Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer

Development and validation of nomograms for predicting overall survival and cancer-specific survival in unresected colorectal cancer patients undergoing chemotherapy

This study aims to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in colorectal cancer (CRC) patients who did not receive primary site surgery but underwent chemotherapy. We analyzed data from 3,050 patients treated with chemotherapy without primary site surgery from 2010 to 2015, sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The data were randomly divided into training and validation sets. Initial variable selection was performed using the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis was used to identify independent prognostic factors. Two nomograms were subsequently constructed based on these factors. Survival analysis was conducted using Kaplan-Meier plots and the log-rank test. We identified nine significant predictors of OS and CSS: age, marital status, primary site, grade, histology, T stage, M stage, tumor size, and CEA levels. The models for OS and CSS exhibited excellent predictability, with time-dependent area under the receiver operating characteristic curves (AUCs) exceeding 0.7. Calibration curves confirmed the accuracy of these predictions in the training and validation sets. Additionally, decision curve analysis (DCA) indicated that our models provide greater clinical benefit than traditional TNM staging. Notably, survival outcomes varied significantly across risk categories, affirming the models' effective discrimination. For CRC patients who did not receive primary site surgery but underwent chemotherapy, this validated nomogram enables precision prognostication fundamentally shifting the paradigm from population-level TNM estimates to individualized risk-adaptive management.

A predictive nomogram developed and validated for gastric cancer patients with triple-negative tumor markers

Aim: To predict the prognosis of gastric cancer patients with triple-negative tumor markers. Materials & methods: Prognostic factors of the nomogram were identified through univariate and multivariate Cox regression analyses. Calibration and receiver operating characteristic curves were used to assess accuracy. Decision curve analysis and concordance indexes were utilized to compare the nomogram with the pathological tumor, node, metastasis stage. Results: A nomogram incorporating log odds of positive lymph nodes, tumor size and lymphocyte-to-monocyte ratio was constructed. The calibration and receiver operating characteristic curves (area under the curve >0.85) showed high accuracy in predicting overall survival. The concordance indexes (0.832 vs 0.760; p < 0.001) and decision curve analysis demonstrated that the nomogram was superior to the pathological tumor, node, metastasis stage. Conclusion: A prediction and risk stratification nomogram has been developed and validated for gastric cancer patients with triple-negative tumor markers.

Development and validation of a clinical survival model for young-onset colorectal cancer with synchronous liver-only metastases: a SEER population-based study and external validation

Background

The morbidity and mortality of young-onset colorectal cancer (YO-CRC) patients have been increasing in recent years. Moreover, YO-CRC patients with synchronous liver-only metastases (YO-CRCSLM) have various survival outcomes. Therefore, the purpose of this study was to construct and validate a prognostic nomogram for patients with YO-CRCSLM.

Methods

The YO-CRCSLM patients were rigorously screened from the Surveillance, Epidemiology, and End Results (SEER) database in January 2010 and December 2018 and then assigned to a training and validation cohort randomly (1488 and 639 patients, respectively). Moreover, the 122 YO-CRCSLM patients who were enrolled in The First Affiliated Hospital of Nanchang University were served as a testing cohort. The variables were selected using the multivariable Cox model based on the training cohort and then developed a nomogram. The validation and testing cohort were used to validate the model's predictive accuracy. The calibration plots were used to determine the Nomogram's discriminative capabilities and precision, and the decision analysis (DCA) was performed to evaluate the Nomogram's net benefit. Finally, the Kaplan-Meier survival analyses were performed for the stratified patients based on total nomogram scores classified by the X-tile software.

Results

The Nomogram was constructed including ten variables: marital status, primary site, grade, metastatic lymph nodes ratio (LNR), T stage, N stage, carcinoembryonic antigen (CEA), Surgery, and chemotherapy. The Nomogram performed admirably in the validation and testing group according to the calibration curves. The DCA analyses showed good clinical utility values. Low-risk patients (score<234) had significantly better survival outcomes than middle-risk (234-318) and high-risk (>318) patients (P < 0.001).

Conclusion

A nomogram predicting the survival outcomes for patients with YO-CRCSLM was developed. In addition to facilitating personalized survival prediction, this nomogram may assist in developing clinical treatment strategies for patients with YO-CRCSLM who are undergoing treatment.

A population-based predictive model identifying optimal candidates for primary and metastasis resection in patients with colorectal cancer with liver metastatic

BACKGROUND

The survival benefit of primary and metastatic resection for patients with colorectal cancer with liver metastasis (CRLM) has been observed, but methods for discriminating which individuals would benefit from surgery have been poorly defined. Herein, a predictive model was developed to stratify patients into sub-population based on their response to surgery.

METHODS

We assessed the survival benefits for adults diagnosed with colorectal liver metastasis by comparing patients with curative surgery vs. those without surgery. CRLM patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were identified for model construction. Other data including CRLM patients from our center were obtained for external validation. Calibration plots, the area under the curve (AUC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram compared with the tumor-node-metastasis (TNM) classification. The Kaplan-Meier analysis was performed to examine whether this model would distinguish patients who could benefit from surgery.

RESULTS

A total of 1,220 eligible patients were identified, and 881 (72.2%) underwent colorectal and liver resection. Cancer-specific survival (CSS) for the surgery group was significantly better than that for the no-surgery group (41 vs. 14 months, p < 0.001). Five factors were found associated with CSS and adopted to build the nomograms, i.e., age, T stage, N stage, neoadjuvant chemotherapy, and primary tumor position. The AUC of the CRLM nomogram showed a better performance in identifying patients who could obtain benefits in the surgical treatment, compared with TNM classification (training set, 0.826 [95% CI, 0.786-0.866] vs. 0.649 [95% CI, 0.598-0.701]; internal validation set, 0.820 [95% CI, 0.741-0.899] vs. 0.635 [95% CI, 0.539-0.731]; external validation set, 0.763 [95% CI, 0.691-0.836] vs. 0.626 [95% CI, 0.542-0.710]). The calibration curves revealed excellent agreement between the predicted and actual survival outcomes. The DCA showed that the nomogram exhibited more clinical benefits than the TNM staging system. The beneficial and surgery group survived longer significantly than the non-beneficial and surgery group (HR = 0.21, 95% CI, 0.17-0.27, p < 0.001), but no difference was observed between the non-beneficial and surgery and non-surgery groups (HR = 0.89, 95% CI, 0.71-1.13, p = 0.344).

CONCLUSIONS

An accurate and easy-to-use CRLM nomogram has been developed and can be applied to identify optimal candidates for the resection of primary and metastatic lesions among CRLM patients.

Development and Validation of Novel Nomograms to Predict the Overall Survival and Cancer-Specific Survival of Cervical Cancer Patients With Lymph Node Metastasis

Objective

The objective of this study was to establish and validate novel individualized nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in cervical cancer patients with lymph node metastasis.

Methods

A total of 2,956 cervical cancer patients diagnosed with lymph node metastasis (American Joint Committee on Cancer, AJCC N stage=N1) between 2000 and 2018 were included in this study. Univariate and multivariate Cox regression models were applied to identify independent prognostic predictors, and the nomograms were established to predict the OS and CSS. The concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were applied to estimate the precision and discriminability of the nomograms. Decision-curve analysis (DCA) was used to assess the clinical utility of the nomograms.

Results

Tumor size, log odds of positive lymph nodes (LODDS), radiotherapy, surgery, T stage, histology, and grade resulted as significant independent predictors both for OS and CSS. The C-index value of the prognostic nomogram for predicting OS was 0.788 (95% CI, 0.762–0.814) and 0.777 (95% CI, 0.758–0.796) in the training and validation cohorts, respectively. Meanwhile, the C-index value of the prognostic nomogram for predicting CSS was 0.792 (95% CI, 0.767–0.817) and 0.781 (95% CI, 0.764–0.798) in the training and validation cohorts, respectively. The calibration curves for the nomograms revealed gratifying consistency between predictions and actual observations for both 3- and 5-year OS and CSS. The 3- and 5-year area under the curves (AUCs) for the nomogram of OS and CSS ranged from 0.781 to 0.828. Finally, the DCA curves emerged as robust positive net benefits across a wide scale of threshold probabilities.

Conclusion

We have successfully constructed nomograms that could predict 3- and 5-year OS and CSS of cervical cancer patients with lymph node metastasis and may assist clinicians in decision-making and personalized treatment planning.