Pilot Feasibility Study: 18 F-DPA-714 PET/CT Macrophage Imaging in Triple-Negative Breast Cancers (EITHICS).

Abstract: Tumor-associated macrophages are targets of interest in triple-negative breast cancer (TNBC). The translocator protein 18 kDa (TSPO) is a sensitive marker for macrophages and holds potential relevance in TNBC stratification. This pilot prospective study (EITHICS, NCT04320030) aimed to assess the potential of TSPO PET/CT imaging using 18 F-DPA-714 in primary TNBC, compared with immunohistochemistry, autoradiography, and TSPO polymorphism.

[Second reading in breast cancer screening program: State of knowledge and future].

Second reading is an important part of breast cancer organized screening program. Image quality control and detection of non-diagnosed cancer by first reader are the two goals of this process. In France, 6 % of all screening cancer are diagnosed by second reading, actually done on screen film.

Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation.

Purpose: The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4.

Materials And Methods: An ensemble of mask region-based convolutional neural networks (Mask-RCNN) combining nodule segmentation and classification were trained to explicitly localize the nodule and generate a probability of the nodule to be malignant on two-dimensional B-mode ultrasound. These probabilities were aggregated at test time to produce final results.

[Surgery or not on an atypical breast lesion? Taking anxiety into account in shared decision support from a prospective cohort of 300 patients].

Introduction: Organized and individual breast screening have been accompanied by an increase in the detection of "atypical breast lesions (ABL)". Recently, the NOMAT multicenter study proposed a predictive model of the risk of developing breast cancer after detection of an ABL in order to avoid surgical removal of "low-risk" lesions. It also aimed to provide information on psychological experience, in particularly anxiety, to assist in the shared medical decision process.

Three artificial intelligence data challenges based on CT and ultrasound.

Purpose: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers.

Materials And Methods: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams.