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.

Objective. To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. Method. Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model’s performance. The final model was shown by a website calculator. Result. Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879–0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. Conclusion. This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation.

Objective To analyze prognostic factors of antisynthetase syndrome (ASS)‐related interstitial lung disease (ILD). Methods We retrospectively collected the data of 77 inpatients with ASS‐ILD at our hospital from January 1, 2018, to January 1, 2021. The improvement/stability group and deterioration/death group were defined according to their follow‐up outcome. Clinical data of the 2 groups were compared. Univariate analysis was adopted to screen the possible prognostic factors and then logistic regression was used for multivariate analysis. Result After 6 to 42 months of follow‐up, 52 patients (67.5%) were classified into the improvement/stability group, and 25 patients (32.5%) were classified into the deterioration/death group. According to the multivariate stepwise logistic regression analysis, respiratory failure (odds ratio [OR] = 6.71, 95% CI: 1.64–27.38, P = .008) and elevated muscle enzymes (OR = 4.31, 95% CI: 1.03–18.05, P = .045) were found to be independent risk factors, while mechanic's hands (OR = 0.06, 95% CI: 0.01–0.37, P = .003) and anti‐Jo‐1 antibody (OR = 0.24, 95% CI: 0.06–0.93, P = .039) were protective factors. Conclusion The prognostic assessment of ASS‐ILD patients should be emphasized. Patients with a poor prognosis should be identified early based on their risk factors to guide clinical management decisions.

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