A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Wang, Ching-Wei; Lee, Yu-Ching; Chang, Cheng‐Chang; Lin, Yun-Lian; Liou, Yi-An; Hsu, Po-Chao; Chang, Chun-Chieh; Sai, Aung-Kyaw-Oo; Wang, Chih‐Hung; Chao, Tai-Kuang

doi:10.3390/cancers14071651

Cited by 25 publications

(19 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fully convolutional network (FCN) was introduced by Shelhamer et al [ 33 ] and it was demonstrated to be successful in the tumor segmentation of breast cancer metastases [ 36 ], thyroid cancer [ 37 ], cervical cancer [ 38 ], and ovarian cancer [ 39 ]. Inspired by the fully convolutional network (FCN) framework of Shelhamer et al [ 33 ], the proposed deep learning network architecture has two improvements, as shown in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…They used planned dose distributions, pre-radiotherapy CT, and PET images as the CNN inputs to predict treatment response. Wang et al [ 141 ] proposed a weakly supervised deep-learning method for guiding ovarian cancer treatment and identifying an effective biomarker on immunohistochemical (IHC) stained histopathological dataset.…”

Section: DL Methods By Applicationsmentioning

confidence: 99%

“…Yang et al [ 6 ] proposed a deep-learning-based predicting model to differentiate immunotherapy responders from nonresponders in non-small-cell lung cancer patients by using CT images. Wang et al [ 141 ] proposed an automatic weakly supervised deep learning framework for patient selection and guiding ovarian cancer treatment using effective biomarkers for bevacizumab on histopathological WSIs by considering the cost, potential adverse effects, including hypertension, proteinuria, bleeding, thromboembolic events, poor wound healing, and gastrointestinal perforation.…”

Section: DL Methods By Applicationsmentioning

confidence: 99%

“…Wang et al [ 141 ] proposed an automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. They developed a hybrid deep learning framework and weakly supervised deep-learning models for each potential biomarker.…”

Section: DL Methods By Anatomical Application Areasmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Deep Learning for Precision Oncology

2022

Self Cite

View full text Add to dashboard Cite

Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Lin

et al. 2022

Cancers

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Cited by 25 publications

References 61 publications

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

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

A Survey on Deep Learning for Precision Oncology

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

Contact Info

Product

Resources

About