Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI : A feasibility study

We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests. From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (P < 0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor subtype tumors (P < 0.0001) and the triple-negative subtype tumors (P = 0.0039), but not for tumors of the HER2 subtype (P = 0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer

Estimating B1+ in the breast at 7 T using a generic template

Dynamic contrast-enhanced MRI is the workhorse of breast MRI, where the diagnosis of lesions is largely based on the enhancement curve shape. However, this curve shape is biased by RF transmit (B1+) field inhomogeneities. B1+field information is required in order to correct these. The use of a generic, coil-specific B1+template is proposed and tested. Finite-difference time-domain simulations for B1+were performed for healthy female volunteers with a wide range of breast anatomies. A generic B1+template was constructed by averaging simulations based on four volunteers. Three-dimensional B1+maps were acquired in 15 other volunteers. Root mean square error (RMSE) metrics were calculated between individual simulations and the template, and between individual measurements and the template. The agreement between the proposed template approach and a B1+mapping method was compared against the agreement between acquisition and reacquisition using the same mapping protocol. RMSE values (% of nominal flip angle) comparing individual simulations with the template were in the range 2.00-4.01%, with mean 2.68%. RMSE values comparing individual measurements with the template were in the range8.1-16%, with mean 11.7%. The agreement between the proposed template approach and a B1+mapping method was only slightly worse than the agreement between two consecutive acquisitions using the same mapping protocol in one volunteer: the range of agreement increased from ±16% of the nominal angle for repeated measurement to ±22% for the B1+template. With local RF transmit coils, intersubject differences in B1+fields of the breast are comparable to the accuracy of B1+mapping methods, even at 7 T. Consequently, a single generic B1+template suits subjects over a wide range of breast anatomies, eliminating the need for a time-consuming B1+mapping protocol

Computer-aided Diagnosis in Breast MRI: Do Adjunct Features Derived from T2-weighted Images Improve Classification of Breast Masses?

Abstract. In the field of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer, current research efforts in computer-aided diagnosis (CADx) are mainly focused on the temporal series of T1-weighted images acquired during uptake of a contrast agent, processing morphological and kinetic information. Although static T2weighted images are usually part of DCE-MRI protocols, they are seldom used in CADx systems. The aim of this work was to evaluate to what extent T2-weighted images provide complementary information to a CADx system, improving its performance for the task of discriminating benign breast masses from life-threatening carcinomas. In a preliminary study considering 64 masses, inclusion of lesion features derived from T2weighted images increased the classification performance from Az=0.94 to Az=0.99.

Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT

Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural network behavior. This study aimed at assessing the feasibility of regression concept activation to explain detection and classification of multi-modal volumetric data. Proof-of-concept was demonstrated in metastatic prostate cancer patients imaged with positron emission tomography/computed tomography (PET/CT). Multi-modal volumetric concept activation was used to provide global and local explanations. Sensitivity was 80% at 1.78 false positive per patient. Global explanations showed that detection focused on CT for anatomical location and on PET for its confidence in the detection. Local explanations showed promise to aid in distinguishing true positives from false positives. Hence, this study demonstrated feasibility to explain detection and classification of multi-modal volumetric data using regression concept activation