ArticleDiagnostics (Basel, Switzerland)2023
Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- Improving differentiation of hemorrhagic brain metastases from non-neoplastic hematomas using radiomics and clinical feature fusion.Neuroradiology · 2025Article
- The next frontier in antibody-drug conjugates: challenges and opportunities in cancer and autoimmune therapy.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI.NPJ breast cancer · 2024Article
- Applying oversampling before cross-validation will lead to high bias in radiomics.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are different breast cancer molecular subtypes with differences in incidence, treatment response and outcome. They are roughly divided into estrogen and progesterone receptor (ER and PR) negative and positive cancers. In this retrospective study, we included 185 patients augmented with 25 SMOTE patients and divided them into two groups: the training group consisted of 150 patients and the validation cohort consisted of 60 patients. Tumors were manually delineated and whole-volume tumor segmentation was used to extract first-order radiomic features. The ADC-based radiomics model reached an AUC of 0.81 in the training cohort and was confirmed in the validation set, which yielded an AUC of 0.93, in differentiating ER/PR positive from ER/PR negative status. We also tested a combined model using radiomics data together with ki67% proliferation index and histological grade, and obtained a higher AUC of 0.93, which was also confirmed in the validation group. In conclusion, whole-volume ADC texture analysis is able to predict hormonal status in breast cancer masses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.