A population-based predictive model predicting candidate for primary tumor surgery in patients with metastatic esophageal cancer

A machine learning model predicting candidates for surgical treatment modality in patients with distant metastatic esophageal adenocarcinoma: A propensity score-matched analysis

Objective

To explore the role of surgical treatment modality on prognosis of metastatic esophageal adenocarcinoma (mEAC), as well as to construct a machine learning model to predict suitable candidates.

Method

All mEAC patients pathologically diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A 1:4 propensity score-matched analysis and a multivariate Cox analysis were performed to verify the prognostic value of surgical treatment modality. To identify suitable candidates, a machine learning model, classification and regression tree (CART), was constructed, and its predictive performance was evaluated by the area under receiver operating characteristic curve (AUC).

Results

Of 4520 mEAC patients, 2901 (64.2%) were aged over 60 years and 4012 (88.8%) were males. There were 411 (9.1%) patients receiving surgical treatment modality. In the propensity score-matched analysis, surgical treatment modality was significantly associated with a decreased risk of death (HR: 0.47, 95% CI: 0.40-0.55); surgical patients had almost twice as much median survival time (MST) as those without resection (MST with 95% CI: 23 [17-27] months vs. 11 [11-12] months, P <0.0001). The similar association was also observed in the multivariate Cox analysis (HR: 0.47, 95% CI: 0.41-0.53). Then, a CART was constructed to identify suitable candidates for surgical treatment modality, with a relatively good discrimination ability (AUC with 95% CI: 0.710 [0.648-0.771]).

Conclusion

Surgical treatment modality may be a promising strategy to prolong survival of mEAC patients. The CART in our study could serve as a useful tool to predict suitable candidates for surgical treatment modality. Further creditable studies are warranted to confirm our findings.

The Effect of the Appropriate Timing of Radiotherapy on Survival Benefit in Patients with Metastatic Esophageal Cancer Who Have Undergone Resection of Primary Site: A SEER Database Analysis

Background

Metastatic esophageal cancer (MEC) is an advanced stage of esophageal cancer. However, still, resection of primary site and radiotherapy are considered treatment modalities to treat patients with MEC. Hence, this study is aimed at exploring the effect of the appropriate timing of radiotherapy on the survival benefit of these patients by comparing cancer-specific survival (CSS).

Method

The patient information was obtained from the National Surveillance Epidemiology and End Results (SEER) database between the years 2004 and 2017. We used the SEER∗ STAT (V8.3.9.2) software to search and download data. Patients treated with pre- and postoperative radiotherapy were divided into two groups. The propensity score matching (PSM) analysis was performed to increase the comparability of data within two groups. We used the Kaplan-Meier method to analyze and compare the CSS between the two groups. The Cox risk model was used to analyze variables affecting patient survival.

Results

A total of 599 patients with MEC who experienced resection of the primary site and radiotherapy were recruited. 144 pairings formed through PSM. The 5-year CSS was 23.0% and 11.7% for patients who have undergone pre- and postoperative radiotherapy, respectively. Patients who have undergone preoperative radiotherapy showed better CSS than those who received postoperative radiotherapy (P < 0.001). The multivariate Cox analysis of the entire cohort showed that age > 60 years at the time of diagnosis (HR = 1.481, 95% CI: 1.1341-1.934, and P = 0.04) and other histological types of esophageal cancer (HR = 1.581, 95% CI: 1.067-2.341, and P = 0.022) increased the risk of cancer-related death. Inversely, marriage (HR = 0.696, 95% CI: 0.514-0.942, and P = 0.019) and preoperative radiotherapy (HR = 0.664, 95% CI: 0.517-0.853, and P < 0.001) reduced the risk of death from cancer.

Conclusions

For patients with MEC, preoperative radiotherapy might have a significant effect on the survival benefit over those who receive postoperative radiotherapy.

A population-based predictive model to identify patients with signet ring cell carcinoma of the stomach who are most suitable for primary tumor resection

Background

Though the survival benefit of primary tumor operation for patients with signet ring cell carcinoma of the stomach is known, the specific characteristics of those patients who would profit from the operation are yet to be determined. To this end, a predictive model was developed to identify the conjecture that the survival profit from primary tumor operation would only be obtained by patients.

Method

The clinical data of the patients with signet ring cell carcinoma of the stomach were obtained from the Surveillance, Epidemiology, and End Results database, and then divided into operation and no-operation groups based on whether the patients underwent the primary tumor operation. To remove the confounding factors, propensity score matching was employed, and it was hypothesized that the patients who had been operated on and lived a longer life than the median cancer-specific survival time of those who hadn’t must have profited from the surgery. To discuss the independent factors of cancer-specific survival time in the beneficial group and the non-beneficial group, the Cox model was used, and based on the various vital predictive factors, a nomogram was drawn using logistic regression.

Result

The number of eligible patients was 12,484, with 43.9% (5483) of them having received surgery. After employing propensity score matching, the cancer-specific survival time of the operation group was found to be apparently longer (median: 21 vs. 5 months; p < 0.001) than the no-operation group. In the operation group, 4757 (86.7%) of the patients lived longer than five months (beneficial group). The six indexes (beneficial and non-beneficial group) included gender, age, Tumor Node Metastasis stage, histologic type, differentiation grade, and tumor position, and were used as predictors to draw the nomogram. The nomogram was used to divide the patients who had taken operations into two groups: the beneficial operation group and the non-beneficial operation group. The beneficial operation group, it was found, survived longer than the non-beneficial operation group (median cancer-specific survival time: 28 vs. 3 months, p < 0.001). Moreover, there was we could tell little difference in survival between the two groups (median cancer-specific survival time: 3 vs. 5 months).

Conclusions

The predictive model created to select suitable candidates for surgical treatment from patients with signet ring carcinoma of the stomach could be adopted to identify certain patients benefiting from the primary tumor operation.

A Model for Identifying Optimal Patients for Primary Tumor Resection in Patients With Metastatic Bladder Cancer

Background

A survival benefit was observed in metastatic bladder cancer patients who underwent primary tumor resection, but it was still confusing which patients are suitable for the surgery. For this purpose, we developed a model to screen stage M1 patients who would benefit from primary tumor resection.

Methods

Patients with metastatic bladder cancer were screened from the Surveillance, Epidemiology, and End Results database (2004–2016) and then were divided into surgery (partial or complete cystectomy) group and non-surgery group. To balance the characteristics between them, a 1:1 propensity score matching analysis was applied. A hypothesis was proposed that the received primary tumor resection group has a more optimistic prognosis than the other group. The multivariable Cox model was used to explore the independent factors of survival time in two groups (beneficial and non-beneficial groups). Logistic regression was used to build a nomogram based on the significant predictive factors. Finally, a variety of methods are used to evaluate our model.

Results

A total of 7,965 patients with metastatic bladder cancer were included. And 3,314 patients met filtering standards, of which 545 (16.4%) received partial or complete cystectomy. Plots of the Kaplan–Meier and subgroup analyses confirmed our hypothesis. After propensity score matching analysis, a survival benefit was still observed that the surgery group has a longer median overall survival time (11.0 vs. 6.0 months, p < 0.001). Among the surgery cohort, 303 (65.8%) patients lived longer than 6 months (beneficial group). Differentiated characteristics included age, gender, TNM stage, histologic type, differentiation grade, and therapy, which were integrated as predictors to build a nomogram. The nomogram showed good discrimination in both training and validation cohorts (area under the receiver operating characteristic curve (AUC): 0.806 and 0.742, respectively), and the calibration curves demonstrated good consistency. Decision curve analysis showed that the nomogram was clinically useful. Compared with TNM staging, our model shows a better predictive value in identifying optimal patients for primary tumor resection.

Conclusions

A practical predictive model was created and verified, which might be used to identify the optimal candidates for the partial or complete cystectomy group of the primary tumor among metastatic bladder cancer.

Models for Predicting Early Death in Patients With Stage IV Esophageal Cancer: A Surveillance, Epidemiology, and End Results-Based Cohort Study

Background

Despite enormous progress in the stage IV esophageal cancer (EC) treatment, some patients experience early death after diagnosis. This study aimed to identify the early death risk factors and construct models for predicting early death in stage IV EC patients.

Methods

Stage IV EC patients diagnosed between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected. Early death was defined as death within 3 months of diagnosis, with or without therapy. Early death risk factors were identified using logistic regression analyses and further used to construct predictive models. The concordance index (C-index), calibration curves, and decision curve analyses (DCA) were used to assess model performance.

Results

Out of 4411 patients enrolled, 1779 died within 3 months. Histologic grade, therapy, the status of the bone, liver, brain and lung metastasis, marriage, and insurance were independent factors for early death in stage IV EC patients. Histologic grade and the status of the bone and liver metastases were independent factors for early death in both chemoradiotherapy and untreated groups. Based on these variables, predictive models were constructed. The C-index was .613 (95% confidence interval (CI), [.573–.653]) and .635 (95% CI, [.596–.674]) in the chemoradiotherapy and untreated groups, respectively, while calibration curves and DCA showed moderate performance.

Conclusions

More than 40% of stage IV EC patients suffered from an early death. The models could help clinicians discriminate between low and high risks of early death and strategize individually-tailed therapeutic interventions in stage IV EC patients.

Short-term response might influence the treatment-related benefit of adjuvant chemotherapy after concurrent chemoradiotherapy for esophageal squamous cell carcinoma patients

BACKGROUND

Whether adjuvant chemotherapy (AC) after concurrent chemoradiotherapy (CCRT) could provide benefit to esophageal squamous cell carcinoma (ESCC) patients is controversial. Therefore, we decided to investigate the potential benefit of AC after CCRT for ESCC and to identify biomarkers predictive of a clinical benefit.

METHODS

We retrospectively analysed the clinical data of ESCC patients with clinical stage II-IVa who underwent CCRT. Then, we compared patients who received CCRT and AC (CCRT + AC group) with those who received CCRT alone (CCRT group). Propensity score analysis, subgroup analysis and an additional Cox regression model were conducted to analyse the predictive factors. The overall survival (OS) and progression-free survival (PFS) rates were taken as the endpoints.

RESULTS

From January 2013 to December 2017, 244 patients were recruited (n = 131 for CCRT + AC; n = 113 for CCRT alone) for the analysis. After propensity score matching was performed (1:1 and 99 patients for each group) with consideration of the basic clinical characteristics, no significant differences were found in OS (HR = 1.024; 95% CI 0.737-1.423; P = 0.886) or PFS (HR = 0.809; 95% CI 0.582-1.126; P = 0.197) between the two groups. The good short-term response subgroup showed a better PFS and favoured CCRT + AC treatment (HR = 0.542; 95% CI 0.336-0.876; P = 0.008), the independent predictive role of which was confirmed in additional multivariate Cox regression analysis.

CONCLUSIONS

Although AC did not significantly improve PFS and OS for all ESCC patients after CCRT, the short-term response to CCRT might help identify a subgroup that will benefit, which needs further prospective research to confirm.