2 research outputs found
Current role of machine learning and radiogenomics in precision neuro-oncology
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Predicting response to neoadjuvant therapy is a vexing challenge in breast
cancer. In this study, we evaluate the ability of deep learning to predict
response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment
dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a
retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast
cancer patients from 5 institutions, we developed and validated a deep learning
approach for predicting pathological complete response (pCR) to HER2-targeted
NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant
chemotherapy at a single institution were used to train (n=85) and tune (n=15)
a convolutional neural network (CNN) to predict pCR. A multi-input CNN
leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was
identified to achieve optimal response prediction within the validation set
(AUC=0.93). This model was then tested on two independent testing cohorts with
pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient
testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and
a 29 patient multicenter trial including data from 3 additional institutions
(AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction
model was found to exceed a multivariable model incorporating predictive
clinical variables (AUC < .65 in testing cohorts) and a model of
semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing
cohorts). The results presented in this work across multiple sites suggest that
with further validation deep learning could provide an effective and reliable
tool to guide targeted therapy in breast cancer, thus reducing overtreatment
among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3
figures in main tex