±3.9)cm], as well as in the CT value in both the unenhanced phase[(-18.63±48.73)HU vs. (-2.13±51.58)HU] and corticomedullary phase[(13.25±64.01)HU vs. (47.25±66.99)HU]. Additionally, tumor CT enhancement also decreased as compared with that before treatment [(31.88±18.20)HU vs. (49.38±20.63)HU]. Patients who received preoperative mTOR inhibitor showed a trend towards shorter operative time for removing per milliliter of tumor compared to those without preoperative mTOR inhibitor [1.06(0.18, 2.40) min/ml vs. 1.98(0.39, 5.03) min/ml] and so was the renal artery clamping time [0.17(0.03, 0.79) min/ml vs. 0.34(0.10, 1.71) min/ml]. Additionally, the amount of intraoperative bleeding for removing per milliliter of tumor was lower in patients with preoperative mTOR inhibitors compared to those without [0.72(0.19, 0.88) ml/ml vs. 1.69(0.59, 4.54) ml/ml].Conclusions:The administration of mTOR inhibitors before partial nephrectomy in patients with TSC-RAM have the potential to reduce tumor size and blood supply, as well as operative time, renal artery clamping time and intraoperative bleeding, which might contribute to surgery safety and preservation of renal function.

Intrauterine device(IUD)migrating to the bladder is rare,especially the migration far away from the uterus into the bladder wall.Due to no obvious clinical symptom in the early stage and being far away from the uterus,the IUD totally embedded in the bladder wall is prone to misdiagnosis and delay in treatment.We reported one case of such migration,aiming to improve the clinical management of the IUD totally embedded in the bladder wall.

Objective:To explore the feasibility of deep learning technology for renal artery recognition in retroperitoneal laparoscopic renal surgery videos.Methods:From January 2020 to July 2021, the video data of 87 cases of laparoscopic retroperitoneal nephrectomy, including radical nephrectomy, partial nephrectomy, and hemiurorectomy, were retrospectively analyzed. Two urological surgeons screened video clips containing renal arteries. After frame extraction, annotation, review, and proofreading, the labeled targets were divided into training set and test set by the random number table in a ratio of 4∶1. The training set was used to train the neural network model. The test set was used to test the ability of the neural network to identify the renal artery in scenes with different difficulties, which was uniformly transmitted to the YOLOv3 convolutional neural network model for training. According to the opinion of two senior doctors, the test set was divided into high, medium, and low discrimination of renal artery and surrounding tissue. High identification means a clean renal artery and a large exposed area. For middle recognition degree, the renal artery had a certain degree of blood immersion, and the exposed area was medium. Low identification means that the exposed area of the renal artery was small, often located at the edge of the lens, and the blood immersion was severe, which may lead to lens blurring. In the surgical video, the annotator annotated the renal artery truth box frame by frame. After normalization and preprocessing, all images were input into the neural network model for training. The neural network output the renal artery prediction box, and if the overlap ratio (IOU) with the true value box was higher than the set threshold, it was judged that the prediction was correct. The neural network test results of the test set were recorded, and the sensitivity and accuracy were calculated according to IOU.Results:In the training set, 1 149 targets of 13 videos had high recognition degree, 1 891 targets of 17 videos had medium recognition degree, and 349 targets of 18 videos had low recognition degree. In the test set, 267 targets in 9 videos had high recognition degree, 519 targets in 11 videos had medium recognition degree, and 349 targets in 18 videos had low recognition degree. When the IOU threshold was 0.1, the sensitivity and accuracy were 52.78% and 82.50%, respectively. When the IOU threshold was 0.5, the sensitivity and accuracy were 37.80% and 59.10%, respectively. When the IOU threshold was 0.1, the sensitivity and accuracy of high, medium and low recognition groups were 89.14% and 87.82%, 45.86% and 78.03%, 32.95%, and 76.67%, respectively. The frame rate of the YOLOv3 algorithm in real-time surgery video was ≥15 frames/second. The false detection rate and missed detection rate of neural network for renal artery identification in laparoscopic renal surgery video were 47.22% and 17.49%, respectively (IOU=0.1). The leading causes of false detection were similar tissue and reflective light. The main reasons for missed detection were image blurring, blood dipping, dark light, fascia interference, or instrument occlusion, etc.Conclusions:Deep learning-based renal artery recognition technology is feasible. It may assist the surgeon in quickly identifying and protecting the renal artery during the operation and improving the safety of surgery.