ArticleFrontiers in oncology2022
Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- Multiclassifier Radiomics Analysis of Ultrasound for Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma in Children.International journal of medical sciences · 2023Trial
- Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2026Article
- Article
- A deep learning model based on chest CT to predict benign and malignant breast masses and axillary lymph node metastasis.Biomolecules & biomedicine · 2025Article
- Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.Scientific reports · 2024Article
- A prediction model based on dual-layer spectral detector computed tomography for distinguishing nonluminal from luminal invasive breast cancer.Quantitative imaging in medicine and surgery · 2024Article
- Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.Scientific reports · 2024Article
- Application of SHAP for Explainable Machine Learning on Age-Based Subgrouping Mammography Questionnaire Data for Positive Mammography Prediction and Risk Factor Identification.Healthcare (Basel, Switzerland) · 2023Article
- Radiomics analysis in medical imaging research.Journal of medical radiation sciences · 2023Article
- Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis.Annals of translational medicine · 2022Article
- Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.Translational cancer research · 2022Review
- Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma.Frontiers in oncology · 2022Article
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6 authors.
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Abstract
Objectives: The molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively. Methods: A total of 300 breast cancer patients (153 luminal types and 147 non-luminal types) who underwent routine chest CT examination were included in the study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model. Results: During internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish luminal from the non-luminal type were 0.842 [95% CI: 0.728-0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640-0.866], 0.713, 0.767, and 0.676 in the test set. Conclusion: The radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.
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