Diagnostic Value of Multimodal Magnetic Resonance Imaging in Discriminating Between Metastatic and Non-Metastatic Pelvic Lymph Nodes in Cervical Cancer

Xu, Jing; Ma, Yangmin; Mei, Haibing; Wang, Qimin

doi:10.2147/ijgm.s372154

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Cervical cancer is the fourth most common cancer in women worldwide, accounting for nearly 8% of all cancer deaths in women each year [ 10 ]. Studies have shown that lymph node metastasis can occur in the early stage of cervical cancer, with an incidence of 10% to 20% [ 11 ]. Compared with patients without lymph node metastasis as confirmed by pathology after surgery (5-year survival rate can be as high as 90%), the 5-year survival rate of patients with positive pelvic lymph nodes is reduced to 50 ~ 60% [ 12 ].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer

Yan,

Huang,

Yang

et al. 2024

J Digit Imaging. Inform. med.

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Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922–0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703–0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947–0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848–0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.

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Diffusion Weighted Imaging for the Assessment of Lymph Node Metastases in Women with Cervical Cancer: A Meta-analysis of the Apparent Diffusion Coefficient Values

Distelbrink,

Celebi,

Mom

et al. 2024

Academic Radiology

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Diagnostic Value of Multimodal Magnetic Resonance Imaging in Discriminating Between Metastatic and Non-Metastatic Pelvic Lymph Nodes in Cervical Cancer

Cited by 5 publications

References 20 publications

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

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

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer

Diffusion Weighted Imaging for the Assessment of Lymph Node Metastases in Women with Cervical Cancer: A Meta-analysis of the Apparent Diffusion Coefficient Values

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