Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study

BACKGROUND

This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques.

METHODS

Data on esophageal cancer patients from 2010 to 2020 were extracted from the surveillance, epidemiology, and end results (SEER) database. Through univariate and multivariate logistic regression analyses, eight indicators related to the risk of lung metastasis were selected. These indicators were incorporated into six machine learning classifiers to develop corresponding predictive models. The performance of these models was evaluated and compared using metrics such as The area under curve (AUC), accuracy, sensitivity, specificity, and F1 score.

RESULTS

A total of 20,249 confirmed cases of esophageal cancer were included in this study. Among them, 14,174 cases (70%) were assigned to the training set while 6075 cases (30%) constituted the internal test set. Primary site location, tumor histology, tumor grade classification system T staging criteria N staging criteria brain metastasis bone metastasis liver metastasis emerged as independent risk factors for esophageal cancer with lung metastasis. Amongst the six constructed models, the GBM algorithm-based machine learning model demonstrated superior performance during internal dataset validation. AUC, accuracy, sensitivity, and specificity values achieved by this model stood at respectively at 0.803, 0.849, 0.604, and 0.867.

CONCLUSION

We have developed an online calculator based on the GBM model ( https://lvgrkyxcgdvo7ugoyxyywe.streamlit.app/)to aid clinical decision-making and treatment planning.

Prognostic factors for patients with pathologic T1-T2N+ esophageal squamous cell carcinoma: A retrospective study with external validation

BACKGROUND

Lymph node metastasis is significantly associated with a worse prognosis in patients with localized early-stage esophageal squamous cell carcinoma. This study aimed to explore the prognostic factors and develop a nomogram for predicting survival in patients with pathologic T1-2N+ esophageal squamous cell carcinoma.

METHODS

Between 2014 and 2022, patients with pT1-2N+ esophageal squamous cell carcinoma who underwent esophagectomy with lymphadenectomy at 2 institutes were reviewed and assigned to training and external validation cohorts. Independent prognostic factors were identified via univariate and multivariate Cox regression analyses. The nomogram model was developed and evaluated by the area under the receiver operating characteristic curve and calibration curve.

RESULTS

In total, 268 patients with a median age of 65 years (range, 40-82) were included and assigned to training (n = 190) and external validation (n = 78) cohorts. The Cox proportional hazards model demonstrated that body mass index (P = .031), surgical approach (P < .001), T stage (P = .015), and Clavien-Dindo classification (P < .001) were independent prognostic factors in the training cohort. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve for 1-year, 3-year, and 5-year of 0.810, 0.789, and 0.809 in the training cohort and 0.782, 0.679, and 0.698 in the validation cohort. The calibration curve showed that the predicted survival probability was in good agreement with the actual survival probability.

CONCLUSION

Lower body mass index, left surgical approach, T2 stage, and Clavien-Dindo classification grade III to V were related to worse prognosis in patients with pT1-T2N+ esophageal squamous cell carcinoma. The developed nomogram may predict individual survival accurately.

Prognostic prediction model for patients with pathological T1N0 stage esophageal squamous cell carcinoma undergone esophagectomy

Background

There is a shortage of reliable predictive models to provide valuable prognostic information for early esophageal squamous cell carcinoma (ESCC) without lymph node metastasis (LNM). We aimed to develop and validate a nomogram using the prognostic factors in T1N0 ESCC patients.

Methods

Patients with pathological T1N0 ESCC who underwent esophagectomy between 2014 and 2021 at three institutes were reviewed. The prognostic factors were evaluated by Cox proportional hazards model and a nomogram was developed. Patients were divided into high- and low-risk groups based on cut-off value of total points in the nomogram. Overall survival (OS) was estimated by the Kaplan-Meier method and compared using the log-rank test.

Results

A total of 275 patients were included and split into training (n=180) and external validation (n=95) cohorts. In the training cohort, multivariable analysis showed that the surgical approach, T1 substage, and carcinoembryonic antigen (CEA) level were independent prognostic factors. The developed nomogram had relatively high performance, with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.783, 0.711 and 0.612 for 1-, 3-, and 5-year OS, respectively. The calibration curves showed that the predicted probability was in good agreement with the actual probability. Forty-seven was determined as cut-off value of total points. High-risk group (n=148) showed a significant poor OS than low-risk group (n=127) (P<0.001).

Conclusions

Left surgical approach, stage T1b, and higher CEA were associated with poorer prognosis in T1N0 ESCC patients. The nomogram demonstrated a good performance to predict the individual survival.

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

Development and validation of a novel nomogram to predict the overall survival of patients with large cell lung cancer: A surveillance, epidemiology, and end results population-based study

Background

Large cell lung cancer (LCLC) is a rare subtype of non-small cell lung carcinoma (NSCLC), and little is known about its clinical and biological characteristics.

Methods

LCLC patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. All patients were randomly divided into a training group and a validation group at a ratio of 7:3. The independent prognostic factors that were identified (P < 0.01) by stepwise multivariate Cox analysis were incorporated into an overall survival (OS) prediction nomogram, and risk-stratification systems, C-index, time-ROC, calibration curve, and decision curve analysis (DCA) were applied to evaluate the quality of the model.

Results

Nine factors were incorporated into the nomogram: age, sex, race, marital status, 6th AJCC stage, chemotherapy, radiation, surgery and tumor size. The C-index of the predicting OS model in the training dataset and in the test dataset was 0.757 ± 0.006 and 0.764 ± 0.009, respectively. The time-AUCs exceeded 0.8. The DCA curve showed that the nomogram has better clinical value than the TNM staging system.

Conclusions

Our study summarized the clinical characteristics and survival probability of LCLC patients, and a visual nomogram was developed to predict the 1-year, 3-year and 5-year OS of LCLC patients. This provides more accurate OS assessments for LCLC patients and helps clinicians make personal management decisions.

Influence of adverse effects of neoadjuvant chemoradiotherapy on the prognosis of patients with early-stage esophageal cancer (cT1b-cT2N0M0) based on the SEER database

Objective

To analyze the prognostic impact of neoadjuvant chemoradiotherapy (NCRT) on early-stage (cT1b-cT2N0M0) esophageal cancer (ESCA) and construct a prognostic nomogram for these patients.

Methods

We extracted the clinical data about patients diagnosed with early-stage esophageal cancer from the 2004-2015 period of the Surveillance, Epidemiology, and End Results (SEER) database. We applied the independent risk factors affecting the prognosis of patients with early-stage esophageal cancer obtained after screening by univariate and multifactorial COX regression analyses to establish the nomogram and performed model calibration using bootstrapping resamples. The optimal cut-off point for continuous variables is determined by applying X-tile software. After balancing the confounding factors by propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) method, Kaplan-Meier(K-M) curve, and log-rank test were applied to evaluate the prognostic impact of NCRT on early-stage ESCA patients.

Results

Among patients who met the inclusion criteria, patients in the NCRT plus esophagectomy (ES) group had a poorer prognosis for overall survival (OS) and esophageal cancer-specific survival (ECSS) than patients in the ES alone group (p < 0.05), especially in patients who survived longer than 1 year. After PSM, patients in the NCRT + ES group had poorer ECSS than patients in the ES alone group, especially after 6 months, while OS was not significantly different between the two groups. IPTW analysis showed that, prior to 6 months patients in the NCRT + ES group had a better prognosis than patients in the ES group, regardless of OS or ECSS, whereas after 6 months, patients in the NCRT + ES group had a poorer prognosis. Based on multivariate COX analysis, we established a prognostic nomogram which showed areas under the ROC curve (AUC) for 3-, 5-, and 10-year OS 0.707, 0.712, and 0.706, respectively, with the calibration curves showing that the nomogram was well calibrated.

Conclusions

Patients with early-stage ESCA (cT1b-cT2) did not benefit from NCRT, and we established a prognostic nomogram to provide clinical decision aid for the treatment of patients with early-stage ESCA.

The risk and prognostic factors for liver metastases in esophageal cancer patients: A large-cohort based study

BACKGROUND

This retrospective study aimed to explore risk factors for liver metastases (LiM) in patients with esophageal cancer (EC) and to identify prognostic factors in patients initially diagnosed with LiM.

METHODS

A total of 28 654 EC patients were retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2018. A multivariate logistic regression model was utilized to identify risk factors for LiM. A Cox regression model was used to identify prognostic factors for patients with LiM.

RESULTS

Of 28 654 EC patients, 4062 (14.2%) had LiM at diagnosis. The median overall survival (OS) for patients with and without LiM was 6.00 (95% CI: 5.70-6.30) months and 15.00 (95% CI: 14.64-15.36) months, respectively. Variables significantly associated with LiM included gender, age, tumor site, histology, tumor grade, tumor size, clinical T stage, clinical N stage, bone metastases (BoM), brain metastases (BrM) and lung metastases (LuM). Variables independently predicting survival for EC patients with LiM were age, histology, tumor grade, BoM, BrM, LuM, and chemotherapy. A risk prediction model and two survival prediction models were then constructed revealing satisfactory predictive accuracy.

CONCLUSIONS

Based on the largest known cohort of EC, independent predictors of LiM and prognostic indicators of survival for patients with LiM were identified. Two models for predicting survival as well as a risk prediction model were developed with robust predictive accuracy.

A modified survival model for patients with esophageal squamous cell carcinoma based on lymph nodes: A study based on SEER database and external validation

Background

The counts of examined lymph nodes (ELNs) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) is a controversial issue. We conducted a retrospective study to develop an ELNs-based model to individualize ESCC prognosis.

Methods

Patients with ESCC from the SEER database and our center were strictly screened. The optimal threshold value was determine by the X-tile software. A prognostic model for ESCC patients was developed and validated with R. The model’s efficacy was evaluated by C-index, ROC curve, and decision curve analysis (DCA).

Results

3,629 cases and 286 cases were screened from the SEER database and our center, respectively. The optimal cut-off value of ELNs was 10. Based on this, we constructed a model with a favorable C-index (training group: 0.708; external group 1: 0.687; external group 2: 0.652). The model performance evaluated with ROC curve is still reliable among the groups. 1-year AUC for nomogram in three groups (i.e., 0.753, 0.761, and 0.686) were superior to that of the TNM stage (P < 0.05). Similarly, the 3-year AUC and the 5-year AUC results for the model were also higher than that of the 8th TNM stage. By contrast, DCA showed the benefit of this model was better in the same follow-up period.

Conclusion

More than 10 ELNs are helpful to evaluate the survival of ESCC patients. Based on this, an improved model for predicting the prognosis of ESCC patients was proposed.

Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma

BACKGROUND

In China, it has been well recognized that some female patients with esophageal squamous cell carcinoma (ESCC) have different overall survival (OS) time, even with the same tumor-node-metastasis (TNM) stage, challenging the prognostic value of the TNM system alone. An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.

AIM

To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC, and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.

METHODS

A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort (n = 175). The results were recognized using the internal (n = 111) and independent external (n = 85) validation cohorts. The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index (C-index), Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC), calibration curve and decision curve analysis. The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.

RESULTS

A clinical–reproductive model including incidence area, age, tumor differentiation, lymph node metastasis (N) stage, estrogen receptor alpha (ESR1) and beta (ESR2) expression, menopausal age, and pregnancy number was constructed to predict OS in female ESCC patients. Compared to the clinical model and TNM stage, the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-, 3-, and 5-years OS in the primary training, internal and external validation sets. Based on the optimal cut-off value of total prognostic scores, patients were classified into high- and low-risk groups with significantly different OS. The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.

CONCLUSION

The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.