Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data

Niu, Sihua; Huang, Jianhua; Li, Jia; Li, Xueling; Wang, Dan; Wang, Yingyan; Shen, Huiming; Qi, Min; Xiao, Yingying; Guan, Mengyao; Li, Diancheng; Liu, Feifei; Wang, Xiuming; Xiong, Yu; Gao, Siqi; Wang, Xue; Yu, Pengfei; Zhu, Jiaan

doi:10.21037/qims-20-919

Cited by 13 publications

(9 citation statements)

References 25 publications

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“…The results were analyzed and plotted using Python v. 3.8 (Python Software Foundation, Wilmington, DE, USA). The areas under the receiver operating characteristic (ROC) curves ( 25 ) were compared using the DeLong test via the Python rpy2 package. A P value less than 0.05 was considered statistically significant.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the statistical optimization strategies and clinical evaluation of an artificial intelligence-based automated diagnostic system for thyroid nodule screening

Guo¹,

Chang²,

Zhao³

et al. 2023

Quant Imaging Med Surg

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Background: Thyroid cancer is the most common endocrine cancer in the world. Accurately distinguishing between benign and malignant thyroid nodules is particularly important for the early diagnosis and treatment of thyroid cancer. This study aimed to investigate the best possible optimization strategies for an already-trained artificial intelligence (AI)-based automated diagnostic system for thyroid nodule screening and, in addition, to scrutinize the clinically relevant limitations using stratified analysis to better standardize the application in clinical workflows. Methods:We retrospectively reviewed a total of 1,092 ultrasound images associated with 397 thyroid nodules collected from 287 patients between April 2019 and January 2021, applying postoperative pathology as the gold standard. We applied different statistical approaches, including averages, maximums, and percentiles, to estimate per-nodule-based malignancy scores from the malignancy scores per image predicted by AI-SONIC Thyroid v. 5.3.0.2 (Demetics Medical Technology Ltd., Hangzhou, China) system, and we assessed its diagnostic efficacy on nodules of different sizes or tumor types with per-nodule analysis using performance metrics.Results: Of the 397 thyroid nodules, 272 thyroid nodules were overrepresented by malignant nodules according to the results of the surgical pathological examinations. Taking the median of the malignancy scores per image to estimate the nodule-based score with a cutoff value of 0.56 optimized for the means of sensitivity and specificity, the AI-based automated detection system demonstrated slightly lower sensitivity, significantly higher specificity (almost independent of nodule size), and similar accuracy to that of the senior radiologist. Both the AI system and the senior radiologist demonstrated higher sensitivity in diagnosing smaller nodules (≤25 mm) and comparable diagnostic performances for larger nodules. The mean diagnostic time per nodule of the AI system was 0.146 s, which was in sharp contrast to the 2.8 to 4.5 min of the radiologists.Conclusions: Using our optimization strategy to achieve nodule-based diagnosis, the AI-SONIC Thyroid automated diagnostic system demonstrated an overall diagnostic accuracy equivalent to that of senior ^ ORCID: 0000-0003-1212-9525.radiologists. Thus, it is expected that it can be used as a reliable auxiliary diagnostic method by radiologists for the screening and preoperative evaluation of malignant thyroid nodules.

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Section: Methodsmentioning

confidence: 99%

Assessment of the statistical optimization strategies and clinical evaluation of an artificial intelligence-based automated diagnostic system for thyroid nodule screening

Guo¹,

Chang²,

Zhao³

et al. 2023

Quant Imaging Med Surg

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“…Figure 1 shows how we labeled the lung nodules with high quality, and the CT scan of a patient with a lung nodule in the upper left lung (the right half of the patient's left lung appears in the CT image). Figure 1 [1][2][3][4][5][6] represents the order of the images. In each image, the left half is the unlabeled image and the right half is the labeled image.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Manual feature extraction processes are also complicated and fail to effectively mine the abundant information contained in images. Recently, the application of artificial intelligence in medicine has addressed several complex medical challenges, and image detection has emerged as a new research focus (4)(5)(6). In image classification, AI techniques identify and differentiate images based on different features extracted from a large number of images for the purpose of intelligent diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A lightweight neural network for lung nodule detection based on improved ghost module

Yang¹,

Cai²,

Luo³

et al. 2023

Quant Imaging Med Surg

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Background: Computer tomography images are the preferred method of preoperative evaluation for lung disease. However, it remains difficult to detect and recognize nodules accurately and efficiently due to poor data imaging quality, heavy reliance on physician experience and the need for more human-computer interaction for diagnosis. Currently, image nodule detection based on deep convolutional neural networks has gained much momentum.Methods: To alleviate doctors' tremendous labor in the diagnosis procedure, and improve the accuracy of intelligent detection of lung nodules, we improved GhostNet and proposed a lightweight neural network for object detection for lung nodule image detection. Firstly, the bneck structure in the backbone feature extraction network is adopted and improved from the structure of MobileNetV3. The weights are adjusted by changing the initial channel attention mechanism and introducing a spatial-temporal attention mechanism. Then, in the enhanced feature extraction part, we mainly use depth-separable convolution blocks to replace the 3×3 convolution of the original network for the purpose of reducing the model parameters, and make more improvements based on the network structure to enhance the applicability of the network. Diagnostic precision, recall, F1-score, mAP and parameter count were calculated.Results: According to our lightweight neural network, F1-score, precision, and recall were 0.87, 86.34%, and 86.69%, respectively. Based on our dataset, the Yolov4-GNet network proposed in this research outperforms the current neural networks on both precision and recall as well as F1. Conclusions:The lung nodule detection method proposed in this research not only simplifies the processing of images, but also outperforms comparable methods in nodule detection rate and positioning accuracy, providing a new way for lung nodule detection.

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“…Deep learning approaches are increasingly being used for medical image segmentation and quantitative information regarding the morphology and textural features of lesions [ 9 ]. Several neural network architectures developed from convolutional neural networks (CNNs) have shown satisfactory segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography

Guo,

Chen,

Yang

et al. 2024

BioMed Eng OnLine

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Background Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing.

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Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data

Cited by 13 publications

References 25 publications

Assessment of the statistical optimization strategies and clinical evaluation of an artificial intelligence-based automated diagnostic system for thyroid nodule screening

Assessment of the statistical optimization strategies and clinical evaluation of an artificial intelligence-based automated diagnostic system for thyroid nodule screening

A lightweight neural network for lung nodule detection based on improved ghost module

A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography

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