PubMed · 6536634
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M Basu, K Moitra, S S Gupta. A few observations on measles immunisation programme.. https://pubmed.ncbi.nlm.nih.gov/6536634/
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PURPOSE: To investigate in three institutions, The Netherlands Cancer Institute (Antoni van Leeuwenhoek Huis [AvL]), Dr. Daniel den Hoed Cancer Center (DDHC), and Dr, Bernard Verbeeten Institute (BVI), how much the patient setup accuracy for irradiation of prostate cancer can be improved by an offline setup verification and correction procedure, using portal imaging. METHODS AND MATERIALS: The verification procedure consisted of two stages. During the first stage, setup deviations were measured during a number (Nmax) of consecutive initial treatment sessions. The length of the average three dimensional (3D) setup deviation vector was compared with an action level for corrections, which shrunk with the number of setup measurements. After a correction was applied, Nmax measurements had to be performed again. Each institution chose different values for the initial action level (6, 9, and 10 mm) and Nmax (2 and 4). The choice of these parameters was based on a simulation of the procedure, using as input preestimated values of random and systematic deviations in each institution. During the second stage of the procedure, with weekly setup measurements, the AvL used a different criterion ("outlier detection") for corrective actions than the DDHC and the BVI ("sliding average"). After each correction the first stage of the procedure was restarted. The procedure was tested for 151 patients (62 in AvL, 47 in DDHC, and 42 in BVI) treated for prostate carcinoma. Treatment techniques and portal image acquisition and analysis were different in each institution. RESULTS: The actual distributions of random and systematic deviations without corrections were estimated by eliminating the effect of the corrections. The percentage of mean (systematic) 3D deviations larger than 5 mm was 26% for the AvL and the DDHC, and 36% for the BVI. The setup accuracy after application of the procedure was considerably improved (percentage of mean 3D deviations larger than 5 mm was 1.6% in the AvL and 0% in the DDHC and BVI), in agreement with the results of the simulation. The number of corrections (about 0.7 on the average per patient) was not larger than predicted. CONCLUSION: The verification procedure appeared to be feasible in the three institutions and enabled a significant reduction of mean 3D setup deviations. The computer simulation of the procedure proved to be a useful tool, because it enabled an accurate prediction of the setup accuracy and the required number of corrections.
PURPOSE: The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. METHODS AND MATERIALS: Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. RESULTS: Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. CONCLUSIONS: An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit.
A method for quantifying peak mitral and tricuspid regurgitant jet flow rate that utilizes a measure of jet orifice velocity (Uo, m s-1), a distal centerline velocity (Um, m s-1), and the intervening distance (X, cm) was recently developed. This method, however, modeled the regurgitant jet as a free jet, whereas many atrial jets are counterflowing jets because of jet opposing intra-atrial flow fields (counterflows). This study evaluated the feasibility of using the free jet quantification equation in the atrium where ambient flow fields may alter jet centerline velocities and therefore reduce the accuracy of jet flow rate calculations. A 4 cm wide chamber was used to pump counterflows of 0, 4, and 22 cm s-1 against jets of 2.3, 4.8, and 6.4 s-1 originating from a 2 mm diameter orifice. For each counterflow-jet combination, jet centerline velocities were measured using laser Doppler anemometry. For free jets (no counterflow), flow rate was calculated with 98% mean accuracy. For all jets in counterflow, the calculation was less accurate as (i) the ratio of jet orifice velocity to counterflow velocity decreased (Uo/Uc, where Uc is counterflow velocity), i.e. the counterflow was relatively more intense, an (ii) centerline measurements were mad further from the orifice. But although counterflow lowered jet centerline velocities beneath free jet values, it did so only significantly in the jet's distal portion, while the initial portion (X/D < 16, where D is jet orifice diameter) of a jet in counterflow behaved essentially as a free jet. Therefore, regurgitant jets, although not classically free because of systolic atrial inflow, will decay in their initial portions as free jets and hence are candidates for quantification with the centerline technique.