A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5-30 mm pulmonary nodules: a retrospective analysis

Background

The diagnosis of benign and malignant solitary pulmonary nodules based on personal experience has several limitations. Therefore, this study aims to establish a nomogram for the diagnosis of benign and malignant solitary pulmonary nodules using clinical information and computed tomography (CT) results.

Methods

Retrospectively, we collected clinical and CT characteristics of 1,160 patients with pulmonary nodules in Guang'an People's Hospital and the hospital affiliated with North Sichuan Medical College between 2019 and 2021. Among these patients, data from 773 patients with pulmonary nodules were used as the training set. We used the least absolute shrinkage and selection operator (LASSO) to optimize clinical and imaging features and performed a multivariate logistic regression to identify features with independent predictive ability to develop the nomogram model. The area under the receiver operating characteristic curve (AUC), C-index, decision curve analysis, and calibration plot were used to evaluate the performance of the nomogram model in terms of predictive ability, discrimination, calibration, and clinical utility. Finally, data from 387 patients with pulmonary nodules were utilized for validation.

Results

In the training set, the predictors for the nomogram were gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction, selected through LASSO and logistic regression analysis. The resulting model had a C-index of 0.842 (95% CI [0.812-0.872]) and AUCs of 0.842 (95% CI [0.812-0.872]). In the validation set, the C-index was 0.856 (95% CI [0.811-0.901]), and the AUCs were 0.844 (95% CI [0.797-0.891]). Results from the calibration curve and clinical decision curve analyses indicate that the nomogram has a high fit and clinical benefit in both the training and validation sets.

Conclusion

The establishment of a nomogram for predicting the benign or malignant diagnosis of solitary pulmonary nodules by this study has shown good efficacy. Such a nomogram may help to guide the diagnosis, follow-up, and treatment of patients.

Development and validation of a prediction model for malignant pulmonary nodules: A cohort study

This study is to develop and validate a preoperative prediction model for malignancy of solitary pulmonary nodules. Data from 409 patients who underwent solitary pulmonary nodule resection at the First Affiliated Hospital of Nanjing Medical University, China between June 2018 and December 2020 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Clinical features, imaging parameters and laboratory data were then collected. Logistic regression analysis was used to develop a prediction model to identify variables significantly associated with malignant pulmonary nodules (MPNs) that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by concordance index and calibration plot, respectively. MPNs were confirmed in 215 (52.6%) patients by a pathological examination. Multivariate logistic regression analysis identified 6 risk factors independently associated with MPN: gender (female, odds ratio [OR] = 2.487; 95% confidence interval [CI]: 1.313-4.711; P = .005), location of nodule (upper lobe of lung, OR = 1.126; 95%CI: 1.054-1.204; P < .001), density of nodule (pure ground glass, OR = 4.899; 95%CI: 2.572-9.716; P < .001; part-solid nodules, OR = 6.096; 95%CI: 3.153-14.186; P < .001), nodule size (OR = 1.193; 95%CI: 1.107-1.290; P < .001), GAGE7 (OR = 1.954; 95%CI: 1.054-3.624; P = .033), and GBU4-5 (OR = 2.576; 95%CI: 1.380-4.806; P = .003). The concordance index was 0.86 (95%CI: 0.83-0.91) and 0.88 (95%CI: 0.84-0.94) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes. We have developed and validated a preoperative prediction model for MPNs. The model could aid physicians in clinical treatment decision making.

[Advances and Clinical Application of Malignant Probability Prediction Models for Solitary Pulmonary Nodule]

随着计算机断层扫描（computed tomography, CT）的普及，孤立性肺结节（solitary pulmonary nodule, SPN）发病率明显上升。肺结节良恶性评估对肺结节诊治决策至关重要。许多肺结节恶性概率预测模型已被开发，这些模型基于患者临床和影像学特征来评估肺结节恶性概率。近年来，肺结节恶性概率预测模型在国内逐渐引起关注。本文基于肺结节恶性概率预测模型研究，重点关注在中国患者群体中模型的建立或验证情况，综述肺结节恶性概率预测模型的研究进展和临床应用情况，并为模型未来发展提出思路。

Development and validation of a risk prediction score for patients with nasopharyngeal carcinoma

Background

We aimed to develop and validate a predictive model for the overall survival (OS) of patients with nasopharyngeal carcinoma (NPC).

Methods

Overall, 519 patients were retrospectively reviewed in this study. In addition, a random forest model was used to identify significant prognostic factors for OS among NPC patients. Then, calibration plot and concordance index (C-index) were utilized to evaluate the predictive accuracy of the nomogram model.

Results

We used a random forest model to select the three most important features, dNLR, HGB and EBV DNA, which were significantly associated with the OS of NPC patients. Furthermore, the C-index of our model for OS were 0.733 (95% CI 0.673 ~ 0.793) and 0.772 (95% CI 0.691 ~ 0.853) in the two cohorts, which was significantly higher than that of the TNM stage, treatment, and EBV DNA. Based on the model risk score, patients were divided into two groups, associated with low-risk and high-risk. Kaplan–Meier curves demonstrated that the two subgroups were significantly associated with OS in the primary cohort, as well as in the validation cohort. The nomogram for OS was established using the risk score, TNM stage and EBV DNA in the two cohorts. The nomogram achieved a higher C-index of 0.783 (95% CI 0.730 ~ 0.836) than that of the risk score model 0.733 (95% CI 0.673 ~ 0.793) in the primary cohort (P = 0.005).

Conclusions

The established risk score model and nomogram resulted in more accurate prognostic prediction for individual patient with NPC.