Comparison of accuracy captured by different controlled languages in oral pathology diagnoses.

This project was comparing the accuracy of capturing the oral pathology diagnoses among different coding systems. 55 diagnoses were selected for comparison among 5 coding systems. The results of accuracy in capturing oral diagnoses are: AFIP (96.4%), followed by Read 99 (85.5%), SNOMED 98 (74.5%), ICD-9 (43.6%), and CDT-3 (14.5%). It shows that the currently used coding systems, ICD-9 and CDT-3, were inadequate, whereas the AFIP coding system captured the majority of oral diagnoses. In conclusion, the most commonly used medical and dental coding systems lack terms for the diagnosis of oral and dental conditions.

Accuracy and self correction of information received from an internet breast cancer list: content analysis.

OBJECTIVES: To determine the prevalence of false or misleading statements in messages posted by internet cancer support groups and whether these statements were identified as false or misleading and corrected by other participants in subsequent postings. DESIGN: Analysis of content of postings. SETTING: Internet cancer support group Breast Cancer Mailing List. MAIN OUTCOME MEASURES: Number of false or misleading statements posted from 1 January to 23 April 2005 and whether these were identified and corrected by participants in subsequent postings. RESULTS: 10 of 4600 postings (0.22%) were found to be false or misleading. Of these, seven were identified as false or misleading by other participants and corrected within an average of four hours and 33 minutes (maximum, nine hours and nine minutes). CONCLUSIONS: Most posted information on breast cancer was accurate. Most false or misleading statements were rapidly corrected by participants in subsequent postings.

EVALUATION OF THE QUANTITATIVE ACCURACY OF A COMMERCIALLY-AVAILABLE POSITRON EMISSION MAMMOGRAPHY SCANNER

Objective: The PEM Flex Solo II (Naviscan, Inc., San Diego, CA) is currently the only commercially-available positron emission mammography (PEM) scanner. This scanner does not apply corrections for count rate effects, attenuation or scatter during image reconstruction, potentially affecting the quantitative accuracy of images. This work measures the overall quantitative accuracy of the PEM Flex system, and determines the contributions of error due to count rate effects, attenuation and scatter. Materials and Methods: Gelatin phantoms were designed to simulate breasts of different sizes (4 – 12 cm thick) with varying uniform background activity concentration (0.007 – 0.5 μCi/cc), cysts and lesions (2:1, 5:1, 10:1 lesion-to-background ratios). The overall error was calculated from ROI measurements in the phantoms with a clinically relevant background activity concentration (0.065 μCi/cc). The error due to count rate effects was determined by comparing the overall error at multiple background activity concentrations to the error at 0.007 μCi/cc. A point source and cold gelatin phantoms were used to assess the errors due to attenuation and scatter. The maximum pixel values in gelatin and in air were compared to determine the effect of attenuation. Scatter was evaluated by comparing the sum of all pixel values in gelatin and in air. Results: The overall error in the background was found to be negative in phantoms of all thicknesses, with the exception of the 4-cm thick phantoms (0%±7%), and it increased with thickness (-34%±6% for the 12-cm phantoms). All lesions exhibited large negative error (-22% for the 2:1 lesions in the 4-cm phantom) which increased with thickness and with lesion-to-background ratio (-85% for the 10:1 lesions in the 12-cm phantoms). The error due to count rate in phantoms with 0.065 μCi/cc background was negative (-23%±6% for 4-cm thickness) and decreased with thickness (-7%±7% for 12 cm). Attenuation was a substantial source of negative error and increased with thickness (-51%±10% to -77% ±4% in 4 to 12 cm phantoms, respectively). Scatter contributed a relatively constant amount of positive error (+23%±11%) for all thicknesses. Conclusion: Applying corrections for count rate, attenuation and scatter will be essential for the PEM Flex Solo II to be able to produce quantitatively accurate images.

Improving the accuracy of radiation pneumonitis dose response models

The prognosis for lung cancer patients remains poor. Five year survival rates have been reported to be 15%. Studies have shown that dose escalation to the tumor can lead to better local control and subsequently better overall survival. However, dose to lung tumor is limited by normal tissue toxicity. The most prevalent thoracic toxicity is radiation pneumonitis. In order to determine a safe dose that can be delivered to the healthy lung, researchers have turned to mathematical models predicting the rate of radiation pneumonitis. However, these models rely on simple metrics based on the dose-volume histogram and are not yet accurate enough to be used for dose escalation trials. The purpose of this work was to improve the fit of predictive risk models for radiation pneumonitis and to show the dosimetric benefit of using the models to guide patient treatment planning. The study was divided into 3 specific aims. The first two specifics aims were focused on improving the fit of the predictive model. In Specific Aim 1 we incorporated information about the spatial location of the lung dose distribution into a predictive model. In Specific Aim 2 we incorporated ventilation-based functional information into a predictive pneumonitis model. In the third specific aim a proof of principle virtual simulation was performed where a model-determined limit was used to scale the prescription dose. The data showed that for our patient cohort, the fit of the model to the data was not improved by incorporating spatial information. Although we were not able to achieve a significant improvement in model fit using pre-treatment ventilation, we show some promising results indicating that ventilation imaging can provide useful information about lung function in lung cancer patients. The virtual simulation trial demonstrated that using a personalized lung dose limit derived from a predictive model will result in a different prescription than what was achieved with the clinically used plan; thus demonstrating the utility of a normal tissue toxicity model in personalizing the prescription dose.

Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^