scholarly journals Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianyu Zhang ◽  
Hui Li ◽  
Chaoyun Wang ◽  
Wen Cheng ◽  
Yuntao Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Molecular Subtypes ◽  
Learning Model ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Test Set ◽  
External Test ◽  
Deep Learning Model

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

scholarly journals Performance Comparison of the Deep Learning and the Human Endoscopist for Bleeding Peptic Ulcer Disease

2021 ◽  
Author(s):  
Hsu-Heng Yen ◽  
Ping-Yu Wu ◽  
Pei-Yuan Su ◽  
Chia-Wei Yang ◽  
Yang-Yuan Chen ◽  
...  
Keyword(s):  
Peptic Ulcer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Endoscopic Therapy ◽  
Learning Model ◽  
Ulcer Bleeding ◽  
Learning Models ◽  
Peptic Ulcer Bleeding ◽  
Ulcer Disease ◽  
Deep Learning Model

Abstract Purpose Management of peptic ulcer bleeding is clinically challenging. Accurate characterization of the bleeding during endoscopy is key for endoscopic therapy. This study aimed to assess whether a deep learning model can aid in the classification of bleeding peptic ulcer disease. Methods Endoscopic still images of patients (n = 1694) with peptic ulcer bleeding for the last 5 years were retrieved and reviewed. Overall, 2289 images were collected for deep learning model training, and 449 images were validated for the performance test. Two expert endoscopists classified the images into different classes based on their appearance. Four deep learning models, including Mobile Net V2, VGG16, Inception V4, and ResNet50, were proposed and pre-trained by ImageNet with the established convolutional neural network algorithm. A comparison of the endoscopists and trained deep learning model was performed to evaluate the model’s performance on a dataset of 449 testing images. Results The results first presented the performance comparisons of four deep learning models. The Mobile Net V2 presented the optimal performance of the proposal models. The Mobile Net V2 was chosen for further comparing the performance with the diagnostic results obtained by one senior and one novice endoscopists. The sensitivity and specificity were acceptable for the prediction of “normal” lesions in both 3-class and 4-class classifications. For the 3-class category, the sensitivity and specificity were 94.83% and 92.36%, respectively. For the 4-class category, the sensitivity and specificity were 95.40% and 92.70%, respectively. The interobserver agreement of the testing dataset of the model was moderate to substantial with the senior endoscopist. The accuracy of the determination of endoscopic therapy required and high-risk endoscopic therapy of the deep learning model was higher than that of the novice endoscopist. Conclusions In this study, the deep learning model performed better than inexperienced endoscopists. Further improvement of the model may aid in clinical decision-making during clinical practice, especially for trainee endoscopist.

Deep learning to predict subtypes of poorly differentiated lung cancer from biopsy whole slide images.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8536-8536
Author(s):  
Gouji Toyokawa ◽  
Fahdi Kanavati ◽  
Seiya Momosaki ◽  
Kengo Tateishi ◽  
Hiroaki Takeoka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Test Set ◽  
Cancer Subtypes ◽  
Independent Test ◽  
Poorly Differentiated ◽  
Test Sets ◽  
Deep Learning Model ◽  
Whole Slide Images

8536 Background: Lung cancer is the leading cause of cancer-related death in many countries, and its prognosis remains unsatisfactory. Since treatment approaches differ substantially based on the subtype, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC) and small cell lung cancer (SCLC), an accurate histopathological diagnosis is of great importance. However, if the specimen is solely composed of poorly differentiated cancer cells, distinguishing between histological subtypes can be difficult. The present study developed a deep learning model to classify lung cancer subtypes from whole slide images (WSIs) of transbronchial lung biopsy (TBLB) specimens, in particular with the aim of using this model to evaluate a challenging test set of indeterminate cases. Methods: Our deep learning model consisted of two separately trained components: a convolutional neural network tile classifier and a recurrent neural network tile aggregator for the WSI diagnosis. We used a training set consisting of 638 WSIs of TBLB specimens to train a deep learning model to classify lung cancer subtypes (ADC, SCC and SCLC) and non-neoplastic lesions. The training set consisted of 593 WSIs for which the diagnosis had been determined by pathologists based on the visual inspection of Hematoxylin-Eosin (HE) slides and of 45 WSIs of indeterminate cases (64 ADCs and 19 SCCs). We then evaluated the models using five independent test sets. For each test set, we computed the receiver operator curve (ROC) area under the curve (AUC). Results: We applied the model to an indeterminate test set of WSIs obtained from TBLB specimens that pathologists had not been able to conclusively diagnose by examining the HE-stained specimens alone. Overall, the model achieved ROC AUCs of 0.993 (confidence interval [CI] 0.971-1.0) and 0.996 (0.981-1.0) for ADC and SCC, respectively. We further evaluated the model using five independent test sets consisting of both TBLB and surgically resected lung specimens (combined total of 2490 WSIs) and obtained highly promising results with ROC AUCs ranging from 0.94 to 0.99. Conclusions: In this study, we demonstrated that a deep learning model could be trained to predict lung cancer subtypes in indeterminate TBLB specimens. The extremely promising results obtained show that if deployed in clinical practice, a deep learning model that is capable of aiding pathologists in diagnosing indeterminate cases would be extremely beneficial as it would allow a diagnosis to be obtained sooner and reduce costs that would result from further investigations.

scholarly journals Deep learning model for fully automated breast cancer detection system from thermograms

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262349
Author(s):  
Esraa A. Mohamed ◽  
Essam A. Rashed ◽  
Tarek Gaber ◽  
Omar Karam
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Detection System ◽  
Diagnostic Technique ◽  
Learning Model ◽  
The Body ◽  
Breast Cancer Detection ◽  
Benchmark Database ◽  
Deep Learning Model

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

scholarly journals Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zhongyi Han ◽  
Benzheng Wei ◽  
Yuanjie Zheng ◽  
Yilong Yin ◽  
Kejian Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Histopathological Images ◽  
Multi Classification ◽  
Deep Learning Model

scholarly journals IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2556
Author(s):  
Liyang Wang ◽  
Yao Mu ◽  
Jing Zhao ◽  
Xiaoya Wang ◽  
Huilian Che
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Test Set ◽  
Non Invasive ◽  
Deep Learning Model

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

scholarly journals Lesion Segmentation in Breast Ultrasound Images using Optimized Marked Watershed Method

2021 ◽  
Author(s):  
Xiaoyan Shen ◽  
He Ma ◽  
Ruibo Liu ◽  
Hong Li ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Stage ◽  
Speckle Noise ◽  
Histogram Equalization ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Early Screening ◽  
Side Window

Abstract Background: Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumors in early stage. However, due to the lack of radiologists with professional skills, ultrasound based breast cancer screening has not been widely used in rural area. Computer-aided diagnosis (CAD) technology can effectively alleviates this problem. Since Breast Ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD system, is challenging.Results: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 BUS images from open source. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model, RDAU–NET. And its’ Accuracy(Acc), Dice efficient(DSC) and Jaccard Index(JI) reached 96.25%, 78.4% and 65.34% on dataset A, and ACC, DC and Sen reached 97.96%, 86.25% and 88.79% on dataset B.Conclusions: We proposed an adaptive morphology snake based on marked watershed(AMSMW) algorithm for BUS images segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. What’s more, since the Rectangular Region of Interest(RROI) in the proposed method is drawn manually, we will consider adding deep learning network to automatically identify RROI, and completely liberate the hands of radiologists.Methods: The proposed method consists of two main steps. In the first step, we used Contrast Limited Adaptive Histogram Equalization(CLAHE) and Side Window Filter(SWF) to preprocess BUS images. Therefore, lesion contours can be effectively highlighted and the influence of noise can be eliminated to a great extent. In the second step, we proposed adaptative morphology snake(AMS) as an embedded segmentation function of AMSMW. It can adjust working parameters adaptively, according to different lesions’ size. By combining segmentation results of AMS with marker region obtained by morphological method, we got the marker region of marked watershed (MW). Finally, we obtained candidate contours by MW. And the best lesion contour was chosen by maximum Average Radial Derivative(ARD).

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