Prognostic Parameters for Patients with Cervical Cancer FIGO Stages IA2-IIB: A Long-Term Follow-Up

Obrzut, Bogdan; Semczuk, Andrzej; Naróg, Maciej; Obrzut, Marzanna; Król, Piotr

doi:10.1159/000471766

Cited by 35 publications

(37 citation statements)

References 66 publications

(60 reference statements)

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“…In the analysis of tumor size, we grouped the tumor size using a limit of 4 cm, and the diameter of the tumor was the result of the postoperative pathology, which is different from previous studies (21,22). Pelvic lymph node metastasis has been observed in several studies and is included in the latest FIGO staging (15,23,24). It is also critical to evaluate the condition of pelvic lymph nodes before surgery (23,25).…”

Section: Discussionmentioning

confidence: 99%

Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach

et al. 2020

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Introduction: The International Federation of Gynecology and Obstetrics (FIGO) staging system is considered the most powerful prognostic factor in patients with cervical cancer. In addition, other surgical-pathological risk factors have been demonstrated to have significance in predicting the prognosis of patients. Therefore, the purpose of this study was to investigate the effects of the FIGO staging system and surgical-pathological risk factors on the prognosis of cervical cancer patients. Methods: A retrospective study was performed on patients diagnosed with cervical cancer at FIGO stage IB1-IIA2. Kaplan-Meier, Cox proportional hazards regression analysis and the support vector machine (SVM) algorithm were used to assess and validate the high-risk factors related to recurrence and death. Results: A total of 647 patients were included. Kaplan-Meier analysis showed that five high-risk factors, including FIGO stage, status of pelvic lymph node, parametrial involvement, tumor size, and depth of cervical cancer, had a significant effect on the prognosis of patients. In multivariate analysis, pelvic lymph node metastasis (hazard ratio [HR] 2.415, 95% confidence interval [CI] 1.471-3.965), parametrial involvement (HR 2.740, 95% CI 1.092-6.872) and >2/3 depth of cervical invasion (HR 2.263, 95% CI 1.045-4.902) were three independent risk factors of disease-free survival. Pelvic lymph node metastasis (HR 3.855, 95% CI 2.125-6.991) and parametrial involvement (HR 3.871, 95% CI 1.375-10.900) were two independent risk factors for overall survival. When all five high-risk factors were assembled and used for classification prediction through SVM, it achieved the highest prediction accuracy of recurrence (accuracy = 69.1%). The highest prediction accuracy for survival was 94.3% when only using the two independent predictors (the pathological status of lymph nodes and parametrium involvement) by SVM classifiers. Among the 13 groups of intermediate-risk factor, the Xie et al. Prognostic Assessment of Cervical Cancer combination of tumor size, histology and grade of differentiation was more accurate in predicting prognosis than the intermediate-risk factors in the Sedlis criteria (recurrence: 86.8% vs. 60.0%; death: 92.0% vs. 71.6%). Conclusions: The combination of FIGO stage and surgical-pathological risk factors can further enhance the prediction accuracy of the prognosis in patients with early-stage cervical cancer. Histology and grade of differentiation can further improve the prediction accuracy of intermediate-risk factors in the Sedlis criteria.

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Section: Discussionmentioning

confidence: 99%

Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach

et al. 2020

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“…Several studies report a close relationship between patient survival and lymph node involvement [ 57 – 61 ]. Moreover, the number of positive lymph nodes is a more accurate prognostic factor than the presence of metastases [ 62 – 65 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

et al. 2017

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BackgroundComputational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5–year overall survival prediction in patients with cervical cancer treated by radical hysterectomy.MethodsThe data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model.ResultsThe best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse.ConclusionsThe PNN model is an effective tool for predicting 5–year overall survival in cervical cancer patients treated with radical hysterectomy.

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“…Pelvic lymph node (LN) metastasis is a critical independent prognostic factor and one of the leading causes of cervical cancer death [2]. However, there is still no effective and reliable means to prevent or control LN metastasis.…”

Section: Introductionmentioning

confidence: 99%

LNMICC Promotes Nodal Metastasis of Cervical Cancer by Reprogramming Fatty Acid Metabolism

et al. 2018

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Running Title: LNMICC promotes lymph node metastasis in cervical cancer.Key words: lymph node metastasis; fatty acid metabolism; long non-coding RNA; cervical cancer. Conflict of Interest:The authors declare no potential conflicts of interest.Research. AbstractCancer spread to lymph nodes (LN) predicts poor survival but underlying mechanisms remain little understood. In this study, we show that overexpression of the long non-coding RNA LNMICC associates with LN metastasis of primary cervical cancer, where it serves as an independent high-risk factor in patient survival. Functional investigations demonstrated that LNMICC promoted LN metastasis by reprogramming fatty acid metabolism, by recruiting the nuclear factor NPM1 to the promoter of the fatty acid binding protein FABP5. We also found that the pro-metastatic effects of LNMICC were directly targeted and suppressed by miR-190. Our results establish a new mechanism of LN metastasis and highlight LNMICC as a candidate prognostic biomarker and therapeutic target in cervical cancer.

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Prognostic Parameters for Patients with Cervical Cancer FIGO Stages IA2-IIB: A Long-Term Follow-Up

Cited by 35 publications

References 66 publications

Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach

Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach

Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

LNMICC Promotes Nodal Metastasis of Cervical Cancer by Reprogramming Fatty Acid Metabolism

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