数据挖掘主要是以肿瘤方向为主，但是非肿瘤呢，数据很少，光靠生信很难搞出文章的。但是这种情况很好发，收集一定的病例数据，然后绘制下面的这几张图就可以发SCI了。这里同样为大家提供参考范文，这篇文章属于影像学方向的预测模型，之前很多人问过影像学的文章，现在它来了。文章题目：Development and validation of CT imaging–basedpreoperative nomogram in the prediction of unfavorable high-grade small renal masses文章摘要：Purpose: In recent years, there has been an increase in the incidence of small renal masses (SRMs) and nephrectomy was the standard management of this disease in the past. Currently,the use of active surveillance has been recommended as an alternative option in the case of some patients with SRMs due to its heterogenicity. However, limited studies focused on the regarding risk stratification. Therefore, in the current study, we developed a nomogram for the purpose of predicting the presence of high-grade SRMs on the basis of the patient information provided (clinical information, hematological indicators, and CT imaging data).Patients and methods: A total of 329 patients (consisting of development and validation cohort) who had undergone nephrectomy for SRMs between January 2013 and May 2016retrospectively were recruited for the present study. All preoperative information, including clinical predictors, hematological indicators, and CT predictors, were obtained. Lasso regression model was used for data dimension reduction and feature selection. Multivariablelogistic regression analysis was applied for the establishment of the predicting model. The performance of the nomogram was assessed with respect to its calibration and discriminationproperties and externally validated.Results: The predictors used in the assessment of the nomogram included tumor size, CT tumor contour, CT necrosis, CT tumor exophytic properties, and CT collecting system oppression. Based on these parameters, the nomogram was evaluated to have an effectivediscrimination and calibration ability, and the C-index was found to be 0.883 after internal validation and 0.887 following external validation.Conclusion: Based on the aforementioned findings, it can be concluded that CT imaging–based preoperative nomogram is an effective predictor of SRMs and hence can be used in the preoperative evaluation of SRMs, due to its calibration and discrimination abilities.
