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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.3
- 수록면
- 1 - 12 (12page)
- DOI
- https://doi.org/10.3802/jgo.2024.35.e17
이용수
초록· 키워드
오류제보하기
Objective: To develop a novel machine learning-based preoperative prediction model forpelvic lymph node metastasis (PLNM) in early-stage cer vical cancer by combining the clinicalfindings and preoperative computerized tomography (CT) of the whole abdomen and pelvis.
Methods: Patients diagnosed with International Federation of Gynecology and Obstetricsstage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinomaof the cer vix who had primar y radical surger y with bilateral pelvic lymphadenectomy fromJanuar y 1, 2003 to December 31, 2020, were included. Seven super vised machine learningalgorithms, including logistic regression, random forest, support vector machine, adaptiveboosting, gradient boosting, extreme gradient boosting, and categor y boosting, were used toevaluate the risk of PLNM.
Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumorsize, higher stage, no prior conization, tumor appearance, adenosquamous histology, andvaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis,pelvic lymph node enlargement, and vaginal metastasis, were significantly associated withPLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area underthe receiver operating characteristics cur ve (86.9%–91.0%), sensitivity (77.4%–82.4%),specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictivevalue (93.0%–94.4%), appeared satisfactor y and comparable among all the algorithms.
After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%,the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNMprediction.
Conclusion: We developed prediction models for PLNM in early-stage cer vical cancer withpromising prediction performance in our setting. Further external validation in otherpopulations is needed with potential clinical applications.
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