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. 2022 Jun 10;12(6):953.
doi: 10.3390/jpm12060953.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

Raffaella Massafra  1 , Maria Colomba Comes  1 , Samantha Bove  1 , Vittorio Didonna  1 , Gianluca Gatta  2 , Francesco Giotta  3 , Annarita Fanizzi  1 , Daniele La Forgia  4 , Agnese Latorre  2 , Maria Irene Pastena  5 , Domenico Pomarico  1 , Lucia Rinaldi  6 , Pasquale Tamborra  1 , Alfredo Zito  5 , Vito Lorusso  3 , Angelo Virgilio Paradiso  7
Affiliations

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

Raffaella Massafra et al. J Pers Med. .

Abstract

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori "Giovanni Paolo II" in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

Keywords: deep learning; early prediction; magnetic resonance imaging; pathological complete response.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Examples of baseline MR image acquired according to (a) a sagittal view of a public DB patient and (b) an axial view of a private DB patient.
Figure 2
Figure 2
Workflow of the proposed AI framework for early pCR prediction. The approach consists of three main steps: (a) Automatic feature extraction through a pre-trained CNN; (b) Stratified feature selection; (c) Classification on the independent test. The method has been applied on sagittal and axial baseline MRIs separately.
See this image and copyright information in PMC

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