Biomedical subjects

Ross D Shachter

Publications and source records attributed to Ross D Shachter.

5 recordsLinked to original sources

PubMed2006

Value of quantitative D-dimer assays in identifying pulmonary embolism: implications from a sequential decision model.

OBJECTIVES: To examine the cost-effectiveness of a quantitative D-dimer assay for the evaluation of patients with suspected pulmonary embolism (PE) in an urban emergency department (ED). METHODS: The authors analyzed different diagnostic strategies over pretest risk categories on the basis of Wells criteria by using the performance profile of the ELISA D-dimer assay (over five cutoff values) and imaging strategies used in the ED for PE: compression ultrasound (CUS), ventilation-perfusion (VQ) scan (over three cutoff values), CUS with VQ (over three cutoff values), computed tomography (CT) angiogram (CTA) with pulmonary portion (CTP) and lower-extremity venous portion, and CUS with CTP. Data used in the analysis were based on literature review. Incremental costs and quality-adjusted-life-years were the outcomes measured. RESULTS: Computed tomography angiogram with pulmonary portion and lower-extremity venous portion without D-dimer was the preferred strategy. CUS-VQ scanning always was dominated by CT-based strategies. When CTA was infeasible, the dominant strategy was D-dimer with CUS-VQ in moderate- and high-Wells patients and was D-dimer with CUS for low-Wells patients. When CTP specificity falls below 80%, or if its overall performance is markedly degraded, preferred strategies include D-dimer testing. Sensitivity analyses suggest that pessimistic assessments of CTP accuracy alter the results only at extremes of parameter settings. CONCLUSIONS: In patients in whom PE is suspected, when CTA is available, even the most sensitive quantitative D-dimer assay is not likely to be cost-effective. When CTA is not available or if its performance is markedly degraded, use of the D-dimer assay has value in combination with CUS and a pulmonary imaging study. These conclusions may not hold for the larger domain of patients presenting to the ED with chest pain or shortness of breath in whom PE is one of many competing diagnoses.

Cost-Benefit Analysis↗

PubMed2006

Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.

PURPOSE: To retrospectively determine whether a Bayesian network (BN) computer model can accurately predict the probability of breast cancer on the basis of risk factors and mammographic appearance of microcalcifications, to improve the positive predictive value (PPV) of biopsy, with pathologic examination and follow-up as reference standards. MATERIALS AND METHODS: The institutional review board approved this HIPAA-compliant study; informed consent was not required. Results of 111 consecutive image-guided breast biopsies performed for microcalcifications deemed suspicious by radiologists were analyzed. Mammograms obtained before biopsy were analyzed in a blinded manner by a breast imager who recorded Breast Imaging Reporting and Data System (BI-RADS) descriptors and provided a probability of malignancy. The BN uses probabilistic relationships between breast disease and mammography findings to estimate the risk of malignancy. Probability estimates from the radiologist and the BN were used to create receiver operating characteristic (ROC) curves, and area under the ROC curve (A(z)) values were compared. PPV of biopsy was also evaluated on the basis of these probability estimates. RESULTS: The BN and the radiologist achieved A(z) values of 0.919 and 0.916, respectively, which were not significantly different. If the 34 patients estimated by the BN to have less than a 10% probability of malignancy had not undergone biopsy, the PPV of biopsy would have increased from 21.6% to 31.2% without missing a breast cancer (P < .001). At this level, the radiologist's probability estimation improved the PPV to 30.0% (P < .001). CONCLUSION: A probabilistic model that includes BI-RADS descriptors for microcalcifications can distinguish between benign and malignant abnormalities at mammography as well as a breast imaging specialist can and may be able to improve the PPV of image-guided breast biopsy.

Adult↗

PubMed2005

Individualizing generic decision models using assessments as evidence.

Complex decision models in expert systems often depend upon a number of utilities and subjective probabilities for an individual. Although these values can be estimated for entire populations or demographic subgroups, a model should be customized to the individual's specific parameter values. This process can be onerous and inefficient for practical decisions. We propose an interactive approach for incrementally improving our knowledge about a specific individual's parameter values, including utilities and probabilities, given a decision model and a prior joint probability distribution over the parameter values. We define the concept of value of elicitation and use it to determine dynamically the next most informative elicitation for a given individual. We evaluated the approach using an example model and demonstrate that we can improve the decision quality by focusing on those parameter values most material to the decision.

Algorithms↗

PubMed2004

A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience.

OBJECTIVE: We sought to determine whether a probabilistic expert system can provide accurate automated imaging-histologic correlations to aid radiologists in assessing the concordance of mammographic findings with the results of imaging-guided breast biopsies. MATERIALS AND METHODS: We created a Bayesian network in which Breast Imaging Reporting and Data System (BI-RADS) descriptors are used to convey the level of suspicion of mammographic abnormalities. Our system is a computer model that links BI-RADS descriptors with diseases of the breast using probabilities derived from the literature. Mammographic findings are used to update pretest probabilities (prevalence of disease) into posttest probabilities applying Bayes' theorem. We evaluated the histologic results of 92 consecutive imaging-guided breast biopsies for concordance with the mammographic findings during radiology-pathology review sessions. First, radiologists with no knowledge of the biopsy results chose BI-RADS descriptors for the mammographic findings. After the histologic diagnosis was revealed, the radiologists assessed concordance between the pathologic results and the mammographic findings. We then input the information gathered from these sessions into the Bayesian network to produce an automated mammographic-histologic correlation. RESULTS: We had a sampling error rate of 1.1% (1/92 biopsies). Our expert system was able to integrate pathologic diagnoses and mammographic findings to obtain probabilities of sampling error, thereby enabling us to identify the incorrect pathologic diagnosis with 100% sensitivity while maintaining a specificity of 91%. CONCLUSION: Our probabilistic expert system has the potential to help radiologists in identifying breast biopsy results that are discordant with mammographic findings and discovering cases in which biopsy sampling errors may have occurred.

Adult↗

PubMed2004

Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography.

Since the widespread adoption of mammographic screening in the 1980's there has been a significant increase in the detection and biopsy of both benign and malignant microcalcifications. Though current practice standards recommend that the positive predictive value (PPV) of breast biopsy should be in the range of 25-40%, there exists significant variability in practice. Microcalcifications, if malignant, can represent either a non-invasive or an invasive form of breast cancer. The distinction is critical because distinct surgical therapies are indicated. Unfortunately, this information is not always available at the time of surgery due to limited sampling at image-guided biopsy. For these reasons we conducted an experiment to determine whether a previously created Bayesian network for mammography could predict the significance of microcalcifications. In this experiment we aim to test whether the system is able to perform two related tasks in this domain: 1) to predict the likelihood that microcalcifications are malignant and 2) to predict the likelihood that a malignancy is invasive to help guide the choice of appropriate surgical therapy.

Bayes Theorem↗