ArticleJournal of medical imaging (Bellingham, Wash.)2019
Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer.Oncology letters · 2023Article
- Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study.Frontiers in oncology · 2022Article
- Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths.Magnetic resonance imaging · 2021Article
- Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset.Academic radiology · 2019Article
- The Application of Radiomics in Breast MRI: A Review.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Radiomic features extracted from magnetic resonance (MR) images have potential for diagnosis and prognosis of breast cancer. However, presentation of lesions on images may be affected by biopsy. Thirty-four nonsize features were extracted from 338 dynamic contrast-enhanced MR images of benign lesions and luminal A cancers (80 benign/34 luminal A prebiopsy; 46 benign/178 luminal A postbiopsy). Feature value distributions were compared by biopsy condition using the Kolmogorov-Smirnov test. Classification performance was assessed by biopsy condition in the task of distinguishing between lesion types using the area under the receiver operating characteristic curve (AUCROC) as performance metric. Superiority and equivalence testing of differences in AUCROC between biopsy conditions were conducted using Bonferroni-Holm-adjusted significance levels. Distributions for most nonsize features for each lesion type failed to show a statistically significant difference between biopsy conditions. Fourteen features outperformed random guessing in classification. Their differences in AUCROC by biopsy condition failed to reach statistical significance, but we were unable to prove equivalence using a margin of
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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 OpenQuestion graph.