Dataset Information

M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection.

ABSTRACT: A good prediction model is useful to accurately predict patient prognosis. Tumor-node-metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown that the infiltration of M2 macrophages in many tumors indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging, but there is less research in gastric cancer. A multivariate analysis demonstrated that CD163 expression, TNM staging, age, and gender were independent risk factors for overall survival. Thus, these parameters were assessed to develop the nomogram in the training data set, which was tested in the validation and whole data sets. The model showed a high degree of discrimination, calibration, and good clinical benefit in the training, validation, and whole data sets. In conclusion, we combined CD163 expression in macrophages, TNM staging, age, and gender to develop a nomogram to predict 3- and 5-year overall survivals after curative resection for gastric cancer. This model has the potential to provide further diagnostic and prognostic value for patients with gastric cancer.

SUBMITTER: Hu J

PROVIDER: S-EPMC8397443 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection.

Hu Jianwen J Ma Yongchen Y Ma Ju J Yang Yanpeng Y Ning Yingze Y Zhu Jing J Wang Pengyuan P Chen Guowei G Liu Yucun Y

Frontiers in oncology 20210812

A good prediction model is useful to accurately predict patient prognosis. Tumor-node-metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown that the infiltration of M2 macrophages in many tumors indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging, but there is less research in gastric cancer. A multivariate analysis demonstrated that CD163 expression, TNM ...[more]

PMID: 34458140

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Project description:Objective: The purpose of this study was to develop and validate a nomogram model for the prediction of survival outcome in rectal cancer patients who underwent surgical resection. Methods: A total of 9,919 consecutive patients were retrospectively identified using the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors were determined by the univariate and multivariate Cox analysis. The nomogram model for the prediction of cancer-specific survival (CSS) in rectal cancer patients were developed based on these prognostic variables, and its predictive power was assessed by the concordance index (C-index). Calibration curves were plotted to evaluate the associations between predicted probabilities and actual observations. The internal and external cohort were used to further validate the predictive performance of the prognostic nomogram. Results: All patients from the SEER database were randomly split into a training cohort (n = 6,944) and an internal validation cohort (n = 2,975). The baseline characteristics of two cohorts was comparable. Independent prognostic factors were identified as age, pT stage, lymph node metastasis, serum CEA level, tumor size, differentiation type, perineural invasion, circumferential resection margin involvement and inadequate lymph node yield. In the training cohort, the C-index of the nomogram was 0.719 (95% CI: 0.696-0.742), which was significantly higher than that of the TNM staging system (C-index: 0.606, 95% CI: 0.583-0.629). The nomogram had a C-index of 0.726 (95% CI: 0.691-0.761) for the internal validation cohort, indicating a good predictive power. In addition, an independent cohort composed of 202 rectal cancer patients from our institution were enrolled as the external validation. Compared with the TNM staging system (C-index: 0.573, 95% CI: 0.492-0.654), the prognostic nomogram still showed a better predictive performance, with the C-index of 0.704 (95% CI: 0.626-0.782). Calibration plots showed a good consistency between predicted probability and the actual observation in the training and two validation cohorts. Conclusion: The nomogram showed an excellent predictive ability for survival outcome of rectal cancer patients, and it might provide an accurate prognostic stratification and help clinicians determine individualized treatment strategies.

Project description:Objective: We sought to investigate the prognostic significance of body composition and weight change during the first 6 months of adjuvant chemotherapy after R0 resection and develop novel nomograms to accurately predict relapse-free survival (RFS) and overall survival (OS). Methods: This retrospective study included 190 patients who underwent curative radical gastrectomy for gastric cancer and received adjuvant chemotherapy. The changes in weight and body composition including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) were analyzed for 6 months. LASSO Cox regression and multivariate Cox regression were conducted to evaluate other clinical characteristics, which were used to construct a nomogram for the prediction of 3- and 5-year RFS and OS. The constructed nomogram was subjected to 1,000 resamples bootstrap for internal validation. The Concordance index (C-index) and time-dependent receiver operating characteristic (t-ROC) curves were used to evaluate and compare the discriminative abilities of the new nomograms, non-nutritional nomograms, and pTNM stage. Results: The median follow-up duration was 42.0 (25.2-55.1) months. Factors included in the newly-built nomogram for RFS were pT stage, pN stage, tumor site, tumor size, nerve invasion or not, surgery type, and change of L3SMI, while factors included in the nomogram for OS were pT stage, pN stage, tumor size, nerve invasion or not, surgery type, and change of L3SMI. The C-index and t-ROC indicated that our newly-built nomograms had greater potential to accurately predict prognosis than the non-nutritional nomograms and pTNM stage system. Besides, oral nutritional supplements can reduce the degree of weight and L3SMI loss. Conclusion: Change in skeletal muscle mass during adjuvant chemotherapy can be incorporated into predictive prognostic nomograms for RFS and OS in GC patients after radical resection. Dynamic changes in body composition and weight during adjuvant chemotherapy contribute to the early detection of poor outcomes.

Project description:BackgroundThe purpose of this study was to develop a large population-based nomogram incorporating the log odds of positive nodes (LODDS) for predicting the overall survival (OS) of stage II/III rectal cancer patients treated with neoadjuvant chemoradiotherapy (NCRT) followed by surgical resection.MethodsThe Surveillance, Epidemiology, and End Results database was used to collect information on patients diagnosed with stage II/III rectal cancer between 2010 and 2015 and treated with NCRT followed by surgical resection. The Cox regression analyses were performed to determine the independent prognostic factors. In this study, LODDS was employed instead of American Joint Committee on Cancer (AJCC) 7th N stage to determine lymph node status. Then a nomogram integrating independent prognostic factors was developed to predict the 24-, 36-, and 60-month overall survival. The receiver operating characteristic (ROC) curves and calibration curves were used to validate the nomogram. Furthermore, patients were stratified into three risk groups (high-, middle-, and low-risk) based on the total points obtained from the nomogram. And Kaplan-Meier curves were plotted to compare the OS of the three groups.ResultsA total of 3829 patients were included in the study. Race, sex, age, marital status, T stage, tumor grade, tumor size, LODDS, CEA level, and postoperative chemotherapy were identified as independent prognostic factors, based on which the prognostic nomogram was developed. The area under curve values of the nomogram for the 24-, 36-, and 60-month OS in the training cohort were 0.736, 0.720, and 0.688, respectively; and 0.691, 0.696, and 0.694 in the validation cohort, respectively. In both the validation and training cohorts, the calibration curves showed a high degree of consistency between actual and nomogram-predicted survival rates. The Kaplan-Meier curves showed that the three risk groups had significant differences in overall survival (P < 0.001).ConclusionA large population-based nomogram incorporating LODDS was developed to assist in evaluating the prognosis of stage II/III rectal cancer patients treated with NCRT followed by surgical resection. The nomogram showed a satisfactorily discriminative and stable ability to predict the OS for those patients.

Dataset Information

M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection.

Publications

M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection.

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