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Abstract：
BACKGROUND: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.
METHODS: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample\'s predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.
RESULTS: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.
CONCLUSIONS: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.
METHODS: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample\'s predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.
RESULTS: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.
CONCLUSIONS: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.
摘要：
背景：术前准确识别卵巢肿瘤亚型对患者来说是必要的，因为它使医生能够定制精确和个性化的管理策略。所以,我们已经开发了一种基于超声（US）的多类预测算法，用于区分良性，边界线，和恶性卵巢肿瘤。
方法：我们以8:2的比例将849例卵巢肿瘤患者的数据随机分为训练和测试集。对US图像上的感兴趣区域进行分割，并提取和筛选手工制作的影像组学特征。我们在多类别分类中应用了一休法。我们将最佳特征输入到机器学习（ML）模型中，并构建了放射学签名（Rad_Sig）。将最大修剪的卵巢肿瘤切片的US图像输入到预先训练的卷积神经网络(CNN)模型中。经过内部增强和复杂的算法，每个样本的预测概率，称为深度迁移学习签名(DTL_Sig)，产生了。分析临床基线数据。训练集中的统计上显著的临床参数和US语义特征用于构建临床签名(Clinic_Sig)。Rad_Sig的预测结果，DTL_Sig,将每个样本的Clinic_Sig融合为新的特征集，为了建立组合模型，即,深度学习基因组签名(DLR_Sig)。我们使用接受者工作特征（ROC）曲线和ROC曲线下面积（AUC）来估计多类分类模型的性能。
结果：训练集包括440个良性，44边界线，和196例恶性卵巢肿瘤。测试集包括109个良性，11边界线，和49例恶性卵巢肿瘤。DLR_Sig三类预测模型具有最佳的总体和特定类别分类性能，微观和宏观平均AUC分别为0.90和0.84，在测试集上。鉴定AUC的类别是良性的0.84，0.85和0.83，边界线，卵巢恶性肿瘤，分别。在混乱矩阵中，Clinic_Sig和Rad_Sig的分类器模型不能识别卵巢交界性肿瘤。然而,DLR_Sig确定的卵巢交界性肿瘤和恶性肿瘤的比例最高，分别为54.55%和63.27%，分别。
结论：基于US的DLR_Sig的三级预测模型可以区分良性，边界线，和恶性卵巢肿瘤。因此,它可以指导临床医生确定卵巢肿瘤患者的差异化管理.
方法：我们以8:2的比例将849例卵巢肿瘤患者的数据随机分为训练和测试集。对US图像上的感兴趣区域进行分割，并提取和筛选手工制作的影像组学特征。我们在多类别分类中应用了一休法。我们将最佳特征输入到机器学习（ML）模型中，并构建了放射学签名（Rad_Sig）。将最大修剪的卵巢肿瘤切片的US图像输入到预先训练的卷积神经网络(CNN)模型中。经过内部增强和复杂的算法，每个样本的预测概率，称为深度迁移学习签名(DTL_Sig)，产生了。分析临床基线数据。训练集中的统计上显著的临床参数和US语义特征用于构建临床签名(Clinic_Sig)。Rad_Sig的预测结果，DTL_Sig,将每个样本的Clinic_Sig融合为新的特征集，为了建立组合模型，即,深度学习基因组签名(DLR_Sig)。我们使用接受者工作特征（ROC）曲线和ROC曲线下面积（AUC）来估计多类分类模型的性能。
结果：训练集包括440个良性，44边界线，和196例恶性卵巢肿瘤。测试集包括109个良性，11边界线，和49例恶性卵巢肿瘤。DLR_Sig三类预测模型具有最佳的总体和特定类别分类性能，微观和宏观平均AUC分别为0.90和0.84，在测试集上。鉴定AUC的类别是良性的0.84，0.85和0.83，边界线，卵巢恶性肿瘤，分别。在混乱矩阵中，Clinic_Sig和Rad_Sig的分类器模型不能识别卵巢交界性肿瘤。然而,DLR_Sig确定的卵巢交界性肿瘤和恶性肿瘤的比例最高，分别为54.55%和63.27%，分别。
结论：基于US的DLR_Sig的三级预测模型可以区分良性，边界线，和恶性卵巢肿瘤。因此,它可以指导临床医生确定卵巢肿瘤患者的差异化管理.
