The Construction and Validation of Nomogram to Predict the Prognosis with Small-Cell Lung Cancer Followed Surgery

Development and Validation of a Prognostic Model for Postoperative Anastomotic Recurrence in Siewert II or III Adenocarcinomas Without Neoadjuvant Therapy in an East Asian Population

PURPOSE

Anastomotic recurrence leads to poor prognosis in patients with Siewert II or III adenocarcinoma who undergo radical gastrectomy and do not receive neoadjuvant therapy. We aimed to establish a prognostic model to evaluate the risk of postoperative anastomotic recurrence in patients with Siewert II or III adenocarcinoma who did not receive neoadjuvant therapy.

METHODS

We included 366 patients with Siewert II or III adenocarcinoma who were treated with radical gastrectomy without neoadjuvant therapy at Fujian Provincial Hospital (FPH) between 2012 and 2018 as the development cohort. Cox regression was used to verify prognostic factors for anastomotic recurrence, and a nomogram was established. The nomogram was externally validated using a combined cohort of two external centers. Patients were classified into high- or low-risk groups according to the diagnostic threshold and nomogram scores, and recurrence-related survival analysis was analyzed.

RESULTS

The average age was 64.6 years, and 285 patients were male. All surgeries were successfully performed (185 open vs 181 laparoscopic). The 3-year anastomotic recurrence rate was significantly lower in the low-risk group (3.5% vs 18.8%, P < 0.001). The predictive performance was verified in the external validation cohort. This model better stratified patient survival than the American Joint Committee on Cancer (AJCC) TNM staging system.

CONCLUSIONS

This novel nomogram with surgical margin, postoperative tumor node metastasis (pTNM) stage, and neural invasion as prognostic factors has a significant predictive performance for the risk of anastomotic recurrence after radical gastrectomy in patients with Siewert II or III adenocarcinoma.

Delta Radiomics Model for the Prediction of Overall Survival and Local Recurrence in Small Cell Lung Cancer Patients After Chemotherapy

RATIONALE AND OBJECTIVES

To evaluate the validity of CT-based delta radiomics signatures in predicting overall survival (OS) and local recurrence (LR) in small cell lung cancer (SCLC) patients after chemotherapy.

MATERIALS AND METHODS

Retrospectively enrolled 136 SCLC patients were split into training and testing cohorts. Radiomics features were extracted from CT images before, after the second, and the fourth cycle of chemotherapy. Delta radiomics features were obtained by calculating the net changes of features. Three radiomics signatures (R1, R2, and R3) and three delta radiomics signatures (R21, R31, and R32) were developed. The best signature was defined as the radiomics risk signature (RRS). The significant clinicoradiological factors and RRS of OS or LR were applied to build the combined model. RRS was also investigated in the subgroups based on stage and treatment regimens, respectively.

RESULTS

Delta radiomics models presented improved performance. R32 signature demonstrated the highest C-indices in the training and testing cohorts, with C-indices of 0.850 and 0.834 in the OS arm, and 0.723 and 0.737 in the LR arm, respectively. The incremental performance was observed after the clinicoradiological characteristics integrated into the RRSOS, with C-indexes of 0.857 and 0.836, respectively. Furthermore, the stratified analysis also confirmed the ability of RRS based on the stage and treatment regimen subgroups in the OS and LR arms, respectively.

CONCLUSION

Delta radiomics signatures could improve the personalized prediction of OS and LR at the early stage of chemotherapy in SCLC patients. R32 signature performed the highest performance.

Construction and validation of a nomogram to predict the overall survival of small cell lung cancer: a multicenter retrospective study in Shandong province, China

BACKGROUND

Patients diagnosed with small cell lung cancer (SCLC) typically experience a poor prognosis, and it is essential to predict overall survival (OS) and stratify patients based on distinct prognostic risks.

METHODS

Totally 2309 SCLC patients from the hospitals in 15 cities of Shandong from 2010 - 2014 were included in this multicenter, population-based retrospective study. The data of SCLC patients during 2010-2013 and in 2014 SCLC were used for model development and validation, respectively. OS served as the primary outcome. Univariate and multivariate Cox regression were applied to identify the independent prognostic factors of SCLC, and a prognostic model was developed based on these factors. The discrimination and calibration of this model were assessed by the time-dependent C-index, time-dependent receiver operator characteristic curves (ROC), and calibration curves. Additionally, Decision Curve Analysis (DCA) curves, Net Reclassification Improvement (NRI), and Integrated Discriminant Improvement (IDI) were used to assess the enhanced clinical utility and predictive accuracy of the model compared to TNM staging systems.

RESULTS

Multivariate analysis showed that region (Southern/Eastern, hazard ratio [HR] = 1.305 [1.046 - 1.629]; Western/Eastern, HR = 0.727 [0.617 - 0.856]; Northern/Eastern, HR = 0.927 [0.800 - 1.074]), sex (female/male, HR = 0.838 [0.737 - 0.952]), age (46-60/≤45, HR = 1.401 [1.104 - 1.778]; 61-75/≤45, HR = 1.500 [1.182 - 1.902]; >75/≤45, HR = 1.869 [1.382 - 2.523]), TNM stage (II/I, HR = 1.119[0.800 - 1.565]; III/I, HR = 1.478 [1.100 - 1.985]; IV/I, HR = 1.986 [1.477 - 2.670], surgery (yes/no, HR = 0.677 [0.521 - 0.881]), chemotherapy (yes/no, HR = 0.708 [0.616 - 0.813]), and radiotherapy (yes/no, HR = 0.802 [0.702 - 0.917]) were independent prognostic factors of SCLC patients and were included in the nomogram. The time-dependent AUCs of this model in the training set were 0.699, 0.683, and 0.683 for predicting 1-, 3-, and 5-year OS, and 0.698, 0.698, and 0.639 in the validation set, respectively. The predicted calibration curves aligned with the ideal curves, and the DCA curves, the IDI, and the NRI collectively demonstrated that the prognostic model had a superior net benefit than the TNM staging system.

CONCLUSION

The nomogram using SCLC patients in Shandong surpassed the TNM staging system in survival prediction accuracy and enabled the stratification of patients with distinct prognostic risks based on nomogram scores.

Construction and validation of a nomogram based on the log odds of positive lymph nodes to predict cancer-specific survival in patients with small cell lung cancer after surgery

Background

The lymph node ratio (LNR) is useful for predicting survival in patients with small cell lung cancer (SCLC). The present study compared the effectiveness of the N stage, number of positive LNs (NPLNs), LNR, and log odds of positive LNs (LODDS) to predict cancer-specific survival (CSS) in patients with SCLC.

Materials and methods

674 patients were screened using the Surveillance Epidemiology and End Results database. The Kaplan-Meier survival and receiver operating characteristic (ROC) curves were performed to address optimal estimation of the N stage, NPLNs, LNR, and LODDS to predict CSS. The optimal LN status group was incorporated into a nomogram to estimate CSS in SCLC patients. The ROC curve, decision curve analysis, and calibration plots were utilized to test the discriminatory ability and accuracy of this nomogram.

Results

The LODDS model showed the highest accuracy compared to the N stage, NPLNs, and LNR in predicting CSS for SCLC patients. LODDS, age, sex, tumor size, and radiotherapy status were included in the nomogram. The results of calibration plots provided evidences of nice consistency. The ROC and DCA plots suggested a better discriminatory ability and clinical applicability of this nomogram than the 8th TNM and SEER staging systems.

Conclusions

LODDS demonstrated a better predictive power than other LN schemes in SCLC patients after surgery. A novel LODDS-incorporating nomogram was built to predict CSS in SCLC patients after surgery, proving to be more precise than the 8th TNM and SEER staging.