Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble

Zhu, Xueliang; Ying, Jie; Yang, Haima; Fu, Le; Li, Boyang; Jiang, Bin

doi:10.1016/j.compbiomed.2021.104487

Cited by 28 publications

(8 citation statements)

References 31 publications

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“…The risk stratification of the patient will rise to a medium-to-high risk level when deep muscle infiltration occurs. Prior research has also demonstrated that EC patients with deep myometrial invasion (DMI) have a notably lower overall survival rate compared to those without DMI [ [16] , [17] , [18] , [19] ]. This highlights the importance of accurately assessing the depth of myometrium invasion for proper tumor staging and prognosis prediction.…”

Section: Discussionmentioning

confidence: 99%

Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis

Chen,

Wang,

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis

Chen,

Wang,

et al. 2024

Heliyon

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“…Moreover, it is a great challenge for the radiologist to label the dataset as well as for the predictive performance of the model since the UCL is difficult to find on most MRI images. Zhu et al established an ML model for identifying deep MI, obtaining ACC, SEN, SPE, and F1 scores of 93.7%, 94.7%, 93.3%, and 87.8% [ 17 ]. However, the feature extraction used to train the model is tedious (geometric features, first-order histogram-based features, GLCM-based features) and requires human intervention, which is not fully automated.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, there has been increasingly attention to employing CAD methods based on machine learning (ML) to help radiologists analyze MRI images of EC patients. Examples include assessment of the depth of myometrial infiltration (MI) [ 15 , 16 ], classification of stage IA and stage IB in patients with FIGO stage I [ 17 ], and detection of LNM [ 18 , 19 ]. In Chen et al’s study, a YOLOv3 model was used to detect uterine and tumor regions, and then the detected regions were cropped out and fed into a CNN model for classification, with an Accuracy (ACC) of 84.78% with the Sensitivity (SEN) of 66.67% and the Specificity (SPE) of 87.50% [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images

Xiong,

Chen,

Lin

et al. 2023

BioMed Eng OnLine

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To classify early endometrial cancer (EC) on sagittal T2-weighted images (T2WI) by determining the depth of myometrial infiltration (MI) using a computer-aided diagnosis (CAD) method based on a multi-stage deep learning (DL) model. This study retrospectively investigated 154 patients with pathologically proven early EC at the institution between January 1, 2018, and December 31, 2020. Of these patients, 75 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA and 79 were in FIGO stage IB. An SSD-based detection model and an Attention U-net-based segmentation model were trained to select, crop, and segment magnetic resonance imaging (MRl) images. Then, an ellipse fitting algorithm was used to generate a uterine cavity line (UCL) to obtain MI depth for classification. In the independent test datasets, the uterus and tumor detection model achieves an average precision rate of 98.70% and 87.93%, respectively. Selecting the optimal MRI slices method yields an accuracy of 97.83%. The uterus and tumor segmentation model with mean IOU of 0.738 and 0.655, mean PA of 0.867 and 0.749, and mean DSC of 0.845 and 0.779, respectively. Finally, the CAD method based on the calculated MI depth reaches an accuracy of 86.9%, a sensitivity of 81.8%, and a specificity of 91.7% for early EC classification. In this study, the CAD method implements an end-to-end early EC classification and is found to be on par with radiologists in terms of performance. It is more intuitive and interpretable than previous DL-based CAD methods.

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“…Hidden myometrial invasion detection using multi-feature fusion and probabilistic SVM was done by Zhu et al [48]. SVM provided a high F1-Score of 79.1% over a dataset which had a positive to negative sample ratio of 1:3.…”

Section: Existing Methodsmentioning

confidence: 99%

Image processing and machine learning approaches for the automatic diagnosis of endometrial cancer

Libish²

2022

ijhs

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In many medical imaging based diagnosis, Deep Learning (DL) algorithms play an essential role. A DL algorithm can be used to identify abnormal and normal cells in the uterus's endometrium in order to discover Endometrial Cancer (EC) cells. EC is difficult to diagnose since it develops without causing any symptoms. DL algorithm can distinguish between normal, abnormal, and malignant cells, producing more accurate findings than screening by hand procedures such as liquid cytology and Pap smear test. For the accurate and easier detection of EC cells, DL employs multiple architectures. The findings of an analysis and survey of the many forms of DL architecture, as well as their accuracy and performance, are addressed in this work. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the most modalities of advanced imaging used for the non-invasive diagnosis of EC. EC is considered as the fourth most prevalent malignancy in women and one of the common most gynaecological cancers. Diagnostic imaging in clinical evaluation with has not been proven yet to be precise enough to substitute surgical staging in determining the spread of cancer. It may allow for improved surgical process optimization and a more customised therapeutic plan.

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Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble

Cited by 28 publications

References 31 publications

Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis

Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis

A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images

Image processing and machine learning approaches for the automatic diagnosis of endometrial cancer

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