Purpose
The recently released EANM/SNMMI guideline, endorsed by several important clinical and imaging societies in the field of breast cancer (BC) care (ACR, ESSO, ESTRO, EUSOBI/ESR, EUSOMA), emphasized the role of [18F]FDG PET/CT in management of patients with no special type (NST) BC. This review identifies and summarizes similarities, discrepancies and novelties of the EANM/SNMMI guideline compared to NCCN, ESMO and ABC recommendations.

Methods
The EANM/SNMMI guideline was based on a systematic literature search and the AGREE tool. The level of evidence was determined according to NICE criteria, and 85 % agreement or higher was reached regarding each statement. Comparisons with NCCN, ESMO and ABC guidelines were examined for specific clinical scenarios in patients with early stage through advanced and metastatic BC.

Results
Regarding initial staging of patients with NST BC, [18F]FDG PET/CT is the preferred modality in the EANM-SNMMI guideline, showing superiority as a single modality to a combination of contrast-enhanced CT of thorax-abdomen-pelvis plus bone scan in head-to-head comparisons and a randomized study. Its use is recommended in patients with clinical stage IIB or higher and may be useful in certain stage IIA cases of NST BC. In NCCN, ESMO, and ABC guidelines, [18F]FDG PET/CT is instead recommended as complementary to conventional imaging to solve inconclusive findings, although ESMO and ABC also suggest [18F]FDG PET/CT can replace conventional imaging for staging patients with high-risk and metastatic NST BC. During follow up, NCCN and ESMO only recommend diagnostic imaging if there is suspicion of recurrence. Similarly, EANM-SNMMI states that [18F]FDG PET/CT is useful to detect the site and extent of recurrence only when there is clinical or laboratory suspicion of recurrence, or when conventional imaging methods are equivocal. The EANM-SNMMI guideline is the first to emphasize a role of [18F]FDG PET/CT for assessing early metabolic response to primary systemic therapy, particularly for HER2+ BC and TNBC. In the metastatic setting, EANM-SNMMI state that [18F]FDG PET/CT may help evaluate bone metastases and determine early response to treatment, in agreement with guidelines from ESMO.

Conclusions
The recently released EANM/SNMMI guideline reinforces the role of [18F]FDG PET/CT in the management of patients with NST BC supported by extensive evidence of its utility in several clinical scenarios.@en

Introduction: There is much literature about the role of 2-[¹⁸F]FDG PET/CT in patients with breast cancer (BC). However, there exists no international guideline with involvement of the nuclear medicine societies about this subject. Purpose: To provide an organized, international, state-of-the-art, and multidisciplinary guideline, led by experts of two nuclear medicine societies (EANM and SNMMI) and representation of important societies in the field of BC (ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA). Methods: Literature review and expert discussion were performed with the aim of collecting updated information regarding the role of 2-[¹⁸F]FDG PET/CT in patients with no special type (NST) BC and summarizing its indications according to scientific evidence. Recommendations were scored according to the National Institute for Health and Care Excellence (NICE) criteria. Results: Quantitative PET features (SUV, MTV, TLG) are valuable prognostic parameters. In baseline staging, 2-[¹⁸F]FDG PET/CT plays a role from stage IIB through stage IV. When assessing response to therapy, 2-[¹⁸F]FDG PET/CT should be performed on certified scanners, and reported either according to PERCIST, EORTC PET, or EANM immunotherapy response criteria, as appropriate. 2-[¹⁸F]FDG PET/CT may be useful to assess early metabolic response, particularly in non-metastatic triple-negative and HER2+ tumours. 2-[¹⁸F]FDG PET/CT is useful to detect the site and extent of recurrence when conventional imaging methods are equivocal and when there is clinical and/or laboratorial suspicion of relapse. Recent developments are promising. Conclusion: 2-[¹⁸F]FDG PET/CT is extremely useful in BC management, as supported by extensive evidence of its utility compared to other imaging modalities in several clinical scenarios.

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Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from different vendors, with diverse characteristics between and even within vendors. Physical properties of these devices and postprocessing of the images can greatly influence the resulting mammogram. This results in the fact that a deep learning model trained on data from one vendor cannot readily be applied to data from another vendor. This paper investigates the use of tailored transfer learning methods based on adversarial learning to tackle this problem. We consider a database of DM exams (mostly bilateral and two views) generated by Hologic and Siemens vendors. We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available. We propose tailored variants of recent state-of-the-art methods for transfer learning which take into account the class imbalance and incorporate knowledge provided by the annotations at exam level. Results of experiments indicate the beneficial effect of transfer learning in both transfer settings. Notably, at 0.02 false positives per image, we achieve a sensitivity of 0.37, compared to 0.30 of a baseline with no transfer. Results indicate that using exam level annotations gives an additional increase in sensitivity.

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Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers try to solve in several ways. In this study we propose an extension of the Learned Primal- Dual algorithm for digital breast tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network consisting of several a€reconstruction blocks', which take in raw sinogram data as the initial input, perform a forward and a backward pass by taking projections and back-projections, and use a convolutional neural network to produce an intermediate reconstruction result which is then improved further by the successive reconstruction block. We extend the architecture by providing breast thickness measurements as a mask to the neural network and allow it to learn how to use this thickness mask. We have trained the algorithm on digital phantoms and the corresponding noise-free/noisy projections, and then tested the algorithm on digital phantoms for varying level of noise. Reconstruction performance of the algorithms was compared visually, using MSE loss and Structural Similarity Index. Results indicate that the proposed algorithm outperforms the baseline iterative reconstruction algorithm in terms of reconstruction quality for both breast edges and internal structures and is robust to noise.

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Computer-aided detection or decision support systems aim to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. Commonly such methods proceed in two steps: Selection of candidate regions for malignancy, and later classification as either malignant or not. In this study, we present a candidate detection method based on deep learning to automatically detect and additionally segment soft tissue lesions in DM. A database of DM exams (mostly bilateral and two views) was collected from our institutional archive. In total, 7196 DM exams (28294 DM images) acquired with systems from three different vendors (General Electric, Siemens, Hologic) were collected, of which 2883 contained malignant lesions verified with histopathology. Data was randomly split on an exam level into training (50%), validation (10%) and testing (40%) of deep neural network with u-net architecture. The u-net classifies the image but also provides lesion segmentation. Free receiver operating characteristic (FROC) analysis was used to evaluate the model, on an image and on an exam level. On an image level, a maximum sensitivity of 0.94 at 7.93 false positives (FP) per image was achieved. Similarly, per exam a maximum sensitivity of 0.98 at 7.81 FP per image was achieved. In conclusion, the method could be used as a candidate selection model with high accuracy and with the additional information of lesion segmentation.

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