A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database

Liu, Michael Z.; Mutasa, Simukayi; Chang, Peter D.; Siddique, Maham; Jambawalikar, Sachin; Ha, Richard

doi:10.1016/j.mri.2020.08.021

Cited by 35 publications

(18 citation statements)

References 16 publications

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“…So far, there have been several attempts to develop approaches based on customised CNNs with the goal to predict pCR to NAC for breast patients using the only pre-treatment MRI exams from I-SPY1 TRIAL public database. Liu et al 32 developed a customised CNN exploiting first post-contrast pre-treatment MRI examinations from 131 patients (40 pCR; 91 non-pCR): a mean AUC value of 0.72 was reached. Similarly, Ravichandran et al 33 designed a customised CNN utilizing pre-contrast and post-contrast pre-treatment MRI scans in isolation or in conjunction.…”

Section: Discussionmentioning

confidence: 99%

Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs

Comes

Fanizzi

Bove

et al. 2021

Sci Rep

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The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.

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Section: Discussionmentioning

confidence: 99%

Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs

Comes

Fanizzi

Bove

et al. 2021

Sci Rep

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“…With the increased use of deep learning techniques in all areas of the biomedical field [ 38 , 39 ], several attempts to solve the early prediction of pCR to NAC in breast cancer patients have been proposed. Liu et al [ 40 ] used the first post-contrast pre-treatment MRI examinations from 131 patients (40 pCR; 91 non-pCR) of the I-SPY1 TRIAL public database to design a CNN-based method to predict pCR. As a result, a mean AUC value of 72% was returned.…”

Section: Discussionmentioning

confidence: 99%

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

Massafra

Comes

Bove

et al. 2022

JPM

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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.

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“…In the latter model, the molecular subtype was added to radiomics [28]. From the I-SPY TRIAL breast MRI database, an implemented CNN algorithm on MRI (N = 131) showed an AUC of 0.72 [29]. Similarly, CNN used in a pre-NACT MRI study by Ha et al…”

Section: Ai and Treatment Response Evaluationmentioning

confidence: 99%

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept

Skarping

Larsson²,

Förnvik

2021

Eur Radiol

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Objectives In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. Methods Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. Results A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. Conclusions Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. Key Points • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.

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A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database

Cited by 35 publications

References 16 publications

Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs

Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs

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

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept

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