Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study

Zhang, Yuhan; Yi, Xinglin; Tang, Zhe; Xie, Pan; Yin, Na; Deng, Qiumiao; Zhu, Lin; Luo, Hu; Peng, Kanfu

doi:10.3389/fpubh.2023.1104931

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…The limited sample size also restricted us to analyze the influence of each variant of urothelial variants in LNM in BUC patients. Finally, some new model constructing algorithms, such as machine learning, have been used to predict the risk of LNM in prostate cancer and renal cell carcinoma (Li et al 2022 ; Sabbagh et al 2023 ; Zhang et al 2023 ). Our model was developed using traditional univariate and multivariate logistic regression analyses.…”

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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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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“…[21]However, TNM staging contains only tumor size, lymph node metastatic status, as well as information on distant metastases. [22], [23], [24]Therefore, we designed a nomogram combining more information to predict the survival prognosis of young renal cancer patients. In our study, our nomogram shows greater accuracy and clinical utility than TNM staging based on the results of ROC curves, C index, and DCA curves, which demonstrates the strength of our nomogram.…”

Section: Discussionmentioning

confidence: 99%

Prognostic nomogram of the young renal cell carcinoma patient: a SEER-based study

Huang,

Chen

2023

Preprint

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Objective We have constructed and verified nomograms to predict survival in young renal cancer survivors. METHODS We used the US SEER database to conduct a population-based cohort study. Patients diagnosed with young renal cancer were abstracted from the 2000–2018 Surveillance, Endemic Epidemiology, and End Results (SEER) data base, and variables were screened by both univariate and multivariate logistic regression analysis. Risk factors that were statistically meaningful were used to establish predictive models. The areas of the curve (AUC) of the working characteristics of the subjects were used to assess the models' performance.Accuracy and clinical usability were evaluated based on the results of ROC curve, C-index and DCA curve. RESULTS The training cohort in this study includes 3306 patients with a validation cohort of 1416 patients.Multifactorial analysis indicated that patients who were young, married, well-differentiated, low T, N, M stage, and surgery had improved prognosis. The nomogram included 12 independent factors affecting overall survival. The model had a c-index of 0.82, which represents predictive accuracy.The AUC values in the validation set were 0.91, 0.85 and 0.82 for 1, 3 and 5 years, depending on the type of the validation set.The model was shown to have high computational accuracy.The DCA results indicated that the model had a favorable net benefit for patients with renal cell carcinoma. CONCLUSION We have successfully structured the accurate and effective nomograms to predict the prognosis of young renal cancer patients, which helps physicians to choose individualized treatment strategies for young renal cancer patients.

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“…As a technological tool, ML has yielded remarkable results in assisting epidemiologists [ 32 ]. Using ML algorithms, Handelman predicted a reduction in diagnostic errors by addressing complex and tedious clinical work [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study

Zhang

Cai

et al. 2023

International Journal of Clinical Practice

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The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions.

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Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study

Cited by 4 publications

References 29 publications

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

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

Prognostic nomogram of the young renal cell carcinoma patient: a SEER-based study

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study

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