MRI-Based Machine Learning Radiomics Can Predict HER2 Expression Level and Pathologic Response after Neoadjuvant Therapy in HER2 Overexpressing Breast Cancer

Bitencourt, Almir; Gibbs, Peter; Saccarelli, Carolina Rossi; Daimiel, Isaac; Gullo, Roberto Lo; Fox, Michael J.; Thakur, Sunitha B.; Pinker, Katja; Morris, Elizabeth A.; Morrow, Monica; Jochelson, Maxine S.

doi:10.2139/ssrn.3582723

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“…The article by Bitencourt et al. published this month in EBioMedicine, is in line with the recent progress in radiomics research in breast cancer patients by focusing on developing a specific radiomic model for HER2-positive breast cancer [5] . Bitencourt and colleagues report the performance of a machine learning model incorporating clinical MRI-based parameters and radiomic MRI features for predicting pCR in HER2 overexpressing breast cancer.…”

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confidence: 66%

Expanding applications of MRI-based radiomics in HER2-positive breast cancer

Park

2020

EBioMedicine

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confidence: 66%

Expanding applications of MRI-based radiomics in HER2-positive breast cancer

Park

2020

EBioMedicine

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“…To date, the development of such decision-support algorithms for breast cancer evaluation has mainly relied on radiomics data derived from DCE-MRI. This development has had applications for the characterization of different molecular profiles of breast cancer [ 19 , 20 ], the prediction of likelihood for axillary lymph node metastatic involvement [ 21 ], and the probability of tumor response to chemotherapy treatment [ 22 ], as well as the differentiation between breast lesions [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. However, the facets of clinical implementation of these support decision models are still to be determined, particularly in the setting of multiparametric MRI.…”

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confidence: 99%

Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists’ Performance

Naranjo

Gibbs

Reiner

et al. 2022

Cancers

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This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

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MRI-Based Machine Learning Radiomics Can Predict HER2 Expression Level and Pathologic Response after Neoadjuvant Therapy in HER2 Overexpressing Breast Cancer

Cited by 2 publications

References 0 publications

Expanding applications of MRI-based radiomics in HER2-positive breast cancer

Expanding applications of MRI-based radiomics in HER2-positive breast cancer

Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists’ Performance

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