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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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Criterion function values  of the proposed IIFDL and CNN: (a)  of IIFDL and CNN according to the number of clusters, (b)  of IIFDL according to the breathing feature combination, and (c)  of CNN according to the breathing feature combination. In IIFDL, the number of cluster  was 11 and the optimal breathing feature combination Y was chosen as BRF and MLE by (8). In CNN,  was 12 and its Y was a combination of ACC, VEL, and PCA.
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fig4: Criterion function values of the proposed IIFDL and CNN: (a) of IIFDL and CNN according to the number of clusters, (b) of IIFDL according to the breathing feature combination, and (c) of CNN according to the breathing feature combination. In IIFDL, the number of cluster was 11 and the optimal breathing feature combination Y was chosen as BRF and MLE by (8). In CNN, was 12 and its Y was a combination of ACC, VEL, and PCA.

Mentions: We present the patient clustering results with the calculated criterion function values . Fig. 4(a) shows criterion function values regarding the number of clusters, where we represented the proposed IIFDL as a red line with a ‘’ maker, and the alternate CNN [33] as a blue dotted line with a ‘’ marker. Fig. 4(b) and 4(c) show criterion function values of IIFDL and CNN with regard to the possible breathing feature combination.FIGURE 4.


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)

Criterion function values  of the proposed IIFDL and CNN: (a)  of IIFDL and CNN according to the number of clusters, (b)  of IIFDL according to the breathing feature combination, and (c)  of CNN according to the breathing feature combination. In IIFDL, the number of cluster  was 11 and the optimal breathing feature combination Y was chosen as BRF and MLE by (8). In CNN,  was 12 and its Y was a combination of ACC, VEL, and PCA.
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getmorefigures.php?uid=PMC4862314&req=5

fig4: Criterion function values of the proposed IIFDL and CNN: (a) of IIFDL and CNN according to the number of clusters, (b) of IIFDL according to the breathing feature combination, and (c) of CNN according to the breathing feature combination. In IIFDL, the number of cluster was 11 and the optimal breathing feature combination Y was chosen as BRF and MLE by (8). In CNN, was 12 and its Y was a combination of ACC, VEL, and PCA.
Mentions: We present the patient clustering results with the calculated criterion function values . Fig. 4(a) shows criterion function values regarding the number of clusters, where we represented the proposed IIFDL as a red line with a ‘’ maker, and the alternate CNN [33] as a blue dotted line with a ‘’ marker. Fig. 4(b) and 4(c) show criterion function values of IIFDL and CNN with regard to the possible breathing feature combination.FIGURE 4.

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