Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

A review of research into the development of radiologic expertise: Implications for computer-based training

Rationale and Objectives. Studies of radiologic error reveal high levels of variation between radiologists. Although it is known that experts outperform novices, we have only limited knowledge about radiologic expertise and how it is acquired.Materials and Methods. This review identifies three areas of research: studies of the impact of experience and related factors on the accuracy of decision-making; studies of the organization of expert knowledge; and studies of radiologists' perceptual processes.Results and Conclusion. Interpreting evidence from these three paradigms in the light of recent research into perceptual learning and studies of the visual pathway has a number of conclusions for the training of radiologists, particularly for the design of computer-based learning programs that are able to illustrate the similarities and differences between diagnoses, to give access to large numbers of cases and to help identify weaknesses in the way trainees build up a global representation from fixated regions

A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection

Breast cancer is the second leading cause of death among a large number of women worldwide. It may be challenging for radiologists to diagnose and treat breast cancer. Consequently, primary care improves disease prevention and death. Early detection increases treatment options and saves life, which is the major target of this research. This research indicates the versatility of the methodology by integrating contemporary segmentation approaches with machine learning methods, which are developing areas of research. In the pre-processing process, an adaptive median filter is utilized for noise removal, enhancement of image quality, conservation of edges, and smoothing. This research makes a significant contribution by proposing a new parameter for evaluating K-means and a Gaussian mixture model (GMM) performance. A hybrid combination of segmentation and detection was applied to breast cancer. The proposed technique is significant for classifying benign and malignant tumors. The simulated results are discussed and evaluated to determine the competence of this method for the early diagnosis of breast cancer. This method allows medical experts to recognize breast cancer at a faster rate and provide higher accuracy. An ANOVA test was used to determine the multi-variant analysis and prediction rate for the proposed method

Comparison between two packages for pectoral muscle removal on mammographic images

Background: Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. Purpose: To compare performance of the two packages on a single database of FFDM images. Methods: Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA’s and OpenBreast’s removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal–Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. Results: FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA’s Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P < 0.05). No dependence on breast density or laterality has been found (Kruskal–Wallis test P > 0.05). Conclusion: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation

Psychosocial consequences of false-positive mammography among women attending breast cancer screening. Assessment, prediction, and coping.

One side-effect of breast cancer (BC) screening is a false-positive mammogram among healthy women. That is, finding(s) on a screening mammogram that lead to additional breast examinations but where the woman is eventually considered free from BC. There is evidence of short-term psychosocial consequences of false-positive BC screening. Regarding long-term consequences, research findings are inconsistent. Lack of validated condition-specific questionnaires targeting such consequences has been postulated as a potential reason for the inconsistencies. Therefore, the Consequences of Screening - Breast Cancer (COS-BC) questionnaire was developed in Denmark. However, before the COS-BC can be used for studying psychosocial consequences of false-positive BC screening across countries, it needs to be adapted and psychometrically (validity and reliability) tested therein. Furthermore, studies of prediction of long-term psychosocial consequences of false-positive BC screening and coping with such consequences might identify women at risk as well as interventions to prevent consequences of screening. Thus, the aim of this thesis was to validate measures of and study the psychosocial consequences of false-positive mammography among women in a Swedish breast cancer screening programme, and to explore how women cope with such a situation. Interviews with 26 women experiencing false-positive screening mammography (Paper I) provided support for the content validity of a Swedish version of the COS-BC; questionnaire items were generally found relevant, understandable, and covering the psychosocial consequences of false-positive BC screening. Psychometric tests (Paper II) of the COS-BC among 1442 women with false-positive or negative mammography demonstrated support for five COS-BC scales (Sense of dejection, Anxiety, Behavioural, Sleep, and Existential values) for cross-sectional and longitudinal group assessments. The remaining seven COS-BC scales should be used more cautiously. One year follow-up study (Paper III, framework) of 399 recalled women and 449 controls showed that women experience psychosocial consequences targeted by the COS-BC scales, except for breast self-examination consequences. Early recall for subsequent mammography demonstrated the strongest prediction of long-term consequences. Dissatisfaction with information at recall, worry about BC, lack of social support, and being foreign-born were also identified as potential predictors. Interviews with 13 women (Paper IV) experiencing psychosocial consequences of false-positive screening mammography revealed that coping with the situation implied a roller coaster of emotions and sense. Social support, sisterhood, and being professionally taken care of were identified as important aspects of coping with the perceived psychosocial consequences of false-positive BC screening (Paper IV). In conclusion, findings of this thesis confirm the occurrence of short-term psychosocial consequences and demonstrated long-term consequences of false-positive screening mammography among women. Early recall should be avoided and personalized information and communication could be of value in order to diminish the risk of long-term psychosocial consequences of false-positive BC screening. Further research is needed to investigate adequate communication styles, especially in order to face multicultural populations in the context of BC screening

Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. 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Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care