Nomogram Predicting the Likelihood of Parametrial Involvement in Early-Stage Cervical Cancer: Avoiding Unjustified Radical Hysterectomies

Benoit, Louise; Balaya, Vincent; Guani, Benedetta; Bresset, Arnaud; Bonsang-Kitzis, Hélène; Ngô, Charlotte; Mathevet, Patrice; Lécuru, Fabrice

doi:10.3390/jcm9072121

Cited by 13 publications

(12 citation statements)

References 44 publications

(74 reference statements)

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“…However, there are still some potentially high-risk patients who have missed adjuvant treatment. 29 For example, these patients may show relatively good clinicopathological characteristics, but in fact, they may have excessive inflammatory reaction and poor nutritional status, which may also lead to a poor prognosis for patients to a large extent. 11,12 Therefore, the…”

Section: Discussionmentioning

confidence: 99%

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Jiang¹,

Kong²,

Gong³

et al. 2022

JIR

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The purpose of this study was to evaluate the prognostic value of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in patients with operable cervical cancer, and on this basis, combined with classical clinicopathological parameters to predict the recurrence of patients. Methods: A total of 1580 patients with stage IA-IIA cervical cancer were randomly divided into training cohort (n=1054) and validation cohort (n=526) according to the predefined ratio of 2:1. In the training cohort, the receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of HALP score for predicting cervical cancer recurrence. On this basis, the independent related factors with cervical cancer recurrence were screened through univariate and multivariate Cox regression analysis, and then a nomogram model was further established. The internal and external validation of the model was carried out in the training cohort and the validation cohort respectively through the consistency index (C-index) and calibration curve. Results: ROC curve and Youden index showed that the optimal threshold of HALP score for predicting cervical cancer recurrence was 39.50. Multivariate analysis confirmed that HALP score and some other classic clinicopathological parameters were independently associated with cervical cancer recurrence. Based on the results of multivariate analysis, a nomogram model for predicting cervical cancer recurrence was successfully constructed. The internal and external calibration curves showed that the fitting degree of the model was good, and the C-index (the C-index of the training cohort and the validation cohort were 0.862 and 0.847, respectively) showed that the prediction accuracy of the model proposed in this study was better than other similar models. Conclusion: HALP score may be a novel predictor for predicting the cervical cancer recurrence. Nomogram model based on HALP score and classical clinicopathological parameters can better predict the recurrence of cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Jiang¹,

Kong²,

Gong³

et al. 2022

JIR

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“…Several risk factors commonly associated [ 2 , 3 , 12 , 34 ] with an increased risk of PLNM in cervical cancer were assessed. Increased age or surgical approach (laparoscopy vs. laparotomy) were not associated with an increased risk of PLNM.…”

Section: Resultsmentioning

confidence: 99%

Lymph Node Involvement in Early-Stage Cervical Cancer: Is Lymphangiogenesis a Risk Factor? Results from the MICROCOL Study

Tantari

Bogliolo²,

Morotti

et al. 2022

Cancers

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Background: In patients with cervical cancer, the presence of tumoral lymph-vascular space invasion (LVSI) is the main risk factor for pelvic lymph node metastasis (PLNM). The objective of this study was to evaluate the presence of several markers of lymphangiogenesis in early-stage cervical cancer and their correlation with PLNM and tumoral recurrence. Materials and Methods: Seventy-five patients with early-stage cervical carcinoma underwent sentinel lymph node (SLN) sampling in association with complete pelvic lymph node dissection. Primary tumors were stained with the following markers: Ki67, D2-40, CD31 and VEGF-C. A 3-year follow-up was performed to evaluate the disease-free survival. Results: Overall, 14 patients (18.6%) had PLNM. Positive LVSI was seen in 29 patients (38.6%). There was a significant correlation between LVSI evidenced by H/E staining and PLNM (p < 0.001). There was no correlation between high Ki67, CD31, D2-40, and VEGF-C staining with PLNM or tumor recurrence. Conclusions: Our data support that lymphatic spread does not require the proliferation of new lymphatic endothelial cells in early-stage cervical cancer. These results emphasize the importance of pre-existing peritumoral lymphatic vessels in the metastatic process in early cervical cancer. None of the markers of lymphangiogenesis and proliferation assessed in this study were predictive of PLNM or recurrence.

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“…As a new statistical prediction model [11], the nomogram has many advantages over traditional prognostic models, such as high accuracy, exibility, and ease of generalization, and it has been widely used with various tumor types, including liver, bladder, prostate, cervical, and gastric cancers [12][13][14][15][16]. In this study, a nomogram was constructed to predict the 1-, 3-, and 5-year OS of NSCLC patients using eight variables that showed statistical signi cance in a multifactorial Cox risk regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Using Nomogram and Machine Learning Models to Predict Non-Small Cell Lung Cancer Prognosis

Lei

et al. 2021

Preprint

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BackgroundPatients with non-small cell lung cancer (NSCLC) often have a poor prognosis. Overall survival (OS) prediction through the early diagnosis of cancer has many benefits, such as allowing providers to design the best treatment plan for patients. In this study, we aimed to evaluate the prognostic factors in NSCLC patients, construct a nomogram, and develop machine learning models to predict the OS. We also conducted feature importance analysis to understand how relevant factors of NSCLC patients impact their OS.ResultsMultiple machine learning models were adopted in a retrospective cohort of patients from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors for NSCLC were determined using Cox proportional hazards regression analysis. We modeled OS and vital status as the outcomes and constructed and validated a nomogram to predict the OS of NSCLC. Furthermore, we applied logistic regression, random forest, XGBoost, decision tree, multilayer perceptron, and LightGBM to predict the patients’ vital status. We tested the prediction ability of the models and evaluated their performances using accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve. A total of 34,567 patients selected from the SEER database that met our criteria were included in this study. The nomogram visualized the OS prediction results of the Cox regression model. Among the classifiers, XGBoost had the best prediction performance, with an area under the curve of 0.733.ConclusionsThe results demonstrated that machine learning-based classifier models are capable of predicting the outcomes of patients with NSCLC. And Cox regression model-based nomogram interpreted the results well and supports potential medical applications.

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Nomogram Predicting the Likelihood of Parametrial Involvement in Early-Stage Cervical Cancer: Avoiding Unjustified Radical Hysterectomies

Cited by 13 publications

References 44 publications

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Lymph Node Involvement in Early-Stage Cervical Cancer: Is Lymphangiogenesis a Risk Factor? Results from the MICROCOL Study

Using Nomogram and Machine Learning Models to Predict Non-Small Cell Lung Cancer Prognosis

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