Validation of Nomograms for Survival and Metastases after Hysterectomy and Adjuvant Therapy in Uterine Cervical Cancer with Risk Factors

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal

Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research. Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool. Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable. Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

Prognosis impact of posttreatment pelvic MRI in patients treated for stage IB2-IIB cervical cancer with chemoradiation therapy

OBJECTIVES

To evaluate the performances of systematic posttreatment pelvic magnetic resonance imaging (PPMRI) in predicting prognosis of patients treated with chemoradiation therapy (CRT) for locally advanced cervical cancer (LACC).

MATERIALS AND METHODS

Multi-institutional data from 216 patients presenting FIGO IB2-IIB cervical cancer for which PPMRI was performed following CRT were retrospectively reviewed. Incomplete response was defined as the identification of persistent lesion on PPMRI. Primary endpoints were patients' 5-year recurrence free (RFS) and overall (OS) survivals. Secondary endpoint was the identification of residual histologic disease on hysterectomy specimens when completion surgery was performed.

RESULTS

PPMRI identified an incomplete response in 102 (47.2%) cases. A 70% or more reduction in tumor size on PPMRI was identified as the best predictive cut-off for recurrence (37.7% sensitivity and 78.7% specificity) and death (50% sensitivity and 77.9% specificity) with significant impact on those risks (HRa: 0.42; 95%CI: 0.23-0.77 and HRa: 0.18; 95%CI: 0.06-0.50, respectively). Completion hysterectomy was performed in 117 (54.4%) cases, with histologic residual disease in 55 (47.4%). PPMRI demonstrated 74.5% sensitivity and 50.8% specificity in predicting residual disease. Although survival of patients with complete response at PPMRI was not impacted by completion hysterectomy, it significantly increased 5-year RFS and OS of those with incomplete response: 38.7% vs. 65.3% (p < 0.001) and 63% vs. 82.9% (p = 0.038), respectively.

CONCLUSION

A 70% or more reduction of in tumor size on PPMRI following CRT in patients with LACC is predictive of RFS and OS. PPMRI could help triaging patients who could benefit from completion hysterectomy.

Performance of a Nomogram Based on the Integration of Inflammation Markers with Tumor Staging in Prognosis Prediction of Stage III Colorectal Cancer

Introduction

The aim of the present study was to evaluate a nomogram model for predicting the 5-year overall survival (OS) in lymph node-metastatic colorectal cancer (CRC) patients by combining inflammation markers with some traditional prognostic factors.

Methods

A total of 399 patients with stage III (pT_XN_1-3M₀) CRC operated from January 2007 to December 2012 were enrolled in this retrospective study. All patients underwent D₂ lymphadenectomy in the hospital. A prognostic nomogram based on the integration of traditional prognostic factors and NLR (neutrophil-to-lymphocyte ratio) and PLR (platelet-to-lymphocyte ratio) was established and compared with the nomogram based on the traditional prognostic factors alone. ROC curves were further applied to verify the predictive accuracy of the established model.

Results

Both NLR (P=0.00) and PLR (P=0.01) predicted the 5-year OS. In multivariate analysis, age, T3 category, T4 category, N2 category, N3 category, Pgp (P-glycoprotein), NLR and PLR are proven to be independent (all P≤0.05). The established nomogram showed better predictive power than that of traditional profile (c-index: 0.66 versus 0.63) in both training and validation cohorts. External assessment by ROC curve analysis demonstrated that the established model had a good prediction accuracy of 5-year OS in stage III CRC patients, with area under curve values of 0.657 and 0.629 in training and validating sets, respectively.

Conclusion

A nomogram based on the integration of traditional prognostic factors and inflammatory markers (NLR and PLR) could provide more precise long-term prognosis information for lymph node-metastatic CRC patients than the model based on traditional profile alone. This model might be useful for clinical application in personalized evaluation.

A Novel Diagnostic Nomogram for Noninvasive Evaluating Liver Fibrosis in Patients with Chronic Hepatitis B Virus Infection

Objective

To establish a novel nomogram for diagnosing liver fibrosis in patients with chronic hepatitis B virus (HBV) infection and verify the diagnostic performance of the established nomogram.

Methods

Patients with chronic HBV infection who met the inclusion and exclusion criteria were enrolled in this retrospective study; 70% and 30% of patients were randomly assigned to training dataset and validation dataset, respectively. The risk factors for liver fibrosis were screened using the univariate and multivariate logistic regression analyses. Based on the results, a nomogram was established and verified.

Results

508 patients with chronic HBV infection were included in this study (n = 355 for training dataset and n = 153 for validation dataset). The logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT) count, and prothrombin time (PT) were independent risk factors for liver fibrosis (P < 0.01), which were used to establish the nomogram. The consistency index (C-index) of the nomogram established for diagnosing liver fibrosis was 0.875. The calibration line and the ideal line were consistent, which indicated that diagnosis of liver fibrosis by the established model was accurate. The values of area under the receiver operator characteristic (ROC) curve (AUROC) for diagnosing liver fibrosis by the nomogram were 0.857 and 0.862 in the training dataset and validation dataset, respectively, which were noticeably higher than those in the well-known serological models, including the aspartate aminotransferase- (AST-) to-platelet ratio index (APRI) scoring model, fibrosis-4 (FIB-4) scoring model, APAG model (including age, PT, albumin, and γ-glutamyl transferase), and S-index model (all P < 0.05).

Conclusion

LSM, PT, and PLT were found as independent risk factors for liver fibrosis. The established nomogram exhibited an excellent diagnostic performance, and it can more visually and individually evaluate the probability of liver fibrosis in patients with chronic HBV infection.