Evaluation of the role of intraoperative frozen section and magnetic resonance imaging in endometrial cancer

Ooka, Reina; Nanki, Yoshiko; Yamagami, Wataru; Kawaida, Miho; Nagai, Shimpei; Hirano, Takuro; Sakai, Kensuke; Makabe, Takeshi; Chiyoda, Tatsuyuki; Kobayashi, Yusuke; Kataoka, Fumio; Aoki, Daisuke

doi:10.1002/ijgo.14389

Cited by 1 publication

(1 citation statement)

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“…Although the diagnosis of LVSI depends on postoperative pathological analysis, there are currently no effective biomarkers to determine LVSI status before or during surgery 11 . The intraoperative frozen section technique was also limited by time and sample size in the detection of LVSI, making it difficult to make an accurate identification 12 . Therefore, predictive models, as a statistical tool, have been widely used to simplify the clinical prediction process by graphing complex regression equations 13 .…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer

Wang,

Pang,

Shi

et al. 2024

Sci Rep

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This study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a clinical risk assessment model based on these factors. Samples were collected from May 2017 to March 2022, including 312 EC patients who received treatment at Xuzhou Medical University Affiliated Hospital of Lianyungang. Of these, 219 cases were collected for the training group and 93 for the validation group. Clinical data and laboratory indicators were analyzed. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used to analyze risk factors and construct risk models. The LVSI and non-LVSI groups showed statistical significance in clinical data and laboratory indicators (P < 0.05). Multivariable logistic regression analysis identified independent risk factors for LVSI in EC, which were myometrial infiltration depth, cervical stromal invasion, lymphocyte count (LYM), monocyte count (MONO), albumin (ALB), and fibrinogen (FIB) (P < 0.05). LASSO regression identified 19 key feature factors for model construction. In the training and validation groups, the risk scores for the logistic and LASSO models were significantly higher in the LVSI group compared with that in the non-LVSI group (P < 0.001). The model was built based on machine learning and can effectively predict LVSI in EC and enhance preoperative decision-making. The reliability of the model was demonstrated by the significant difference in risk scores between LVSI and non-LVSI patients in both the training and validation groups.

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Section: Introductionmentioning

confidence: 99%