Development and validation of a nomogram for predicting overall survival of gastric cancer patients after D2R0 resection

Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

BACKGROUND

It has previously been observed that the prognostic value of tumor size varied according to different stages patients enrolled in gastric cancer. We aimed to investigate the influence of T stage on the prognostic and predicting value of tumor size.

MATERIAL AND METHODS

A total of 13,585 patients with stage I-III gastric cancer were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and multivariate cox regression analysis stratified by T stage were performed. C-index and time-dependent receiver operating characteristic curve (ROC) curve were applied to assess discrimination ability of tumor size and other factors. Nomograms were constructed to further assess the performance of tumor size in a specific model. Calibration ability, discrimination ability, reclassification ability and clinical benefits were executed to judge the performance of models.

RESULTS

Stratified analyses according to T stage illustrated that with the increase of T stage, the effect of tumor size on overall survival (OS) and cancer-specific survival (CSS) significantly decreased. Moreover, tumor size showed superior discrimination ability in T1 gastric cancer, outperformed other prognostic factors in predicting both CSS (C-index: 0.666, AUC: 0.687) and OS (C-index: 0.635, AUC: 0.660). The cox regression model included tumor size showed better performance than the model excluded tumor size in every aspect.

CONCLUSION

T stage had a negative impact on the predicting value of tumor size. Tumor size showed significant prognostic value in T1 gastric cancer, which may be effective in clinical practice.

Multiview Deep Forest for Overall Survival Prediction in Cancer

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

Validation of the Memorial Sloan Kettering Gastric Cancer Post-Resection Survival Nomogram: Does It Stand the Test of Time?

BACKGROUND

The Memorial Sloan Kettering Cancer Center (MSK) nomogram combined both gastroesophageal junction (GEJ) and gastric cancer patients and was created in an era from patients who generally did not receive neoadjuvant chemotherapy. We sought to reevaluate the MSK nomogram in the era of multidisciplinary treatment for GEJ and gastric cancer.

STUDY DESIGN

Using data on patients who underwent R0 resection for GEJ or gastric cancer between 2002 and 2016, the C-index of prediction for disease-specific survival (DSS) was compared between the MSK nomogram and the American Joint Committee on Cancer (AJCC) 8th edition staging system after segregating patients by tumor location (GEJ or gastric cancer) and neoadjuvant treatment. A new nomogram was created for the group for which both systems poorly predicted prognosis.

RESULTS

During the study period, 886 patients (645 gastric and 241 GEJ cancer) underwent up-front surgery, and 999 patients (323 gastric and 676 GEJ) received neoadjuvant treatment. Compared with the AJCC staging system, the MSK nomogram demonstrated a comparable C-index in gastric cancer patients undergoing up-front surgery (0.786 vs 0.753) and a better C-index in gastric cancer patients receiving neoadjuvant treatment (0.796 vs 0.698). In GEJ cancer patients receiving neoadjuvant chemotherapy, neither the MSK nomogram nor the AJCC staging system performed well (C-indices 0.647 and 0.646). A new GEJ nomogram was created based on multivariable Cox regression analysis and was validated with a C-index of 0.718.

CONCLUSIONS

The MSK gastric cancer nomogram's predictive accuracy remains high. We developed a new GEJ nomogram that can effectively predict DSS in patients receiving neoadjuvant treatment.

Assessment of indocyanine green fluorescence lymphography on lymphadenectomy during minimally invasive gastric cancer surgery: a systematic review and meta-analysis

BACKGROUND

In recent years, indocyanine green fluorescence lymphography has been introduced for lymphatic mapping in gastric cancer surgery. The aim of this study was to investigate the efficacy of ICGFL in lymph node dissection during minimally invasive surgery for gastric cancer.

METHODS

A systematic review of electronic databases including PubMed, Embase, Web of Science, the Cochrane Library, and China National Knowledge Infrastructure was performed from the inception to January 2021 for all studies comparing ICGFL with non-ICGFL in GC patients undergoing minimal access gastrectomy. The primary outcome was the total number of harvested lymph nodes. The secondary endpoints were the number of metastatic LNs, operative time, estimated blood loss, and postoperative complications. The registration number of this protocol is PROSPERO CRD42020203443.

RESULTS

A total of 13 studies including 1882 participants were included. In this meta-analysis, the use of ICGFL was associated with a higher number of harvested LNs (40.33 vs. 33.40; MD = 6.93; 95%CI: 4.28 to 9.58; P < 0.0001; I2 = 86%). No significant difference was found between the ICGFL and control groups in terms of metastatic LNs (2.63 vs. 2.42; MD = 0.21; 95%CI: -0.46 to 0.87; P = 0.54; I2 = 0%). In addition, the use of ICGFL could be safely performed without increasing the operative time (P = 0.49), estimated blood loss (P = 0.26) and postoperative complications (P = 0.54).

CONCLUSION

The use of ICGFL may be a useful tool facilitating complete lymph node dissection during minimally invasive GC resection. However, more high-quality RCTs with large sample size are needed to validate this issue.

Development and External Validation of a Nomogram for Predicting Overall Survival in Stomach Cancer: A Population-Based Study

Objective

The study was to develop and externally validate a prognostic nomogram to effectively predict the overall survival of patients with stomach cancer.

Methods

Demographic and clinical variables of patients with stomach cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2007–2016 were retrospectively collected. Patients were then divided into the Training Group (n = 4,456) for model development and the Testing Group (n = 4,541) for external validation. Univariate and multivariate Cox regressions were used to explore prognostic factors. The concordance index (C-index) and the Kolmogorov–Smirnov (KS) value were used to measure the discrimination, and the calibration curve was used to assess the calibration of the nomogram.

Results

Prognostic factors including age, race, marital status, TNM stage, surgery, chemotherapy, grade, and the number of regional nodes positive were used to construct a nomogram. The C-index was 0.790 and the KS value was 0.45 for the Training Group, and the C-index was 0.789 for the Testing Group, all suggesting the good performance of the nomogram.

Conclusion

We have developed an effective nomogram with ten easily acquired prognostic factors. The nomogram could accurately predict the overall survival of patients with stomach cancer and performed well on external validation, which would help improve the individualized survival prediction and decision-making, thereby improving the outcome and survival of stomach cancer.