Department of Surgical Sciences, 9314University of Turin, Torino, Italy.
Breast, Gynecology and Reconstructive Surgery Unit, 419441Curie Institute, Paris, France.
Cancer Control. 2023 Jan-Dec;30:10732748231159553. doi: 10.1177/10732748231159553.
In patients affected by epithelial ovarian cancer (EOC) complete cytoreduction (CC) has been associated with higher survival outcomes. Artificial intelligence (AI) systems have proved clinical benefice in different areas of healthcare.
To systematically assemble and analyze the available literature on the use of AI in patients affected by EOC to evaluate its applicability to predict CC compared to traditional statistics.
Data search was carried out through PubMed, Scopus, Ovid MEDLINE, Cochrane Library, EMBASE, international congresses and clinical trials. The main search terms were: Artificial Intelligence AND surgery/cytoreduction AND ovarian cancer. Two authors independently performed the search by October 2022 and evaluated the eligibility criteria. Studies were included when data about Artificial Intelligence and methodological data were detailed.
A total of 1899 cases were analyzed. Survival data were reported in 2 articles: 92% of 5-years overall survival (OS) and 73% of 2-years OS. The median area under the curve (AUC) resulted 0,62. The model accuracy for surgical resection reported in two articles reported was 77,7% and 65,8% respectively while the median AUC was 0,81. On average 8 variables were inserted in the algorithms. The most used parameters were age and Ca125.
AI revealed greater accuracy compared against the logistic regression models data. Survival predictive accuracy and AUC were lower for advanced ovarian cancers. One study analyzed the importance of factors predicting CC in recurrent epithelial ovarian cancer and disease free interval, retroperitoneal recurrence, residual disease at primary surgery and stage represented the main influencing factors. Surgical Complexity Scores resulted to be more useful in the algorithms than pre-operating imaging.
AI showed better prognostic accuracy if compared to conventional algorithms. However further studies are needed to compare the impact of different AI methods and variables and to provide survival informations.
在患有上皮性卵巢癌(EOC)的患者中,完全肿瘤细胞减灭术(CC)与更高的生存结果相关。人工智能(AI)系统已在医疗保健的不同领域证明了临床益处。
系统地收集和分析关于 AI 在患有 EOC 的患者中使用的现有文献,以评估其与传统统计学相比预测 CC 的适用性。
通过 PubMed、Scopus、Ovid MEDLINE、Cochrane 图书馆、EMBASE、国际会议和临床试验进行数据搜索。主要搜索词为:人工智能 AND 手术/肿瘤细胞减灭术 AND 卵巢癌。两位作者于 2022 年 10 月独立进行了搜索,并评估了纳入标准。当详细报告了关于人工智能和方法学数据的数据时,研究被纳入。
共分析了 1899 例病例。有 2 篇文章报道了生存数据:5 年总生存率(OS)为 92%,2 年 OS 为 73%。曲线下面积(AUC)的中位数为 0.62。两篇文章报道的手术切除的模型准确性分别为 77.7%和 65.8%,中位数 AUC 为 0.81。平均有 8 个变量被插入到算法中。最常用的参数是年龄和 Ca125。
与逻辑回归模型数据相比,AI 显示出更高的准确性。在晚期卵巢癌中,生存预测准确性和 AUC 较低。有一项研究分析了预测复发性上皮性卵巢癌和无疾病间期、腹膜后复发、初次手术残留疾病和分期的因素对 CC 的重要性,这些因素是主要的影响因素。与术前影像学相比,手术复杂性评分在算法中更有用。
与传统算法相比,AI 显示出更好的预后准确性。然而,需要进一步的研究来比较不同 AI 方法和变量的影响,并提供生存信息。