Development and validation of a surgical-pathologic staging and scoring system for cervical cancer.
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ABSTRACT: Background
Most cervical cancer patients worldwide receive surgical treatments, and yet the current International Federation of Gynecology and Obstetrics (FIGO) staging system do not consider surgical-pathologic data. We propose a more comprehensive and prognostically valuable surgical-pathologic staging and scoring system (SPSs).Methods
Records from 4,220 eligible cervical cancer cases (Cohort 1) were screened for surgical-pathologic risk factors. We constructed a surgical-pathologic staging and SPSs, which was subsequently validated in a prospective study of 1,104 cervical cancer patients (Cohort 2).Results
In Cohort 1, seven independent risk factors were associated with patient outcome: lymph node metastasis (LNM), parametrial involvement, histological type, grade, tumor size, stromal invasion, and lymph-vascular space invasion (LVSI). The FIGO staging system was revised and expanded into a surgical-pathologic staging system by including additional criteria of LNM, stromal invasion, and LVSI. LNM was subdivided into three categories based on number and location of metastases. Inclusion of all seven prognostic risk factors improves practical applicability. Patients were stratified into three SPSs risk categories: zero-, low-, and high-score with scores of 0, 1 to 3, and ?4 (P=1.08E-45; P=6.15E-55). In Cohort 2, 5-year overall survival (OS) and disease-free survival (DFS) outcomes decreased with increased SPSs scores (P=9.04E-15; P=3.23E-16), validating the approach. Surgical-pathologic staging and SPSs show greater homogeneity and discriminatory utility than FIGO staging.Conclusions
Surgical-pathologic staging and SPSs improve characterization of tumor severity and disease invasion, which may more accurately predict outcome and guide postoperative therapy.
SUBMITTER: Li S
PROVIDER: S-EPMC4991512 | biostudies-literature | 2016 Apr
REPOSITORIES: biostudies-literature
Publications
Development and validation of a surgical-pathologic staging and scoring system for cervical cancer.
Li Shuang S Li Xiong X Zhang Yuan Y Zhou Hang H Tang Fangxu F Jia Yao Y Hu Ting T Sun Haiying H Yang Ru R Chen Yile Y Cheng Xiaodong X Lv Weiguo W Wu Li L Zhou Jin J Wang Shaoshuai S Huang Kecheng K Wang Lin L Yao Yuan Y Yang Qifeng Q Yang Xingsheng X Zhang Qinghua Q Han Xiaobing X Lin Zhongqiu Z Xing Hui H Qu Pengpeng P Cai Hongbing H Song Xiaojie X Tian Xiaoyu X Shen Jian J Xi Ling L Li Kezhen K Deng Dongrui D Wang Hui H Wang Changyu C Wu Mingfu M Zhu Tao T Chen Gang G Gao Qinglei Q Wang Shixuan S Hu Junbo J Kong Beihua B Xie Xing X Ma Ding D
Oncotarget 20160401 15
<h4>Background</h4>Most cervical cancer patients worldwide receive surgical treatments, and yet the current International Federation of Gynecology and Obstetrics (FIGO) staging system do not consider surgical-pathologic data. We propose a more comprehensive and prognostically valuable surgical-pathologic staging and scoring system (SPSs).<h4>Methods</h4>Records from 4,220 eligible cervical cancer cases (Cohort 1) were screened for surgical-pathologic risk factors. We constructed a surgical-patho ...[more]
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Project description:BackgroundDevelopment of a prediction model using baseline characteristics of COVID-19 patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring system for predicting the COVID-19 severity in South India.MethodsWe undertook this retrospective cohort study among COVID-19 patients reporting to Hindu Mission Hospital, India. Multivariable logistic regression using the LASSO procedure was used to select variables for the model building, and the nomogram scoring system was developed with the final selected model. Model discrimination, calibration, and decision curve analysis (DCA) was performed.ResultsIn total, 35.1% of the patients in the training set developed severe COVID-19 during their follow-up period. In the basic model, nine variables (age group, sex, education, chronic kidney disease, tobacco, cough, dyspnea, olfactory-gustatory dysfunction [OGD], and gastrointestinal symptoms) were selected and a nomogram was built using these variables. In the advanced model, in addition to these variables (except OGD), C-reactive protein, lactate dehydrogenase, ferritin, D-dimer, and CT severity score were selected. The discriminatory power (c-index) for basic model was 0.78 (95%CI: 0.74-0.82) and advanced model was 0.83 (95%CI: 0.79-0.87). DCA showed that both the models are beneficial at a threshold probability around 10-95% than treat-none or treat-all strategies.ConclusionThe present study has developed two separate prognostic-scoring systems to predict the COVID-19 severity. This scoring system could help the clinicians and policymakers to devise targeted interventions and in turn reduce the COVID-19 mortality in India.
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