Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

Zhang, Yan; Gong, Cuilan; Zheng, Ling; Li, Xiaoyan; Yang, Xiaomei

doi:10.1155/2021/1148309

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Deep learning research on uterine lesions has mainly focused on MRIs and ultrasound images. Zhang et al trained and evaluated the LeNet-5 neural network using MRIs from 158 patients with endometrial cancer, achieving an area-under-the-curve value of 0.897 [ 23 ]. Dong et al applied a U-Net neural network to MRI scans, seeking the depth of endometrial cancer invasion and achieving a model accuracy of 79.2%, which was not significantly different from the diagnostic accuracy achieved by radiologists [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Zhao

Yuan

et al. 2023

Diagnostics

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Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.

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Section: Discussionmentioning

confidence: 99%

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Zhao

Yuan

et al. 2023

Diagnostics

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“…Convolutional neural network (CNN) has been developed from MLP to be more efficient and accurate in processing image data, but requires higher sample sizes and are currently commonly used for segmentation and identification. Zhang et al 29 construct a CNN model for EC prediction based on features. The AUC is 0.889 in the test group with good performance, indicating that the CNN radiomics model can be used as a noninvasive marker for EC prediction.…”

Section: Discussionmentioning

confidence: 99%

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer

Ding,

Sun,

Meng

et al. 2023

Sci Rep

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To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

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“…On the other hand, the use of AI-models during the radiological diagnosis of endometrial cancer was also addressed in a few works. In 2021, Zhang et al analyzed preoperative MRI from 158 patients with endometrial cancer and designed a CNN architecture to predict endometrial cancer based on radiomic features from MRI [56]. The AUC of the radiomic model was 0.897 in the training group.…”

Section: Endometrial Cancermentioning

confidence: 99%

Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

Brandão,

Mendes,

Martins

et al. 2024

JCM

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Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women’s reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

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Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

Cited by 10 publications

References 15 publications

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer

Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

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