Aim: This study proposes a method to bypass the requirement of large amounts of original training data to develop a 1- to 4-year breast cancer risk prediction model using transfer learning from a breast cancer detection model with digital mammography images as input. Methods: The study utilizes a labelled dataset of 423 low risk cases and 423 high risk cases, which is considered a small amount of data in terms of AI development, but from the viewpoint of a regional screening organization this represents a large number of high risk cases, given the rarity of such cases compared to the large number of low risk cases available. A breast cancer detection model was used to obtain a latent representation of features extracted from ‘FOR PRESENTATION’ screening mammography images from three systems from a single vendor (Siemens). Dimensionality reduction was performed on the latent space using an Autoencoder architecture. The reduced latent space was then mapped to 1- to 4-year breast cancer risk with a fully-connected model. Results: The resulting model achieved an AUC of 0.77 for differentiating high and low risk cases, outperforming the Tyrer-Cuzick model and achieving state-of-the-art performance. Conclusions: The use of transfer learning from breast cancer detection models can produce image-based breast cancer risk prediction models that are comparable to the state-of-the-art, while requiring only moderate amounts of data.
Purpose: Quantitative measures derived from positron emission tomography (PET) images are subject to statistical uncertainty, depending critically on system parameters, including the spatial resolution of the scanner. Predictions of statistical uncertainty of quantitative measures were compared with measurements.
Approach: Measurements were performed on the dual-ring PET prototype setup at the University of Michigan. The setup consisted of multiple detectors that, in combination, span a system resolution ranging between 1 and 5.5 mm full-width-at-half-maximum (FWHM). A Micro Jaszczak hot-spot phantom with rod diameters between 1.2 and 4.8 mm was imaged and independently reconstructed for different detector combinations. Statistical properties of quantitative measures were evaluated for different reconstructions.
Results: Measured signal-to-noise ratios (SNR) of 108 ± 14, 85 ± 11, and 40 ± 5 for high (0.92 to 0.98 mm FWHM), medium (1.3 to 1.5 mm FWHM), and low (5.5 mm FWHM) resolution detector configurations and 1 million events in general terms followed predications based on the detector resolution.
Conclusions: The unique tomograph configuration allowed for experimental comparison of the impact of spatial resolution on statistical properties of reconstructions in the same setup. An SNR advantage in systems with high resolutions was predicted and confirmed even for object features significantly larger than the detector resolution.
Aim: To develop and subsequently perform a systematic study on the impact of parameter settings on the biological reproducibility and sensitivity of extracted radiomic features from Full Field Digital Mammography (FFDM) images for the task of Breast Cancer Risk assessment. Methods: Cranio-caudal (CC) ”FOR PRESENTATION” images (88 in total, two centers: Slovenia and Belgium) were used for this study. Biological reproducibility of radiomic features was evaluated with two tests: reproducibility of extracted features between left and right breasts and by reproducibility of extracted features between the original and 4 perturbed images. The quantification was done using the intra-class correlation (ICC) coefficient between values of extracted radiomic features. To determine biological sensitivity, AUC between groups with low and high breast cancer risk was calculated. For the selection of optimal radiomic feature parameters, thresholds of 0.75 and 0.7 were defined for ICC and AUC, respectively. Results: Parameters bin Count and distances highly influenced biological reproducibility and sensitivity of specific radiomic features. Parameters weightingNorm and symmetricalGLCM had no effect. Overall, only 12/93 radiomic features passed the reproducibility and sensitivity tests in both centers. For five of these features, parameter ranges were crucial. Reproducibility varied greatly between the centers of Belgium and Slovenia. Conclusions: Rather than single radiomic parameters, parameter ranges were found to be a reasonable description for acceptable biological reproducibility and sensitivity. Overall, 12/93 radiomic features were found to be potential candidates for breast cancer risk prediction tasks, however further analysis is needed before definitive recommendations can be made.
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.