Development and Validation of a Nomogram to Predict Lymph Node Metastasis in Patients With T1 High-Grade Urothelial Carcinoma of the Bladder

Ou, Ningjing; Song, Yuxuan; Liu, Mohan; Jun, Zhu; Yang, Yongjiao; Liu, Xiaoqiang

doi:10.3389/fonc.2020.532924

Cited by 8 publications

(9 citation statements)

References 36 publications

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“…First, the largest diameter of the primary bladder tumor was a selected predictor, and patients with primary tumor larger than 49-57 mm had higher risk of pelvic LN involvement. This is in line with previously published studies that reported the primary bladder tumor size to be associated with the risk of LN involvement [19] and distant metastases [20]. Of note, the largest diameter of primary tumor was measured when visible on PET and/or non-contrast enhanced CT from [ 18 F]-FDG PET/CT performed after TURBT.…”

Section: Discussionsupporting

confidence: 90%

A Machine-learning Signature Performs as Well as Experts' Consensus to Diagnose Pelvic Lymph Node Involvement in Bladder Cancer on [18F]-FDG PET/CT: A Pathology-controlled Study

Girard

Dercle

Vila-Reyes

et al. 2021

Preprint

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PurposeTo develop and validate an objective machine-learning-based signature for the diagnosis of pelvic lymph node (LN) involvement based on [18F]-FDG PET/CT before radical cystectomy in patients with muscle-invasive bladder cancer (MIBC).Methods Between 2010-2017, consecutive patients with localized MIBC were retrospectively and randomly assigned to a training (n=115) and a validation (n=58) sets. The reference standard used for LN involvement was pathological status after extended pelvic LN dissection. In the training set a signature was obtained with a random forest algorithm built from 21 morphological and metabolic imaging features. The signature performance was evaluated using areas under ROC curves (AUCs).Results The prevalence of pelvic LN involvement was 26% (n=30/115) in the training set, and 21% (n=12/58) in the validation set. In the training set, the top 5 features selected by the algorithm were derived from pelvic LNs (SUVmax, sum of the products of diameters, product of diameters of the largest LN), primary bladder tumor (tumor largest diameter), and urine (SUVmax). In the training set, the AUC of the signature was 0.79 (95%CI 0.71-0.88), versus 0.72 (95%CI 0.62-0.81) with the consensus of experts, p=0.24. In the validation set, AUCs were 0.67 (95%CI 0.50-0.84) and 0.62 (95%CI 0.48-0.77) p=0.70, respectively.Conclusion The diagnostic performance of a machine-learning signature was not inferior to the expert consensus review for pelvic LN staging, providing an objective and accurate tool for this task.

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Section: Discussionsupporting

confidence: 90%

A Machine-learning Signature Performs as Well as Experts' Consensus to Diagnose Pelvic Lymph Node Involvement in Bladder Cancer on [18F]-FDG PET/CT: A Pathology-controlled Study

Girard

Dercle

Vila-Reyes

et al. 2021

Preprint

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“…In the present study, we used preoperative laboratory measurements and inflammatory biomarkers, including NLR, PLR, MLR, NPR, and SII, to predict LNM in BUC. Two previously published studies identified MLR, fibrinogen, and NLR as preoperative predictors of LNM in BUC and included them in nomograms (Ou et al 2020 ; Schuettfort et al 2022 ). Although most laboratory measurements (including MLR, fibrinogen, and NLR) included in our study were significantly different between BUC patients with LNM and those without in the univariate analysis, serum creatine was the only variable capable of independently predicting LNM in BUC before RC in the multivariate analysis.…”

Section: Discussionmentioning

confidence: 99%

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

Yao

Sun

et al. 2023

J Cancer Res Clin Oncol

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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.

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“…Predicting models of tumor LNM have been established in thyroid papillary carcinoma, bladder urothelial carcinoma and early esophageal squamous cell carcinoma ( 14 – 16 ). To our knowledge, there are no predicting models for LNM in ES.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing’s Sarcoma

Qian

Liu

et al. 2022

Front. Med.

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ObjectiveIn order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms.MethodsClinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application.ResultsLNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py).ConclusionWith the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.

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Development and Validation of a Nomogram to Predict Lymph Node Metastasis in Patients With T1 High-Grade Urothelial Carcinoma of the Bladder

Cited by 8 publications

References 36 publications

A Machine-learning Signature Performs as Well as Experts' Consensus to Diagnose Pelvic Lymph Node Involvement in Bladder Cancer on [18F]-FDG PET/CT: A Pathology-controlled Study

A Machine-learning Signature Performs as Well as Experts' Consensus to Diagnose Pelvic Lymph Node Involvement in Bladder Cancer on [18F]-FDG PET/CT: A Pathology-controlled Study

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

A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing’s Sarcoma

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