Development and validation of a nomogram for predicting cancer-specific survival of surgical resected stage I-II adenosquamous carcinoma of the lung

Development and Validation of a Nomogram for Predicting the 1-, 3-, and 5-year Survival in Patients with Acinar-predominant Lung Adenocarcinoma

OBJECTIVE

This study aimed to develop a nomogram to predict the overall survival (OS) of patients with acinar-predominant adenocarcinoma (APA).

METHODS

Data from patients with APA obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2008 and 2016 were used. Significant prognostic factors were incorporated to construct a nomogram for predicting the 1-, 3-, and 5-year OS in these patients. The discrimination and calibration abilities of the nomogram were assessed using a C-index and calibration curves, respectively.

RESULTS

A total of 2242 patients with APA were randomly divided into a training cohort (n=1576) and validation cohort (n=666). The independent prognostic factors for OS incorporated into the nomogram included marital status, age, gender, differentiation grade, T stage, N stage, and M stage. The nomogram showed good prediction capability, as indicated by the C-index [0.713, 95% confidence interval (CI): 0.705-0.721 in the training cohort, and 0.662, 95% CI: 0.649-0.775 in the validation cohort]. The calibration curves demonstrated that the 1-, 3-, and 5-year OS probabilities were consistent between the observed and predicted outcome frequencies. Patients were divided into the high-risk and low-risk groups with the former showing significantly worse survival than the latter (P<0.001).

CONCLUSION

Using the SEER database, a nomogram was established to predict the 1-, 3-, and 5-year OS of patients with APA and was superior to the tumor size, lymph node, and metastasis staging system in terms of evaluating long-term prognosis.

Establishment and Validation of a Predictive Nomogram for Postoperative Survival of Stage I Non-Small Cell Lung Cancer

Background

Surgical procedure is the preferred option for people with early-stage non-small cell lung cancer (NSCLC), while nearly 30% of patients experienced metastatic or recurrent tumor after operation. The primary intention of this context is to summarize high-risk prognostic factors and set up a novel nomogram to predict the overall survival of individuals with stage I NSCLC after resection.

Methods

Research objects, 10,218 patients with stage I NSCLC after operation from 2010 to 2015, were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors, confirmed by Cox regression analyses, were integrated into a nomogram, to predict the 3-and 5-year overall survival of these individuals. The model experienced internal validation of testing cohorts above and external validation crewed by 160 patients from China. Finally, the nomogram was evaluated through several verification methods such as concordance index (C-index), calibration plots and receiver operating characteristic curve (ROC).

Results

Multivariate analysis identified that age, gender, histologic type, differentiation class, type of operation, T stage and treatment were significant predictive factors for the survival of stage I NSCLC. Based on these factors, a nomogram was constructed to predict the 3- and 5-year overall survival of these individuals. Meanwhile, in the training set, this nomogram displayed excellent superiority over the TNM staging system with abroad application, especially in C-index (0.669 vs 0.580) and the AUC (the Area Under ROC Curve) for the 3- and 5-year survival (0.678 vs 0.582; 0.650 vs 0.576). In the calibration curve, the curve representing predicted survival tended to align with the line representing actual survival as well.

Conclusion

A nomogram was successfully created and verified to achieve the goal that made a rounded accurate prediction on the survival of postoperative I NSCLC patients in terms of the SEER database.

Clinicopathological characteristics and prognosis of resectable lung adenosquamous carcinoma: a population-based study of the SEER database

OBJECTIVE

Adenosquamous carcinoma is a rare subtype of non-small cell lung cancer characterized by aggressive behavior, with combination of adenocarcinoma and squamous cell carcinoma components. The clinicopathological characteristics and prognosis of resectable adenosquamous carcinoma are incompletely understood and this study aimed to depict those in a large population.

METHODS

A total of 805 adenosquamous carcinoma, 7875 squamous cell carcinoma and 23 957 adenocarcinoma patients who underwent lobectomy or sublobectomy were queried from the Surveillance, Epidemiology, and End Results database (2010-17). Clinicopathological characteristics of adenosquamous carcinoma patients were compared with those of squamous cell carcinoma and adenocarcinoma patients. Prognostic factors were identified by univariable and multivariable Cox regression analyses. Propensity score matching was applied to reduce confounding effects.

RESULTS

Adenosquamous carcinoma was associated with higher pleural invasion incidence and poorer differentiation compared with squamous cell carcinoma or adenocarcinoma (P values < 0.001). The independent risk factors of cancer-specific survival of adenosquamous carcinoma patients were increasing age, male sex, invading through visceral pleura, poor differentiation and higher stage. Stage IB adenosquamous carcinoma patients whose tumor invaded through visceral pleura had significantly worse survival than those not (P = 0.003). Adenosquamous carcinoma patients had worse survival compared with squamous cell carcinoma (5-year-survival: 64.55 vs. 69.09%, P = 0.003) and adenocarcinoma (5-year-survival: 64.55 vs. 76.79%, P < 0.001) patients before match. And this difference persisted after match.

CONCLUSIONS

Resectable adenosquamous carcinoma patients had higher pleural invasion incidence, poorer differentiation and worse survival compared with squamous cell carcinoma and adenocarcinoma patients. Visceral pleural invasion status and differentiation grade were vital prognostic factors of adenosquamous carcinoma patients on the basis of stage.

The Effect of Examined Lymph Nodes and Lymph Node Ratio on Pathological Nodal Classification in the Lung Adenosquamous Carcinoma After Lobectomy

Objective

The effects of examined lymph nodes (LNs) and lymph node ratio (LNR) on pN classification and the prognosis are unclear in lung adenosquamous carcinoma (ASC) patients. Thus, this study aimed to investigate the significance of LNs and LNR in the prognosis of ASC and the impact of the abovementioned factors on the pN classification.

Methods

Patients diagnosed with pathological stage T1-4N0-2M0 ASC from the Surveillance Epidemiology and End Results database were included in the study. The primary clinical endpoint was cancer-specific survival (CSS). The optimal cutoff values of the LNs and LNR were determined. An LN indicator, including pN0 #LNs ≤9, pN0 #LNs >9, pN⁺ #LNR ≤0.53, and pN⁺ #LNR > 0.53, was developed. Concordance index (C-index) was used to compare the prognostic predictive ability between N classification and LN indicator. The univariable and multivariable Cox regression analyses were used in this study.

Results

The cohort of 1,416 patients were included in the study. The level of LNs stratified the patients without metastasis of lymph nodes (pN0 #LNs ≤9 vs. pN0 #LNs >9, unadjusted hazard ratio [HR] = 1.255, P = 0.037). Two groups based on the cutoff value of LNR differentiated prognosis of patients with metastasis of lymph nodes (pN⁺ #LNR >0.53 vs. pN⁺ #LNR ≤0.53, unadjusted HR = 1.703, P = 0.001). The LN indicator had a much better predictive ability over N classification in this cohort (LN indicator: C-index = 0.615; N classification: C-index = 0.602, P = 0.001).

Conclusions

We explored clinicopathological factors affecting prognosis in resected lung ASC patients. Besides, the LN indicator was confirmed to be played an essential role in affecting the survival rate in ASC patients. The high-level LNs or low-level LNR might be corelated to improved survival outcomes.

A Novel Clinical Nomogram for Predicting Cancer-Specific Survival in Adult Patients After Primary Surgery for Epithelial Ovarian Cancer: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database and External Validation in a Tertiary Center

Background

The present study aimed to construct and validate a nomogram that can be used to predict cancer-specific survival (CSS) in patients with epithelial ovarian cancer (EOC).

Methods

A total of 7,129 adult patients with EOC were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Patients were randomly divided into the training and validation cohorts (7:3). Cox regression was conducted to evaluate prognostic factors of CSS. The internal validation of the nomogram was performed using concordance index (C-index), AUC, calibration curves, and decision curve analyses (DCAs). Data from 53 adult EOC patients at Shengjing Hospital of China Medical University from 2008 to 2012 were collected for external verification. Kaplan-Meier curves were plotted to compare survival outcomes among risk subgroups.

Results

Age, grade, histological types, stage, residual lesion size, number of regional lymph nodes resected, number of positive lymph nodes, and chemotherapy were independent risk factors for CSS. Based on the above factors, we constructed a nomogram. The C-indices of the training cohort, internal validation cohort, and external verification group were 0.763, 0.750, and 0.920, respectively. The calibration curve indicated good agreement between the nomogram prediction and actual survival. AUC and DCA results indicated great clinical usefulness of the nomogram. The differences in the Kaplan-Meier curves among different risk subgroups were statistically significant.

Conclusions

We constructed a nomogram to predict CSS in adult patients with EOC after primary surgery, which can assist in counseling and guiding treatment decision making.

A nomogram for predicting overall survival in patients with resected non-small cell lung cancer treated with chemotherapy

Background

Chemotherapy is a common treatment for patients with resected non-small cell lung cancer (NSCLC). However, there are few models for predicting the survival outcomes of these patients. Here, we developed a clinical nomogram for predicting overall survival (OS) in this cohort.

Methods

A total of 16,661 patients with resected NSCLC treated with chemotherapy were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified prognostic factors and integrated them into a nomogram. The model was subjected to bootstrap internal validation using the SEER database and external validation using a database in China and the National Cancer Database (NCDB). The model’s predictive accuracy and discriminative ability were tested by calibration and concordance index (C-index).

Results

Age, sex, number of dissected lymph nodes, extent of surgery, N stage, T stage, and grade were independent factors for OS and were integrated into the model. The calibration curves for probability of 1-, 3-, and 5-year OS showed excellent agreement between the predicted and actual survivals. The C-index of the nomogram was higher than that of the Tumor-Node-Metastasis staging system for predicting OS (training cohort, 0.62 vs. 0.58; China cohort, 0.68 vs. 0.63; NCDB cohort, 0.59 vs. 0.57).

Conclusions

We developed a nomogram that can present individual prediction of OS for patients with resected NSCLC who are undergoing chemotherapy. This practical prognostic tool may help clinicians in treatment planning.

Prognostic factors of patients with initially diagnosed T1a glottic cancer: Novel nomograms and a propensity-score matched cohort analysis

The option of T1a glottic cancer treatments remarkably varied in different countries. This study aimed to construct predictive models to predict overall survival (OS) and cancer-specific survival (CSS) of patients with initially diagnosed T1a glottic cancer. And we used propensity score matching (PSM) to reassess the effect of treatments.Data of patients with initially diagnosed T1a glottic cancer were extracted from the Surveillance, Epidemiology, and End Results database. Patients with complete information were randomly divided into the training and the validation cohorts (7:3). Cox regression was conducted to screen significant predictors of the OS and the CSS. PSM was performed to mimic randomized controlled trials. Survival analyses were performed by Kaplan-Meier survival methods, and log-rank tests were utilized.A total of 2342 patients met the inclusion criteria. Survival analyses showed that patients who underwent primary site surgery would have better OS and CSS. Univariate analyses and multivariate analyses proved that stage, N stage, primary site surgery, and chemotherapy significantly affected both the OS and the CSS. Predictive nomograms were established to predict patients' prognosis. Finally, the OS and the CSS for patients who underwent primary site surgery alone were significantly longer than those who underwent radiation alone before and after PSM.We constructed nomograms predicting the OS and the CSS of patients with initially diagnosed T1a glottic cancer. Compared to our previous studies, this study indicated that primary site surgery may be superior to radiation and chemotherapy. At present, chemotherapy should be not recommended for T1a glottic cancer patients.

Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China

An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 10⁹/L versus (4–10) × 10⁹/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 10⁹/L versus (0.8–4) × 10⁹/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1–4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.

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21,392 COVID-19 patients constituted one of the largest cohort studies to date

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Elderly male patients with critical illness and comorbidities had higher death rate

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The death probability increased with time, which was evident for critically ill patients

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The highest death probability within 1 month can reach 54% by the predictive model

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The predictive model could guide the allocation of medical resources