Mămuleanu Mădălin, Urhuț Cristiana Marinela, Săndulescu Larisa Daniela, Kamal Constantin, Pătrașcu Ana-Maria, Ionescu Alin Gabriel, Șerbănescu Mircea-Sebastian, Streba Costin Teodor
Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania.
Oncometrics S.R.L., 200677 Craiova, Romania.
Diagnostics (Basel). 2023 Mar 10;13(6):1062. doi: 10.3390/diagnostics13061062.
Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software, and then the signal can be analyzed for further investigations.
The purpose of the study was to build an automated method for extracting TICs and classifying liver lesions in CEUS liver investigations. The cohort contained 50 anonymized video investigations from 49 patients. Besides the CEUS investigations, clinical data from the patients were provided. A method comprising three modules was proposed. The first module, a lesion segmentation deep learning (DL) model, handled the prediction of masks frame-by-frame (region of interest). The second module performed dilation on the mask, and after applying colormap to the image, it extracted the TIC and the parameters from the TIC (area under the curve, time to peak, mean transit time, and maximum intensity). The third module, a feed-forward neural network, predicted the final diagnosis. It was trained on the TIC parameters extracted by the second model, together with other data: gender, age, hepatitis history, and cirrhosis history.
For the feed-forward classifier, five classes were chosen: hepatocarcinoma, metastasis, other malignant lesions, hemangioma, and other benign lesions. Being a multiclass classifier, appropriate performance metrics were observed: categorical accuracy, F1 micro, F1 macro, and Matthews correlation coefficient. The results showed that due to class imbalance, in some cases, the classifier was not able to predict with high accuracy a specific lesion from the minority classes. However, on the majority classes, the classifier can predict the lesion type with high accuracy.
The main goal of the study was to develop an automated method of classifying liver lesions in CEUS video investigations. Being modular, the system can be a useful tool for gastroenterologists or medical students: either as a second opinion system or a tool to automatically extract TICs.
超声造影（CEUS）是肝脏肿瘤诊断中的一种重要成像方式。通过使用造影剂，可以获得更详细的图像。时间强度曲线（TIC）可使用专门软件提取，然后对信号进行分析以作进一步研究。
本研究的目的是构建一种自动方法，用于在CEUS肝脏检查中提取TIC并对肝脏病变进行分类。该队列包含来自49名患者的50份匿名视频检查资料。除了CEUS检查外，还提供了患者的临床数据。提出了一种包含三个模块的方法。第一个模块是病变分割深度学习（DL）模型，逐帧处理掩码预测（感兴趣区域）。第二个模块对掩码进行膨胀操作，在对图像应用颜色映射后，提取TIC及其参数（曲线下面积、达峰时间、平均通过时间和最大强度）。第三个模块是前馈神经网络，预测最终诊断结果。它基于第二个模型提取的TIC参数以及其他数据进行训练：性别、年龄、肝炎病史和肝硬化病史。
对于前馈分类器，选择了五个类别：肝癌、转移瘤、其他恶性病变、血管瘤和其他良性病变。作为一个多类别分类器，观察到了合适的性能指标：分类准确率、微观F1值、宏观F1值和马修斯相关系数。结果表明，由于类别不平衡，在某些情况下，分类器无法高精度预测少数类别的特定病变。然而，对于多数类别，分类器能够高精度预测病变类型。
本研究的主要目标是开发一种在CEUS视频检查中对肝脏病变进行分类的自动方法。该系统具有模块化特点，对于胃肠病学家或医学生可能是一个有用的工具：既可以作为第二意见系统，也可以作为自动提取TIC的工具。
