Complete surgical excision of the primary tumor improves survival of patients with metastatic breast cancer at diagnosis, particularly among women with only bone metastases.

CARDIAC IMAGINGC oronary artery calcium (CAC) scoring in dedicated CT examinations is frequently performed to measure coronary atherosclerotic plaque burden and predict cardiovascular disease (CVD) risk. The CAC score may have increasing applications, as noted in the 2018-2019 American Heart Association and American College of Cardiology Guidelines for Cholesterol and Prevention, whereby the CAC score is a tool to refine the 10-year risk of atherosclerotic CVD when the CVD risk may be uncertain (1). CAC scoring is performed by using nonenhanced electrocardiographical-Purpose: To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods:The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199-568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted k values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1-10, 11-100, 101-400, .400).Results: At baseline, the DL algorithm yielded ICCs of 0.79-0.97 for CAC and 0.66-0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84-0.99 (CAC) and 0.92-0.99 (TAC) for CT protocol-specific training and to 0.85-0.99 (CAC) and 0.96-0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the k value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion:A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol-specific images further improved algorithm performance.

Preoperative axillary ultrasound-guided biopsy is a useful step in the process of axillary staging. Approximately 50 % of women with axillary involvement can be identified preoperatively. Still, one in four women with an ultrasound-guided biopsy-"proven" negative axilla has a positive SNB.

An international research consortium has been formed to facilitate evidence-based introduction of MR-guided radiotherapy (MR-linac) and to address how the MR-linac could be used to achieve an optimized radiation treatment approach to improve patients’ survival, local, and regional tumor control and quality of life. The present paper describes the organizational structure of the clinical part of the MR-linac consortium. Furthermore, it elucidates why collaboration on this large project is necessary, and how a central data registry program will be implemented.

The pace of innovation in radiation oncology is high and the window of opportunity for evaluation narrow. Financial incentives, industry pressure, and patients’ demand for high-tech treatments have led to widespread implementation of innovations before, or even without, robust evidence of improved outcomes has been generated. The standard phase I–IV framework for drug evaluation is not the most efficient and desirable framework for assessment of technological innovations. In order to provide a standard assessment methodology for clinical evaluation of innovations in radiotherapy, we adapted the surgical IDEAL framework to fit the radiation oncology setting. Like surgery, clinical evaluation of innovations in radiation oncology is complicated by continuous technical development, team and operator dependence, and differences in quality control. Contrary to surgery, radiotherapy innovations may be used in various ways, e.g., at different tumor sites and with different aims, such as radiation volume reduction and dose escalation. Also, the effect of radiation treatment can be modeled, allowing better prediction of potential benefits and improved patient selection. Key distinctive features of R-IDEAL include the important role of predicate and modeling studies (Stage 0), randomization at an early stage in the development of the technology, and long-term follow-up for late toxicity. We implemented R-IDEAL for clinical evaluation of a recent innovation in radiation oncology, the MRI-guided linear accelerator (MR-Linac). MR-Linac combines a radiotherapy linear accelerator with a 1.5-T MRI, aiming for improved targeting, dose escalation, and margin reduction, and is expected to increase the use of hypofractionation, improve tumor control, leading to higher cure rates and less toxicity. An international consortium, with participants from seven large cancer institutes from Europe and North America, has adopted the R-IDEAL framework to work toward coordinated, evidence-based introduction of the MR-Linac. R-IDEAL holds the promise for timely, evidence-based introduction of radiotherapy innovations with proven superior effectiveness, while preventing unnecessary exposure of patients to potentially harmful interventions.

Women who refuse surgery for breast cancer have a strongly impaired survival. This information might help patients who are hesitant toward surgery make a better informed decision.

Background: Systematic evaluation and validation of new prognostic and predictive markers, technologies and interventions for colorectal cancer (CRC) is crucial for optimizing patients' outcomes. With only 5-15% of patients participating in clinical trials, generalizability of results is poor. Moreover, current trials often lack the capacity for post-hoc subgroup analyses. For this purpose, a large observational cohort study, serving as a multiple trial and biobanking facility, was set up by the Dutch Colorectal Cancer Group (DCCG). Methods/design: The Prospective Dutch ColoRectal Cancer cohort is a prospective multidisciplinary nationwide observational cohort study in the Netherlands (yearly CRC incidence of 15 500). All CRC patients (stage I-IV) are eligible for inclusion, and longitudinal clinical data are registered. Patients give separate consent for the collection of blood and tumor tissue, filling out questionnaires, and broad randomization for studies according to the innovative cohort multiple randomized controlled trial design (cmRCT), serving as an alternative study design for the classic RCT. Objectives of the study include: 1) systematically collected long-term clinical data, patient-reported outcomes and biomaterials from daily CRC practice; and 2) to facilitate future basic, translational and clinical research including interventional and cost-effectiveness studies for both national and international research groups with short inclusion periods, even for studies with stringent inclusion criteria. Results: Seven months after initiation 650 patients have been enrolled, eight centers participate, 15 centers await IRB approval and nine embedded cohort-or cmRCT-designed studies are currently recruiting patients. Conclusion: This cohort provides a unique multidisciplinary data, biobank, and patient-reported outcomes collection initiative, serving as an infrastructure for various kinds of research aiming to improve treatment outcomes in CRC patients. This comprehensive design may serve as an example for other tumor types.

Prostate specific antigen (PSA) screening was introduced to detect prostate cancer at an early stage and to reduce prostate cancer‐specific mortality. Until results from clinical trials are available, the efficacy of PSA screening in reducing prostate cancer mortality can be estimated by surveillance of prostate cancer mortality trends. Our study analyzes recent trends in prostate cancer mortality in 38 countries. We used the IARC‐WHO cancer mortality database and performed joinpoint analysis to examine prostate cancer mortality trends and identified 3 patterns. In USA, and to a lesser extent in Germany, Switzerland, Canada, France, Italy and Spain, prostate cancer‐specific mortality decreased to a level lower than before the introduction of PSA screening. In Australia, New Zealand, Austria, Finland, The Netherlands, Norway, United Kingdom, Hungary, Slovakia, Israel, Singapore, Sweden and Portugal, mortality from prostate cancer decreased but rates remain higher than before the introduction of PSA screening. Prostate cancer mortality continued to increase in Belgium, Denmark, Greece, Ireland, Bulgaria, Czech Republic, Belarus, Ukraine, Russian Federation, Romania, Poland, Argentina, Chile, Cuba, Mexico, Japan, China Hong Kong and the Republic of Korea. The trends in prostate cancer mortality rates in examined countries suggest that PSA screening may be effective in reducing mortality from prostate cancer. © 2008 Wiley‐Liss, Inc.