前列腺癌（prostate cancer，PCa）是欧美地区男性发病率及死亡率均排第二位的恶性肿瘤[1]。近年来我国PCa发病率及死亡率呈逐年增长趋势[2]。研究发现PCa转移是导致患者预后较差的重要因素[3]。众所周知，转移性PCa中以淋巴结转移最为常见。盆腔淋巴结清扫（pelvic lymph node dissection，PLND）是诊断PCa淋巴结转移的"金标准"，但目前PLND的适应证、范围和患者获益程度仍存在较大争议[4]。术前明确诊断PCa淋巴结转移有助于患者准确地分期及选择最佳的治疗方案。如何在前列腺癌根治术（radical prostatectomy，RP）前准确判断是否有淋巴结转移及转移范围是临床工作中的重点和难点。前列腺特异性抗原（prostate specific antigen，PSA）、Gleason评分、临床分期、前列腺穿刺阳性针数及其百分比、前列腺特异性抗原密度（PSA density，PSAD）、前列腺健康指数（prostate health index，PHI）、体质量指数（body mass index，BMI）、多参数磁共振成像（multiparametric magnetic resonance Imaging，mpMRI）、前列腺影像和数据系统（prostate imaging reporting and data system，PI-RADS）评分、前列腺特异性膜抗原正电子发射断层扫描成像（prostate-specific membrane antigen positron-emission tomography，PSMA PET-CT）等临床指标；MSKCC、Briganti 2012、Partin 2016、Yale、Briganti 2017和Briganti 2019等列线图模型；17-gene Oncotype DX前列腺基因组评分（genomic prostate score，GPS）、塌陷反应调节蛋白4（collapsin response mediator proteins，CRMP4）甲基化、99mTc硫胶体、光学示踪剂如吲哚菁绿（indocyanine green，ICG）、混合示踪剂等新技术均在预测PCa淋巴结转移中有相关报道。本研究系统总结了术前预测PCa淋巴结转移的各种参数及其进展。

ObjectivesClear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC).Patients and MethodsData of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system.ResultsDiagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks.ConclusionIn this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Objectives To investigate the clinical and non-clinical characteristics that may affect the prognosis of patients with renal collecting duct carcinoma (CDC) and to develop an accurate prognostic model for this disease. Methods The characteristics of 215 CDC patients were obtained from the U.S. National Cancer Institute’s surveillance, epidemiology and end results database from 2004 to 2016. Univariate Cox proportional hazard model and Kaplan-Meier analysis were used to compare the impact of different factors on overall survival (OS). 10 variables were included to establish a machine learning (ML) model. Model performance was evaluated by the receiver operating characteristic curves (ROC) and calibration plots for predictive accuracy and decision curve analysis (DCA) were obtained to estimate its clinical benefits. Results The median follow-up and survival time was 16 months during which 164 (76.3%) patients died. 4.2, 32.1, 50.7 and 13.0% of patients were histological grade I, II, III, and IV, respectively. At diagnosis up to 61.9% of patients presented with a pT3 stage or higher tumor, and 36.7% of CDC patients had metastatic disease. 10 most clinical and non-clinical factors including M stage, tumor size, T stage, histological grade, N stage, radiotherapy, chemotherapy, age at diagnosis, surgery and the geographical region where the care delivered was either purchased or referred and these were allocated 95, 82, 78, 72, 49, 38, 36, 35, 28 and 21 points, respectively. The points were calculated by the XGBoost according to their importance. The XGBoost models showed the best predictive performance compared with other algorithms. DCA showed our models could be used to support clinical decisions in 1-3-year OS models. Conclusions Our ML models had the highest predictive accuracy and net benefits, which may potentially help clinicians to make clinical decisions and follow-up strategies for patients with CDC. Larger studies are needed to better understand this aggressive tumor.

The incidence of extra-osseous Ewing sarcoma is low, and extra-osseous Ewing sarcoma of renal origin is even less frequently reported. The clinical manifestation of Ewing sarcoma is non-specific and early diagnosis is difficult, and the diagnosis mainly relies on pathological histology and immunohistochemistry. The disease is highly malignant, with a high rate of local recurrence and distant metastasis, and is currently treated with a combination of surgery, chemotherapy and radiation therapy. The Department of Urology of the First Affiliated Hospital of Jinan University admitted a 71-year-old male patient in 2021 with carnal hematuria and lumbar and abdominal pain as the first manifestation, and the preoperative examination showed a type of round mixed signal mass in the right kidney. After admission, the patient underwent mass resection and inferior vena cava dissection, and the postoperative pathology showed a small round cell malignant tumor, which was considered as extraosseous Ewing sarcoma in combination with immunohistochemical results. Three weeks after surgery, the patient developed multiple organ metastases.