Nephron-Sparing Approaches in Upper Tract Urothelial Carcinoma: Current and Future Strategies

Endoscopic intervention versus radical nephroureterectomy for the management of localized upper urinary tract urothelial carcinoma: a systematic review and meta-analysis of comparative studies

OBJECTIVE

Localized Upper Urinary Tract Urothelial Carcinoma (UTUC) is an uncommon cancer typically detected at an advanced stage. Currently, radical nephroureterectomy (RNU) with bladder cuff excision is the standard treatment for high-risk UTUC. This meta-analysis aims to evaluate the 5-year overall and cancer-specific survival and bladder recurrence rates in studies comparing endoscopic kidney-sparing surgeries (E-KSS) with RNU in localized UTUC.

EVIDENCE ACQUISITION

We performed a literature search on 20th April 2023 through PubMed, Web of Science, and Scopus. The PICOS model was used for study inclusion: P: adult patients with localized UTUC; I: E-KSS. C: RNU; O: primary: overall survival (OS); secondary: cancer-specific survival (CSS), bladder recurrence rate, and metastasis-free survival (MFS). S: retrospective, prospective, and randomized studies.

EVIDENCE SYNTHESIS

Overall, 11 studies involving 2284 patients were eligible for this meta-analysis, 737 in the E-KSS group and 1547 in the RNU group. E-KSS showed a similar overall 5-year OS between E-KSS and RNU, and for low-grade tumors, while 5-year OS favored RNU for high-grade tumors (RR 1.84, 95% CI 1.26-2.69, p = 0.002). No difference emerged for 5-year CSS between the two groups, even when the results were stratified for low- and high grade tumors. Bladder recurrence rate and 5-year MFS were also similar between the two groups.

CONCLUSIONS

Our review showed that E-KSS is a viable option for patients with localized UTUC with non-inferior oncological outcomes as compared with RNU, except for 5-year OS in high-grade tumors which favoured RNU.

Current Evidence on Utility, Outcomes, and Limitations of Endoscopic Laser Ablation for Localized Upper Urinary Tract Urothelial Carcinoma: Results from a Scoping Review

Context

The occurrence of upper urinary tract urothelial carcinoma (UTUC) is uncommon and is usually identified at an advanced and multifocal stage. Currently, there is growing interest in utilizing endoscopic laser ablation (ELA).

Objective

To evaluate the survival rates and perioperative complications of ELA.

Evidence acquisition

We performed a literature search through PubMed, Web of Science, and Scopus. The analysis included observational studies that examined the oncological outcomes of patients with UTUC treated with ELA.

Evidence synthesis

Neodymium and diode lasers are no longer used due to their high complication rates. Holmium:yttrium-aluminum-garnet (YAG) and thulium:YAG lasers provided excellent tumor ablation and hemostasis in both the collecting system and the ureter. These lasers offer good disease-free and cancer-specific survival, especially for low-grade tumors.

Conclusions

Advancements in laser technology and ablation techniques, and understanding of UTUC tumor biology hold significant promise in improving the use of conservative UTUC treatment, with excellent safety and good oncological outcomes for low-grade diseases.

Patient summary

With the advancement of technology, the conservative approach utilizing endoscopic laser ablation for upper tract urothelial tumors has been proved to be both safe and effective, showcasing promising survival rates.

Lymph Node Dissection in Upper Tract Urothelial Carcinoma: Current Status and Future Perspectives

PURPOSE OF REVIEW

This narrative review aims to evaluate the role of lymph node dissection (LND) in upper tract urothelial carcinoma (UTUC) and its implications for staging and management outcomes, as well as future perspectives.

RECENT FINDINGS

Multiple studies have demonstrated the limitations of conventional imaging techniques in accurately localizing lymph node metastasis (LNM) in UTUC. While 18F-fluorodeoxyglucose positron emission tomography with computed tomography (18FDG-PET/CT) shows promise for preoperative LNM detection, its specificity is low. Alternative methods such as choline PET/CT and sentinel lymph node detection are under consideration but require further investigation. Additionally, various preoperative factors associated with LNM hold potential for predicting nodal involvement, thereby improving nodal staging and oncologic outcomes of LND. Several surgical approaches, including segmental ureterectomy and robot-assisted nephroureterectomy, provide a possibility for LND, while minimizing morbidity. LND remains the primary nodal staging tool for UTUC, but its therapeutic benefit is still uncertain. Advances in imaging techniques and preoperative risk assessment show promise in improving LNM detection. Further research and multi-center studies are needed to comprehensively assess the advantages and limitations of LND in UTUC, as well as the long-term outcomes of alternative staging and treatment strategies.

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

Objective

The purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images.

Materials and methods

The computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA).

Results

The selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA.

Conclusion

Machine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively.