Development and internal validation of nomograms for the prediction of postoperative survival of patients with grade 4 renal cell carcinoma (RCC)

Zhu, Jun; Liu, Zhifu; Zhang, Zhongyuan; Fan, Yu; Chen, Yuke; He, Zhisong; Zhou, Liqun; Jin, Jie; Shen, Cheng; Yu, Wenyan

doi:10.21037/tau-19-687

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…In our study, T stage, M stage, and pathological grade were independent predictors of LNM, Patients with high-level pathological grade, and advanced T and M stages had a higher risk of LNM, probably indicating that tumors were closely related to much more drastic aggressive, which was similar to previous studies (17,18,(36)(37)(38). Meanwhile, Figure 5 also showed that the T stage and M stage were the top two important variables in our five models.…”

Section: Discussionsupporting

confidence: 88%

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

Feng¹,

Hong²,

Liu³

et al. 2022

Front. Endocrinol.

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Simple summaryStudies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.BackgroundLymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.MethodsData on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.ResultsThe training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.ConclusionsThe predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

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Section: Discussionsupporting

confidence: 88%

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

Feng¹,

Hong²,

Liu³

et al. 2022

Front. Endocrinol.

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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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“…Survival prediction is an ongoing challenge in RCC. Multiple different models have been developed already, but none of them has incorporated AI-based image analysis [29][30][31]. Thus, in our study, we trained a CNN to predict 5y-OS.…”

Section: Discussionmentioning

confidence: 99%

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma

et al. 2022

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For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9–68.1%), 86.2% (95%-CI: 81.8–90.5%), 44.9% (95%-CI: 40.2–49.6%), and 0.70 (95%-CI: 0.69–0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70–8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60–5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92–4.94, p = 0.08) on external validation. The results demonstrate that the CNN’s image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.

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Development and internal validation of nomograms for the prediction of postoperative survival of patients with grade 4 renal cell carcinoma (RCC)

Cited by 5 publications

References 29 publications

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

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

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma

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