Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway.

Purpose To explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661 695 digital mammographic examinations performed among 242 629 female individuals screened as a part of BreastScreen Norway, 2004-2018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. A continuous examination-level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92, 0.93) for screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10 987 of 16 040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16 040) of the false-positive results were considered negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload. Keywords: Mammography, Breast, Screening, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article. © RSNA, 2024 See also commentary by Bahl and Do in this issue.

Can I trust my fake data - A comprehensive quality assessment framework for synthetic tabular data in healthcare.

BACKGROUND: Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient data for training, testing and validation. Synthetic data has been suggested in response to privacy concerns and regulatory requirements and can be created by training a generator on real data to produce a dataset with similar statistical properties. Competing metrics with differing taxonomies for quality evaluation have been proposed, resulting in a complex landscape. Optimising quality entails balancing considerations that make the data fit for use, yet relevant dimensions are left out of existing frameworks. METHOD: We performed a comprehensive literature review on the use of quality evaluation metrics on synthetic data within the scope of synthetic tabular healthcare data using deep generative methods. Based on this and the collective team experiences, we developed a conceptual framework for quality assurance. The applicability was benchmarked against a practical case from the Dutch National Cancer Registry. CONCLUSION: We present a conceptual framework for quality assuranceof synthetic data for AI applications in healthcare that aligns diverging taxonomies, expands on common quality dimensions to include the dimensions of Fairness and Carbon footprint, and proposes stages necessary to support real-life applications. Building trust in synthetic data by increasing transparency and reducing the safety risk will accelerate the development and uptake of trustworthy AI tools for the benefit of patients. DISCUSSION: Despite the growing emphasis on algorithmic fairness and carbon footprint, these metrics were scarce in the literature review. The overwhelming focus was on statistical similarity using distance metrics while sequential logic detection was scarce. A consensus-backed framework that includes all relevant quality dimensions can provide assurance for safe and responsible real-life applications of synthetic data. As the choice of appropriate metrics are highly context dependent, further research is needed on validation studies to guide metric choices and support the development of technical standards.

Cross-population evaluation of cervical cancer risk prediction algorithms.

BACKGROUND: Cervical cancer is a preventable disease, despite being one of the most common types of female cancers worldwide. Integrating existing programs for cervical cancer screening with personalized risk prediction algorithms can improve population-level cancer prevention by enabling more targeted screening and contrive preventive healthcare innovations. While algorithms developed for cervical cancer risk prediction have shown promising performance in internal validation on more homogeneous populations, their ability to generalize to external populations remains to be assessed. METHODS: To address this gap, we perform a cross-population comparative study of personalized prediction algorithms for more personalized cervical cancer screening. Using data from the Norwegian and Estonian populations, the algorithms are validated on internal and external datasets to study their potential biases and limitations when applied to different populations. We evaluate the algorithms in predicting progression from low-grade precancerous cervical lesions, simulating a clinically relevant application of more personalized risk stratification. RESULTS: As expected, our numerical experiments show that algorithm performance varies depending on the population. However, some algorithms show strong generalization capacity across different data sources. Using Kaplan-Meier estimates, we demonstrate the strengths and limitations of the algorithms in detecting cancer progression over time by comparing to the trends observed from data. We assess their overall discrimination performance in personalized risk predictions by analyzing the accuracy and confidence in individual risk estimates. DISCUSSION AND CONCLUSION: This study examines the effectiveness of personalized prediction algorithms across different populations. Our results demonstrate the potential for generalizing risk prediction algorithms to external populations. These findings highlight the importance of considering population diversity when developing risk prediction algorithms.

AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis.

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.

Prolonged screening interval due to the COVID-19 pandemic and its association with tumor characteristics and treatment; a register-based study from BreastScreen Norway.

OBJECTIVE: During the COVID-19 pandemic Norway had to suspend its national breast cancer screening program. We aimed to investigate the effect of the pandemic-induced suspension on the screening interval, and its subsequent association with the tumor characteristics and treatment of screen-detected (SDC) and interval breast cancer (IC). METHODS: Information about women aged 50-69, participating in BreastScreen Norway, and diagnosed with a SDC (N = 3799) or IC (N = 1806) between 2018 and 2021 was extracted from the Cancer Registry of Norway. Logistic regression was used to investigate the association between COVID-19 induced prolonged screening intervals and tumor characteristics and treatment. RESULTS: Women with a SDC and their last screening exam before the pandemic had a median screening interval of 24.0 months (interquartile range: 23.8-24.5), compared to 27.0 months (interquartile range: 25.8-28.5) for those with their last screening during the pandemic. The tumor characteristics and treatment of women with a SDC, last screening during the pandemic, and a screening interval of 29-31 months, did not differ from those of women with a SDC, last screening before the pandemic, and a screening interval of 23-25 months. ICs detected 24-31 months after screening, were more likely to be histological grade 3 compared to ICs detected 0-23 months after screening (odds ratio: 1.40, 95% confidence interval: 1.06-1.84). CONCLUSIONS: Pandemic-induced prolonged screening intervals were not associated with the tumor characteristics and treatment of SDCs, but did increase the risk of a histopathological grade 3 IC. This study provides insights into the possible effects of extending the screening interval.

Impact of the COVID-19 pandemic on breast cancer incidence and tumor stage in the Netherlands and Norway: A population-based study.

BACKGROUND: Comparing the impact of the COVID-19 pandemic on the incidence of newly diagnosed breast tumors and their tumor stage between the Netherlands and Norway will help us understand the effect of differences in governmental and social reactions towards the pandemic. METHODS: Women newly diagnosed with breast cancer in 2017-2021 were selected from the Netherlands Cancer Registry and the Cancer Registry of Norway. The crude breast cancer incidence rate (tumors per 100,000 women) during the first (March-September 2020), second (October 2020-April 2021), and Delta COVID-19 wave (May-December 2021) was compared with the incidence rate in the corresponding periods in 2017, 2018, and 2019. Incidence rates were stratified by age group, method of detection, and clinical tumor stage. RESULTS: During the first wave breast cancer incidence declined to a larger extent in the Netherlands than in Norway (27.7% vs. 17.2% decrease, respectively). In both countries, incidence decreased in women eligible for screening. In the Netherlands, incidence also decreased in women not eligible for screening. During the second wave an increase in the incidence of stage IV tumors in women aged 50-69 years was seen in the Netherlands. During the Delta wave an increase in overall incidence and incidence of stage I tumors was seen in Norway. CONCLUSION: Alterations in breast cancer incidence and tumor stage seem related to a combined effect of the suspension of the screening program, health care avoidance due to the severity of the pandemic, and other unknown factors.

Comparing quality of breast cancer care in the Netherlands and Norway by federated propensity score analytics.

PURPOSE: The aim of the study was to benchmark and compare breast cancer care quality indicators (QIs) between Norway and the Netherlands using federated analytics preventing transfer of patient-level data. METHODS: Breast cancer patients (2017-2018) were retrieved from the Netherlands Cancer Registry and the Cancer Registry of Norway. Five European Society of Breast Cancer Specialists (EUSOMA) QIs were assessed: two on magnetic resonance imaging (MRI), two on surgical approaches, and one on postoperative radiotherapy. The QI outcomes were calculated using 'Vantage 6' federated Propensity Score Stratification (PSS). Likelihood of receiving a treatment was expressed in odds ratios (OR). RESULTS: In total, 39,163 patients were included (32,786 from the Netherlands and 6377 from Norway). PSS scores were comparable to the crude outcomes of the QIs. The Netherlands scored higher on the QI 'proportions of patients preoperatively examined with breast MRI' [37% vs.17.5%; OR 2.8 (95% CI 2.7-2.9)], the 'proportions of patients receiving primary systemic therapy examined with breast MRI' [83.3% vs. 70.8%; OR 2.3 (95% CI 1.3-3.3)], and 'proportion of patients receiving a single breast operation' [95.2% vs. 91.5%; OR 1.8 (95% CI 1.4-2.2)]. Country scores for 'immediate breast reconstruction' and 'postoperative radiotherapy after breast-conserving surgery' were comparable. The EUSOMA standard was achieved in both countries for 4/5 indicators. CONCLUSION: Both countries achieved high scores on the QIs. Differences were observed in the use of MRI and proportion of patients receiving single surgery. The federated approach supports future possibilities on benchmark QIs without transfer of privacy-sensitive data.

Matrix factorization for the reconstruction of cervical cancer screening histories and prediction of future screening results.

BACKGROUND: Mass screening programs for cervical cancer prevention in the Nordic countries have strongly reduced cancer incidence and mortality at the population level. An alternative to the current mass screening is a more personalised screening strategy adapting the recommendations to each individual. However, this necessitates reliable risk prediction models accounting for disease dynamics and individual data. Herein we propose a novel matrix factorisation framework to classify females by the time-varying risk of being diagnosed with cervical cancer. We cast the problem as a time-series prediction model where the data from females in the Norwegian screening population are represented as sparse vectors in time and then combined into a single matrix. Using novel temporal regularisation and discrepancy terms for the cervical cancer screening context, we reconstruct complete screening profiles from this scarce matrix and use these to predict the next exam results indicating the risk of cervical cancer. The algorithm is validated on both synthetic and registry screening data by measuring the probability of agreement (PoA) between Kaplan-Meier estimates. RESULTS: In numerical experiments on synthetic data, we demonstrate that the novel regularisation and discrepancy term can improve the data reconstruction ability as well as prediction performance over varying data scarcity. Using a hold-out set of screening data, we compare several numerical models and find that the proposed framework attains the strongest PoA. We observe strong correlations between the empirical survival curves from our method and the hold-out data, and evaluate the ability of our framework to predict the females' next results for up to five years ahead in time using only their current screening histories as input. CONCLUSIONS: We have proposed a matrix factorization model for predicting future screening results and evaluated its performance in a female cohort to demonstrate the potential for developing prediction models for more personalized cervical cancer screening.

Cervical Cancer in the Baltic States: Can Intelligent and Personalized Cancer Screening Change the Situation?

The three Baltic States (Estonia, Latvia, and Lithuania) are among the European Union countries with the highest incidence and mortality rates for cervical cancer. In order to tackle this public health challenge, there is an urgent need to implement more advanced and effective methods in cervical cancer prevention in Baltic countries. Nationwide cervical cancer screening programs in the Baltic States commenced in 2004-2009. While the organized screening programs in these countries differ in some relevant details (target age groups, screening interval), the underlying principles and problems, barriers are universal. However, the outcomes of present screening programs are unsatisfactory. In addition, universal screening programs are extremely costly. There is a potential need for more intelligent and personalized cervical cancer screening program. In 2019 the project "Towards elimination of cervical cancer: intelligent and personalized solutions for cancer screening" (2020-2023) was developed with the main objective - to develop improved and personalized cancer screening methods within a sustainable health care system. It is expected, that more sophisticated cervical cancer screening model will be implemented in Estonia, Latvia, and Lithuania, and will have a positive impact to epidemiology of cervical cancer and public health in general.

Hierarchical continuous-time inhomogeneous hidden Markov model for cancer screening with extensive followup data.

Continuous-time hidden Markov models are an attractive approach for disease modeling because they are explainable and capable of handling both irregularly sampled, skewed and sparse data arising from real-world medical practice, in particular to screening data with extensive followup. Most applications in this context consider time-homogeneous models due to their relative computational simplicity. However, the time homogeneous assumption is too strong to accurately model the natural history of many diseases including cancer. Moreover, cancer risk across the population is not homogeneous either, since exposure to disease risk factors can vary considerably between individuals. This is important when analyzing longitudinal datasets and different birth cohorts. We model the heterogeneity of disease progression and regression using piece-wise constant intensity functions and model the heterogeneity of risks in the population using a latent mixture structure. Different submodels under the mixture structure employ the same types of Markov states reflecting disease progression and allowing both clinical interpretation and model parsimony. We also consider flexible observational models dealing with model over-dispersion in real data. An efficient, scalable Expectation-Maximization algorithm for inference is proposed with the theoretical guaranteed convergence property. We demonstrate our method's superior performance compared to other state-of-the-art methods using synthetic data and a real-world cervical cancer screening dataset from the Cancer Registry of Norway. Moreover, we present two model-based risk stratification methods that identify the risk levels of individuals.

Towards a data-driven system for personalized cervical cancer risk stratification.

Mass-screening programs for cervical cancer prevention in the Nordic countries have been effective in reducing cancer incidence and mortality at the population level. Women who have been regularly diagnosed with normal screening exams represent a sub-population with a low risk of disease and distinctive screening strategies which avoid over-screening while identifying those with high-grade lesions are needed to improve the existing one-size-fits-all approach. Machine learning methods for more personalized cervical cancer risk estimation may be of great utility to screening programs shifting to more targeted screening. However, deriving personalized risk prediction models is challenging as effective screening has made cervical cancer rare and the exam results are strongly skewed towards normal. Moreover, changes in female lifestyle and screening habits over time can cause a non-stationary data distribution. In this paper, we treat cervical cancer risk prediction as a longitudinal forecasting problem. We define risk estimators by extending existing frameworks developed on cervical cancer screening data to incremental learning for longitudinal risk predictions and compare these estimators to machine learning methods popular in biomedical applications. As input to the prediction models, we utilize all the available data from the individual screening histories.Using data from the Cancer Registry of Norway, we find in numerical experiments that the models are strongly biased towards normal results due to imbalanced data. To identify females at risk of cancer development, we adapt an imbalanced classification strategy to non-stationary data. Using this strategy, we estimate the absolute risk from longitudinal model predictions and a hold-out set of screening data. Comparing absolute risk curves demonstrate that prediction models can closely reflect the absolute risk observed in the hold-out set. Such models have great potential for improving cervical cancer risk stratification for more personalized screening recommendations.

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection. © RSNA, 2022.

Federated Networks for Distributed Analysis of Health Data.

Access to health data, important for population health planning, basic and clinical research and health industry utilization, remains problematic. Legislation intended to improve access to personal data across national borders has proven to be a double-edged sword, where complexity and implications from misinterpretations have paradoxically resulted in data becoming more siloed. As a result, the potential for development of health specific AI and clinical decision support tools built on real-world data have yet to be fully realized. In this perspective, we propose federated networks as a solution to enable access to diverse data sets and tackle known and emerging health problems. The perspective draws on experience from the World Economic Forum Breaking Barriers to Health Data project, the Personal Health Train and Vantage6 infrastructures, and industry insights. We first define the concept of federated networks in a healthcare context, present the value they can bring to multiple stakeholders, and discuss their establishment, operation and implementation. Challenges of federated networks in healthcare are highlighted, as well as the resulting need for and value of an independent orchestrator for their safe, sustainable and scalable implementation.

Improving five-year survival prediction via multitask learning across HPV-related cancers.

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

A hidden Markov model for population-level cervical cancer screening data.

The Cancer Registry of Norway has been administrating a national cervical cancer screening program since 1992 by coordinating triennial cytology exam screenings for the female population between 25 and 69 years of age. Up to 80% of cancers are prevented through mass screening, but this comes at the expense of considerable screening activity and leads to overtreatment of clinically asymptomatic precancers. In this article, we present a continuous-time, time-inhomogeneous hidden Markov model which was developed to understand the screening process and cervical cancer carcinogenesis in detail. By leveraging 1.7 million individual's multivariate time-series of medical exams performed over a 25-year period, we simultaneously estimate all model parameters. We show that an age-dependent model reflects the Norwegian screening program by comparing empirical survival curves from observed registry data and data simulated from the proposed model. The model can be generalized to include more detailed individual-level covariates as well as new types of screening exams. By utilizing individual screening histories and covariate data, the proposed model shows potential for improving strategies for cancer screening programs by personalizing recommended screening intervals.

Using clinical cancer registry data for estimation of quality indicators: Results from the Norwegian breast cancer registry.

INTRODUCTION: Increased focus on quality indicators and the use of clinical registries for breast cancer for real world studies have shown higher compliance to recommended therapy and better survival. In 2010, the European Society of Breast Cancer Specialist (EUSOMA) proposed quality indicators (QI) covering diagnosis, treatment and follow-up. To become a EUSOMA certified Breast Cancer Unit, 14 specified quality indicators, in addition to other requirements, need to be met. To evaluate the compliance and results of recommended treatment in breast cancer care in Norway and to improve the quality of epidemiological data, the Cancer Registry of Norway (CRN) in cooperation with the Norwegian Breast Cancer Group (NBCG) developed the Norwegian Breast Cancer Registry (NBCR). The objective of this study is to assess the feasibility of using the NBCR for estimating the EUSOMA QI individually for all hospitals diagnosing and treating breast cancer in Norway. METHODS: To provide researchers with high quality cancer data as well as for the purpose of national cancer statistics, the CRN employs a cancer registry system to 1) longitudinal capture data from all patients from all medical entities that diagnose and/or treat cancer patients (e.g., pathology, radiology and clinical departments) in Norway; 2) curate data, i.e. validate the correctness of collected data, and assemble the validated cancer data as cancer cases; 3) provide data for analytics and presentation. Estimates for 10 EUSOMA QI were calculated at national and hospital level. To compare hospitals, a summary score of QIs was defined for each hospital. RESULTS: All hospitals currently treating breast cancer patients have the technical ability to submit data to the NBCR for estimation of QIs defined by EUSOMA. Data from pathology and surgery are of high quality. However, data from oncological and radiological departments are incomplete, but improving. This currently hinders three QIs from being calculated. QI on benign to malign diagnosis needs to be calculated at the individual Breast Centre. Over time the adherence to guidelines have improved and the hospital variation for the respective QI have decreased. Two hospitals met all minimum standard on ten QIs in year 2016 and one hospital did not meet one minimum standard, but met all other targets. CONCLUSION: The NBCR has since 2012 published annual reports on breast cancer care and for the year 2016 measured 10 of 14 QI defined by EUSOMA. Increased compliance of recommended treatment in Norway has been observed during the years the registry has been active.

Bayesian multitask learning regression for heterogeneous patient cohorts.

Multitask learning (MTL) leverages commonalities across related tasks with the aim of improving individual task performance. A key modeling choice in designing MTL models is the structure of the tasks' relatedness, which may not be known. Here we propose a Bayesian multitask learning model that is able to infer the task relationship structure directly from the data. We present two variations of the model in terms of a priori information of task relatedness. First, a diffuse Wishart prior is placed on a task precision matrix so that all tasks are assumed to be equally related a priori. Second, a Bayesian graphical LASSO prior is used on the task precision matrix to impose sparsity in the task relatedness. Motivated by machine learning applications in the biomedical domain, we emphasize interpretability and uncertainty quantification in our models. To encourage model interpretability, linear mappings from the shared input spaces to task-dependent output spaces are used. To encourage uncertainty quantification, conjugate priors are used so that full posterior inference is possible. Using synthetic data, we show that our model is able to recover the underlying task relationships as well as features jointly relevant for all tasks. We demonstrate the utility of our model on three distinct biomedical applications: Alzheimer's disease progression, Parkinson's disease assessment, and cervical cancer screening compliance. We show that our model outperforms Single Task (STL) models in terms of predictive performance, and performs better than existing MTL methods for the majority of the scenarios.

Better survival after breast-conserving therapy compared to mastectomy when axillary node status is positive in early-stage breast cancer: a registry-based follow-up study of 6387 Norwegian women participating in screening, primarily operated between 1998 and 2009.

BACKGROUND: Recent registry studies on early-stage breast cancer have shown better survival rates when women underwent breast-conserving therapy (BCT) compared with mastectomy (MTX). The aim of this study is to investigate women participating in screening, in all four stages of early breast cancer (T1N0M0, T2N0M0, T1N1M0, and T2N1M0), as to whether there is a survival benefit when women undergo BCT compared to MTX. METHOD: A cohort of 6387 women aged 50-69, with primary-operated breast cancer from January 1998 to December 2009, participating in screening and followed-up until the end of 2010. Life tables were calculated by stages (pT1N0M0, pT2N0M0, pT1N1M0, and pT2N1M0), surgery groups (BCT and MTX), and screening detection (first screening, later screening, or interval cancer). Cox regression was used to calculate hazard ratios (HR) between BCT and MTX in crude and adjusted analyses. RESULTS: In stage T1N1M0, women who underwent MTX had an HR of 2.91 (95% CI 1.30-6.48) for breast cancer death compared to women who underwent BCT, after adjusting for screening detection, years of diagnosis, age at diagnosis, histology, grade, and hormone receptor status. For all other TNM categories of early breast cancer, there was no difference in survival. 10-year breast cancer-specific survival (BCSS) in T1N0M0 was 98% for women undergoing BCT and 96% for women undergoing MTX. 10-year BCSS in T1N1M0 was 97% for women undergoing BCT and 89% for women undergoing MTX. CONCLUSIONS: For women participating in screening, there is a benefit of BCT over MTX in stage T1N1M0. No such effects were observed in the other early stages of breast cancer.

Anticipating anonymity in screening program databases.

In this paper, we propose a technique for improving anonymity in screening program databases to increase the privacy for the participants in these programs. The data generated by the invitation process (screening centre, appointment date) is often made available to researchers for medical research and for evaluation and improvement of the screening program. This information, combined with other personal quasi-identifiers such as the ZIP code, gender or age, can pose a risk of disclosing the identity of the individuals participating in the program, and eventually their test results. We present two algorithms that produce a set of screening appointments that aim to increase anonymity of the resulting dataset. The first one, based on the constraint programming paradigm, defines the optimal appointments, while the second one is a suboptimal heuristic algorithm that can be used with real size datasets. The level of anonymity is measured using the new concept of generalized k-anonymity, which allows us to show the utility of the proposal by means of experiments, both using random data and data based on screening invitations from the Norwegian Cancer Registry.