Malignant melanoma located in the ureter and gallbladder: A case report and literature review.

RATIONALE: Melanoma is one of a common cutaneous malignancy. Currently, metastatic malignant melanoma is difficult to be diagnosed through imaging examinations. Furthermore, the incidence of metastatic melanoma affecting the gallbladder and ureter is exceptionally rare. PATIENT CONCERNS: A 54-year-old female was admitted to the hospital with a half-month history of left lower back pain. Correlative examination revealed an occupying lesion in the mid-left ureter and the neck of the gallbladder. DIAGNOSES: The patient was initially diagnosed with gallbladder cancer and left ureteral carcinoma based on imaging. Following 2 operations, immunohistochemical staining confirmed the presence of metastatic melanoma involving both the gallbladder and ureter. INTERVENTION: After multidisciplinary consultation and obtaining consent from the patient and her family, the patient underwent left radical nephroureterectomy, radical cholecystectomy, laparoscopic partial hepatectomy (Hep IV, Hep V), and lymph node dissection of hepatoduodenal ligament. OUTCOMES: One month after treatment, the patient imaging showed no disease progression, and at 6 months of follow-up, the patient was still alive. LESSONS: It is difficult to distinguish metastatic melanoma from carcinoma in situ by imaging. In addition, metastatic malignant melanoma lacks specific clinical manifestations and is prone to misdiagnosis, which emphasizes the highly aggressive nature of malignant melanoma.

A computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.

OBJECTIVES: Development and validation of a computed tomography urography (CTU)-based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC). METHODS: A total of 140 patients with UTUC who underwent CTU examination from January 2017 to August 2023 were retrospectively enrolled. Tumor lesions on the unenhanced, medullary, and excretory periods of CTU were used to extract Features, respectively. Feature selection was screened by the Pearson and Spearman correlation analysis, least absolute shrinkage and selection operator algorithm, random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). The logistic regression (LR) was used to screen for independent influencing factors of clinical baseline characteristics. Machine learning models based on different feature datasets were constructed and validated using algorithms such as LR, RF, SVM, and XGBoost. By computing the selected features, a radiomics score was generated, and a diverse feature dataset was constructed. Based on the training set, 16 ML models were created, and their performance was evaluated using the validation set for metrics including sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and others. RESULTS: The training set consisted of 98 patients (mean age: 64.5 ± 10.5 years; 30 males), whereas the validation set consisted of 42 patients (mean age: 65.3 ± 9.78 years; 17 males). Hydronephrosis was the best independent influence factor (p < 0.05). The RF model had the best performance in predicting high-grade UTUC, with AUC of 0.914 (95% Confidence Interval [95%CI] 0.852-0.977) and 0.903 (95%CI 0.809-0.997) in the training set and validation set, and accuracy of 0.878 and 0.857, respectively. CONCLUSIONS: An ML model based on the RF algorithm exhibits excellent predictive performance, offering a non-invasive approach for predicting preoperative high-grade UTUC.

Prognostic nutritional index combined with NLR to construct a survival prediction model and decision analysis of patients with muscle-invasive bladder cancer after surgery.

OBJECTIVES: To build a nomogram prediction model, assess its predictive ability, and perform a survival decision analysis on patients with muscle-invasive bladder cancer (MIBC) to study risk factors affecting overall survival (OS). METHODS: A retrospective analysis was performed on the clinical information of 262 patients with MIBC who underwent radical cystectomy (RC) at the Urology Department of the Second Affiliated Hospital of Kunming Medical University between July 2015 and August 2021. The final model variables that were included were chosen using single-factor stepwise Cox regression, optimal subset regression, and LASSO regression + cross-validation with the minimum AIC value. The next step was to do a multivariate Cox regression analysis. The establishment of a nomogram model by fitting and the screening out of independent risk factors impacting the survival of patients with MIBC having radical resection. Receiver Activity Characteristic curves, C-index, and a calibration plot evaluated the prediction accuracy, validity, and clinical benefit of the model. The 1-, 3-, and 5-year survival rates were then computed for each risk factor using a Kaplan-Meier survival analysis. RESULTS: 262 eligible patients in total were enrolled. With a median follow-up of 32 months, the follow-up period ranged from 2 to 83 months. 171 cases (65.27%) survived while 91 cases (34.73%) perished. Age (HR = 1.06 [1.04; 1.08], p = 0.001), preoperative hydronephrosis (HR = 0.69 [0.46, 1.05], p = 0.087), T stage (HR = 2.06 [1.09, 3.93], p = 0.027), lymphovascular invasion (LVI, HR = 1.73 [1.12, 2.67], p = 0.013), prognostic nutritional index (PNI, HR = 1.70 [1.09, 2.63], p = 0.018), and neutrophil-to-lymphocyte ratio (NLR, HR = 0.52 [0.29, 0.93)], p = 0.026) were independent risk factor for the survival of bladder cancer patients. Create a nomogram based on the aforementioned findings, and then draw the 1-year, 3-year, and 5-year OS receiver operating characteristic curves by the nomogram. The AUC values were 0.811 (95% CI [0.752, 0.869]), 0.814 (95% CI [0.755, 0.873]), and 0.787 (95% CI [0.708, 0.865]), respectively, and the calibration plot matched the predicted value well. The 1-year, 3-year, and 5-year decision curve analyses were higher than the ALL line and None line at threshold values of >5%, 5%-70%, and 20%-70% indicating that the model has good clinical applicability. The calibration plot for the Bootstrap 1000-time resampled validation model was similar to the actual value. Patients with preoperative combination hydronephrosis, higher T-stage, combined LVI, low PNI, and high NLR had worse survival, according to Kaplan-Meier survival analysis for each variable. CONCLUSIONS: This study might conclude that PNI and NLR were separate risk factors that affect a patient's OS after RC for MIBC. The prognosis of bladder cancer may be predicted by PNI and NLR, but additional confirmation in randomized controlled trials is required.