A Pre-operative Nomogram for Prediction of Lymph Node Metastasis in Bladder Urothelial Carcinoma

A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study

BACKGROUND

The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients.

METHODS

Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC).

RESULTS

A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI.

DISCUSSION

The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.

Development and validation of a preoperative nomogram to predict lymph node metastasis in patients with bladder urothelial carcinoma

PURPOSE

Predicting lymph node metastasis (LNM) in patients with bladder urothelial carcinoma (BUC) before radical cystectomy aids clinical decision making. Here, we aimed to develop and validate a nomogram to preoperatively predict LNM in BUC patients.

METHODS

Patients with histologically confirmed BUC, who underwent radical cystectomy and bilateral lymphadenectomy, were retrospectively recruited from two institutions. Patients from one institution were enrolled in the primary cohort, while those from the other were enrolled in the external validation cohort. Patient demographic, pathological (using transurethral resection of the bladder tumor specimens), imaging, and laboratory data were recorded. Univariate and multivariate logistic regression analyses were performed to explore the independent preoperative risk factors and develop the nomogram. Internal and external validation was conducted to assess nomogram performance.

RESULTS

522 and 215 BUC patients were enrolled in the primary and external validation cohorts, respectively. We identified tumor grade, infiltration, extravesical invasion, LNM on imaging, tumor size, and serum creatinine levels as independent preoperative risk factors, which were subsequently used to develop the nomogram. The nomogram showed a good predictive accuracy, with area under the receiver operator characteristic curve values of 0.817 and 0.825 for the primary and external validation cohorts, respectively. The corrected C-indexes, calibration curves (after 1000 bootstrap resampling), decision curve analysis results, and clinical impact curves demonstrated that the nomogram performed well in both cohorts and was highly clinically applicable.

CONCLUSION

We developed a nomogram to preoperatively predict LNM in BUC, which was highly accurate, reliable, and clinically applicable.

Predicting skip metastasis in lateral lymph nodes of papillary thyroid carcinoma based on clinical and ultrasound features

Background

Skip metastasis in papillary thyroid cancer (PTC), defined as lateral lymph node metastasis (LLNM) without the involvement of central lymph node metastasis (CLNM), is generally unpredictable. Our study aimed to develop a model to predict skip metastasis by using clinicopathological and ultrasound factors of PTC.

Methods

We retrospectively reviewed the medical records of patients who underwent total thyroidectomy and central lymph node dissection (CLND) plus lateral lymph node dissection (LLND) between January 2019 and December 2021 at the First Affiliated Hospital of Soochow University. Furthermore, univariate and multivariate analyses assessed the clinical and ultrasound risk factors. Receiver operating characteristic (ROC) curves were used to find the optimal cut-off values for age and dominant nodule diameter. Multivariate logistic regression analysis results were used to construct a nomogram and were validated internally.

Results

In all patients, the skip metastasis rate was 15.4% (41/267). Skip metastasis was more frequently found in patients with a tumour size ≤10 mm (OR 0.439; P = 0.033), upper tumour location (OR 3.050; P=0.006) and fewer CLNDs (OR 0.870; P = 0.005). After analysing the clinical and ultrasound characteristics of the tumour, five factors were ultimately associated with lateral lymph node skip metastasis and were used to construct the model. These factors were an age >40 years, tumour diameter <9.1 mm, upper tumour location, non-smooth margin and extrathyroidal extension. The internally evaluated calibration curves indicated an excellent correlation between the projected and actual skip metastasis probability. The nomogram performed well in discrimination, with a concordance index of 0.797 (95% CI, 0.726 to 0.867).

Conclusions

This study screened for predictors of skip metastasis in PTC and established a nomogram that effectively predicted the risk of potential skip metastasis in patients preoperatively. The method can predict and distinguish skip metastases in PTC in a simple and inexpensive manner, and it may have future therapeutic utility.

Uncovering the potential functions of lymph node metastasis-associated aberrant methylation differentially expressed genes and their association with the immune infiltration and prognosis in bladder urothelial carcinoma

Background

Bladder urothelial carcinoma (BLCA) is a malignant tumor of the urinary system. This study aimed to explore the potential role of lymph node metastasis-associated aberrant methylation differentially expressed genes (DEGs) in BLCA.

Methods

CHAMP and limma packages were used to identify lymph node metastasis-associated aberrant methylation DEGs. Univariate Cox analysis and Lasso analysis were performed to identify the signature genes, and multivariate Cox analysis was used to construct the risk score. Subsequently, the molecular characteristics of the signature genes and the relationship between risk score and prognosis, clinical characteristics and immune cell infiltration were analyzed. The signature gene AKAP7 was selected for functional verification.

Results

A novel risk score model was constructed based on 12 signature genes. The risk score had a good ability to predict overall survival (OS). The nomogram constructed based on age, N stage and risk score had a higher value in predicting the prognosis of patients. It was also found that stromal activation in TIME may inhibit the antitumor effects of immune cells. Functional enrichment analysis revealed that ECM receptor interaction and focal adhesion were two important pathways involved in the regulation of BLCA. Immunohistochemistry showed that AKAP7 may be associated with the occurrence, clinical stages and grades, and lymph node metastasis of BLCA. In vitro cell experiments showed that the migration and invasion ability of EJ cells was significantly inhibited after AKAP7 overexpression, while the migration and invasion ability of T24 cells was significantly promoted after AKAP7 knockdown.

Conclusion

The risk score model based on lymph node metastasis-associated aberrant methylation DEGs has a good ability to predict OS and is an independent prognostic factor for BLCA. It was also found that stromal activation in TIME may inhibit the antitumor effects of immune cells. This implicates aberrant methylation modifications as an important factor contributing to the heterogeneity and complexity of individual tumor microenvironments. Functional enrichment analysis revealed that ECM receptor interaction and focal adhesion were two important pathways involved in the regulation of BLCA, which contributed to the exploration of the pathological mechanism of BLCA. In addition, immunohistochemistry showed that AKAP7 may be associated with the occurrence, progression and lymph node metastasis of BLCA. In vitro cell experiments showed that AKAP7 could also inhibit the migration and invasion of cancer cells.

Development of a novel signature derived from single cell RNA-sequencing for preoperative prediction of lymph node metastasis in head and neck squamous cell carcinoma

BACKGROUND

Lymph node metastasis (LNM) is considered as an adverse prognostic indicator for cancer patients. Preoperative knowledge of LNM is valuable for pretreatment decision making. Here, we sought to develop and validate an LNM signature for preoperative prediction of LNM in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

By studying single cell RNA-sequencing data (scRNA-seq), differentially expressed mRNA were selected and analyzed through univariate logistic regression and least absolute shrinkage and selection operator (LASSO) to identify an LNM signature. Multivariate logistic regression was utilized to establish an LNM nomogram incorporating LNM signature and T-classification.

RESULTS

The LNM signature was significantly associated with lymph node status and prognosis. The LNM signature and LNM nomogram displayed a robust predictive effect.

CONCLUSION

Our study reveals that LNM signature is a powerful biomarker for preoperative prediction of LNM in patients with HNSCC, which may be effective to realize individualized outcome prediction.

Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

Background

Lymph node metastasis (LNM) is a well-established prognostic factor for colon cancer. Preoperative LNM evaluation is relevant for planning colon cancer treatment. The aim of this study was to construct and evaluate a nomogram for predicting LNM in primary colon cancer according to pathological features.

Patients and Methods

Six-hundred patients with clinicopathologically confirmed colon cancer (481 cases in the training set and 119 cases in the validation set) were enrolled in the Affiliated Cancer Hospital of Guangxi Medical University from January 2010 to December 2019. The expression of molecular markers (p53 and β-catenin) was determined by immunohistochemistry. Multivariate logistic regression was used to screen out independent risk factors, and a nomogram was established. The accuracy and discriminability of the nomogram were evaluated by consistency index and calibration curve.

Results

Univariate logistic analysis revealed that LNM in colon cancer is significantly correlated (P <0.05) with tumor size, grading, stage, preoperative carcinoembryonic antigen (CEA) level, and peripheral nerve infiltration (PNI). Multivariate logistic regression analysis confirmed that CEA, grading, and PNI were independent prognostic factors of LNM (P <0.05). The nomogram for predicting LNM risk showed acceptable consistency and calibration capability in the training and validation sets.

Conclusions

Preoperative CEA level, grading, and PNI were independent risk factor for LNM. Based on the present parameters, the constructed prediction model of LNM has potential application value.

Emerging Biomarkers for Predicting Bladder Cancer Lymph Node Metastasis

Bladder cancer is one of the leading causes of cancer deaths worldwide. Early detection of lymph node metastasis of bladder cancer is essential to improve patients' prognosis and overall survival. Current diagnostic methods are limited, so there is an urgent need for new specific biomarkers. Non-coding RNA and m6A have recently been reported to be abnormally expressed in bladder cancer related to lymph node metastasis. In this review, we tried to summarize the latest knowledge about biomarkers, which predict lymph node metastasis in bladder cancer and their mechanisms. In particular, we paid attention to the impact of non-coding RNA on lymphatic metastasis of bladder cancer and its specific molecular mechanisms, as well as some prediction models based on imaging, pathology, and biomolecules, in an effort to find more accurate diagnostic methods for future clinical application.

A nomogram to predict skip metastasis in papillary thyroid cancer

Background

Skip metastases are defined as lateral lymph node metastasis (LNM) without the involvement of central LNM in papillary thyroid cancer (PTC), and it is difficult to predict in clinical practice. Our study aimed to investigate the risk factors of skip metastasis and establish a nomogram for predicting the probability of skip metastasis in PTC patients.

Patients and methods

A total of 378 consecutive PTC patients with clinically suspected LNM who underwent modified radical neck dissection (MRND) from March 2018 to July 2019 in our hospital were enrolled. Univariate and multivariate analyses were used to examine risk factors of skip metastasis, and a nomogram prediction model was established and internally validated.

Results

The incidence of skip metastases was 11.6% (44/378). Primary tumor size of ≤ 1 cm (OR = 2.703; 95% CI, 1.342–5.464; P = 0.005), age (OR = 1.051; 95% CI, 1.017–1.805; P = 0.005), and primary tumor location in the upper portion (OR = 6.799; 95% CI, 2.710–17.060; P < 0.001) were found to be independent risk factors for skip metastasis in PTC patients. A nomogram based upon these predictors performed well. The area under the curve (AUC) was 0.806 (95% CI, 0.736–0.876), and the P value of the Hosmer-Lemeshow goodness of fit test was 0.66. Decision curve analysis revealed that the nomogram was clinically useful.

Conclusion

Based on the risk factors of skip metastasis, a high-performance nomogram was established, which can provide an individual risk assessment and can guide treatment decisions for patients.

Nomogram for predicting overall survival of patients with bladder cancer: A population-based study

OBJECTIVE

The aim of this study was to develop and validate a reliable nomogram to estimate overall survival in bladder cancer.

METHOD

Patients diagnosed with bladder cancer identified in the Surveillance, Epidemiology, and End Results database were randomly divided into training and validation cohorts. The powerful prognostic variables were examined using Cox regression analyses. A nomogram was developed on the prognostic factors.

RESULTS

The results suggested that age, sex, race, grade, histologic type, primary site, pathological stage, surgical treatment, and number of primary tumors, were the powerful prognostic factors. All these factors were integrated to construct the nomogram. The nomogram for predicting overall survival showed better discrimination power than the tumor-node-metastasis (TNM) stage system 8th edition.

CONCLUSION

The nomogram has the potential to provide an individualized prediction of overall survival in patients with bladder cancer.