The Surgical Complexity Score (SCS) has been widely used to describe the surgical effort during advanced stage epithelial ovarian cancer (EOC) cytoreduction. Referring to a variety of multi-visceral resections, it best combines the numbers with the complexity of the sub-procedures. Nevertheless, not all potential surgical procedures are described by this score. Lately, the European Society for Gynaecological Oncology (ESGO) has established standard outcome quality indicators pertinent to achieving complete cytoreduction (CC0). There is a need to define what weight all these surgical sub-procedures comprising CC0 would be given. Prospectively collected data from 560 surgically cytoreduced advanced stage EOC patients were analysed at a UK tertiary referral centre.We adapted the structured ESGO ovarian cancer report template. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to model a long list of surgical sub-procedures. We applied the Shapley Additive explanations (SHAP) framework to provide global (cohort) explainability. We used Cox regression for survival analysis and constructed Kaplan-Meier curves. The XGBoost model predicted CC0 with an acceptable accuracy (area under curve [AUC] = 0.70; 95% confidence interval [CI] = 0.63–0.76). Visual quantification of the feature importance for the prediction of CC0 identified upper abdominal peritonectomy (UAP) as the most important feature, followed by regional lymphadenectomies. The UAP best correlated with bladder peritonectomy and diaphragmatic stripping (Pearson’s correlations > 0.5). Clear inflection points were shown by pelvic and para-aortic lymph node dissection and ileocecal resection/right hemicolectomy, which increased the probability for CC0. When UAP was solely added to a composite model comprising of engineered features, it substantially enhanced its predictive value (AUC = 0.80, CI = 0.75–0.84). The UAP was predictive of poorer progression-free survival (HR = 1.76, CI 1.14–2.70, P: 0.01) but not overall survival (HR = 1.06, CI 0.56–1.99, P: 0.86). The SCS did not have significant survival impact. Machine Learning allows for operational feature selection by weighting the relative importance of those surgical sub-procedures that appear to be more predictive of CC0. Our study identifies UAP as the most important procedural predictor of CC0 in surgically cytoreduced advanced-stage EOC women. The classification model presented here can potentially be trained with a larger number of samples to generate a robust digital surgical reference in high output tertiary centres. The upper abdominal quadrants should be thoroughly inspected to ensure that CC0 is achievable.
手术复杂性评分（SCS）已被广泛用于描述晚期上皮性卵巢癌（EOC）肿瘤细胞减灭术中的手术难度。该评分涉及多种多脏器切除操作，能较好地结合手术步骤的数量与各子步骤的复杂性。然而，该评分并未涵盖所有可能的手术操作。近期，欧洲妇科肿瘤学会（ESGO）制定了与实现完全肿瘤细胞减灭（CC0）相关的标准结局质量指标。因此，需要明确构成CC0的所有手术子步骤应被赋予何种权重。本研究在英国一家三级转诊中心前瞻性收集了560例接受手术减瘤的晚期EOC患者数据进行分析。 我们采用结构化ESGO卵巢癌报告模板，运用极限梯度提升（XGBoost）算法对一系列手术子步骤进行建模，并应用沙普利加性解释（SHAP）框架提供群体层面的可解释性分析。采用Cox回归进行生存分析并构建Kaplan-Meier曲线。XGBoost模型预测CC0的准确度可接受（曲线下面积[AUC]=0.70；95%置信区间[CI]=0.63–0.76）。通过可视化量化各特征对CC0预测的重要性，发现上腹部腹膜切除术（UAP）是最重要的预测特征，其次是区域淋巴结清扫术。UAP与膀胱腹膜切除术及膈肌剥除术相关性最强（皮尔逊相关系数>0.5）。盆腔及腹主动脉旁淋巴结清扫术、回盲部切除术/右半结肠切除术均显示明确的拐点效应，可提高实现CC0的概率。在由工程化特征组成的复合模型中单独加入UAP后，模型预测价值显著提升（AUC=0.80，CI=0.75–0.84）。UAP可预测较差的无进展生存期（HR=1.76，CI 1.14–2.70，P=0.01），但对总生存期无显著影响（HR=1.06，CI 0.56–1.99，P=0.86）。SCS对生存结局无显著影响。 机器学习可通过加权评估各手术子步骤的相对重要性，实现对CC0更具预测性特征的操作性筛选。本研究确定UAP是手术减瘤晚期EOC女性患者实现CC0最重要的手术预测指标。本研究提出的分类模型可通过更大样本量训练，在高产出量三级医疗中心形成可靠的数字化手术参考标准。为实现CC0，应对上腹部象限进行彻底探查评估。
肿瘤(癌症)患者之家