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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-23252-3 |
| _version_ | 1828330420163313664 |
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| author | Yuka Asami Kengo Hiranuma Daisuke Takayanagi Maiko Matsuda Yoko Shimada Mayumi Kobayashi Kato Ikumi Kuno Naoya Murakami Masaaki Komatsu Ryuji Hamamoto Takashi Kohno Akihiko Sekizawa Koji Matsumoto Tomoyasu Kato Hiroshi Yoshida Kouya Shiraishi |
| author_facet | Yuka Asami Kengo Hiranuma Daisuke Takayanagi Maiko Matsuda Yoko Shimada Mayumi Kobayashi Kato Ikumi Kuno Naoya Murakami Masaaki Komatsu Ryuji Hamamoto Takashi Kohno Akihiko Sekizawa Koji Matsumoto Tomoyasu Kato Hiroshi Yoshida Kouya Shiraishi |
| author_sort | Yuka Asami |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T20:39:48Z |
| format | Article |
| id | doaj.art-65bcdc6953a2477681527a5dcc956d6e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T20:39:48Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-65bcdc6953a2477681527a5dcc956d6e2022-12-22T02:30:55ZengNature PortfolioScientific Reports2045-23222022-11-0112111010.1038/s41598-022-23252-3Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancerYuka Asami0Kengo Hiranuma1Daisuke Takayanagi2Maiko Matsuda3Yoko Shimada4Mayumi Kobayashi Kato5Ikumi Kuno6Naoya Murakami7Masaaki Komatsu8Ryuji Hamamoto9Takashi Kohno10Akihiko Sekizawa11Koji Matsumoto12Tomoyasu Kato13Hiroshi Yoshida14Kouya Shiraishi15Division of Genome Biology, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDepartment of Radiation Oncology, National Cancer Center HospitalDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Medical AI Research and Development, National Cancer Center Research InstituteDivision of Genome Biology, National Cancer Center Research InstituteDepartment of Obstetrics and Gynecology, Showa University School of MedicineDepartment of Obstetrics and Gynecology, Showa University School of MedicineDepartment of Gynecology, National Cancer Center HospitalDivision of Diagnostic Pathology, National Cancer Center HospitalDivision of Genome Biology, National Cancer Center Research InstituteAbstract 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.https://doi.org/10.1038/s41598-022-23252-3 |
| spellingShingle | Yuka Asami Kengo Hiranuma Daisuke Takayanagi Maiko Matsuda Yoko Shimada Mayumi Kobayashi Kato Ikumi Kuno Naoya Murakami Masaaki Komatsu Ryuji Hamamoto Takashi Kohno Akihiko Sekizawa Koji Matsumoto Tomoyasu Kato Hiroshi Yoshida Kouya Shiraishi Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer Scientific Reports |
| title | Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer |
| title_full | Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer |
| title_fullStr | Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer |
| title_full_unstemmed | Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer |
| title_short | Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer |
| title_sort | predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer |
| url | https://doi.org/10.1038/s41598-022-23252-3 |
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