ArticleBMC cancer2020
Diagnosis of triple negative breast cancer based on radiomics signatures extracted from preoperative contrast-enhanced chest computed tomography.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 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
- Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study.BMC cancer · 2023Trial
- Assessment of breast cancer and feasibility of subtyping of breast cancer using thoracoabdominal staging photon-counting detector computed tomography.Scientific reports · 2026Article
- Assessment of CuFeSeNano convergence · 2025Article
- CT-based radiomics for predicting breast cancer radiotherapy side effects.Scientific reports · 2024Article
- Article
- Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer.La Radiologia medica · 2024Article
- Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.BJR artificial intelligence · 2024Review
- The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2024Article
- Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT.International journal of computer assisted radiology and surgery · 2023Article
- A Simultaneous MultiparametricCancers · 2022Article
- Predictive Value ofDiagnostics (Basel, Switzerland) · 2022Article
- Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease.Journal of clinical medicine · 2022Review
- Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study.Frontiers in oncology · 2022Article
- AI in spotting high-risk characteristics of medical imaging and molecular pathology.Precision clinical medicine · 2021Review
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5 authors.
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Abstract
backgroundTo explore the diagnostic value of radiomics features of preoperative computed tomography (CT) for triple negative breast cancer (TNBC) for better treatment of patients with breast cancer.
methodsA total of 890 patients with breast cancer admitted to our hospital from June 2016 to January 2018 were analyzed. They were diagnosed by surgery and pathology to have mass and invasive breast cancer and had contrast-enhanced chest CT examination before operation. 300 patients were randomly selected for the study, including 100 TNBC and 200 non-TNBC (NTNBC) patients. Among them 180 were used in discovery group and 120 were used in validation group. The molecular subtypes of breast cancer in the patients were determined immunohistochemistrially. Radiomics features were extracted from three dimensional CT-images. The LASSO logistic method was used to select image features and calculate radiomics scores. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of radiomics scores for TNBC.
resultsFive image features were found to be related to TNBC subtype (P < 0.001). These image features based-radiomic signatures had good predictive values for TNBC with the areas under ROC curve (AUC) of 0.881 (95% CI: 0.781-0.921) in the discovery group and 0.851 (95% CI: 0.761-0.961) in the validation group, respectively. The sensitivities and specificities were 0.767, and 0.873 in the discovery group and 0.785 and 0.915 in the validation group.
conclusionsRadiomic signature based on preoperative CT is capable of distinguishing patients with TNBC and NTNBC. It adds additional value for conventional chest contrast-enhanced CT and helps plan the strategy for clinical treatment of the patients.
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