Women with locally advanced breast cancer are generally given neoadjuvant chemotherapy (NAC), in which chemotherapy and optionally targeted treatment is administered prior to the surgery. In current clinical practice, prior to the start of NAC, it is not possible to accurately predict whether the patient is likely to encounter metastasis after treatment. Metastasis (or distant recurrence) is the development of secondary malignant growths at a distance from a primary site of cancer. We explore the use of tumor thickness features computed from MRI imaging to predict the risk of post treatment metastasis. We performed a retrospective study on a cohort of 1738 patients who were administered NAC. Of these patients, 551 patients had magnetic resonance imaging (MRI) before the treatment started. We analyzed the multimodal data using deep learning and classical machine learning algorithms to increase the set of discriminating features. Our results demonstrate the ability to predict metastasis prior to the initiation of NAC treatment, using each modality alone. We then show the significant improvement achieved by combining the clinical and MRI modalities, as measured by the AUC, sensitivity, and specificity. The overall combined model achieved 0.747 AUC and 0.379 specificity at a sensitivity operation point of 0.99. We also use interpretability methods to explain the models and identify important clinical features for the early prediction of metastasis.
Women who are diagnosed with breast cancer are referred to Neoadjuvant Chemotherapy Treatment (NACT) before surgery when treatment guidelines indicate that. Achieving complete response in this treatment is correlated with improved overall survival compared with those experiencing a partial or no response at all. In this paper, we explore multi modal clinical and radiomics metrics including quantitative features from medical imaging, to assess in advance complete response to NACT. Our dataset consists of a cohort from Institut Curie with 1383 patients; from which 528 patients have mammogram imaging. We analyze the data via image processing, machine learning and deep learning algorithms to increase the set of discriminating features and create effective models. Our results show ability to classify the data in this problem settings, using the clinical data. We then show the possible improvement we may achieve in combining clinical and mammogram data measured by the AUC, sensitivity and specificity. We show that for our cohort the overall model achieves sensitivity 0.954 while keeping good specificity of 0.222. This means that almost all patients that achieved pathologic complete response will also be correctly classified by our model. At the same time, for 22% of the patients, the model could correctly predict in advance that they won’t achieve pathologic complete response, enabling them to reassess in advance this treatment. We also describe our system architecture that includes the Biomedical Framework, a platform to create configurable reusable pipelines and expose them as micro-services on-premise or in-thecloud.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.