Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

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

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Deep learning algorithms for tumor detection in screening mammography

Population-wide mammography screening was fully implemented in Sweden in 1997. The implementation has helped to identify breast cancer at earlier stages and thereby lowered mortality by 30-40%. However, it still has its limitations, many studies have shown a discrepancy between radiologist when assessing mammographic examinations. Additionally, women with very dense breasts have a lower mammographic sensitivity and cancers are easily missed. There is also a shortage on breast radiologists and the workload is increasing due to more women being screened. These challenges could be addressed with the help of artificial intelligence systems. The artificial intelligence system can serve both as an assistant to replace one radiologist in a double-reading setting and as a tool to triage women with a high risk of breast cancer for additional screening using other modalities. In this thesis we used data from two cohorts: the cohort of screen aged women (CSAW) and the ScreenTrust MRI cohort. The primary objectives were to establish performance benchmarks based on radiologists recorded assessments (study I), compare the diagnostic performance of various AI CAD systems (study II), investigate differences and similarities in false assessments between AI CAD and radiologists (study III), and evaluate the potential of artificial intelligence in triaging women for complementary MRI screening (study IV). The data for studies I-III were obtained from CSAW, while the data for study IV were obtained from the MRI ScreenTrust cohort. CSAW is a collection of data from Stockholm County between the years of 2008 and 2015. Study I was a retrospective multicenter cohort study that examined radiologist performance benchmarks in screening mammography. Operating performance was assessed in terms of abnormal interpretation rate, false negative rate, sensitivity, and specificity. Measures were determined for each quartile of radiologists classified according to performance, and performance was evaluated overall and by different tumor characteristics. The study included a total of 418,041 women and 1,186,045 digital mammograms, and involved 110 radiologists, of which 24 were defined as high-volume readers. Our analysis revealed significant differences in performance between highvolume readers, as well as a variability in sensitivity based on tumor characteristics. This study was presented during the 2019 annual meeting of the Radiological Society of North America, and was awarded the Trainee research prize that same year. Study II was a retrospective case-control study that evaluated the performance of three commercial algorithms. We performed an external evaluation of these algorithms and compared the retrospective mammography assessments of radiologists with those of the algorithms. Operating performance was determined in terms of abnormal interpretation rate, false negative rate, sensitivity, specificity and the AUC. The study included 8,805 women, of whom 740 women had cancer, and a random sample of 8,066 healthy controls. There were 25 radiologists involved. For a binary decision, the cutpoint was defined by the mean specificity of the original first-reader radiologists (96.6%). Our findings showed that one AI algorithm outperformed the other AI algorithm and the original first-reader radiologists. This study was presented during the 2020 annual meeting of the European Society of Radiology. Study III was a retrospective case-control study that evaluated the differences and similarities in false assessments between an artificial intelligence system and a human reader in screening mammography. In this study we included 714 screening examinations for women diagnosed with breast cancer and 8,003 randomly selected healthy controls. The abnormality threshold was predefined from study II. We examined how false positive and false negative assessments by AI CAD and the first radiologist, were associated with breast density, age and tumor characteristics. Our findings showed that AI makes fewer false negative assessments than radiologists. Combining AI with a radiologist resulted in the most pronounced decrease in false negative assessments for high-density women and women over the age of 55. This study was presented at the 2021 annual meeting of the Radiological Society of North America. Study IV is a randomized clinical trial that aims to investigate the effect of applying deep learning methods to select women for MRI-based breast cancer screening. The study examines how effectively AI can identify women who should be offered a complementary MRI screening based on their likelihood of having cancer that is not visible on regular mammography. The results reported in this thesis are preliminary and based on examinations from April 1, 2021 to December 31, 2022. During the indicated time period, 481 MRI examinations have been completed, and 28 cancers have been detected, yielding a cancer detection rate of 58.2 per 1,000 examinations. Although, the trial is still ongoing, the inter-rim results suggest that using AI-based selection for supplemental MRI screening can lead to a higher rate of cancer detection than that reported for density-based selection methods. In conclusion, we have shown that the use of AI for breast cancer detection can increase precision and efficiency in mammography screening