Kucukakcali Zeynep, Cicek Ipek Balikci, Akbulut Sami
Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Turkey.
Department of Surgery, Inonu University Faculty of Medicine, Malatya 44280, Turkey.
J Clin Med. 2025 Sep 2;14(17):6201. doi: 10.3390/jcm14176201.
: Ovarian cancer is often diagnosed at advanced stages due to the absence of specific early symptoms, resulting in high mortality rates. This study aims to develop a robust and interpretable machine learning (ML) model for the early detection of ovarian cancer, enhancing its transparency through the use of the Contrastive Explanation Method (CEM), an advanced technique within the field of explainable artificial intelligence (XAI). : An open-access dataset of 349 patients with ovarian cancer or benign ovarian tumors was used. To improve reliability, the dataset was augmented via bootstrap resampling. A three-layer deep neural network was trained on normalized demographic, biochemical, and tumor marker features. Model performance was measured using accuracy, sensitivity, specificity, F1-score, and the Matthews correlation coefficient. CEM was used to explain the model's classification results, showing which factors push the model toward "Cancer" or "No Cancer" decisions. : The model achieved high diagnostic performance, with an accuracy of 95%, sensitivity of 96.2%, and specificity of 93.5%. CEM analysis identified lymphocyte count (CEM value: 1.36), red blood cell count (1.18), plateletcrit (0.036), and platelet count (0.384) as the strongest positive contributors to the "Cancer" classification, with lymphocyte count demonstrating the highest positive relevance, underscoring its critical role in cancer detection. In contrast, age (change from -0.13 to +0.23) and HE4 (change from -0.43 to -0.05) emerged as key factors in reversing classifications, requiring substantial hypothetical increases to shift classification toward the "No Cancer" class. Among benign cases, a significant reduction in RBC count emerged as the strongest determinant driving a shift in classification. Overall, CEM effectively explained both the primary features influencing the model's classification results and the magnitude of changes necessary to alter its outputs. : Using CEM with ML allowed clear and trustworthy detection of early ovarian cancer. This combined approach shows the promise of XAI in assisting clinicians in making decisions in gynecologic oncology.
卵巢癌由于缺乏特定的早期症状，往往在晚期才被诊断出来，导致死亡率很高。本研究旨在开发一种强大且可解释的机器学习（ML）模型，用于早期检测卵巢癌，并通过使用对比解释方法（CEM）提高其透明度，CEM是可解释人工智能（XAI）领域的一项先进技术。：使用了一个包含349例卵巢癌患者或卵巢良性肿瘤患者的开放获取数据集。为了提高可靠性，通过自助重采样对数据集进行了扩充。在标准化的人口统计学、生化和肿瘤标志物特征上训练了一个三层深度神经网络。使用准确率、灵敏度、特异性、F1分数和马修斯相关系数来衡量模型性能。CEM用于解释模型的分类结果，显示哪些因素促使模型做出“癌症”或“无癌症”的决策。：该模型取得了较高的诊断性能，准确率为95%，灵敏度为96.2%，特异性为93.5%。CEM分析确定淋巴细胞计数（CEM值：1.36）、红细胞计数（1.18）、血小板压积（0.036）和血小板计数（0.384）是“癌症”分类的最强正向贡献因素，其中淋巴细胞计数显示出最高的正相关性，突出了其在癌症检测中的关键作用。相比之下，年龄（从-0.13变为+0.23）和人附睾蛋白4（从-0.43变为-0.05）成为逆转分类的关键因素，需要大幅假设性增加才能将分类转向“无癌症”类别。在良性病例中，红细胞计数的显著降低是推动分类转变的最强决定因素。总体而言，CEM有效地解释了影响模型分类结果的主要特征以及改变其输出所需的变化幅度。：将CEM与ML结合使用能够清晰且可靠地检测早期卵巢癌。这种联合方法显示了XAI在协助临床医生进行妇科肿瘤学决策方面的前景。
