Log odds of positive lymph nodes show better predictive performance on the prognosis of early-onset colorectal cancer

The impact of lymph node ratio on survival in gallbladder cancer: a national cancer database analysis

BACKGROUND

The study aimed to investigate the impact of lymph node ratio (LNR) on survival in patients with resectable gallbladder adenocarcinoma.

METHODS

We retrospectively analyzed the National Cancer Database from 2004 to 2020. We included patients with gallbladder adenocarcinoma who had undergone resection of the primary site as well as adequate lymphadenectomy. Exclusions comprised patients with distant metastasis and missing key data. LNR was calculated as a proportion of positive lymph nodes (LNs) to examined LNs.

RESULTS

Patients were stratified into LNR groups: LNR0 - 343 patients (55%); 168 (26.9%) patients with LNR < 30%; and 113 (18.1%) with LNR ≥ 30%. The mean age was 67.3 ± 10.7 years, with 71.6% being female and 75.8% identifying as white. The mean overall survival (OS) was 52.8 months for the LNR0 group, 36.3 months for LNR < 30%, and 27 months for LNR ≥ 30% (p < 0.001). The difference in survival was significant when adjusted for adjuvant chemotherapy status and surgical margins using Cox regression - HR 3.2 (2.4-4.5 95% CI) for LNR < 30% and HR 4.9 (3.5-6.8 95% CI) for LNR ≥ 30%.

CONCLUSION

The study suggests that LNR is a valuable prognostic factor for resectable gallbladder cancer patients and could potentially guide treatment decisions.

A Prognostic Model Based on the Log Odds Ratio of Positive Lymph Nodes Predicts Prognosis of Patients with Rectal Cancer

OBJECTIVE

This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging.

METHODS

Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification.

RESULTS

While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata.

CONCLUSIONS

Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer.

The survival prediction of advanced colorectal cancer received neoadjuvant therapy-a study of SEER database

PURPOSE

The aim of study was to screen factors associated with the overall survival of colorectal cancer patients with lymph nodes metastasis who received neoadjuvant therapy and construct a nomogram model.

METHODS

All enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model.

RESULTS

A total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients.

CONCLUSION

A nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.

A novel log odds of positive lymph nodes-based nomogram for predicting overall survival in patients with colorectal signet ring cell carcinoma: a SEER population-based study

PURPOSE

Considering the poor prognosis and high lymph node (LN) involvement rate of colorectal signet ring cell carcinoma (SRCC), this study aimed to construct a prognostic nomogram to predict overall survival (OS) with satisfactory accuracy and utility, based on LN status indicators with superior predictability.

METHODS

Using the Surveillance, Epidemiology, and End Results (SEER) database, we obtained cases of colorectal SRCC patients and employed univariate and multivariate Cox analyses to determine independent prognostic factors. Kaplan-Meier curves were utilized to visualize survival differences among these factors. Receiver operating characteristic curves were generated to assess predictive performances of models incorporating various LN status indicators. A novel nomogram, containing optimal LN status indicators and other prognostic factors, was developed to predict OS, whose discriminatory ability and accuracy were evaluated using calibration curves and decision curve analysis.

RESULTS

A total of 1663 SRCC patients were screened from SEER database. Older patients and those with grades III-IV, tumor sizes > 39 mm, T3/T4 stage, N1/N2 stage, M1 stage, and higher log odds of positive lymph nodes (LODDS) values exhibited poorer prognoses. Age, grade, tumor size, TNM stage, and LODDS were independent prognostic factors. The model containing N stage and LODDS outperformed the one relying solely on N stage as LN status indicator, resulting in a validated nomogram for accurately predicting OS in SRCC patients.

CONCLUSION

The integration of LODDS, N stage, and other risk factors into a nomogram offered precise OS predictions, enhancing therapeutic decision-making and tailored follow-up management for colorectal SRCC patients.

Predicting survival and prognosis in early-onset locally advanced colon cancer: a retrospective observational study

OBJECTIVE

To predict cancer-specific survival, a refined nomogram model and brand-new risk-stratifying system were established to classify the risk levels of patients with early-onset locally advanced colon cancer (LACC).

METHODS

The clinical factors and survival outcomes of LACC cases from the SEER database from 2010 to 2019 were retrieved retrospectively. Early-onset and late-onset colon cancer were grouped according to the age (50 years old) at diagnosis. Differences between groups were compared to identify mutual significant variables. A multivariate Cox regression analysis was further performed and then constructed a nomogram. We compared it with the AJCC-TNM system. The external validation was performed for evaluation. Finally, a risk-stratifying system of patients with early-onset LACC was established.

RESULTS

A total of 32,855 LACC patients were enrolled in, 4548 (13.84%) patients were included in the early-onset LACC group, and 28,307 (86.16%) patients were included in the late-onset LACC group. The external validation set included 228 early-onset LACC patients. Early-onset colon cancers had poorer prognosis (T4, N2, TNM stage III, CEA, tumor deposit, and nerve invasion), and a higher proportion received radiotherapy and systemic therapy (P<0.001). In the survival analysis, cancer-specific survival (CSS) was better in patients with early-onset LACC than in those with late-onset LACC (P <0.001). This nomogram constructed based on the results of COX analysis showed better accuracy in CSS prediction of early-onset LACC patients than AJCC-TNM system in the training set and external validation set (0.783 vs 0.728; 0.852 vs 0.773).

CONCLUSION

We developed a novel nomogram model to predict CSS in patients with early-onset LACC it provided a reference in prognosis prediction and selection of individualized treatment, helping clinicians in decision-making.