Computed tomography radiation dosimetry: from the indicators to the indications

CT surveillance for type 1 autoimmune pancreatitis: cumulative radiation dose and diagnostic performance for disease relapse

OBJECTIVES

Long-term follow-up is essential for type 1 autoimmune pancreatitis (AIP) patients due to high relapse rates. The cumulative radiation dose from repeated CT scans during follow-up should not be ignored. We aim to investigate the cumulative radiation dose in AIP patients undergoing CT surveillance and the diagnostic performance of CT in detecting disease relapse. The diagnostic performance of MRI from a secondary cohort during the same period was also investigated.

METHODS

This retrospective single-institutional study included 247 type 1 AIP patients with one or more follow-up CT scans, and 120 patients with MR follow-ups. Four metrics were utilized to report the radiation dose, including the volume computed tomography dose index, the dose length product, size-specific dose estimate and effective dose. The diagnostic performance for AIP relapse was assessed, taking the final clinical diagnosis in retrospect as the reference standard.

RESULTS

With a median 2.3-year follow-up period, AIP patients followed up with CT exhibited a median cumulative radiation dose of 37.5 mSv. 11.3% of patients have accumulated doses exceeding 100 mSv. For the 169 patients followed over a year, 30.8% sustained an average annual radiation dose surpassing 20 mSv. The sensitivity/specificity/accuracy of CT for detecting abdominal organ relapse was 64.1%/99.6%/97.0%. For AIP patients followed up with MRI, the sensitivity for detecting disease relapse was 90.5%.

CONCLUSION

Considering the accumulation of radiation dose in AIP patients and the insufficient sensitivity in detecting disease relapse with CT, safer and more sensitive imaging follow-up strategies should be explored.

KEY POINTS

Question CT, as the primary imaging modality for autoimmune pancreatitis (AIP) follow-up, raises concerns regarding radiation exposure and lacks reported diagnostic performance in detecting AIP relapse. Findings CT in AIP follow-up causes significant cumulative radiation exposure and exhibits insufficient sensitivity in relapse detection. Clinical relevance Type 1 AIP necessitates long-term imaging follow-up, yet current guidelines lack consensus regarding the prioritization of CT or MRI for such follow-up. CT is widely used but has radiation concerns and limited sensitivity, calling for safer, efficient strategies.

Radiation Doses in Cardiovascular Computed Tomography

We discussed the contemporary views on the effects of ionising radiation on living organisms and the process of estimating radiation doses in CT examinations and the definitions of the CTDI, CTDIvol, DLP, SSDE, ED. We reviewed the reports from large analyses on the radiation doses in CT examinations of the coronary arteries prior to TAVI procedures, including the CRESCENT, PROTECTION, German Cardiac CT Registry studies. These studies were carried out over the last 10 years and can help confront the daily practice of performing cardiovascular CT examinations in most centres. The reference dose levels for these examinations were also collected. The methods to optimise the radiation dose included tube voltage reduction, ECG-monitored tube current modulation, iterative and deep learning reconstruction techniques, a reduction in the scan range, prospective study protocols, automatic exposure control, heart rate control, rational use of the calcium score, multi-slices and dual-source and wide-field tomography. We also present the studies that indicated the need to raise the organ conversion factor for cardiovascular studies from the 0.014-0.017 mSv/mGy*cm used for chest studies to date to a value of 0.0264-0.03 mSv/mGy*cm.

Modelling the no-show of patients to exam appointments of computed tomography

BACKGROUND

Patients' no-shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no-shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.

METHODS

We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no-shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).

RESULTS

The no-show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature.

CONCLUSIONS

Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Background

We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated.

Methods

The model’s main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal.

Results

The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation.

Conclusions

Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system’s cost is minimized.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06918-y.