Dataset Information

Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer.

ABSTRACT: Lymph node metastasis (LNM) is a well-established prognostic factor in endometrial cancer (EC). We aimed to construct a model that predicts LNM and prognosis using preoperative factors such as myometrial invasion (MI), enlarged lymph nodes (LNs), histological grade determined by endometrial biopsy, and serum cancer antigen 125 (CA125) level using two independent cohorts consisting of 254 EC patients. The area under the receiver operating characteristic curve (AUC) of the constructed model was 0.80 regardless of the machine learning techniques. Enlarged LNs and higher serum CA125 levels were more significant in patients with low-grade EC (LGEC) and LNM than in patients without LNM, whereas deep MI and higher CA125 levels were more significant in patients with high-grade EC (HGEC) and LNM than in patients without LNM. The predictive performance of LNM in the HGEC group was higher than that in the LGEC group (AUC = 0.84 and 0.75, respectively). Patients in the group without postoperative pathological LNM and positive LNM prediction had significantly worse relapse-free and overall survival than patients with negative LNM prediction (log-rank test, P < 0.01). This study showed that preoperative clinicopathological factors can predict LNM with high precision and detect patients with poor prognoses. Furthermore, clinicopathological factors associated with LNM were different between HGEC and LGEC patients.

SUBMITTER: Asami Y

PROVIDER: S-EPMC9643353 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer.

Asami Yuka Y Hiranuma Kengo K Takayanagi Daisuke D Matsuda Maiko M Shimada Yoko Y Kato Mayumi Kobayashi MK Kuno Ikumi I Murakami Naoya N Komatsu Masaaki M Hamamoto Ryuji R Kohno Takashi T Sekizawa Akihiko A Matsumoto Koji K Kato Tomoyasu T Yoshida Hiroshi H Shiraishi Kouya K

Scientific reports 20221108 1

Lymph node metastasis (LNM) is a well-established prognostic factor in endometrial cancer (EC). We aimed to construct a model that predicts LNM and prognosis using preoperative factors such as myometrial invasion (MI), enlarged lymph nodes (LNs), histological grade determined by endometrial biopsy, and serum cancer antigen 125 (CA125) level using two independent cohorts consisting of 254 EC patients. The area under the receiver operating characteristic curve (AUC) of the constructed model was 0. ...[more]

PMID: 36347927

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Project description:Introduction: Renal cell carcinoma (RCC) is one of the most common urinary tumors. The risk of metastasis for patients with RCC is about 1/3, among which 30-40% have lymph node metastasis, and the existence of lymph node metastasis will greatly reduce the survival rate of patients. However, the necessity of lymph node dissection is still controversial at present. Therefore, a new predictive model is urgently needed to judge the risk of lymph node metastasis and guide clinical decision making before operation. Method: We retrospectively collected the data of 189 patients who underwent retroperitoneal lymph node dissection or enlarged lymph node resection due to suspected lymph node metastasis or enlarged lymph nodes found during an operation in Tongji Hospital from January 2016 to October 2021. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (lasso) regression analyses were used to identify preoperative predictors of pathological lymph node positivity. A nomogram was established to predict the probability of lymph node metastasis in patients with RCC before surgery according to the above independent predictors, and its efficacy was evaluated with a calibration curve and a DCA analysis. Result: Among the 189 patients, 54 (28.60%) were pN1 patients, and 135 (71.40%) were pN0 patients. Three independent impact factors were, finally, identified, which were the following: age (OR = 0.3769, 95% CI = 0.1864-0.7622, p < 0.01), lymph node size according to pre-operative imaging (10-20 mm: OR = 15.0040, 95% CI = 1.5666-143.7000, p < 0.05; >20 mm: OR = 4.4013, 95% CI = 1.4892-7.3134, p < 0.01) and clinical T stage (cT1-2 vs. cT3-4) (OR = 3.1641, 95% CI = 1.0336-9.6860, p < 0.05). The calibration curve and DCA (Decision Curve Analysis) showed the nomogram of this predictive model had good fitting. Conclusions: Low age, large lymph node size in pre-operative imaging and high clinical T stage can be used as independent predictive factors of pathological lymph node metastasis in patients with RCC. Our predictive nomogram using these factors exhibited excellent discrimination and calibration.

Dataset Information

Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer.

Publications

Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer.

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