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The Breast Cancer overall survival rate has raised impressively in the last 20 years mainly due to improved screening and effectiveness of treatments. This increase in survival paralleled the awareness over the long-lasting impact of the side effects of treatments on patient quality of life, emphasizing the motto “a longer but better life for breast cancer patients”. In breast cancer more strikingly than in other cancers, besides the side effects of systemic treatments, there is the visible impact of surgery and radiotherapy on patients’ body image. This has sparked interest on the development of tools for the aesthetic evaluation of Breast Cancer locoregional treatments, which evolved from manual, subjective approaches to computerized, automated solutions. However, although studied for almost four decades, past solutions were not mature enough to become a standard. Recent advancements in machine learning have inspired trends toward deep-learning-based medical image analysis, also bringing new promises to the field of aesthetic assessment of locoregional treatments. In this paper, a review and discussion of the previous state-of-the-art methods in the field is conducted and the extracted knowledge is used to understand the evolution and current challenges. The aim of this paper is to delve into the current opportunities as well as motivate and guide future research in the aesthetic assessment of Breast Cancer locoregional treatments., Highlights • Past, present and future of objective assessment of aesthetic outcome of breast cancer treatments. • Critical discussion of the role and impact of deep-learning-based methodologies in the field. • Recommendation for future efforts in the objective assessment of aesthetic outcome of breast cancer treatments.

Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectations. However, in the last five years, the field of medical image analysis has undergone a revolution due to the introduction of deep learning convolutional neural networks – a form of artificial intelligence (AI). Because of their considerably higher performance compared to conventional computer aided detection methods, these AI algorithms have resulted in renewed interest in their potential for interpreting breast images in stand-alone mode. For this, first the actual capability of the algorithms, compared to breast radiologists, needs to be well understood. Although early studies have pointed to the comparable performance between AI systems and breast radiologists in interpreting mammograms, these comparisons have been performed in laboratory conditions with limited, enriched datasets. AI algorithms with performance comparable to breast radiologists could be used in a number of different ways, the most impactful being pre-selection, or triaging, of normal screening mammograms that would not need human interpretation. Initial studies evaluating this proposed use have shown very promising results, with the resulting accuracy of the complete screening process not being affected, but with a significant reduction in workload. There is a need to perform additional studies, especially prospective ones, with large screening data sets, to both gauge the actual stand-alone performance of these new algorithms, and the impact of the different implementation possibilities on screening programs., Highlights • AI-based mammography interpretation systems are feasible for stand-alone mode use. • Studies to date have shown that their performance approximates that of radiologists. • Larger scale, prospective screening trials are needed to determine their impact. • Once proven, AI identification of normal cases could reduce the radiologist workload.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient’s tumor on multiparametric MRI is insufficient to predict that patient’s response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation., Highlights • Machine and deep learning have shown potential in predicting neoadjuvant treatment outcomes using multiparametric MRI data. • Machine and deep learning-based early identification of chemotherapy non-responders could improve patient management. • Deep learning techniques using CNN may prove to be more powerful and more robust than traditional machine learning classifiers.

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication., Highlights • In the screening setting, radiology sensitivity is suboptimal. • Artificial intelligence hold promise in cancer diagnosis and prognostication. • Radiomics include feature extraction from clinical images.

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases., Highlights • Artificial intelligence is used in target delineation and treatment planning. • Benefits are expected from individualising dose based on recurrence patterns. • Collaboration between different expertises is essential to generate models.

Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient’s breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice., Highlights • MRI/3D surface scan fusion algorithm to create 3D breast cancer models. • A replicable clinical validation protocol for MRI/3D surface scan fusion algorithms. • Anthropometric study that quantifies breast deformations by area in MRI and 3D scans.

As more artificial intelligence (AI)-enhanced mammography screening tools enter the clinical market, greater focus will be placed on external validation in diverse patient populations. In this viewpoint, we outline lessons learned from prior efforts in this field, the need to validate algorithms on newer screening technologies and diverse patient populations, and conclude by discussing the need for a framework for continuous monitoring and recalibration of these AI tools. Sufficient validation and continuous monitoring of emerging AI tools for breast cancer screening will require greater stakeholder engagement and the creation of shared policies and guidelines.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes, and issues of bias and transferability, data ownership, confidentiality and consent, and legal, moral and professional responsibility. We consider potential effects for patients, including on trust in healthcare, and provide some social science explanations for the apparent rush to implement AI solutions. We conclude by anticipating future directions for AI in breast cancer care. Stakeholders in healthcare AI should acknowledge that their enterprise is an ethical, legal and social challenge, not just a technical challenge. Taking these challenges seriously will require broad engagement, imposition of conditions on implementation, and pre-emptive systems of oversight to ensure that development does not run ahead of evaluation and deliberation. Once artificial intelligence becomes institutionalised, it may be difficult to reverse: a proactive role for government, regulators and professional groups will help ensure introduction in robust research contexts, and the development of a sound evidence base regarding real-world effectiveness. Detailed public discussion is required to consider what kind of AI is acceptable rather than simply accepting what is offered, thus optimising outcomes for health systems, professionals, society and those receiving care., Highlights • Artificial intelligence (AI) systems are rapidly developing for breast cancer care. • Excitement about AI’s potential may encourage a rush to implementation. • AI systems must demonstrate accuracy; accuracy alone cannot justify clinical use. • AI must be evaluated on outcomes, and on other ethical, legal and social criteria. • Health systems adopting AI should do so in rigorous trials with public deliberation.

The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction. In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task. Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P = 6e-4) but better for the remaining ones (P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2–3 mm do not have impact in the dissection step. Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case). The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story., Graphical abstract Image 1, Highlights • Automatic perforator detection was better compared to radiology team when estimating calibre of large vessels for DIEP flap. • Software showed more difficulties when estimating the calibre of narrowest perforators. • Time spent by radiology team evaluating AngioCT scans is reduced from about 2h per patient to 30 min when using the software.