ArticleComputers in biology and medicine2020
Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.
Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Preoperative prediction of Ki-67 expression and prognosis in breast cancer based on contrast-enhanced cone-beam breast CT: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.Frontiers in oncology · 2026Review
- Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.Scientific reports · 2025Article
- An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.BMC medical imaging · 2025Article
- Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.BMC medical imaging · 2025Article
- Combined morphology and radiomics of intravoxel incoherent movement as a predictive model for the pathologic complete response before neoadjuvant chemotherapy in patients with breast cancer.Frontiers in oncology · 2025Article
- Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study.European radiology · 2024Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Radiomics nomogram for predicting axillary lymph node metastasis-a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study.La Radiologia medica · 2023Article
- CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.Quantitative imaging in medicine and surgery · 2023Article
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI.Bioengineering (Basel, Switzerland) · 2023Article
- Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy.Scientific reports · 2022Article
- A Convolutional Neural Network Based on Ultrasound Images of Primary Breast Masses: Prediction of Lymph-Node Metastasis in Collaboration With Classification of Benign and Malignant Tumors.Frontiers in physiology · 2022Article
- Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography.Frontiers in oncology · 2022Article
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4 authors.
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Abstract
A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks' lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation.
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