Background and Objective:Machine learning and radiomics (ML/RM) are gaining interest in ovarian cancer (OC) but only a few studies have used these methods to predict treatment response. The objective of this study was to review the literature on the applications of ML/RM in OC assessments, specifically focusing on studies describing algorithms to predict treatment response and survival.Methods:This is a systematic review of the published literature from January 1985 to December 2023 on the use of ML/RM in OC An extensive search of electronic library databases was conducted. Two independent reviewers screened the articles initially by title then by full text. Quality was assessed using the MINORS criteria.p-values were generated using the Pearson’s Chi-squared (x2) test to compare the performances of ML/RM models with traditional statistics.Results:Of the 5576 screened articles, 225 studies were included. Between 2021 and 2023, 49 studies were published, highlighting the rapidly growing interest in ML/RM. Median-quality scores using the MINORS scale were similar between studies published between 1985–2021 and 2021–2023 (both 8). Neural Networks (22.6%) and LASSO (15.3%) were the most common ML/RM algorithms in OC. Among these studies, 13 focused specifically on prediction of treatment response using radiomics. A total of 5113 patients were analyzed. The most common algorithms were Random Forest (4/13) followed by Neural Networks (3/13) and Support Vectors (3/13). Radiomic analysis was used to predict response to neoadjuvant chemotherapy in seven studies, with a median AUC of 0.77 (range 0.72–0.93), while the median AUC was 0.82 (range 0.77–0.89) in the six studies assessing the prediction of optimal or complete cytoreduction. Median model accuracy reported in 7/13 studies was 73% (range 66–98%). Additionally, four studies investigated the use of ML/RM for survival prediction for OC. The XGBoost model had 80.9% accuracy in predicting 5-year survival compared to linear regression, which achieved 79% accuracy. The Random Forest model has 93.7% accuracy in predicting 12-month progression-free survival, compared to 82% for linear regression.Conclusions:In conclusion, we found that the use of ML/RM algorithms is becoming a more frequent method to predict responses to treatment of OC. These models should be validated in a prospective multicenter trial prior to integration into clinical use.
背景与目的：机器学习和影像组学在卵巢癌领域日益受到关注，但仅有少数研究运用这些方法预测治疗反应。本研究旨在系统综述机器学习与影像组学在卵巢癌评估中的应用文献，重点关注预测治疗反应及生存期的算法研究。 方法：本研究系统回顾了1985年1月至2023年12月期间发表的关于机器学习与影像组学在卵巢癌中应用的文献。通过全面检索电子图书馆数据库，由两名独立评审员依次通过标题和全文进行文献筛选。研究质量采用MINORS标准进行评估，并运用皮尔逊卡方检验生成p值，以比较机器学习/影像组学模型与传统统计方法的性能表现。 结果：在筛选的5576篇文献中，最终纳入225项研究。2021年至2023年间发表的研究达49项，凸显了该领域快速发展的趋势。1985-2021年与2021-2023年间发表的研究在MINORS量表上的质量评分中位数均为8分。神经网络（22.6%）和LASSO算法（15.3%）是卵巢癌研究中最常用的机器学习/影像组学算法。其中13项研究专门聚焦于利用影像组学预测治疗反应，共涉及5113例患者。随机森林（4/13）、神经网络（3/13）和支持向量机（3/13）是最常用的预测算法。在七项预测新辅助化疗反应的研究中，影像组学分析获得的曲线下面积中位数为0.77（范围0.72-0.93）；而在六项评估肿瘤细胞减灭术效果预测的研究中，曲线下面积中位数为0.82（范围0.77-0.89）。13项研究中有7项报告了模型准确率，中位值为73%（范围66-98%）。此外，四项研究探讨了机器学习/影像组学在卵巢癌生存预测中的应用：XGBoost模型预测5年生存率的准确率达80.9%，而线性回归模型为79%；随机森林模型预测12个月无进展生存期的准确率为93.7%，线性回归模型为82%。 结论：机器学习与影像组学算法正日益成为预测卵巢癌治疗反应的常用方法。这些模型在投入临床使用前，需通过前瞻性多中心试验进行验证。
肿瘤(癌症)患者之家