Development and validation of a nomogram for predicting pelvic lymph node metastasis and prognosis in patients with cervical cancer

Wang, Mengting; Ma, Min; Yang, Lei; Liang, Chengtong

doi:10.3389/fonc.2022.952347

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…Nomograms are based on multivariate regression analysis and integrating multiple predictors, so as to express the interrelationship between various variables in a prediction model. Nomograms have been widely used in the diagnosis and prognosis of various diseases ( 16 - 21 ). In the present study, the area under the ROC curve of the training set of the predictive model was 0.755 (95% confidence interval: 0.673–0.837), indicating that the model has predictive valuable for postoperative pelvic floor dysfunction in cervical cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Identifying the risk factors and developing a predictive model for postoperative pelvic floor dysfunction in cervical cancer patients

Liu¹,

Zhu²,

Li³

et al. 2023

Transl Cancer Res

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Background: Pelvic floor dysfunction is a common complication after cervical cancer surgery, and the key to early prevention and treatment is the timely identification of risk factors and high-risk patients. The present study explored the risk factors of pelvic floor dysfunction in cervical cancer patients after surgery and established a predictive model.Methods: A total of 282 cervical cancer patients admitted to Wuhan No.7 Hospital from January 2020 to June 2022 was retrospectively enrolled in this study. All patients underwent surgery and were followed up after surgery. The patients were divided into a pelvic floor dysfunction group (n=92) and a control group (n=190) according to whether they developed pelvic floor dysfunction or not at 6 months post-surgery. The differences in clinical features between the two groups were observed to identify the risk factors of pelvic floor dysfunction after cervical cancer, and a prediction model was established.Results: There were significant differences in age, surgical method, surgical resection range, and radiotherapy between the two groups (P<0.05). Age greater than 65 years, open surgery, total hysterectomy, and radiotherapy were identified as the risk factors of postoperative pelvic floor dysfunction in patients with cervical cancer (P<0.05). The R4.0.3 statistical software was used to randomly divide the dataset into a training dataset (n=141) and a validation dataset (n=141). The area under the curve was 0.755 (95% confidence interval: 0.673-0.837) in the training set and 0.604 (95% confidence interval: 0.502-0.705) in the verification set. In the validation set, the model was tested with a Hosmer-Lemeshow Goodness-of-Fit test, with a chi-square value of 9.017 and a P value of 0.341. Conclusions: Patients with cervical cancer have a high incidence of postoperative pelvic floor dysfunction. Age greater than 65 years, open surgery, total hysterectomy, and radiotherapy are risk factors of postoperative pelvic floor dysfunction in cervical cancer patients, and the present model helps to identify patients at highrisk of pelvic floor dysfunction.

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Section: Discussionmentioning

confidence: 99%

Identifying the risk factors and developing a predictive model for postoperative pelvic floor dysfunction in cervical cancer patients

Liu¹,

Zhu²,

Li³

et al. 2023

Transl Cancer Res

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“…However, RH combined with adjuvant therapy might lead to more serious comorbidities, such as genitourinary complications [ 4 ]. Because LNM is an important factor affecting prognosis, FIGO staging was revised in 2018, and patients with LNM were classified as stage IIIC and required concurrent chemoradiotherapy (CCRT) [ 5 , 6 ]. Therefore, accurate diagnosis of LNM is crucial for improving prognosis and reducing mortality.…”

Section: Introductionmentioning

confidence: 99%

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study

Qin,

Sun,

Tian

et al. 2024

Insights Imaging

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Objectives To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. Methods A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. Results SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. Conclusion The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Critical relevance statement Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. Key points • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer. Graphical Abstract

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“…With the development of tumour diagnosis and treatment technology, tumour staging is also in the process of change. Lymph node positivity is one of the high-risk factors affecting the prognosis of patients with cervical cancer and can be used as a reference point for guiding adjuvant therapy for cervical cancer [ 6 ]. In 2018, the Federation International of Gynaecology and Obstetrics (FIGO) included lymph node metastasis in stage IIIC [r (imaging) and p (pathology)], and patients with pelvic lymph node metastasis or para-aortic lymph node metastasis were classified as stage IIIC1 and stage IIIC2, respectively [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic analysis of patients with stage IIIC1p cervical cancer treated by surgery

et al. 2023

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Background Cervical cancer (CC) is one of the most common gynaecologic malignancies. The prognosis of stage IIIC1p cervical cancer patients treated by surgery is heterogeneous. Therefore, the aim of this study was to analyse the factors influencing the prognosis in such patients. Methods From January 2012 to December 2017, 102 patients with cervical cancer who underwent surgical treatment in the Department of Gynaecology and Tumours, Changzhou Maternal and Child Health Hospital, and had pelvic lymph node metastasis confirmed by pathology were analysed retrospectively. All patients underwent radical hysterectomy with/without oophorectomy with pelvic lymphadenectomy with/without para-aortic lymphadenectomy. Clinical data was collected including age, surgical method, ovarian status, intraoperative blood loss, perioperative complications, tumour size, pathological type, depth of stromal invasion (DSI), whether the lymphatic vascular space was infiltrated, number of pelvic lymph node metastases, location of pelvic lymph node metastases, total number of lymph nodes resected, lymph node ratio (LNR), nature of vaginal margin, whether parametrium was involved, postoperative adjuvant therapy, preoperative neutrophil–lymphocyte ratio (NLR) and prognostic information of patients. Survival curves for overall survival (OS) and disease-free survival (DFS) were plotted using the Kaplan–Meier method, and the difference between the survival curves was tested using the log-rank test. Univariate and multivariate COX regression models were used to assess the factors associated with overall survival and disease-free survival in patients with stage IIIC1p cervical cancer. Nomogram plots were constructed to predict OS and DFS, and the predictive accuracy of the nomograms was measured by Harrell’s C-index and calibration curves. Results A total of 102 patients with stage IIIC1p cervical cancer were included in the study, and the median follow-up time was 63 months (range from 6 to 130 months). The 5-year OS was 64.7%, and the 5-year DFS was 62.7%. Multivariate analysis showed that no postoperative adjuvant therapy, LNR > 0.3 and NLR > 3.8 were independent risk factors for OS and DFS in patients with stage IIIC1p cervical cancer. Conclusions Patients with stage IIIC1p cervical cancer have a poor prognosis. Lower OS and DFS were associated with no postoperative adjuvant therapy, LNR > 0.3 and NLR > 3.8.

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Development and validation of a nomogram for predicting pelvic lymph node metastasis and prognosis in patients with cervical cancer

Cited by 4 publications

References 45 publications

Identifying the risk factors and developing a predictive model for postoperative pelvic floor dysfunction in cervical cancer patients

Identifying the risk factors and developing a predictive model for postoperative pelvic floor dysfunction in cervical cancer patients

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study

Prognostic analysis of patients with stage IIIC1p cervical cancer treated by surgery

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