Cumulative effective doses from radiologic procedures for pediatric oncology patients.

OBJECTIVE: Our aim was to estimate the cumulative effective doses (CEDs) from radiologic procedures for a cohort of pediatric oncology patients. METHODS: A retrospective cohort study of the imaging histories of 150 pediatric oncology patients (30 each in 5 subgroups, that is, leukemia, lymphomas, brain tumors, neuroblastomas, and assorted solid tumors) for 5 years after diagnosis was performed. All procedures involving ionizing radiation were recorded, including radiography, computed tomography (CT), nuclear medicine (NM) studies, fluoroscopy, and interventional procedures. CED estimates were calculated. RESULTS: Individual CED estimates ranged from <1 mSv to 642 mSv, with a median of 61 mSv. CT and NM were the greatest contributors; CT constituted 30% of procedures but 52% of the total CED, and NM constituted 20% and 46%, respectively. There was considerable variability between tumor subgroups. CED estimates were highest in the neuroblastoma (median: 213 mSv [range: 36-489 mSv]) and lymphoma (median: 191 mSv [range: 10-642 mSv]) groups and lowest in the leukemia group (median: 5 mSv [range: 0.2-57 mSv]). CONCLUSIONS: CEDs from diagnostic and interventional imaging for pediatric oncology patients vary considerably according to diagnoses, individual clinical courses, and imaging modalities used. Increased awareness may promote strategies to reduce the radiation burden to this population.

Imaging in pediatric patients: time to think again about surveillance.

BACKGROUND: Despite concerns regarding ionizing radiation exposures from diagnostic imaging procedures in pediatric patients, many are deemed unavoidable or even mandated by treatment protocols. A prior review at our institution found patients with lymphoma had a higher median cumulative radiation exposure (191 mSv) versus other oncology subgroups (61 mSv). PURPOSE: Estimations of cumulative diagnostic radiation exposures were tabulated for 5 years from the first diagnostic scan for 30 consecutive lymphoma patients diagnosed in 2001. Each individual imaging procedure was reviewed and classified as protocol mandated or discretionary (for disease surveillance, good patient care or radiologist request). RESULTS: Almost all patients (28/29) received chemotherapy; one had surgery only. Individual cumulative radiation exposures ranged from 10 to 642 mSv. Over 5 years, 690 procedures were performed; 303 (44%) X-rays, 203 (29%) CTs, 157 (23%) radionucleotide, and 27 (4%) interventional procedures. Of these, 238 (34%) were protocol required and 452 (66%) discretionary (224 as part of good patient care for a co-morbid illness and 228 for evaluation of possible disease progression/surveillance). A total of 86/217 (40%) studies (including 43 CTs and 38 radionucleotide scans) were performed when the recurrence risk was low (>2 years off therapy). CONCLUSIONS: The majority of ionizing radiation procedures in this lymphoma cohort were discretionary. Given the excellent outcome of this group and the long-term risks; rational use of discretionary surveillance procedures is necessary. Guidelines for the appropriate use of surveillance imaging based on probability of risk recurrence must be developed in order to minimize ionizing radiation exposure.

A comparison of methods for assessing penetrating trauma on retrospective multi-center data.

OBJECTIVE: TraumaSCAN-Web (TSW) is a computerized decision support system for assessing chest and abdominal penetrating trauma which utilizes 3D geometric reasoning and a Bayesian network with subjective probabilities obtained from an expert. The goal of the present study is to determine whether a trauma risk prediction approach using a Bayesian network with a predefined structure and probabilities learned from penetrating trauma data is comparable in diagnostic accuracy to TSW. METHODS: Parameters for two Bayesian networks with expert-defined structures were learned from 637 gunshot and stab wound cases from three hospitals, and diagnostic accuracy was assessed using 10-fold cross-validation. The first network included information on external wound locations, while the second network did not. Diagnostic accuracy of learned networks was compared to that of TSW on 194 previously evaluated cases. RESULTS: For 23 of the 24 conditions modeled by TraumaSCAN-Web, 16 conditions had Areas Under the ROC Curve (AUCs) greater than 0.90 while 21 conditions had AUCs greater than 0.75 for the first network. For the second network, 16 and 20 conditions had AUCs greater than 0.90 and 0.75, respectively. AUC results for learned networks on 194 previously evaluated cases were better than or equal to AUC results for TSW for all diagnoses evaluated except diaphragm and heart injuries. CONCLUSIONS: For 23 of the 24 penetrating trauma conditions studied, a trauma diagnosis approach using Bayesian networks with predefined structure and probabilities learned from penetrating trauma data was better than or equal in diagnostic accuracy to TSW. In many cases, information on wound location in the first network did not significantly add to predictive accuracy. The study suggests that a decision support approach that uses parameter-learned Bayesian networks may be sufficient for assessing some penetrating trauma conditions.

Knowledge-based method for building patient decision-analytic tools.

Numerous health decision aids (HDAs) have been developed to increase the participation of patients in shared decision-making, but many have limited accessibility and narrow applicability in clinical care. In the Health e-Decision project, we address these limitations in our work on building general HDAs targeted for older adults. Our approach uses a decision-support software architecture that enables principled methods for HDAs. We have formalized a novel knowledge-based decision model (KBDM), using Protégé OWL, that developers and clinicians can instantiate to tailor the components of the architecture for a particular health problem. In this paper, we present the methods used in the architecture and the knowledgebase design; the latter encompasses influence-diagram concepts, specific health problems, health outcome states, and probabilistic relationships. We discuss how this approach improves upon prior HDA methods. We also show that our use of computer-interpretable knowledge provides a structured, customizable means of enabling patient-centered decision support.