Development of a nomogram for predicting the operative mortality of patients who underwent pneumonectomy for lung cancer: a population-based analysis

Conditional survival of elderly primary central nervous system lymphoma

BACKGROUND

Recent studies have reported that overall survival of elderly patients with primary central nervous system lymphoma (PCNSL), who have the highest incidence of this disease, had failed to benefit from the advancements in treatment strategies over the past decades. This highlights the necessity for intensified research to guide treatment decisions for this specific patient population.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute (NCI) was used to extract data of elderly PCNSL patients (age ≥ 60) who were divided into training and validation groups at the ratio of 7:3, for our analysis. Conditional survival [CS(y|x)] was defined as the probability at survival additional y years given that the patient had not died of PCNSL at a specified period of time (x years) after initial diagnosis. The CS pattern of elderly PCNSL patients was analyzed. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were applied to develop a novel CS-based nomogram.

RESULTS

A total of 3315 elderly patients diagnosed with CNS lymphoma between 2000 and 2019 were extracted from the SEER database, of whom 2320 patients were divided into the training group and 995 into the internal validation group. CS analysis revealed a noteworthy escalation in the 5-year survival rate among elderly PCNSL patients for every additional year of survival. The rates progressed from an initial 21-49%, 63%, and 75%, culminating in an impressive 88% and the survival improvement over time was nonlinear. The LASSO regression identified nine predictors and multivariate Cox regression was used to successfully construct the CS-based nomogram model with favorable prediction performance.

CONCLUSION

CS of elderly PCNSL patients was dynamic and increased over time. Our newly-established CS-based nomogram can provide a real-time dynamic survival estimation, allowing clinicians to better guide treatment decision for these patients.

Development and validation of web-based dynamic nomograms predictive of disease-free and overall survival in patients who underwent pneumonectomy for primary lung cancer

Background

The tumour-node-metastasis (TNM) staging system is insufficient to precisely distinguish the long-term survival of patients who underwent pneumonectomy for primary lung cancer. Therefore, this study sought to identify determinants of disease-free (DFS) and overall survival (OS) for incorporation into web-based dynamic nomograms.

Methods

The clinicopathological variables, surgical methods and follow-up information of 1,261 consecutive patients who underwent pneumonectomy for primary lung cancer between January 2008 and December 2018 at Sun Yat-sen University Cancer Center were collected. Nomograms for predicting DFS and OS were built based on the significantly independent predictors identified in the training cohort (n = 1,009) and then were tested on the validation cohort (n = 252). The concordance index (C-index) and time-independent area under the receiver-operator characteristic curve (AUC) assessed the nomogram's discrimination accuracy. Decision curve analysis (DCA) was applied to evaluate the clinical utility.

Results

During a median follow-up time of 40.5 months, disease recurrence and death were observed in 446 (35.4%) and 665 (52.7%) patients in the whole cohort, respectively. In the training cohort, a higher C-reactive protein to albumin ratio, intrapericardial pulmonary artery ligation, lymph node metastasis, and adjuvant therapy were significantly correlated with a higher risk for disease recurrence; similarly, the independent predictors for worse OS were intrapericardial pulmonary artery and vein ligation, higher T stage, lymph node metastasis, and no adjuvant therapy. In the validation cohort, the integrated DFS and OS nomograms showed well-fitted calibration curves and yielded good discrimination powers with C-index of 0.667 (95% confidence intervals CIs [0.610-0.724]) and 0.697 (95% CIs [0.649-0.745]), respectively. Moreover, the AUCs for 1-year, 3-year, and 5-year DFS were 0.655, 0.726, and 0.735, respectively, and those for 3-year, 5-year, and 10-year OS were 0.741, 0.765, and 0.709, respectively. DCA demonstrated that our nomograms could bring more net benefit than the TNM staging system.

Conclusions

Although pneumonectomy for primary lung cancer has brought encouraging long-term outcomes, the constructed prediction models could assist in precisely identifying patients at high risk and developing personalized treatment strategies to further improve survival.

Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer

Background

With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is an important class of SPM, but remains poorly understood.

Methods

We retrospectively analyzed 3,322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables and analyze causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate-cancer-specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram.

Results

The median follow-up (interquartile range, IQR) time was 47 (24-75) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32 (16-57) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p < 0.05), tumor-node-metastasis (TNM) stage (p < 0.001), Gleason score (p < 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI, 0.813-0.899) and 0.905 (95% CI, 0.941-0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM.

Conclusion

In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram, which performed well in predicting the 3-, 5-, and 10-year PCSM.

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

AIM

To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

MATERIALS AND METHODS

Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression.

RESULTS

CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability.

CONCLUSION

The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.

Effects of Neoadjuvant Radiotherapy on Survival in Patients with Stage IIIA-N2 Non-Small-Cell Lung Cancer Following Pneumonectomy

Background: Pneumonectomy is a drastic but sometimes inevitable treatment option for patients with non-small-cell lung cancer (NSCLC) to improve their chances for long-term survival. However, the optimal adjuvant radiotherapy used for patients with N2 NSCLC following pneumonectomy remains unclear in the literature. Methods: T1-4N0-2M0 NSCLC patients registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. Propensity score matching was applied to balance the assignment of patients. Cox proportional hazards models and Kaplan−Meier analyses were used to identify the factors related to overall survival rates. Restricted cubic splines were used to detect the possible nonlinear dependency of the relationship between the risk of survival and age. Results: A total of 4308 NSCLC patients were enrolled in this study. In N2 patients, the long-term outcome of the chemotherapy and postoperative radiotherapy groups was the worst (p = 0.014). Subgroup analyses showed that the influence of age on survival outcome was confined to patients who received chemotherapy and neoadjuvant radiotherapy (p = 0.004). Meanwhile, patients >65 years of age who received chemotherapy and neoadjuvant radiotherapy had significantly worse prognoses than those in the chemotherapy group (p = 0.005). Conclusions: Our results show that neoadjuvant radiotherapy may have potential benefits in patients aged ≤ 65 years who are scheduled for pneumonectomy, but not in elderly patients.

Triage by PAX1 and ZNF582 methylation in women with cervical intraepithelial neoplasia grade 3: a multicenter case-control study

Background

The colposcopy-conization inconsistency is common in women with cervical intraepithelial neoplasia grade 3 (CIN3). No adequate method has been reported to identify the final pathology of conization. In this study, we explored the ability of PAX1 and ZNF582 methylation to predict the pathological outcome of conization in advance.

Methods

This was a multicenter study and included 277 histologically confirmed CIN3 women who underwent cold knife conization (CKC) from January 2019 to December 2020. The methylation levels of PAX1 (PAX1^m) and ZNF582 (ZNF582^m) were determined by quantitative methylation-specific polymerase chain reaction (qMSP) and expressed in ΔCp. Receiver operating characteristic curves were used to evaluate predictive accuracy.

Results

The final pathological results in 48 (17.33%) patients were inflammation or low-grade squamous intraepithelial lesion (LSIL), 190 (68.59%) were high-grade squamous intraepithelial lesion (HSIL), and 39 (14.08%) were squamous cervical cancer (SCC). PAX1^m and ZNF582^m increased as lesions progressed from inflammation/LSIL, HSIL, to SCC. PAX1 and ZNF582 methylation yielded better prediction performance compared with common screening strategies, whether individually or combined. A 4.33-fold increase in the probability of inflammation/LSIL was observed in patients with lower ZNF582 methylation levels (ΔCp_ZNF582 ≥ 19.18). A 6.53-fold increase in SCC risk was observed in patients with elevated ZNF582 methylation (ΔCp_ZNF582< 7.09).

Conclusions

DNA methylation would be an alternative screening method to triage and predict the final outcome of conization in CIN3 cases.

Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.