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Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI‐based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n= 35), pathologically complete response (pCR) prediction (n= 15), lymph node metastasis (LNM) detection (n= 7) and recurrence rate prediction (n= 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual‐targeted treatment. However, radiomics is still in the pre‐clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited. This article contributes to the ongoing development of breast cancer detection and classification by discussing the novel field of radiomics, which can aid in personalised, precision medicine for patients. This review presents current research that considers radiomics for breast cancer subtype classification, prediction of pathologically complete response, detection of lymph node metastasis and recurrence prediction, all of which impact treatment and patient outcomes.

Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essential task; however, diagnosis can include ‘detection’ and ‘interpretation’ errors. Studies to reduce these errors have shown the feasibility of using convolution neural networks (CNNs). This narrative review presents recent studies in diagnosing mammographic malignancy investigating the accuracy and reliability of these CNNs. Databases including ScienceDirect, PubMed, MEDLINE, British Medical Journal and Medscape were searched using the terms ‘convolutional neural network or artificial intelligence’, ‘breast neoplasms [MeSH] or breast cancer or breast carcinoma’ and ‘mammography [MeSH Terms]’. Articles collected were screened under the inclusion and exclusion criteria, accounting for the publication date and exclusive use of mammography images, and included only literature in English. After extracting data, results were compared and discussed. This review included 33 studies and identified four recurring categories of studies: the differentiation of benign and malignant masses, the localisation of masses, cancer‐containing and cancer‐free breast tissue differentiation and breast classification based on breast density. CNN's application in detecting malignancy in mammography appears promising but requires further standardised investigations before potentially becoming an integral part of the diagnostic routine in mammography. This article explores the current research and developments in using convolutional neural networks for mammography diagnosis. Current developments are focused on four distinct categories; however, limitations of this artificial intelligence withhold it from clinical implementation.

Post-hysterectomy histopathological examination is currently the main diagnostic tool for differentiating uterine sarcomas from leiomyomas. This study aimed to investigate the diagnostic accuracy of preoperative quantitative metrics based on T2-weighted sequences and contrast-enhanced MRI (CE-MRI) for distinguishing uterine sarcomas from leiomyomas. The institutional review board approved the study. Sixty-five women confirmed to have a total of 105 lesions participated. Routine pelvic MRI sequences, T2 map and CE-MRI images were performed preoperatively using a 3 T MR scanner. Six quantitative metrics—T2 mapping parameter, T2 scaled ratio, tumor myometrium contrast ratio on T2, tumor psoas contrast ratio on T2, tumor myometrium contrast-enhanced ratio, and tumor psoas contrast-enhanced ratio—were extracted from the acquired image sets. Chi-square test was used to compare the percentage of malignant lesions with the central necrosis to the corresponding percentage for the benign masses. Using the area under receiver operating characteristic (AUC) curve, the performance of different metrics for distinguishing uterine sarcomas from leiomyomas was measured. Moreover, for each metric, we extracted the optimal cut-off value. The values of sensitivity, specificity, negative predictive value, and positive predictive value were calculted for the classifiers based on different metrics. The average age, average lesion size, and proportion of premenopausal women in benign and malignant groups were comparable in our dataset. The signal intensity of uterine sarcomas at T2-weighted sequences was significantly higher than that of leiomyomas (p< 0.001), while intensity at T1-weighted sequences exhibited no significant difference between the two masses (p= 0.201). Our data also suggested that a central necrosis was ten times more common among malignant lesions compared to benign ones (p< 0.001). Among different metrics, T2 mapping parameter achieved the highest AUC value and accuracy in differentiating two groups. Three measures—T2 scaled ratio, tumor myometrium contrast ratio on T2, and tumor myometrium contrast-enhanced ratio—achieved a sensitivity of 100%, therefore none of the malignant lesions would have been missed if these metrics had been adopted in patient management. The findings suggested that the evaluated metrics could be useful in the preoperative assessment of myometrial masses to differentiate uterine sarcomas from leiomyomas. The proposed framework has major implications for improving current practice in the management of myometrial masses.

Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21stcentury. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these changes due to our large digital data footprint. Radiomics is transforming medical images into mineable high‐dimensional data to optimise clinical decision‐making; however, some would argue that AI could infiltrate workplaces with very few ethical checks and balances. In this commentary article, we describe how AI is beginning to change medical imaging services and the innovations that are on the horizon. We explore how AI and its various forms, including machine learning, will challenge the way medical imaging is delivered from workflow, image acquisition, image registration to interpretation. Diagnostic radiographers will need to learn to work alongside our ‘virtual colleagues’, and we argue that there are vital changes to entry and advanced curricula together with national professional capabilities to ensure machine‐learning tools are used in the safest and most effective manner for our patients. This article summarises the opportunities for artificial intelligence (AI) translation into medical imaging in the near future. The changing roles for diagnostic radiographers are explored, and a discussion of the challenges for the ethical implementation of AI is included.

Expression and recognition of different emotions play important roles in human daily life. Among different emotions, recognising sympathy is one of the most complicated ones for humans. This paper presents an EEG-based sympathy recognition system using data collected from two channels, f3 and f4, in conventional 10–20 EEG system. Different visual stimuli have been shown to two subjects. A set of features have been extracted from the recorded EEG and each interval has been labelled as neutral or stimulated. The performances of k-nearest neighbour, artificial neural network and support vector machine in classifying data into two groups have been investigated. According to the result, the best correct classification rate was in average 97.05%. It has been also found that the alpha and the delta bands are more related to sympathy, comparing to the other frequency bands.

Mammographic breast density (MBD) has been reported to be one of the strongest risk factors of breast cancer in western countries. However, the MBD profile in China is unclear. We aimed to compare the MBD of women in China with the MBD of women in Australia using percentage density.

An accurate model to stratify breast cancer risk is required to move from a one-size-fits-all screening paradigm to more personalised risk-based screening strategies. Most risk models have limited accuracy and have been calibrated for white women. Considering the rapid increase in breast cancer incidence in China, in this study we aimed to develop a breast cancer model for Chinese women.