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Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning.

Park S, Lee SJ, Weiss E, Motai Y - IEEE J Transl Eng Health Med (2016)

Bottom Line: Moreover, the computation time of the prediction should be reduced.Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods.The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively.

View Article: PubMed Central - PubMed

ABSTRACT
Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

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Overshoot results for inter-fractional variation. The measurement interval is 115.38ms, which is a middle interval [38.46, 192.30ms]. The maximum overshoot value of the proposed IIFDL was 8.4%. However, CNN and HEKF had large overshoot results up to 100% and wider variance of the overshoot rates than the proposed IIFDL. There was no overshoot value in Class 10.
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fig7: Overshoot results for inter-fractional variation. The measurement interval is 115.38ms, which is a middle interval [38.46, 192.30ms]. The maximum overshoot value of the proposed IIFDL was 8.4%. However, CNN and HEKF had large overshoot results up to 100% and wider variance of the overshoot rates than the proposed IIFDL. There was no overshoot value in Class 10.

Mentions: In Fig. 7, we show the overshoot results of IIFDL, CNN, and HEKF for the inter-fractional variation to demonstrate the stability of the proposed IIFDL.FIGURE 7.


Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning.

Park S, Lee SJ, Weiss E, Motai Y - IEEE J Transl Eng Health Med (2016)

Overshoot results for inter-fractional variation. The measurement interval is 115.38ms, which is a middle interval [38.46, 192.30ms]. The maximum overshoot value of the proposed IIFDL was 8.4%. However, CNN and HEKF had large overshoot results up to 100% and wider variance of the overshoot rates than the proposed IIFDL. There was no overshoot value in Class 10.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4862314&req=5

fig7: Overshoot results for inter-fractional variation. The measurement interval is 115.38ms, which is a middle interval [38.46, 192.30ms]. The maximum overshoot value of the proposed IIFDL was 8.4%. However, CNN and HEKF had large overshoot results up to 100% and wider variance of the overshoot rates than the proposed IIFDL. There was no overshoot value in Class 10.
Mentions: In Fig. 7, we show the overshoot results of IIFDL, CNN, and HEKF for the inter-fractional variation to demonstrate the stability of the proposed IIFDL.FIGURE 7.

Bottom Line: Moreover, the computation time of the prediction should be reduced.Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods.The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively.

View Article: PubMed Central - PubMed

ABSTRACT
Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

No MeSH data available.


Related in: MedlinePlus