Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

Prognostic Performance of Three Lymph-Node Staging Systems on Gastric Signet-Ring-Cell Carcinoma

BACKGROUND

The lymph-node staging system can predict the prognosis of gastric signet-ring-cell carcinoma (SRCC). However, there are significant differences in lymph-node status between early SRCC and advanced SRCC. Additionally, the optimal system for early and advanced SRCC remains unknown.

METHODS

This study retrospectively analyzed 693 SRCC patients who underwent radical resection in the Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital. The predicted performance of three lymph-node staging systems, including pN staging, lymph-node metastasis rate (LNR), and log odds of positive lymph nodes (LODDS), was compared using the receiver characteristic operating curve (ROC) and c-index. The Kaplan-Meier method and the log-rank test analyzed the overall survival of patients. The Cox risk regression model identified independent risk factors associated with patient outcomes. The nomogram was made by R studio.

RESULTS

The 693 SRCC included 165 early SRCC and 528 advanced SRCC. ROC showed that LODDS had better predictive performance than pN and LNR in predicting prognosis regardless of early or advanced SRCC. LODDS can be used to predict the prognosis of early and advanced SRCC and was an independent risk factor associated with patient outcomes (p = 0.002, p < 0.001). Furthermore, the nomogram constructed by LODDS and clinicopathological features had good predictive performance.

CONCLUSIONS

LODDS showed clear prognostic superiority over both pN and LNR in early and advanced SRCC.

Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database

BACKGROUND

Gastric signet ring cell carcinoma (GSRCC) is a highly malignant subtype of gastric cancer. We tried to establish and validate a nomogram using common clinical variables to achieve more personalized management.

METHODS

We analyzed patients with GSRCC in the Surveillance, Epidemiology, and End Results database between 2004 and 2017. The survival curve was calculated by the Kaplan-Meier method, and the difference in survival curve was tested by log-rank test. We used the cox proportional hazard model to evaluate independent factors of prognosis, and established a nomogram to predict 1-, 3- and 5- overall survival (OS). Harrell's consistency index and calibration curve were used to measure the discrimination and calibration of the nomogram. In addition, we used decision curve analysis (DCA) to compare the net clinical benefits of the nomogram and American Joint Committee on Cancer (AJCC) staging system.

RESULTS

The prognosis nomogram predicting 1-, 3- and 5-years OS for patients with GSRCC is established for the first time. The C-index and AUC of nomogram were higher than that of the American Joint Committee on Cancer (AJCC) staging system in the training set. Our model also shows better performance than the AJCC staging system in the validation set, and importantly, DCA shows that our model has a better net benefit than the AJCC stage.

CONCLUSIONS

We have developed and validated a new nomogram and risk classification system, which is better than the AJCC staging system. It will help clinicians manage postoperative patients with GSRCC more accurately.

Effectiveness of clear aligners in achieving proclination and intrusion of incisors among Class II division 2 patients: a multivariate analysis

BACKGROUND

The predictability of incisor movement achieved by clear aligners among Class II division 2 patients is poorly understood. The aim of this retrospective study was to determine the effectiveness of clear aligners in proclining and intruding upper incisors and its influencing factors.

METHODS

Eligible patients with Class II division 2 malocclusion were included. For clear aligner therapy, three types of incisor movements were designed: proclination, intrusion and labial movement. Pre-treatment and post-treatment dental models were superimposed. The differences between predicted and actual (DPA) tooth movement of incisors were analyzed. Univariate and multivariate linear regression were used to analyze the potential influencing factors.

RESULTS

A total of 51 patients and their 173 upper incisors were included. Actual incisor proclination and intrusion were less than predicted ones (both P < 0.001), while actual labial movement was greater than predicted one (P < 0.001). Predictability of incisor proclination and intrusion was 69.8% and 53.3%, respectively. Multivariate linear regression revealed that DPA of proclination was significantly positively associated with predicted proclination (B = 0.174, P < 0.001), ipsilateral premolar extraction (B = 2.773, P < 0.001) and ipsilateral canine proclination (B = 1.811, P < 0.05), while negatively associated with molar distalization (B = - 2.085, P < 0.05). The DPA of intrusion was significantly positively correlated with predicted intrusion (B = 0.556, P < 0.001) while negatively associated with labial mini-implants (B = - 1.466, P < 0.001). The DPA of labial movement was significantly positively associated with predicted labial movement (B = 0.481, P < 0.001), while negatively correlated with molar distalization (B = - 1.004, P < 0.001), labial mini-implants (B = - 0.738, P < 0.001) and age (B = - 0.486, P < 0.05).

CONCLUSIONS

For Class II division 2 patients, predicted incisor proclination (69.8%) and intrusion (53.3%) are partially achieved with clear aligner therapy. Excessive labial movement (0.7 mm) of incisors may be achieved. Incisor movement is influenced by predicted movement amount, premolar extraction, canine proclination, molar distalization, mini-implants and age.

Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach

The optimal number of examined lymph nodes (ELNs) for gastric signet ring cell carcinoma recommended by National Comprehensive Cancer Network guidelines remains unclear. This study aimed to determine the optimal number of ELNs and investigate its prognostic significance. In this study, we included 1723 patients diagnosed with gastric signet ring cell carcinoma in the Surveillance, Epidemiology, and End Results database. X-tile software was used to calculate the cutoff value of ELNs, and the optimal number of ELNs was found to be 32 for adequate nodal staging. In addition, we performed propensity score matching (PSM) analysis to compare the 1-, 3-, and 5-year survival rates; 1-, 3-, and 5-year survival rates for total examined lymph nodes (ELNs < 32 vs. ELNs ≥ 32) were 71.7% vs. 80.1% (p = 0.008), 41.8% vs. 51.2% (p = 0.009), and 27% vs. 30.2% (p = 0.032), respectively. Furthermore, a predictive model based on 32 ELNs was developed and displayed as a nomogram. The model showed good predictive ability performance, and machine learning validated the importance of the optimal number of ELNs in predicting prognosis.

Prognostic value of log odds of positive lymph nodes, lymph node ratio, and N stage in patients with colorectal signet ring cell carcinoma: A retrospective cohort study

Aim

Little attention has been paid in the prognosis of colorectal signet ring cell carcinoma (SRCC). This study aims to explore the predictive capacity of log odds of positive lymph nodes (LODDS), lymph node ratio (LNR), and pN stage in the prognosis of patients with colorectal SRCC.

Methods

A retrospective cohort study was designed, and data were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Data on demographic characteristics, clinicopathological features, and treatment were extracted. Outcomes were overall survival (OS) and cancer-specific survival (CSS). Association of LODDS, LNR, and pN stage with OS and CSS were explored using Cox proportional hazard model and Cox competing risk model, respectively, with results showing as hazard ratio and 95% confidence interval (CI). Predictive performance of LODDS, LNR, and pN stage in OS and CSS was assessed by calculating C-index.

Results

A total of 2,198 patients were included in this study. LODDS, LNR, and pN stage were associated with the OS and CSS of colorectal SRCC patients (all P < 0.05). LODDS showed a good performance in the OS (C-index: 0.704, 95% CI: 0.690-0.718), which was superior to LNR (C-index: 0.657, 95% CI: 0.643-0.671) and pN stage (C-index: 0.643, 95% CI: 0.629-0.657). The C-index of LODDS, LNR, and pN stage for CSS was 0.733 (95% CI: 0.719-0.747), 0.713 (95% CI: 0.697-0.729), and 0.667 (95% CI: 0.651-0.683), respectively.

Conclusions

LODDS displayed a better predictive capacity in the OS and CSS than LNR and pN stage, indicating that LODDS may be effective to predict the prognosis of colorectal SRCC in the clinic.

New staging systems for left-sided colon cancer based on the number of retrieved and metastatic lymph nodes provide a more accurate prognosis

Objectives: We aimed to explore reasonable lymph node classification strategies for left-sided colon cancer (LCC) patients. Methods: 48,425 LCC patients from 2010 to 2015 were identified in the US Surveillance, Epidemiology, and End Results database. We proposed an innovative revised nodal (rN) staging of the 8th American Joint Committee on Cancer (AJCC) Tumor/Node/Metastasis (TNM) classification based on the cut-off value of retrieved lymph nodes and survival analyses in patients with LCC. Log odds of positive lymph nodes (LODDS) stage is a numerical classification strategy obtained by a formula that incorporates the numbers of retrieved and positive lymph nodes. To develop the TrN or TLODDS classification, patients with similar survival rates were grouped by combining T and rN or LODDS stage. The TrN or TLODDS classification was further evaluated in a validation set of 12,436 LCC patients from 2016 to 2017 in the same database and a Chinese application set of 958 LCC patients. Results: We developed novel TrN and TLODDS classifications for LCC patients that incorporated 7 stages with reference to the AJCC staging system. In comparison to the 8th AJCC TNM and TrN classifications, TLODDS classification demonstrated significantly better discrimination (area under the receiver operating characteristic curve, 0.650 vs. 0.656 vs. 0.661, p < 0.001), better model-fitting (Akaike information criteria, 309,287 vs. 308,767 vs. 308,467), and superior net benefits. The predictive performance of the TrN and TLODDS classifications was further verified in the validation and application sets. Conclusion: Both the TrN and TLODDS classifications have better discriminatory ability, model-fitting, and net benefits than the existing TNM classification, and represent an alternative to the current TNM classification for LCC patients.

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Background and Aims

Accurate prediction is essential for the survival of patients with nonmetastatic gastric signet ring cell carcinoma (GSRC) and medical decision-making. Current models rely on prespecified variables, limiting their performance and not being suitable for individual patients. Our study is aimed at developing a more precise model for predicting 1-, 3-, and 5-year overall survival (OS) in patients with nonmetastatic GSRC based on a machine learning approach.

Methods

We selected 2127 GSRC patients diagnosed from 2004 to 2014 from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly partitioned them into a training and validation cohort. We compared the performance of several machine learning-based models and finally chose the eXtreme gradient boosting (XGBoost) model as the optimal method to predict the OS in patients with nonmetastatic GSRC. The model was assessed using the receiver operating characteristic curve (ROC).

Results

In the training cohort, for predicting OS rates at 1-, 3-, and 5-year, the AUCs of the XGBoost model were 0.842, 0.831, and 0.838, respectively, while in the testing cohort, the AUCs of 1-, 3-, and 5-year OS rates were 0.749, 0.823, and 0.829, respectively. Besides, the XGBoost model also performed better when compared with the American Joint Committee on Cancer (AJCC) stage. The performance for this model was stably maintained when stratified by age and ethnicity.

Conclusion

The XGBoost-based model accurately predicts the 1-, 3-, and 5-year OS in patients with nonmetastatic GSRC. Machine learning is a promising way to predict the survival outcomes of tumor patients.

Log odds of positive lymph nodes as a novel prognostic predictor for colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent cancer in the world, which remains one of the leading causes of cancer-related deaths. Accurate prognosis prediction of CRC is pivotal to reduce the mortality and disease burden. Lymph node (LN) metastasis is one of the most commonly used criteria to predict prognosis in CRC patients. However, inaccurate surgical dissection and pathological evaluation may lead to inaccurate nodal staging, affecting the effectiveness of pathological N (pN) classification in survival prediction among patients with CRC. In this meta-analysis, we aimed to estimate the prognostic value of the log odds of positive lymph nodes (LODDS) in patients with CRC.

METHODS

PubMed, Medline, Embase, Web of Science and the Cochrane Library were systematically searched for relevant studies from inception to July 3, 2021. Statistical analyses were performed on Stata statistical software Version 16.0 software. To statistically assess the prognostic effects of LODDS, we extracted the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS) and disease-free survival (DFS) from the included studies.

RESULTS

Ten eligible articles published in English involving 3523 cases were analyzed in this study. The results showed that LODDS1 and LODDS2 in CRC patients was correlated with poor OS compared with LODDS0 (LODDS1 vs. LODDS0: HR = 1.77, 95% CI (1.38, 2.28); LODDS2 vs. LODDS0: HR = 3.49, 95% CI (2.88, 4.23)). Meanwhile, LODDS1 and LODDS2 in CRC patients was correlated with poor DFS compared with LODDS0 (LODDS1 vs. LODDS0: HR = 1.82, 95% CI (1.23, 2.68); LODDS2 vs. LODDS0: HR =3.30, 95% CI (1.74, 6.27)).

CONCLUSIONS

The results demonstrated that the LODDS stage was associated with prognosis of CRC patients and could accurately predict the prognosis of patients with CRC.

Revisiting the 8th AJCC system for gastric cancer: A review on validations, nomograms, lymph nodes impact, and proposed modifications

Gastric cancer is the fifth most frequently diagnosed cancer worldwide, behind breast, lung, colorectal, and prostate cancers. In gastric cancer, multimodality treatment shows prospective benefits and also improves survival. Surgery, however, is the mainstay of curative treatment. The staging of gastric cancer patients is critical for harmonization of care. Accurate stages assure that informed clinical decisions are timely made. The American Joint Committee on Cancer (AJCC) staging system is the most widely applied system in to determine the disease's prognosis and survival prediction. The recently adopted 8th AJCC TNM staging system has been revised to enhance its survival predictive power. Subsequent studies have established the validity of the current edition, demonstrating improved stage stratification, discriminatory power, and survival prediction. However, other studies have cast doubt on the superiority of the new edition. Innovations aimed at further improving its prognosis have resulted in developing of novel models. Advances in our understanding of the tumor microenvironment and molecular categorization of cancer have resulted in proposals for their inclusion in TNM staging as potential complementary factors that enhance survival prediction and prognostic assessment ability. The purpose of this study is to conduct a review of the published literature regarding the validity of the 8th AJCC TNM staging system, proposed modifications, and nomograms.

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The 8th AJCC is valid in prognostic stratification of gastric cancer, however, revisions are still required.

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The yPT staging requires some modifications and inclusion of stages that currently don't exist in the 8th AJCC.

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High lymph nodes count and anatomical localization improve the prediction ability of the current AJCC.

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Nomograms comprising of individual prognostic factors are crucial to the current AJCC.

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Molecular markers positively influence survival prediction of gastric cancer.

A novel pN3 gastric cancer staging system with superior prognostic utility based upon the examination of over 31 lymph nodes: a propensity score-matching analysis

Background

Individuals with pN3 gastric cancer (GC) account for a large proportion of pN + GC, and exhibit poor survival outcomes. The pN3 stage is defined based upon the number of metastatic lymph nodes (mLNs), but the subclassification of pN3 patients based upon the number of examined LNs (eLNs) is rarely performed.

Methods

In total, 2894 pTxN3M0 GC patients in the Surveillance, Epidemiology, and End Results database that had undergone surgery from 2000 to 2016 were selected for analysis. The X-tile software was used to select the optimal cutoff values. Cox proportional regression analyses were used to evaluated hazard ratios corresponding to the risk of death. Selection bias was minimized via propensity score matching (PSM).

Results

As the number of eLNs rose, the risk of death for patients trended downwards. Survival analyses indicated that patients with ≤ 31 eLNs exhibited significantly poorer survival outcomes as compared to patients with > 31 eLNs (5-year OS: 18.4% vs. 24.7%), and this result remained significant when analyzing 857 pairs of patients following PSM analysis. Significant differences in prognosis were additionally observed when comparing pN3a and pN3b patients with ≤ 31 or > 31 eLNs under pT3/4a stage. For pT4b stage, pN3a patients with > 31 eLNs also exhibited a better prognosis than other patients. The novel TNM staging system designed exhibited excellent utility as a tool for the prognostic evaluation of this GC patient population.

Conclusions

These results suggest that in pN3 GC, a minimum of 32 LNs should be examined. The novel TNM staging system for pN3 patients described herein, which was developed based upon the number of eLNs, may thus be of value in clinical settings.

Development and validation of a novel classification scheme for combining pathological T stage and log odds of positive lymph nodes for colon cancer

AIM

Log Odds of Positive Lymph Nodes (LODDS) have a better predictive ability than N stage for colon cancer. However, the prognostic value of developing a novel prognostic classification by combining T stage and LODDS (TLODDS) for colon cancer remains unknown. Therefore, in the present study, we aimed to develop a TLODDS classification for colon cancer, and assess whether or not the novel TLODDS classification could improve survival stratification by comparing its discrimination, model-fitting, and net benefits, with the American Joint Committee on Cancer (AJCC) Tumor/Node/Metastasis (TNM) classification.

METHODS

45,558 Western colon cancers were identified in the Surveillance, Epidemiology, and End Results database as a training set. A novel LODDS stage was established and patients with similar survival rates were grouped by combining T and LODDS stages to develop a novel TLODDS classification. The TLODDS classification was further assessed in a Chinese validation set of 3,515 colon cancers and an application set of 3,053 rectal cancers.

RESULTS

We developed a novel TLODDS classification that incorporated 7 stages: stage I (T1LODDS1), IIA (T2LODDS1, T1LODDS2, T1LODDS3), IIB (T2LODDS2-3, T3LODDS1, T1LODDS4), IIC (T3LODDS2, T2LODDS4, T4aLODDS1), IIIA (T3LODDS3, T1-2LODDS5, T4bLODDS1, T4aLODDS2), IIIB (T3LODDS4-5, T4aLODDS3-4, T4bLODDS2) and IIIC (T4bLODDS3-5, T4aLODDS5). In the training set, it showed significantly better discrimination (area under the receiver operating characteristic (ROC) curve, 0.691 vs. 0.664, P < 0.001), better model-fitting (Akaike information criteria, 265,644 vs. 267,410), and superior net benefits, than the latest AJCC TNM classification. The predictive performance of the TLODDS classification was further validated in colon cancers and was successfully applied in rectal cancers with regards to both overall and disease-free survival.

CONCLUSIONS

The TLODDS classification has better discriminatory ability, model-fitting, and net benefits than the existing TNM classification, and represents an alternative to the current TNM classifications for colon and rectal cancers.

Development and validation of a prognostic nomogram for colorectal cancer after surgery

BACKGROUND

A nomogram is a diagram that aggregates various predictive factors through multivariate regression analysis, which can be used to predict patient outcomes intuitively. Lymph node (LN) metastasis and tumor deposit (TD) conditions are two critical factors that affect the prognosis of patients with colorectal cancer (CRC) after surgery. At present, few effective tools have been established to predict the overall survival (OS) of CRC patients after surgery.

AIM

To screen out suitable risk factors and to develop a nomogram that predicts the postoperative OS of CRC patients.

METHODS

Data from a total of 3139 patients diagnosed with CRC who underwent surgical removal of tumors and LN resection from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results program. The data were divided into a training set (n = 2092) and a validation set (n = 1047) at random. The Harrell concordance index (C-index), Akaike information criterion (AIC), and area under the curve (AUC) were used to assess the predictive performance of the N stage from the American Joint Committee Cancer tumor-node-metastasis classification, LN ratio (LNR), and log odds of positive lymph nodes (LODDS). Univariate and multivariate analyses were utilized to screen out the risk factors significantly correlating with OS. The construction of the nomogram was based on Cox regression analysis. The C-index, receiver operating characteristic (ROC) curve, and calibration curve were employed to evaluate the discrimination and prediction abilities of the model. The likelihood ratio test was used to compare the sensitivity and specificity of the final model to the model with the N stage alone to evaluate LN metastasis.

RESULTS

The predictive efficacy of the LODDS was better than that of the LNR based on the C-index, AIC values, and AUC values of the ROC curve. Seven independent predictive factors, namely, race, age at diagnosis, T stage, M stage, LODDS, TD condition, and serum carcinoembryonic antigen level, were included in the nomogram. The C-index of the nomogram for OS prediction was 0.8002 (95%CI: 0.7839-0.8165) in the training set and 0.7864 (95%CI: 0.7604-0.8124) in the validation set. The AUC values of the ROC curve predicting the 1-, 3-, and 5-year OS were 0.846, 0.841, and 0.825, respectively, in the training set and 0.823, 0.817, and 0.835, respectively, in the validation test. Great consistency between the predicted and actual observed OS for the 1-, 3-, and 5-year OS in the training set and validation set was shown in the calibration curves. The final nomogram showed a better sensitivity and specificity than the nomogram with N stage alone for evaluating LN metastasis in both the training set (-4668.0 vs -4688.3, P < 0.001) and the validation set (-1919.5 vs -1919.8, P < 0.001) through the likelihood ratio test.

CONCLUSION

The nomogram incorporating LODDS, TD, and other risk factors showed great predictive accuracy and better sensitivity and specificity and represents a potential tool for therapeutic decision-making.