Andriy I. Bandos

Bio: Andriy I. Bandos is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Mammography & Receiver operating characteristic. The author has an hindex of 24, co-authored 67 publications receiving 3737 citations. Previous affiliations of Andriy I. Bandos include Northwestern University.

Comparison of Digital Mammography Alone and Digital Mammography Plus Tomosynthesis in a Population-based Screening Program

Abstract: We found a significant increase in cancer detection rates, particularly for invasive cancers, and a simultaneous decrease in false-positive rates with use of mammography plus tomosynthesis compared with mammography alone.

Digital breast tomosynthesis: observer performance study.

Abstract: OBJECTIVE. The purpose of this study was to compare in a retrospective observer study the diagnostic performance of full-field digital mammography (FFDM) with that of digital breast tomosynthesis.MATERIALS AND METHODS. Eight experienced radiologists interpreted images from 125 selected examinations, 35 with verified findings of cancer and 90 with no finding of cancer. The four display conditions included FFDM alone, 11 low-dose projections, reconstructed digital breast tomosynthesis images, and a combined display mode of FFDM and digital breast tomosynthesis images. Observers rated examinations using the screening BI-RADS rating scale and the free-response receiver operating characteristic paradigm. Observer performance levels were measured as the proportion of examinations prompting recall of patients for further diagnostic evaluation. The results were presented in terms of true-positive fraction and false-positive fraction. Performance levels were compared among the acquisitions and reading modes. Time ...

Two-View Digital Breast Tomosynthesis Screening with Synthetically Reconstructed Projection Images: Comparison with Digital Breast Tomosynthesis with Full-Field Digital Mammographic Images

Abstract: The two-view combination of a synthesized two-dimensional view plus digital breast tomosynthesis should be considered acceptable for routine use in mammographic screening.

Predictors of clinical improvement in rituximab-treated refractory adult and juvenile dermatomyositis and adult polymyositis

Abstract: The idiopathic inflammatory myopathies (IIM) are a group of acquired, heterogeneous, systemic connective tissue diseases (CTD) that include polymyositis (PM), adult dermatomyositis (DM), childhood myositis (predominantly juvenile DM), myositis associated with cancer or another connective tissue disease, and inclusion body myositis (IBM) (1, 2). Over the last few decades, survival has improved in IIM, with patients experiencing less cumulative damage and better health related quality of life. Despite an improvement in survival, our knowledge about clinical and serological predictors of clinical improvement in IIM is limited by the lack of well-designed, long–term epidemiological studies and clinical trials. IIM patients have heterogeneous features from a mild rash to life threatening muscle weakness or lung involvement. Their course can be self-limited or may require long-term glucocorticoids and multiple immunosuppressive medications. The response to immunosuppressive drugs is quite variable and current data do not allow the accurate prediction of clinical improvement, which poses a significant challenge to treating physicians, as well as investigators. The varying clinical features of myositis are closely linked to myositis autoantibodies, some of which may contribute to the pathogenesis of IIM (3). Although these autoantibodies provide useful prognostic information on patient outcomes (4–6), this relationship has not been established in prospective cohorts with uniform treatment. Previous evidence suggested that patients possessing anti-Mi-2 autoantibodies had a better prognosis, while patients with anti-SRP fared worse and those with anti-synthetase autoantibodies had intermediate outcomes (6, 7). In addition, there is a paucity of literature regarding predictors of clinical improvement by IIM disease subgroups. IBM is associated with poor treatment responses but studies differentiating responses between PM, DM and JDM are lacking (6). Treatment delay, muscle damage and longer disease duration have also been shown to be associated with poor prognosis (7–10). However, published studies are limited by small sample sizes, retrospective design and a limited assessment of prognostic factors. The availability of targeted therapies and validated outcome measures (11–13) spearheaded the recently completed Rituximab in Myositis (RIM) trial that was designed to evaluate the safety and efficacy of B cell depletion in adult and pediatric myositis patients (14). Rituximab has been studied in a wide variety of autoimmune diseases, as B cells play a critical role in the initiation and propagation of the immune response and are specifically implicated in the pathogenesis of myositis (15). As biologic agents are increasingly utilized in autoimmune diseases, it is important to elucidate the factors that predict a favorable outcome so that clinical trials can be designed with stratification of patients with a good and poor likelihood of improvement. The aim of this study was to identify the clinical and laboratory predictors of clinical improvement in a trial of refractory myositis subjects treated with B cell depletion. This is the first comprehensive study in myositis to evaluate factors associated with clinical improvement in a large prospective cohort.

Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration

Abstract: To compare double readings when interpreting full field digital mammography (2D) and tomosynthesis (3D) during mammographic screening. A prospective, Ethical Committee approved screening study is underway. During the first year 12,621 consenting women underwent both 2D and 3D imaging. Each examination was independently interpreted by four radiologists under four reading modes: Arm A—2D; Arm B—2D + CAD; Arm C—2D + 3D; Arm D—synthesised 2D + 3D. Examinations with a positive score by at least one reader were discussed at an arbitration meeting before a final management decision. Paired double reading of 2D (Arm A + B) and 2D + 3D (Arm C + D) were analysed. Performance measures were compared using generalised linear mixed models, accounting for inter-reader performance heterogeneity (P < 0.05). Pre-arbitration false-positive scores were 10.3 % (1,286/12,501) and 8.5 % (1,057/12,501) for 2D and 2D + 3D, respectively (P < 0.001). Recall rates were 2.9 % (365/12,621) and 3.7 % (463/12,621), respectively (P = 0.005). Cancer detection was 7.1 (90/12,621) and 9.4 (119/12,621) per 1,000 examinations, respectively (30 % increase, P < 0.001); positive predictive values (detected cancer patients per 100 recalls) were 24.7 % and 25.5 %, respectively (P = 0.97). Using 2D + 3D, double-reading radiologists detected 27 additional invasive cancers (P < 0.001). Double reading of 2D + 3D significantly improves the cancer detection rate in mammography screening. • Tomosynthesis-based screening was successfully implemented in a large prospective screening trial. • Double reading of tomosynthesis-based examinations significantly reduced false-positive interpretations. • Double reading of tomosynthesis significantly increased the detection of invasive cancers.

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Abstract: Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Clinical practice guidelines in oncology

Abstract: Ductal carcinoma in situ (DCIS) of the breast represents a heterogeneous group of neoplastic lesions in the breast ducts. The goal for management of DCIS is to prevent the development of invasive breast cancer. This manuscript focuses on the NCCN Guidelines Panel recommendations for the workup, primary treatment, risk reduction strategies, and surveillance specific to DCIS.

International evaluation of an AI system for breast cancer screening.

Abstract: Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening. An artificial intelligence (AI) system performs as well as or better than radiologists at detecting breast cancer from mammograms, and using a combination of AI and human inputs could help to improve screening efficiency.

Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement

Abstract: This guideline from the USPSTF is based on current evidence on mammography, digital breast tomography, and supplemental screening for breast cancer. The recommendations apply to asymptomatic women ...