Deep Learning Using Endobronchial-Ultrasound-Guided Transbronchial Needle Aspiration Image to Improve the Overall Diagnostic Yield of Sampling Mediastinal Lymphadenopathy

Abstract

Lung cancer is the biggest cause of cancer-related death worldwide. An accurate nodal staging is critical for the determination of treatment strategy for lung cancer patients. Endobronchial-ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has revolutionized the field of pulmonology and is considered to be extremely sensitive, specific, and secure for lung cancer staging through rapid on-site evaluation (ROSE), but manual visual inspection on the entire slide of EBUS smears is challenging, time consuming, and worse, subjective, on a large interobserver scale. To satisfy ROSE’s needs, a rapid, automated, and accurate diagnosis system using EBUS-TBNA whole-slide images (WSIs) is highly desired to improve diagnosis accuracy and speed, minimize workload and labor costs, and ensure reproducibility. We present a fast, efficient, and fully automatic deep-convolutional-neural-network-based system for advanced lung cancer staging on gigapixel EBUS-TBNA cytological WSIs. Each WSI was converted into a patch-based hierarchical structure and examined by the proposed deep convolutional neural network, generating the segmentation of metastatic lesions in EBUS-TBNA WSIs. To the best of the authors’ knowledge, this is the first research on fully automated enlarged mediastinal lymph node analysis using EBUS-TBNA cytological WSIs. We evaluated the robustness of the proposed framework on a dataset of 122 WSIs, and the proposed method achieved a high precision of 93.4%, sensitivity of 89.8%, DSC of 82.2%, and IoU of 83.2% for the first experiment (37.7% training and 62.3% testing) and a high precision of 91.8 ± 1.2, sensitivity of 96.3 ± 0.8, DSC of 94.0 ± 1.0, and IoU of 88.7 ± 1.8 for the second experiment using a three-fold cross-validation, respectively. Furthermore, the proposed method significantly outperformed the three state-of-the-art baseline models, including U-Net, SegNet, and FCN, in terms of precision, sensitivity, DSC, and Jaccard index, based on Fisher’s least significant difference (LSD) test (p<0.001). For a computational time comparison on a WSI, the proposed method was 2.5 times faster than U-Net, 2.3 times faster than SegNet, and 3.4 times faster than FCN, using a single GeForce GTX 1080 Ti, respectively. With its high precision and sensitivity, the proposed method demonstrated that it manifested the potential to reduce the workload of pathologists in their routine clinical practice.

A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis

“…To produce accurate and detailed segmentations, they defined a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer. In recent years, researchers developed a modified FCN-32s approach and demonstrated that it is beneficial for tumor segmentation in the diagnosis of cervical cancer [ 7 ], thyroid cancer [ 6 ], breast cancer [ 8 ], ovarian cancer [ 10 , 11 ], and EBUS [ 9 ]. Shen et al [ 61 ] developed a modified mini-U-net to segment the touching cells accurately in FISH images and demonstrated that the performance is better than the original mini-U-net [ 62 ].…”

“…Chen et al [ 40 ] proposed DeepLabv3+, a deep learning model with an encoder–decoder structure, and proved its efficacy on the Cityscapes dataset [ 65 ], which includes polygonal annotations of instance segmentation for vehicles and people. In our experiment, we compare the proposed method with the state-of-the-art deep learning models, including FCN [ 36 ], Modified FCN [ 6 , 7 , 8 , 9 , 10 , 11 ], U-Net [ 2 ] +InceptionV4 [ 32 ], Ensemble of U-Net with Inception-v4 [ 32 ], Inception-Resnet-v2 encoder [ 32 ], and ResNet-34 encoder [ 33 ], U-Net [ 2 ], SegNet [ 34 ], YOLOv5 [ 35 ], BCNet [ 39 ], CPN [ 37 ], SOLOv2 [ 38 ], and DeepLabv3+ [ 40 ] with three different backbones, including MobileNet [ 41 ], ResNet [ 33 ], and Xception [ 42 ].…”

“…Deep learning (DL), a subset of AI capable of autonomously extracting valuable properties from images to achieve specified tasks, has been repeatedly shown to outperform standard image-processing algorithms, as demonstrated for image classification [ 1 ] or segmentation [ 2 ]. Deep learning (DL) has recently been widely employed for high-performance image-analysis tasks such as object recognition [ 3 , 4 , 5 ], image segmentation [ 2 , 6 , 7 , 8 , 9 ], and image classification [ 1 , 10 , 11 , 12 ]. The ability to distinguish objects and properties in images (for example, cancer cells in biopsy samples) is changing the way clinical samples are evaluated.…”

“…The pathologists produced a reference standard by manually annotating the HER2, ERBB2, and CEN17 signals in the FISH and DISH images. We compare the proposed algorithms with thirteen popular or recently published deep learning methods, including U-Net [ 2 ] +InceptionV4 [ 32 ], Ensemble of U-net with Inception-v4 [ 32 ], Inception-Resnet-v2 encoder [ 32 ], and ResNet-34 encoder [ 33 ], SegNet [ 34 ], Modified FCN [ 6 , 7 , 8 , 9 , 10 , 11 ], YOLOv5 [ 35 ], FCN [ 36 ], CPN [ 37 ], SOLOv2 [ 38 ], BCNet [ 39 ], and Deeplabv3+ [ 40 ] with three different backbones, including MobileNet [ 41 ], ResNet [ 33 ], and Xception [ 42 ] (see Section 4 ). The algorithms we developed are more objective, precise, and unbiased than the current standard manual interpretation results for anti-HER2 target therapy.…”

“…The reference standard was manually generated by annotating tumor cells in Papanicolaou-stained WSIs. In evaluation, the proposed method is compared with three state-of-the-art deep learning methods, including Modified FCN [ 6 , 7 , 8 , 9 , 10 , 11 ], U-Net [ 2 ], and SegNet [ 34 ].…”