Development and validation of a nomogram for predicting survival of pulmonary invasive mucinous adenocarcinoma based on surveillance, epidemiology, and end results (SEER) database

Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients

BACKGROUND

Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients.

METHODS

Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to assess trends in GBM incidence across populations of different age groups. Subsequently, we identified important prognostic factors by stepwise regression and multivariate Cox regression analysis, and established a Nomogram mathematical model. COX regression model combined with restricted cubic splines (RCS) model was used to analyze the relationship between tumor size and prognosis of GBM patients.

RESULTS

The incidence of GBM has been on the rise since 1978, especially in the age group of 65-84 years. 11498 patients with GBM were included in our study. The multivariate Cox analysis revealed that age, tumor size, sex, primary tumor site, laterality, number of primary tumors, surgery, chemotherapy, radiotherapy, systematic therapy, marital status, median household income, first malignant primary indicator were independent prognostic factors of overall survival (OS) for GBMs. For cancer-specific survival (CSS), race is also independent prognostic factors. Additionally, risk of poor prognosis increased significantly with tumor size in patients with tumors smaller than 49 mm. Moreover, our nomogram model showed favorable discriminative ability.

CONCLUSION

At the population level, the incidence of GBM is on the rise. The relationship between tumor size and patient prognosis is still worthy of further study. Moreover, the proposed nomogram with good performance was constructed and verified to predict the OS and CSS of patients with GBM.

Nomogram predicting overall and cancer specific prognosis for poorly differentiated lung adenocarcinoma after resection based on SEER cohort analysis

The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

The Battle for Accuracy: Identifying the Most Effective Grading System for Lung Invasive Mucinous Adenocarcinoma

BACKGROUND

The prognostic analysis of lung invasive mucinous adenocarcinoma (IMA) is deficient due to the lack of a universally recommended histological grading system, leading to unregulated treatment approaches.

OBJECTIVE

We aimed to examine the clinical trajectory of IMA and assess the viability of utilizing the existing grading system for lung invasive non-mucinous adenocarcinoma in the context of IMA.

METHODS

We retrospectively collected clinicopathological data from 265 IMA patients. Each case re-evaluated the tumor grade using the following three classification systems: the 4th Edition of the World Health Organization classification system, the International Association for the Study of Lung Cancer (IASLC) grading system, and a two-tier grading system. We performed a comparative analysis of these grading systems and identified the most effective grading system for IMA.

RESULTS

The study comprised a total of 214 patients with pure IMA and 51 patients with mixed IMA. The 5-year overall survival (OS) rates for pure IMA and mixed IMA were 86.7% and 57.8%, respectively. All three grading systems proved to be effective prognostic classifiers for IMA. The value of area under the curve at 1-, 3-, and 5-year OS was highest for the IASLC grading system compared with the other grade systems and the clinical stage. The IASLC classification system was an independent prognostic predictor (p = 0.009, hazard ratio 2.243, 95% confidence interval 1.219-4.127).

CONCLUSION

Mixed IMA is more aggressive than pure IMA, with an OS rate on par with that of high-grade pure IMA. The IASLC grading system can better indicate prognosis and is recommended for lung IMA.

Nomogram for predicting prognosis and identifying chemotherapy beneficiaries for completely resected stage I invasive mucinous lung adenocarcinoma

Background

At present, there is a lack of studies in invasive mucinous adenocarcinoma (IMA) that combine clinicopathological and imaging features to stratify risk and select optimal treatment regimen. We aimed to develop and validate a nomogram for predicting recurrence-free survival (RFS) and identifying adjuvant chemotherapy (ACT) beneficiaries for completely resected stage I primary IMA.

Methods

This retrospective study included 750 patients from three hospitals. Patients from two hospitals were divided into training (n=424) and validating cohort (n=185), and patients from the remaining other one hospital constituted external test cohort (n=141) and preoperative computed tomography (CT) image features of each patient were consecutively evaluated. The nomogram was developed by integrating significant prognostic factors of RFS identified in the multivariate analysis. The risk score (RS) based on nomogram was calculated in the entire cohort and the optimal cut-off point for risk stratification was obtained by X-tile software. The Kaplan-Meier method, log-rank test and interaction were used to evaluate the difference in RFS and overall survival (OS) between different risk and treatment groups.

Results

Visceral pleural invasion (VPI, P<0.001), lymph-vascular invasion (LVI, P<0.001), tumor size (P<0.001), smoking history (P<0.001), lobulation (P<0.001) were identified as independent prognostic factors for RFS. The concordance index (C-index) of the nomogram was higher than that of tumor-node-metastasis (TNM) staging system (validation cohort: 0.73±0.09 vs. 0.62±0.08, P<0.001; external test cohort: 0.74±0.10 vs. 0.70±0.09, P=0.035). The patients with higher RS were associated with worse RFS [hazard ratios (HRs) ≥4.76] and OS (HRs ≥2.55) in all included cohorts. Chemotherapy benefits in terms of RFS and OS were observed for patients in higher RS group in both stage IB (interaction P=0.012 for RFS and P=0.037 for OS) and stage I IMA (interaction P<0.001 for both RFS and OS).

Conclusions

The nomogram based on CT image and clinicopathologic features showed superior performance in predicting RFS for stage I IMA and might identify ACT candidates for personalized patient treatment.

Analysis of prognostic factors and establishment of prediction model of lung adenocarcinoma based on SEER database

Background

Few models have been developed to predict survival outcomes for lung adenocarcinoma (LUAD). In this study, we aimed to establish a nomogram for the prediction of cancer-specific survival (CSS) in LUAD patients which can be further developed as a convenient web-based calculator.

Methods

We performed a retrospective analysis of 50,007 LUAD patients selected from the Surveillance, Epidemiology, and End Result (SEER) 18 registry database. To enhance the reliability of the analysis, the patients' data were further randomly divided into the training cohort (70%) and validation cohort (30%). The optimal age cut-off points were determined using X-tile software, and patients were divided into three age groups: 10-72, 73-79, and 80-99 years. We selected independent prognostic factors from 17 variables by Cox regression, and plotted a visual nomogram to predict the 1-, 3-, and 5-year CSS. The predictive performance of the nomogram was evaluated through the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve. To facilitate CSS forecast, a web-based calculator has subsequently been developed.

Results

We selected sex, age, race, marital status, N stage, tumor size, surgery, radiotherapy, chemotherapy, and metastasis (bone, brain, liver, and lung) as independent prognostic factors. The C-index was 0.779 [95% confidence interval (CI): 0.775-0.783] in the training set prediction model, and 0.782 (95% CI: 0.778-0.786) in the validation set. ROC analysis showed that area under the curve (AUC) values were 0.700, 0.733 and 0.669 for the 1-, 3- and 5-year CSS in the training set and 0.700, 0.744 and 0.669 in the validation set, respectively. In the nomogram calibration curve, there was strong correlation between the observed and predictive values. A web-based calculator can be accessed at: https://hjhlovelfb.shinyapps.io/DynNomapp/.

Conclusions

This nomogram model has good predictive power and can help clinicians identify LUAD patients at high risk of cancer-related death. This nomogram is expected to be a precise and personalized tool for predicting the prognosis of patients with LUAD.

Establishment and validation of nomograms for predicting survival of lung invasive adenocarcinoma based on the level of pathological differentiation: a SEER cohort-based analysis

BACKGROUND

The pathological differentiation of invasive adenocarcinoma (IAC) has been linked closely with epidemiological characteristics and clinical prognosis. However, the current models cannot accurately predict IAC outcomes and the role of pathological differentiation is confused. This study aimed to establish differentiation-specific nomograms to explore the effect of IAC pathological differentiation on overall survival (OS) and cancer-specific survival (CSS).

METHODS

The data of eligible IAC patients between 1975 and 2019 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided in a ratio of 7:3 into a training cohort and a validation cohort. The associations between pathological differentiation and other clinical characteristics were evaluated using chi-squared test. The OS and CSS analyses were performed using the Kaplan-Meier estimator, and the log-rank test was used for nonparametric group comparisons. Multivariate survival analysis was performed using a Cox proportional hazards regression model. The discrimination, calibration, and clinical performance of nomograms were assessed by area under receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

A total of 4,418 IAC patients (1,001 high-differentiation, 1,866 moderate-differentiation, and 1,551 low-differentiation) were identified. Seven risk factors [age, sex, race, tumor-node-metastasis (TNM) stage, tumor size, marital status, and surgery] were screened to construct differentiation-specific nomograms. Subgroup analyses showed that disparate pathological differentiation played distinct roles in prognosis, especially in patients with older age, white race, and higher TNM stage. The AUC of nomograms for OS and CSS in the training cohort were 0.817 and 0.835, while in the validation cohort were 0.784 and 0.813. The calibration curves showed good conformity between the prediction of the nomograms and the actual observations. DCA results indicated that these nomogram models could be used as a supplement to the prediction of the TNM stage.

CONCLUSIONS

Pathological differentiation should be considered as an independent risk factor for OS and CSS of IAC. Differentiation-specific nomogram models with good discrimination and calibration capacity were developed in the study to predict the OS and CSS in 1-, 3- and 5-year, which could be used predict prognosis and select appropriate treatment options.

Postoperative survival of pulmonary invasive mucinous adenocarcinoma versus non-mucinous invasive adenocarcinoma

PURPOSE

In 2015, the World Health Organization renamed mucinous bronchioloalveolar adenocarcinoma as pulmonary invasive mucinous adenocarcinoma (IMA). Due to its low incidence and unclear prognosis with surgical treatment, previous studies have presented opposing survival outcomes. We aimed to investigate the differences in surgical prognosis and prognosis-related risk factors by comparing IMA with non-mucinous invasive adenocarcinoma (NMA).

METHODS

A total of 20,914 patients diagnosed with IMA or NMA from 2000 to 2014 were screened from the Surveillance, Epidemiology, and End Results database. The screened patients were subjected to propensity score matching (PSM) in a 1:4 ratio to explore the survival differences between patients with IMA and NMA and the factors influencing prognosis.

RESULTS

For all patients, IMA was prevalent in the lower lobes of the lungs (p < 0.0001), well-differentiated histologically (p < 0.0001), less likely to have lymph node metastases (94.4% vs. 72.0%, p < 0.0001) and at an earlier pathological stage (p = 0.0001). After PSM, the IMA cohort consisted of 303 patients, and the NMA cohort consisted of 1212 patients. Kaplan‒Meier survival analysis showed no difference in overall survival (OS) between patients in the IMA cohort and those in the NMA cohort (p = 0.7). Cox proportional hazards analysis showed that differences in tumor pathological type did not influence OS between the two cohorts (p = 0.65). Age (HR: 1.98, 95% CI 1.7-2.31, p < 0.0001), gender (HR: 0.64, 95% CI 0.55-0.75, p < 0.0001), and radiation treatment (HR: 2.49, 95% CI 1.84-3.37, p < 0.0001) were independent predictors of patient OS.

CONCLUSION

There was no significant difference in OS between patients with IMA and those with NMA after surgical treatment. Age, sex, and radiation treatment can independently predict OS.

Development and Validation of Nomogram for Predicting Survival of Primary Liver Cancers Using Machine Learning

Background and Aims

Primary liver cancer (PLC) is a common malignancy with poor survival and requires long-term follow-up. Hence, nomograms need to be established to predict overall survival (OS) and cancer-specific survival (CSS) from different databases for patients with PLC.

Methods

Data of PLC patients were downloaded from Surveillance, Epidemiology, and End Results (SEER) and the Cancer Genome Atlas (TCGA) databases. The Kaplan Meier method and log-rank test were used to compare differences in OS and CSS. Independent prognostic factors for patients with PLC were determined by univariate and multivariate Cox regression analyses. Two nomograms were developed based on the result of the multivariable analysis and evaluated by calibration curves and receiver operating characteristic curves.

Results

OS and CSS nomograms were based on age, race, TNM stage, primary diagnosis, and pathologic stage. The area under the curve (AUC) was 0.777, 0.769, and 0.772 for 1-, 3- and 5-year OS. The AUC was 0.739, 0.729 and 0.780 for 1-, 3- and 5-year CSS. The performance of the two new models was then evaluated using calibration curves.

Conclusions

We systematically reviewed the prognosis of PLC and developed two nomograms. Both nomograms facilitate clinical application and may benefit clinical decision-making.

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations

OBJECTIVE

To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting.

METHODS

The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs.

RESULTS

A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs.

CONCLUSION

Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.

CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model

Objective

To investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model.

Methods

Fifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model.

Results

Univariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05).

Conclusion

Compared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.

Clinical features and prognosis of resectable pulmonary primary invasive mucinous adenocarcinoma

Background

According to the latest the World Health Organization (WHO) classification in 2015, invasive mucinous adenocarcinoma (IMA) is defined as a new pathological subtype of lung adenocarcinoma (LUAD). However, whether this rare subtype of lung pathology has any difference in prognosis than conventional LUAD is debatable. Our study attempted to compare clinical characteristics and prognosis of IMA vs. noninvasive mucinous adenocarcinomas (NMA).

Methods

A total of 1,857 patients with LUAD who underwent radical resection were screened from 2010 to 2015 at Zhejiang Cancer Hospital. Patients with pulmonary IMA were matched 1:1 by using propensity scores with LUAD adjusted for clinicopathological characteristics. After follow-up, overall survival (OS) and disease-free survival (DFS) were explored by Kaplan-Meier and Cox regression analyses. Forest plots were used for subgroup analyses.

Results

Following screening, 499 patients with LUAD were enrolled, with 97 IMA and 402 NMA. Compared to NMA of the lung, IMA was proportionately lower in women (50.5% vs. 63.4%; P=0.026) and nonsmokers (P<0.001). IMA was also associated with earlier tumor stage I (68.0% vs. 55.5%; P=0.033) and lower frequency of upper lobe tumors compared to NMA (P=0.007). Following propensity score matching, 97 pairs were selected, among which we found that patients with pulmonary IMA had a longer OS than those with NMA (P=0.014). According to the subgroup analysis, improved OS in the IMA cohort versus the NMA cohort was observed across various factors, including the absence of lymphovascular invasion or perineural invasion.

Conclusions

In this study, we found that resectable IMA patients had a better OS than NMA patients. This study contributes to the understanding of IMA in depth, but it needs to be validated through additional multicenter studies.

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study

BACKGROUND

The prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis.

METHODS

The 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test.

RESULTS

Univariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group.

CONCLUSIONS

We established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.