Tongyib Sorawit, Saleewong Teerapol, Chaowawanit Woraphot
Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand.
Department of Obstetrics and Gynecology, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok 10300, Thailand.
World J Oncol. 2025 Jul 8;16(4):365-374. doi: 10.14740/wjon2595. eCollection 2025 Aug.
Ovarian cancer, particularly epithelial ovarian cancer (EOC), is one of the deadliest gynecological malignancies due to nonspecific early symptoms and late diagnosis. Current diagnostic tools, while useful, often do not account for regional variations in disease presentation, particularly in Asian populations. This study aimed to develop and validate a new preoperative diagnostic index tailored to the Thai population by integrating complete blood count (CBC), tumor markers, and ultrasound features.
This retrospective cohort study included patients with pathologic pelvic or adnexal masses scheduled for surgery at Vajira Hospital from April 2022 to October 2024. Clinical data, CBC, cancer antigen 125 (CA125) levels, and ultrasound findings were analyzed to develop and validate a diagnostic index (KMUTT-NMU Diagnostic Index for Ovarian Cancer (KN-DIOC)). The model's performance was compared against established indices like Risk of Malignancy Index (RMI), Risk of Ovarian Malignancy Algorithm (ROMA), and Rajavithi-Ovarian Cancer Predictive Score (R-OPS) through multivariate logistic regression, focusing on key predictors.
The study comprised 195 patients divided into 151 for the development dataset and 44 for the validation dataset. The KN-DIOC showed high discriminative ability with an area under curve (AUC) of 0.866, indicating very good capability in differentiating between benign and malignant ovarian masses. The index achieved a sensitivity of 93.75% and a specificity of 78.57%, demonstrating superior performance to traditional diagnostic tools, especially in the validation dataset.
The novel diagnostic index (KN-DIOC), incorporating CBC, ultrasound features, and tumor markers, provides a robust tool for preoperative assessment of ovarian tumors in Thai patients. It offers significant improvements in sensitivity and specificity over existing models, suggesting its potential for broader application in similar settings. This index supports enhanced decision-making in gynecological oncology, potentially leading to better patient outcomes through timely and accurate diagnosis.
卵巢癌，尤其是上皮性卵巢癌（EOC），是最致命的妇科恶性肿瘤之一，原因是早期症状不具特异性且诊断较晚。当前的诊断工具虽有用，但往往未考虑疾病表现的区域差异，尤其是在亚洲人群中。本研究旨在通过整合全血细胞计数（CBC）、肿瘤标志物和超声特征，开发并验证一种针对泰国人群的新型术前诊断指数。
这项回顾性队列研究纳入了2022年4月至2024年10月在瓦吉拉医院计划接受手术的盆腔或附件病理性肿块患者。分析临床数据、CBC、癌抗原125（CA125）水平和超声检查结果，以开发并验证一种诊断指数（朱拉隆功大学-玛希隆大学卵巢癌诊断指数（KN-DIOC））。通过多因素逻辑回归，将该模型的性能与恶性风险指数（RMI）、卵巢恶性风险算法（ROMA）和拉贾维蒂卵巢癌预测评分（R-OPS）等既定指数进行比较，重点关注关键预测因素。
该研究包括195名患者，其中151名用于开发数据集，44名用于验证数据集。KN-DIOC显示出较高的鉴别能力，曲线下面积（AUC）为0.866，表明在区分良性和恶性卵巢肿块方面能力很强。该指数的敏感性为93.75%，特异性为78.57%，显示出优于传统诊断工具的性能，尤其是在验证数据集中。
结合CBC、超声特征和肿瘤标志物的新型诊断指数（KN-DIOC），为泰国患者卵巢肿瘤的术前评估提供了一个强大的工具。它在敏感性和特异性方面比现有模型有显著提高，表明其在类似环境中更广泛应用的潜力。该指数有助于加强妇科肿瘤学中的决策制定，通过及时准确的诊断可能带来更好的患者预后。
