The first competing risk survival nomogram in patients with papillary renal cell carcinoma

Case report: Uncommon gastric metastasis as a presentation of recurrent clear cell renal cell carcinoma

Renal cell carcinoma (RCC) is a kidney neoplasm that accounts for 85% of cases and has complex genetic pathways that affect its development and progression. RCC metastasis can occur in 20%-50% of patients and usually affects distant organs. Gastric metastases (GM) from RCC are rare and present as polyp-like growths in the submucosal layer, accounting for 0.2%-0.7% of cases. This case report describes an 84-year-old female with Furhman grade II ccRCC who presented with an atherothrombotic ischemic stroke and gastrointestinal bleeding nine years post-radical nephrectomy. Gastroscopy revealed a 12mm pseudopedicled gastric lesion with ulceration and bleeding, diagnosed as metastatic ccRCC. The discussion focuses on the rarity, diagnostic challenges, and prognostic elements of gastric metastasis from RCC. The median survival after detecting digestive metastasis varies widely, and the mechanisms include direct invasion and dissemination through lymphatic, transcelomic, or hematogenous routes. Prognostic markers encompass patient history, symptoms, time since RCC diagnosis, overall health, and genetic factors. Surgical removal of gastric lesions and targeted therapy are treatment options that can improve survival. This case report highlights the need for further research to enhance diagnostic and treatment strategies for this rare aspect of RCC pathophysiology.

Development and External Validation of a Novel Nomogram Predicting Cancer-specific Mortality-free Survival in Surgically Treated Papillary Renal Cell Carcinoma Patients

BACKGROUND

Accurate prediction of cancer control outcomes in renal cell carcinoma (RCC) patients is important for counselling, follow-up planning, and selection of appropriate adjuvant trial designs.

OBJECTIVE

To develop and externally validate a novel contemporary population-based model for predicting cancer-specific mortality-free survival (CSM-FS) in surgically treated papillary RCC (papRCC) patients and to compare it with established risk categories (Leibovich 2018).

DESIGN, SETTING, AND PARTICIPANTS

Within the Surveillance, Epidemiology, and End Results database (2004-2019), we identified surgically treated papRCC patients (n = 3978). The population was randomly divided into development (50%, n = 1989) and external validation (50%, n = 1989) cohorts. Of the external validation cohort, 97% (n = 1930) of patients were included in a head-to-head comparison of the Leibovich 2018 risk categories addressing nonmetastatic patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Univariable Cox regression models tested the statistical significance in the prediction of CSM-FS. The most parsimonious model with the best validation metrics was selected as the multivariable nomogram. Accuracy, calibration, and decision curve analyses (DCAs) tested the Cox regression-based nomogram, as well as the Leibovich 2018 risk categories in the external validation cohort.

RESULTS AND LIMITATIONS

Age at diagnosis, grade, T stage, N stage, and M stage qualified for inclusion in the novel nomogram. In external validation, the accuracy of the novel nomogram was 0.83 at 5 yr and 0.80 at 10 yr. In nonmetastatic patients, 5- and 10-yr accuracy of the novel nomogram was 0.77 and 0.76, respectively. Conversely, 5- and 10-yr accuracy of the Leibovich 2018 risk categories was 0.70 and 0.66, respectively. The novel nomogram exhibited smaller departures from ideal predictions in calibration plots and higher net benefit in DCAs, when it was compared with the Leibovich 2018 risk categories. Limitations include the retrospective nature of the study, absence of a central pathological review, and inclusion of only North American patients.

CONCLUSIONS

The novel nomogram may represent a valuable clinical aid, when papRCC CSM-FS predictions are required.

PATIENT SUMMARY

We developed an accurate tool to predict death due to papillary kidney cancer in a North American population.

Multiple stimulus-response berberine plus baicalin micelles with particle size-charge-release triple variable properties for breast cancer therapy

OBJECTIVE

The aim was to develop a nanoscale drug delivery system with enzyme responsive and acid sensitive particle size and intelligent degradation aiming to research the inhibitory effect on breast cancer.

SIGNIFICANCE

The delivery system addressed the problems of tissue targeting, cellular internalization, and slow drug release at the target site, which could improve the efficiency of drug delivery and provide a feasible therapeutic approach for breast cancer.

METHODS

The acid sensitive functional material DSPE-PEG₂₀₀₀-dyn-PEG-R9 was synthesized by Michael addition reaction. Then, the berberine plus baicalin intelligent micelles were prepared by thin-film hydration. Subsequently, we characterized the physical and chemical properties of berberine plus baicalin intelligent micelles, evaluated its anti-tumor effects in vivo and in vitro.

RESULTS

The target molecule was successfully synthesized, and the intelligent micelles showed excellent chemical and physical properties, delayed drug release and high encapsulation efficiency. In vitro and in vivo experiments also confirmed that the intelligent micelles could effectively target tumor sites, penetrate tumor tissues, enrich in tumor cells, inhibit tumor cell proliferation, inhibit tumor cell invasion and migration, and induce tumor cell apoptosis.

CONCLUSION

Berberine plus baicalin intelligent micelles have excellent anti-tumor effects and no toxicity to normal tissues, which provides a new potential drug delivery strategy for the treatment of breast cancer.

A novel nomogram and risk classification system predicting the overall survival of patients with papillary renal cell carcinoma after nephrectomy: A population-based study

Background

Papillary renal cell carcinoma (pRCC) is the largest histologic subtype of non-clear-cell RCC. To date, there is no reliable nomogram to predict the prognosis of patients with pRCC after nephrectomy. We aimed to first establish an effective nomogram to predict the overall survival (OS) of patients with pRCC after nephrectomy.

Methods

A total of 3,528 eligible patients with pRCC after nephrectomy were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The patients were randomized into the training cohort (n = 2,472) and the validation cohort (n = 1,056) at a 7:3 ratio. In total, 122 real-world samples from our institute (titled the AHMU-pRCC cohort) were used as the external validation cohort. Univariate and subsequent multivariate Cox regression analyses were conducted to identify OS-related prognostic factors, which were further used to establish a prognostic nomogram for predicting 1-, 3-, and 5-year OS probabilities. The performance of the nomogram was evaluated by using the concordance index (C-index), receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA).

Results

Multivariate Cox analysis showed that age, race, marital status, TNM stage, tumor size, and surgery were significant OS-related prognostic factors. A prognostic model consisting of these clinical parameters was developed and virtualized by a nomogram. High C-index and area under the ROC curve (AUC) values of the nomogram at 1, 3, and 5 years were found in the training, validation, and AHMU-pRCC cohorts. The calibration plot and DCA also showed that the nomogram had a satisfactory clinical application value. A risk classification system was established to risk-stratify patients with pRCC.

Conclusion

Based on a large cohort from the public SEER database, a reliable nomogram predicting the OS of patients with pRCC after nephrectomy was constructed, which could optimize the survival assessment and clinical treatment.

Development and Validation of a Nomogram to Predict Cancer-Specific Survival in Elderly Patients With Papillary Renal Cell Carcinoma

Objective

Papillary renal cell carcinoma (pRCC) is the second most common type of renal cell carcinoma and an important disease affecting older patients. We aimed to establish a nomogram to predict cancer-specific survival (CSS) in elderly patients with pRCC.

Methods

Patient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) project, and we included all elderly patients with pRCC from 2004 to 2018. All patients were randomly divided into a training cohort and a validation cohort. Univariate and multivariate Cox proportional risk regression models were used to identify patient independent risk factors. We constructed a nomogram based on a multivariate Cox regression model to predict CSS for 1-, 3-, and 5- years in elderly patients with pRCC. A series of validation methods were used to validate the accuracy and reliability of the model, including consistency index (C-index), calibration curve, and area under the Subject operating curve (AUC).

Results

A total of 13,105 elderly patients with pRCC were enrolled. Univariate and multivariate Cox regression analysis suggested that age, tumor size, histological grade, TNM stage, surgery, radiotherapy and chemotherapy were independent risk factors for survival. We constructed a nomogram to predict patients' CSS. The training and validation cohort's C-index were 0.853 (95%CI: 0.859–0.847) and 0.855 (95%CI: 0.865–0.845), respectively, suggesting that the model had good discrimination ability. The AUC showed the same results. The calibration curve also indicates that the model has good accuracy.

Conclusions

In this study, we constructed a nomogram to predict the CSS of elderly pRCC patients, which has good accuracy and reliability and can help doctors and patients make clinical decisions.

Predictive and Prognostic Assessment Models for Tumor Deposit in Colorectal Cancer Patients With No Distant Metastasis

Background

More and more evidence indicated that tumor deposit (TD) was significantly associated with local recurrence, distant metastasis (DM), and poor prognosis for patients with colorectal cancer (CRC). This study aims to explore the main clinical risk factors for the presence of TD in CRC patients with no DM (CRC-NDM) and the prognostic factors for TD-positive patients after surgery.

Methods

The data of patients with CRC-NDM between 2010 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A logistic regression model was used to identify risk factors for TD presence. Fine and Gray’s competing-risk model was performed to analyze prognostic factors for TD-positive CRC-NDM patients. A predictive nomogram was constructed using the multivariate logistic regression model. The concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration were used to evaluate the predictive nomogram. Also, a prognostic nomogram was built based on multivariate competing-risk regression. C-index, the calibration, and decision-curve analysis (DCA) were performed to validate the prognostic model.

Results

The predictive nomogram to predict the presence of TD had a C-index of 0.785 and AUC of 0.787 and 0.782 in the training and validation sets, respectively. From the competing-risk analysis, chemotherapy (subdistribution hazard ratio (SHR) = 0.542, p < 0.001) can significantly reduce CRC-specific death (CCSD). The prognostic nomogram for the outcome prediction in postoperative CRC-NDM patients with TD had a C-index of 0.727. The 5-year survival of CCSD was 17.16%, 36.20%, and 63.19% in low-, medium-, and high-risk subgroups, respectively (Gray’s test, p < 0.001).

Conclusions

We constructed an easily predictive nomogram in identifying the high-risk TD-positive CRC-NDM patients. Besides, a prognostic nomogram was built to help clinicians identify poor-outcome individuals in postoperative CRC-NDM patients with TD. For the high-risk or medium-risk subgroup, additional chemotherapy may be more advantageous for the TD-positive patients rather than radiotherapy.