An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

Wu, Yimin; Ma, Qianqing; Fan, Lifang; Wu, Shujian; Wang, Junli

doi:10.1016/j.acra.2023.07.004

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Briefly, the SHAP methodology allocates a value to each feature, signifying the influence of that feature on the model's prediction relative to a baseline, thus enhancing model interpretability (38). The insights obtained from the SHAP analysis revealed a robust association between elevated Ki-67 levels and the heterogeneity surrounding the tumor, a finding in line with earlier research (33,39). The prominence of peritumoral features in our model supports the notion that regions adjacent to the tumor may offer enhanced predictive insight into Ki-67 expression (33,39).…”

Section: Discussionsupporting

confidence: 88%

“…The insights obtained from the SHAP analysis revealed a robust association between elevated Ki-67 levels and the heterogeneity surrounding the tumor, a finding in line with earlier research (33,39). The prominence of peritumoral features in our model supports the notion that regions adjacent to the tumor may offer enhanced predictive insight into Ki-67 expression (33,39). Specifically, these peritumoral regions often exhibit complex cellular interactions and microenvironment changes that may reflect the aggressiveness of the tumor, thereby serving as significant indicators for predicting Ki-67 levels (40).…”

Section: Discussionsupporting

confidence: 87%

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Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

Wang,

Gao,

et al. 2023

Front. Oncol.

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BackgroundTraditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-centered radiomics offers sufficient data, its unsuitability for repeated examinations, along with limited accessibility and an intratumoral focus, constrain the application of predictive models in evaluating Ki-67 levels.ObjectiveThis study aims to explore ultrasound (US) image-based radiomics, incorporating both intra- and peritumoral features, to develop an interpretable ML model for predicting Ki-67 expression in BC patients.MethodsA retrospective analysis was conducted on 263 BC patients, divided into training and external validation cohorts. From intratumoral and peritumoral regions of interest (ROIs) in US images, 849 distinctive radiomics features per ROI were derived. These features underwent systematic selection to analyze Ki-67 expression relationships. Four ML models-logistic regression, random forests, support vector machine (SVM), and extreme gradient boosting-were formulated and internally validated to identify the optimal predictive model. External validation was executed to ascertain the robustness of the optimal model, followed by employing Shapley Additive Explanations (SHAP) to reveal the significant features of the model.ResultsAmong 231 selected BC patients, 67.5% exhibited high Ki-67 expression, with consistency observed across both training and validation cohorts as well as other clinical characteristics. Of the 1698 radiomics features identified, 15 were significantly correlated with Ki-67 expression. The SVM model, utilizing combined ROI, demonstrated the highest accuracy [area under the receiver operating characteristic curve (AUROC): 0.88], making it the most suitable for predicting Ki-67 expression. External validation sustained an AUROC of 0.82, affirming the model’s robustness above a 40% threshold. SHAP analysis identified five influential features from intra- and peritumoral ROIs, offering insight into individual prediction.ConclusionThis study emphasized the potential of SVM model using radiomics features from both intra- and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 87%

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

Wang,

Gao,

et al. 2023

Front. Oncol.

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“…In a previous study, Ouyang et al demonstrated good discriminatory ability in preoperative prediction of the Ki-67 proliferation index in patients with meningiomas using the radiomics nomogram ( 44 ), but they only extracted imaging features of the tumor parenchyma. The tissues surrounding tumors likewise contain a vast amount of heterogeneous information ( 45 ), especially vasogenic edema around intracranial tumors, which are sites of altered specific molecular, cellular, biological, and radiological information. Studies in other different classification tasks and our previous studies have shown that the region of the tumor combined with peritumoral edema will effectively improve the diagnostic performance of classification models ( 46 , 47 ).…”

Section: Discussionmentioning

confidence: 99%

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Zhao,

Yang,

Bai

et al. 2024

Front. Med.

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ObjectivesTo investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).Materials and methodsMRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.ResultsThe mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.ConclusionIn this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.Clinical relevance statementKi-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

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Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning

Li,

Zhu,

Lin

et al. 2024

Academic Radiology

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An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

Cited by 3 publications

References 37 publications

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning

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