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Abstract：
BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).
METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).
RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.
CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).
RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.
CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
摘要：
背景：及时识别和治疗卵巢癌是患者预后的关键决定因素。在这项研究中,我们开发并验证了基于超声(US)成像的深度学习影像组学列线图(DLR_Nomogram),以准确预测卵巢肿瘤的恶性风险,并比较了DLR_Nomogram与卵巢附件报告和数据系统(O-RADS)的诊断性能.
方法：本研究包括两项研究任务。对于两项任务，患者均以8：2的比例随机分为训练和测试集。在任务1中，我们评估了849例卵巢肿瘤患者的恶性肿瘤风险。在任务2中，我们评估了391例O-RADS4和O-RADS5卵巢肿瘤患者的恶性风险。开发并验证了三个模型来预测卵巢肿瘤中恶性肿瘤的风险。将每个样本的模型的预测结果合并以形成新的特征集，该特征集用作逻辑回归（LR）模型的输入，以构建组合模型。可视化为DLR_列线图。然后，通过受试者工作特征曲线(ROC)评估这些模型的诊断性能.
结果：DLR_Nomogram在预测卵巢肿瘤的恶性风险方面表现出优异的预测性能，如任务1的训练集和测试集的ROC曲线下面积（AUC）值分别为0.985和0.928。其测试集的AUC值低于O-RADS；然而，差异无统计学意义。DLR_列线图在任务2的训练和测试集中分别表现出0.955和0.869的最高AUC值。DLR_Nomogram在Hosmer-Lemeshow测试中对这两个任务均显示出令人满意的拟合性能。决策曲线分析表明，DLR_Nomogram在特定阈值范围内预测恶性卵巢肿瘤方面产生了更大的净临床益处。
结论：基于美国的DLR_Nomogram显示了准确预测卵巢肿瘤恶性风险的能力，表现出与O-RADS相当的预测功效。
方法：本研究包括两项研究任务。对于两项任务，患者均以8：2的比例随机分为训练和测试集。在任务1中，我们评估了849例卵巢肿瘤患者的恶性肿瘤风险。在任务2中，我们评估了391例O-RADS4和O-RADS5卵巢肿瘤患者的恶性风险。开发并验证了三个模型来预测卵巢肿瘤中恶性肿瘤的风险。将每个样本的模型的预测结果合并以形成新的特征集，该特征集用作逻辑回归（LR）模型的输入，以构建组合模型。可视化为DLR_列线图。然后，通过受试者工作特征曲线(ROC)评估这些模型的诊断性能.
结果：DLR_Nomogram在预测卵巢肿瘤的恶性风险方面表现出优异的预测性能，如任务1的训练集和测试集的ROC曲线下面积（AUC）值分别为0.985和0.928。其测试集的AUC值低于O-RADS；然而，差异无统计学意义。DLR_列线图在任务2的训练和测试集中分别表现出0.955和0.869的最高AUC值。DLR_Nomogram在Hosmer-Lemeshow测试中对这两个任务均显示出令人满意的拟合性能。决策曲线分析表明，DLR_Nomogram在特定阈值范围内预测恶性卵巢肿瘤方面产生了更大的净临床益处。
结论：基于美国的DLR_Nomogram显示了准确预测卵巢肿瘤恶性风险的能力，表现出与O-RADS相当的预测功效。
