336 research outputs found
Optimizing the axillary management in breast cancer:from imaging to surgery
This thesis aimed to provide insights into different aspects of the axillary management of breast cancer patients: optimizing the axillary lymph node staging with non-invasive imaging modalities, a prediction of axillary pathologic complete response (pCR) based on the breast tumour response, an overview of axillary pCR rates for different breast cancer subtypes for de-escalation and escalation of treatment, and the implications of axillary response for the indication of post-mastectomy radiation therapy and the timing of breast reconstruction. Part I of this thesis focused on optimizing the axillary lymph node staging by non-invasive imaging modalities in the preoperative setting and following neoadjuvant systemic therapy. Part II of this thesis focused on the prediction of axillary pCR following neoadjuvant systemic therapy for different breast cancer subtypes. Part III of this thesis focuses on the implications of residual axillary lymph node disease following neoadjuvant systemic therapy for post-mastectomy radiation therapy and immediate breast reconstruction
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
Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art
Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI
PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review
Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence
PET/MRI in Breast Cancer
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147750/1/jmri26298_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147750/2/jmri26298.pd
Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?
Manual delineation of volumes of interest (VOIs) by experts is considered the
gold-standard method in radiomics analysis. However, it suffers from inter- and
intra-operator variability. A quantitative assessment of the impact of
variations in these delineations on the performance of the radiomics predictors
is required to develop robust radiomics based prediction models. In this study,
we developed radiomics models for the prediction of pathological complete
response to neoadjuvant chemotherapy in patients with two different breast
cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired
prior to treatment (baseline MRI scans). Different mathematical operations such
as erosion, smoothing, dilation, randomization, and ellipse fitting were
applied to the original VOIs delineated by experts to simulate variations of
segmentation masks. The effects of such VOI modifications on various steps of
the radiomics workflow, including feature extraction, feature selection, and
prediction performance, were evaluated. Using manual tumor VOIs and radiomics
features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was
achieved for human epidermal growth receptor 2 positive and triple-negative
breast cancer, respectively. For smoothing and erosion, VOIs yielded the
highest number of robust features and the best prediction performance, while
ellipse fitting and dilation lead to the lowest robustness and prediction
performance for both breast cancer subtypes. At most 28% of the selected
features were similar to manual VOIs when different VOI delineation data were
used. Differences in VOI delineation affects different steps of radiomics
analysis, and their quantification is therefore important for development of
standardized radiomics research
- …