Dataset Information

Prognostic role of preoperative D-dimer, fibrinogen and platelet levels in patients with oral squamous cell carcinoma.

ABSTRACT:

Background

The relationship between cancer and coagulation has been intensively studied in recent years; however, the effects of coagulation factors on oral squamous cell carcinoma (OSCC) have rarely been reported. This study aimed to investigate the relationship between preoperative D-dimer (DD), fibrinogen (FIB), platelets (PLT) and OSCC, as well as the prognostic value of DD, FIB and PLT in OSCC.

Methods

We retrospectively investigated a total of 202 patients with OSCC treated at Guanghua Hospital of Stomatology, Sun Yat-sen University. Baseline demographic and clinicopathological information as well as both preoperative and postoperative DD, FIB and PLT results were collected from each patient, and patients with primary OSCC were followed up for disease progression, death or the end of the study. The correlations between preoperative DD, FIB, PLT and other clinical features, as well as the therapeutic effect and PFS were analysed statistically, and postoperative DD and surgical parameters were also analysed.

Results

Preoperative DD was significantly correlated with T stage, N stage, clinical stage and relapse of OSCC (P?=?0.000, 0.001, 0.000 and 0.000, respectively). Univariate Cox regression analyses showed that high preoperative DD predicted poor prognosis in patients with OSCC (HR?=?2.1, P?=?0.033), while FIB and PLT showed no prognostic values. Postoperative DD was significantly correlated with preoperative DD and surgical type but not the duration of surgery (P?=?0.005, 0.001 and 0.244, respectively).

Conclusion

In this study, we suggested that high preoperative DD level may serve as an indicator for synchronous neck dissection in patients with T_{1, 2} OSCC, and the elevated DD level might be the marker of disease progression in patient follow up.

SUBMITTER: Liang YJ

PROVIDER: S-EPMC7863521 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Prognostic role of preoperative D-dimer, fibrinogen and platelet levels in patients with oral squamous cell carcinoma.

Liang Yu-Jie YJ Mei Xue-Ying XY Zeng Bin B Zhang Si-En SE Yang Le L Lao Xiao-Mei XM Liao Gui-Qing GQ

BMC cancer 20210205 1

<h4>Background</h4>The relationship between cancer and coagulation has been intensively studied in recent years; however, the effects of coagulation factors on oral squamous cell carcinoma (OSCC) have rarely been reported. This study aimed to investigate the relationship between preoperative D-dimer (DD), fibrinogen (FIB), platelets (PLT) and OSCC, as well as the prognostic value of DD, FIB and PLT in OSCC.<h4>Methods</h4>We retrospectively investigated a total of 202 patients with OSCC treated ...[more]

PMID: 33546637

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Project description:BackgroundWe aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC).MethodsA total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree.ResultsDiagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845].ConclusionsOur results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.

Dataset Information

Prognostic role of preoperative D-dimer, fibrinogen and platelet levels in patients with oral squamous cell carcinoma.

Background

Methods

Results

Conclusion

Publications

Prognostic role of preoperative D-dimer, fibrinogen and platelet levels in patients with oral squamous cell carcinoma.

Similar Datasets

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