The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.

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.

Characteristics and Prognostic Nomogram for Primary Lung Lepidic Adenocarcinoma

Background

Lepidic adenocarcinoma (LPA) is an infrequent subtype of invasive pulmonary adenocarcinoma (ADC). However, the clinicopathological features and prognostic factors of LPA have not been elucidated.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) database of 4191 LPA patients were retrospectively analyzed and compared with non-LPA pulmonary ADC to explore the clinicopathological and prognosis features of LPA. Univariate and multivariate Cox proportional hazard models were performed to identify independent survival predictors for further nomogram development. The nomograms were validated using the concordance index, receiver operating characteristic curves, and calibration plots, as well as decision curve analysis, in both the training and validation cohorts.

Results

Compared with non-LPA pulmonary ADC patients, those with LPA exhibited unique clinicopathological features, including more elderly and female patients, smaller tumor size, less pleural invasion, and lower histological grade and stage. Multivariate analyses showed that age, sex, race, tumor location, primary tumor size, pleural invasion, histological grade, stage, primary tumor surgery, and chemotherapy were independently associated with overall survival (OS) and cancer-specific survival (CSS) in patients with LPA. The nomograms showed good accuracy compared with the actual observed results and demonstrated improved prognostic capacity compared with the TNM stage.

Conclusions

LPA is more frequently diagnosed in older people and women. LPA was inclined to be smaller in tumor size and lower in tumor grade and staging, which may indicate a favorable prognosis. The constructed nomograms accurately predict the long-term survival of LPA patients.

A novel age-biomarker-clinical history prognostic index for heart failure with reduced left ventricular ejection fraction

Purpose

A model for predicting the prognosis of patients with heart failure with reduced left ventricular ejection fraction (HFrEF) is currently not available. This study aimed to develop an age-biomarker-clinical history prognostic index (ABC-PI) and validate it for the assessment of individual prognosis.

Patients and methods

A total of 5,974 HFrEF patients were enrolled and 1,529 were included in this study after excluding missing values and loss to follow-up. Variables that significantly contributed to prediction of all-cause mortality were assessed by Cox regression and latent trait analysis (LTA) was used to validate discrimination of variables.

Results

After Cox regression, the following seven most significant variables were selected: age, N-terminal pro-B-type natriuretic peptide, renal dysfunction, left ventricular mass index, percutaneous coronary intervention, atrial fibrillation, and New York Heart Association (C-index: 0.801 ± 0.013). After verification by LTA, discrimination of these seven variables was proven. A nomogram was used to form the ABC-PI, and then the total score was set to 100 points. A lower score indicated a higher risk. After verification, the 3-year mortality rate was 34.7% in the high-risk group and only 2.6% in the low-risk group.

Conclusion

Our novel ABC-PI shows a good performance and does not require re-input in the original model. The ABC-PI can be used to effectively and practically predict the prognosis of HFrEF patients.

Immune landscape and a promising immune prognostic model associated with TP53 in early-stage lung adenocarcinoma

Purpose

TP53 mutation, one of the most frequent mutations in early‐stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early‐stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early‐stage LUAD and formulate a TP53‐associated immune prognostic model (IPM) that can estimate prognosis in early‐stage LUAD patients.

Materials and methods

Immune‐associated differentially expressed genes (DEGs) between TP53 mutated (TP53^MUT) and TP53 wild‐type (TP53^WT) early‐stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune‐associated DEGs. We constructed and validated an IPM based on the TCGA and a meta‐GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application.

Results

TP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early‐stage LUAD. Sixty‐seven immune‐associated DEGs were determined between TP53^WT and TP53^MUT cohort. An IPM consisting of two prognostic immune‐associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta‐GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43–3.06, p < 0.001). Increased expressions of PD‐L1, CTLA‐4, and TIGIT were revealed in the high‐risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility.

Conclusion

The IPM based on TP53 status is a reliable and robust immune signature to identify early‐stage LUAD patients with high risk of unfavorable survival.