A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Feng, Ming; Zhao, Yu; Chen, Jie; Zhao, Ting; Martino, Juan; Fan, Yingying; Lin, Zhenyu; Yao, Jianhua; Bu, Hong

doi:10.21037/qims-22-220

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Among them, 11 studies ( 21 , 30 , 31 , 33 , 36 , 45 , 55 , 61 – 63 , 65 ) adopted the method of external validation, and the remaining studies carried out internal validation by random sampling. Nine studies ( 20 – 22 , 31 , 32 , 35 , 45 , 52 ) did not clearly describe the variable screening method and mainly adopted single-factor and multi-factor logistic regression.…”

Section: Resultsmentioning

confidence: 99%

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Ren,

Chen,

Hong

et al. 2023

Front. Oncol.

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BackgroundThe early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.MethodsA systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.ResultsThis systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.ConclusionAlthough the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

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Section: Resultsmentioning

confidence: 99%

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Ren,

Chen,

Hong

et al. 2023

Front. Oncol.

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“…Research also indicates that model-based schemes can make better utilize radiographic information to predict lymph node diseases ( 31 - 33 ). Das et al integrated clinical parameters and radiomics features extracted from three ROIs of gross tumor volume (GTV), peritumoral volume (PTV), and LNs using different methods to create a variety of nomograms for predicting preoperative LNM in adenocarcinoma, and the compared the predictive efficacy of each model ( 34 ).…”

Section: Discussionmentioning

confidence: 99%

Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study

Xie,

Yang,

Niu

et al. 2024

Quant Imaging Med Surg

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Background Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. Methods A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. Results LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Conclusions Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

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“…Sarah Fremond et al proposed an interpretable endometrial cancer classification system, which can further predict four molecular subtypes of endometrial cancer through self-supervised learning [ 32 ]. Min Feng et al develop a deep learning model for predicting lymph node metastasis from histopathologic images of endometrial cancer, which is believed to predict metastatic status and improve accuracy [ 33 ]. In summary, In summary, we note that deep learning models are commonly used for radiology images [ 34 , 35 ] and histopathology images [ 36 – 38 ] in endometrial studies.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles

Wang,

Li,

Shi

et al. 2024

PLoS ONE

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Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objective guidance efficiency. But endometrial cytopathology images often come from different medical centers with different staining styles. It decreases the generalization ability of deep learning models in cytopathology images analysis, leading to poor performance. This study presents a robust automated screening framework for endometrial cancer that can be applied to cytopathology images with different staining styles, and provide an objective diagnostic reference for cytopathologists, thus contributing to clinical treatment. We collected and built the XJTU-EC dataset, the first cytopathology dataset that includes segmentation and classification labels. And we propose an efficient two-stage framework for adapting different staining style images, and screening endometrial cancer at the cellular level. Specifically, in the first stage, a novel CM-UNet is utilized to segment cell clumps, with a channel attention (CA) module and a multi-level semantic supervision (MSS) module. It can ignore staining variance and focus on extracting semantic information for segmentation. In the second stage, we propose a robust and effective classification algorithm based on contrastive learning, ECRNet. By momentum-based updating and adding labeled memory banks, it can reduce most of the false negative results. On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. Our method robustly predicts endometrial cancer on cytopathologic images with different staining styles, which will further advance research in endometrial cancer screening and provide early diagnosis for patients. The code will be available on GitHub.

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A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Cited by 7 publications

References 40 publications

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study

A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles

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