Abstract This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman–Kutcher–Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R 2 , area under the receiver operator curve (AUC), and Hosmer–Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763–0.854), and the best overall performance with a Negelkerke R 2 value of 0.332 (95% CI: 0.266–0.486). Both of the optimism-corrected Negelkerke R 2 values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.
In this study, we aimed to establish a novel nomogram model which was better than the current American Joint Committee on Cancer (AJCC) stage to predict survival for non-small-cell lung cancer (NSCLC) patients who underwent surgery. Patients and Methods. 19617 patients with initially diagnosed NSCLC were screened from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015. These patients were randomly divided into two groups including the training cohort and the validation cohort. The Cox proportional hazard model was used to analyze the influence of different variables on overall survival (OS). Then, using R software version 3.4.3, we constructed a nomogram and a risk classification system combined with some clinical parameters. We visualized the regression equation by nomogram after obtaining the regression coefficient in multivariate analysis. The concordance index (C-index) and calibration curve were used to perform the validation of nomogram. Receiver operating characteristic (ROC) curves were used to evaluate the clinical utility of the nomogram.Univariate and multivariate analyses demonstrated that seven factors including age, sex, stage, histology, surgery, and positive lymph nodes (all, P < 0.001) were independent predictors of OS. Among them, stage (C-index = 0.615), positive lymph nodes (C-index = 0.574), histology (C-index = 0.566), age (C-index = 0.563), and sex (C-index = 0.562) had a relatively strong ability to predict the OS. Based on these factors, we established and validated the predictive model by nomogram. The calibration curves showed good consistency between the actual OS and predicted OS. And the decision curves showed great clinical usefulness of the nomogram. Then, we built a risk classification system and divided NSCLC patients into two groups including high-risk group and low-risk group. The Kaplan-Meier curves revealed that OS in the two groups was accurately differentiated in the training cohort (P < 0.001). And then, we validated this result in the validation cohort which also showed that patients in the high-risk group had worse survival than those in the low-risk group.The results proved that the nomogram model had better performance to predict survival for NSCLC patients who underwent surgery than AJCC stage. These tools may be helpful for clinicians to evaluate prognostic indicators of patients undergoing operation.
The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
OBJECTIVE: To study the antitumor activity of Huanglian Jiedu decoction (HLJDT). METHOD: Antitumor activities were tested in mice with experimental tumor H22 in vivo, and the thymus index, spleen index and tumor inhibitory rate were evaluated. The effects on cancer cells from human were investigated in vitro using serum pharmacological approach. Swille, SPC-A-1, SGC-7901 and MCF-7 cancer cells were incubated in culture media containing serum from mice medicated with HLJDT. The inhibitory effects of HLJDT serum were observed by MTT assay. RESULT: HLJDT showed significant antitumor activities on H22 in mice. All of the HLJDT serum in different dosage groups could highly inhibit the proliferation of 4 cancer cell lines from human. CONCLUSION: The HLJDT can significantly inhibit the tumor H22 in mice in a dose-dependent manner, the drug serum has obvious anticancer effects against Swille, SPC-A-1, SGC-7901 and MCF-7.
Causal inference prevails in the field of laparoscopic surgery. Once the causality between an intervention and outcome is established, the intervention can be applied to a target population to improve clinical outcomes. In many clinical scenarios, interventions are applied longitudinally in response to patients' conditions. Such longitudinal data comprise static variables, such as age, gender, and comorbidities; and dynamic variables, such as the treatment regime, laboratory variables, and vital signs. Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome; in such cases, simple adjustment with a conventional regression model will bias the effect sizes. To address this, numerous statistical methods are being developed for causal inference; these include, but are not limited to, the structural marginal Cox regression model, dynamic treatment regime, and Cox regression model with time-varying covariates. This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery.
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