인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) is associated with improved survival in recurrent ovarian cancer (ROC) but carries a high risk of postoperative complications. Accurate perioperative risk stratification remains an unmet need. To develop and internally validate a perioperative risk model for postoperative complications in ROC patients using information available by the end of surgery (pre- and intra-operative data), and to compare logistic regression (LR) and artificial neural networks (ANN) as possible predictive models. A retrospective analysis of 71 patients treated with CRS and HIPEC between 2011 and 2022 was performed. Clinical, surgical, and perioperative variables were analysed. Predictors were restricted to variables available at or before the end of the index operation, which prevents information leakage by using only data available at prediction time. LR and ANN models were developed and assessed with cross-validation. Performance reporting followed TRIPOD (Type b) and TRIPOD + AI, with Brier score, and calibration slope/intercept from out‑of‑fold (OOF) predictions. Thresholded metrics (accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve, AUC) were summarised at a prespecified probability cut-off. Exploratory univariate odds ratio analyses with Holm-adjusted p-values were used to explore procedure-complication associations. Postoperative complications occurred in 45% of patients. LR identified blood loss (p = 0.005) and number of procedures (p = 0.042) as significant predictors of complications. The LR model achieved an accuracy of 66.2%, precision of 64.3%, recall of 56.2%, F1 score of 60.0%, and AUC of 0.700. The ANN model achieved an accuracy of 97.2%, precision of 94.3%, recall of 100%, F1 score of 97.1%, and AUC of 0.967. Hysterectomy with adnexa (OR = 11.67, p = 0.035) and metastasectomy (OR = 7.42, p = 0.042) were significantly associated with higher postoperative complication rates. ANN demonstrated superior predictive performance compared to LR in identifying postoperative complications after CRS and HIPEC, as indicated by ROC analysis. Combining traditional statistical modelling with modern machine learning may enhance ROC for perioperative risk stratification after CRS with HIPEC. A well-calibrated, interpretable LR model together with a highly discriminative ANN could enable more tailored allocation of intensive care resources and earlier identification of high-risk patients, potentially improving the safety of this demanding but beneficial treatment. However, external multicentre validation is required before clinical implementation.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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