Wang Ching-Wei, Lin Kuan-Yu, Lin Yi-Jia, Khalil Muhammad-Adil, Chu Kai-Lin, Chao Tai-Kuang
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Cancers (Basel). 2022 Oct 28;14(21):5312. doi: 10.3390/cancers14215312.
According to the World Health Organization Report 2022, cancer is the most common cause of death contributing to nearly one out of six deaths worldwide. Early cancer diagnosis and prognosis have become essential in reducing the mortality rate. On the other hand, cancer detection is a challenging task in cancer pathology. Trained pathologists can detect cancer, but their decisions are subjective to high intra- and inter-observer variability, which can lead to poor patient care owing to false-positive and false-negative results. In this study, we present a soft label fully convolutional network (SL-FCN) to assist in breast cancer target therapy and thyroid cancer diagnosis, using four datasets. To aid in breast cancer target therapy, the proposed method automatically segments human epidermal growth factor receptor 2 (HER2) amplification in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images. To help in thyroid cancer diagnosis, the proposed method automatically segments papillary thyroid carcinoma (PTC) on Papanicolaou-stained fine needle aspiration and thin prep whole slide images (WSIs). In the evaluation of segmentation of HER2 amplification in FISH and DISH images, we compare the proposed method with thirteen deep learning approaches, including U-Net, U-Net with InceptionV5, Ensemble of U-Net with Inception-v4, Inception-Resnet-v2 encoder, and ResNet-34 encoder, SegNet, FCN, modified FCN, YOLOv5, CPN, SOLOv2, BCNet, and DeepLabv3+ with three different backbones, including MobileNet, ResNet, and Xception, on three clinical datasets, including two DISH datasets on two different magnification levels and a FISH dataset. The result on DISH breast dataset 1 shows that the proposed method achieves high accuracy of 87.77 ± 14.97%, recall of 91.20 ± 7.72%, and F1-score of 81.67 ± 17.76%, while, on DISH breast dataset 2, the proposed method achieves high accuracy of 94.64 ± 2.23%, recall of 83.78 ± 6.42%, and F1-score of 85.14 ± 6.61% and, on the FISH breast dataset, the proposed method achieves high accuracy of 93.54 ± 5.24%, recall of 83.52 ± 13.15%, and F1-score of 86.98 ± 9.85%, respectively. Furthermore, the proposed method outperforms most of the benchmark approaches by a significant margin (p <0.001). In evaluation of segmentation of PTC on Papanicolaou-stained WSIs, the proposed method is compared with three deep learning methods, including Modified FCN, U-Net, and SegNet. The experimental result demonstrates that the proposed method achieves high accuracy of 99.99 ± 0.01%, precision of 92.02 ± 16.6%, recall of 90.90 ± 14.25%, and F1-score of 89.82 ± 14.92% and significantly outperforms the baseline methods, including U-Net and FCN (p <0.001). With the high degree of accuracy, precision, and recall, the results show that the proposed method could be used in assisting breast cancer target therapy and thyroid cancer diagnosis with faster evaluation and minimizing human judgment errors.
根据《世界卫生组织2022年报告》，癌症是最常见的死亡原因，全球近六分之一的死亡与之相关。早期癌症诊断和预后对于降低死亡率至关重要。另一方面，癌症检测在癌症病理学中是一项具有挑战性的任务。训练有素的病理学家能够检测出癌症，但其判断存在较高的观察者内和观察者间变异性，这可能因假阳性和假阴性结果导致患者护理不佳。在本研究中，我们使用四个数据集提出了一种软标签全卷积网络（SL-FCN），以辅助乳腺癌靶向治疗和甲状腺癌诊断。为辅助乳腺癌靶向治疗，该方法可自动分割荧光原位杂交（FISH）和双原位杂交（DISH）图像中的人表皮生长因子受体2（HER2）扩增情况。为帮助诊断甲状腺癌，该方法可自动在巴氏染色细针穿刺和薄层液基全切片图像（WSIs）上分割甲状腺乳头状癌（PTC）。在评估FISH和DISH图像中HER2扩增的分割情况时，我们将该方法与13种深度学习方法进行比较，包括U-Net、带有InceptionV5的U-Net、带有Inception-v4的U-Net集成、Inception-Resnet-v2编码器和ResNet-34编码器、SegNet、FCN、改进的FCN、YOLOv5、CPN、SOLOv2、BCNet以及带有三种不同骨干网络（包括MobileNet、ResNet和Xception）的DeepLabv3+，涉及三个临床数据集，包括两个不同放大倍数水平的DISH数据集和一个FISH数据集。DISH乳腺癌数据集1的结果表明，该方法的准确率达到87.77±14.97%，召回率为91.20±7.72%，F1分数为81.67±17.76%；而在DISH乳腺癌数据集2上，该方法的准确率达到94.64±2.23%，召回率为83.78±6.42%，F1分数为85.14±6.61%；在FISH乳腺癌数据集上，该方法的准确率达到93.54±5.24%，召回率为83.52±13.15%，F1分数为86.98±9.85%。此外，该方法在很大程度上优于大多数基准方法（p<0.001）。在评估巴氏染色WSIs上PTC的分割情况时，该方法与三种深度学习方法进行比较，包括改进的FCN、U-Net和SegNet。实验结果表明，该方法的准确率达到99.99±0.01%，精确率为92.02±16.6%，召回率为90.90±14.25%，F1分数为89.82±14.92%，显著优于包括U-Net和FCN在内的基线方法（p<0.001）。凭借高度的准确性、精确率和召回率，结果表明该方法可用于辅助乳腺癌靶向治疗和甲状腺癌诊断，实现更快的评估并最大限度减少人为判断错误。
