Risk factors of regional lymph node metastasis in patients with cervical cancer

Wu, Chunchun; Li, Lichun; Xiao, Xue; Sun, Anyi; Lin, Wentao; Li, Ailu

doi:10.1515/biol-2019-0023

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…However, it remains unclear which patients are more likely to have LNM and also if FIGO 2018 changes the groups at risk. Although there have been many studies on LNM of cervical cancer [9][10][11][12][13][14], few included large samples. Our hospital, a large obstetrics and gynecology hospital in China, treats many patients with cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Lymph Node Metastasis and Risk Factors in 975 Patients with FIGO 2009 Stage IA–IIA Cervical Cancer

Cao¹,

Kong²,

Li³

et al. 2022

Gynecol Obstet Invest

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Objectives: To summarize the rate of lymph node metastasis (LNM) of patients with stage IA–IIA cervical cancer and further analyze its distribution characteristics and related risk factors. Design: This study is a retrospective analysis of clinical data from 975 patients with stage IA–IIA cervical cancer treated in our hospital from January 2010 to December 2018, according to the International Federation of Gynecology and Obstetrics (FIGO) 2009. Methods: The incidence and distribution of LNM were analyzed, and the influencing factors of cervical cancer LNM were analyzed using univariate and multivariate logistic regression. Results: In this study, the LNM rate was 14.8% (144/975), and a total of 20,288 lymph nodes were removed, among which 359 lymph nodes had metastasis. According to the number and frequency of metastatic lymph nodes in different regions, the metastatic rate was the highest in the external iliac regions. Univariate analysis showed more than three pregnancies, tumor size > 4 cm, gross type, FIGO stage, pathological type, positive lymphovascular space invasion (LVSI), deep cervical stromal invasion (outer half invasion), parametrial involvement, and uterine corpus invasion (UCI) were correlated with LNM (P < 0.05). Multivariate analysis showed that tumor lesion of >4 cm (odds ratio (OR) = 2.253, 95% confidence interval (CI): 1.486–3.416, P < 0.001), positive LVSI (OR = 5.353, 95% CI: 3.303–8.676, P < 0.001), deep cervical stromal invasion (OR = 3.461, 95% CI: 2.106–5.688, P < 0.001), and deep UCI (myometrial invasion ≥ 50%) (OR = 3.529, 95% CI: 1.321–9.427, P = 0.012) were independent risk factors for LNM. Limitations: Retrospective nature of the study and limitation to a singlecenter study. Conclusions: Patients with cervical cancer are more likely to have LNM with a tumor size of >4 cm, positive LVSI, deep cervical stromal invasion, or deep UCI. When these risk factors are present, the presence of LNM is possible, and attention should be paid. This study provides a certain reference value for predicting LNM risk for patients with early cervical cancer and for the stratified management of early cervical cancer treatment.

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Section: Introductionmentioning

confidence: 99%

Analysis of Lymph Node Metastasis and Risk Factors in 975 Patients with FIGO 2009 Stage IA–IIA Cervical Cancer

Cao¹,

Kong²,

Li³

et al. 2022

Gynecol Obstet Invest

View full text Add to dashboard Cite

show abstract

“…So which patients are more likely to have LNM? There are many studies about LNM of cervical cancer [9][10][11][12][13][14], but few studies on large samples. Since our hospital is a large obstetrics and gynecology hospital in China, many cervical cancer patients were admitted to our hospital.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Lymph Node Metastasis and Risk Factors in 975 Patients with FIGO 2009 Stage ⅠA-ⅡA Cervical Cancer

Cao

Kong

et al. 2022

Preprint

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Objective To summarize the lymph node metastasis (LNM) rate of patients with stage ⅠA-ⅡA cervical cancer, and further analyze its distribution characteristics and related risk factors. Methods According to FIGO 2009, the clinical data of 975 patients with stage ⅠA-ⅡA cervical cancer treated in our hospital from January 2010 to December 2018 were retrospectively analyzed. The incidence and distribution of LNM were analyzed, and the influencing factors of cervical cancer LNM were analyzed by univariate and multivariate Logistic regression. Results In this study, the LNM rate was 14.8%(144/975), and a total of 20288 lymph nodes were removed, among which 359 lymph nodes had metastasis. According to the statistics of the number and frequency of metastatic lymph nodes in different regions, the metastatic rate was the highest in the parauterine/obturator regions. Univariate analysis showed that pregnancy > 3 times, tumor size > 4cm, gross type, FIGO stage, pathological type, positive lymphovascular space invasion (LVSI), deep cervical stromal invasion (outer 1/2 invasion), parametrial involvement and uterine corpus invasion (UCI) were all correlated with LNM (P <0.05). Multivariate analysis showed tumor lesion >4cm (OR=2.253,95%CI:1.486-3.416,P<0.001), positive LVSI (OR=5.353,95%CI: 3.303-8.676, P <0.001), deep cervical stromal invasion (OR=3.461, 95%CI: 2.106-5.688, P<0.001)and deep UCI (myometrial invasion≥50%) (OR=3.529, 95%CI: 1.321-9.427, P=0.012)were independent risk factors for LNM. Conclusion C ervical cancer patients are more likely to have LNM if the tumor size > 4cm, positive LVSI, deep cervical stromal invasion and deep UCI. In clinical treatment, attention should be paid to the evaluation of LNM.

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Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

Arezzo

Cormio

Mongelli

et al. 2022

Arch Gynecol Obstet

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Purpose: Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show con icting data.Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients.Methods: We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with 10-fold cross-validation. The performances of the algorithm were assessed.Results: Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pretreatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre and post-treatment MRI, presence/absence of fornix in ltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79). Conclusions:We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.Authors contributions: Francesca Arezzo and Vera Loizzi performed the study concepyion. Gerardo Cazzato and Claudio Lombardi contributed to the study design. Material preparation and data collection were performed by Vincenzo Venerito, Ambrogio Cazzola and Michele Mongelli.

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Risk factors of regional lymph node metastasis in patients with cervical cancer

Cited by 4 publications

References 17 publications

Analysis of Lymph Node Metastasis and Risk Factors in 975 Patients with FIGO 2009 Stage IA–IIA Cervical Cancer

Analysis of Lymph Node Metastasis and Risk Factors in 975 Patients with FIGO 2009 Stage IA–IIA Cervical Cancer

Analysis of Lymph Node Metastasis and Risk Factors in 975 Patients with FIGO 2009 Stage ⅠA-ⅡA Cervical Cancer

Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

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