Intrafraction tumor motion monitoring and dose reconstruction for liver pencil beam scanning proton therapy.

Background: Pencil beam scanning (PBS) proton therapy can provide highly conformal target dose distributions and healthy tissue sparing. However, proton therapy of hepatocellular carcinoma (HCC) is prone to dosimetrical uncertainties induced by respiratory motion. This study aims to develop intra-treatment tumor motion monitoring during respiratory gated proton therapy and combine it with motion-including dose reconstruction to estimate the delivered tumor doses for individual HCC treatment fractions. Methods: Three HCC-patients were planned to receive 58 GyRBE (n=2) or 67.5 GyRBE (n=1) of exhale respiratory gated PBS proton therapy in 15 fractions. The treatment planning was based on the exhale phase of a 4-dimensional CT scan. Daily setup was based on cone-beam CT (CBCT) imaging of three implanted fiducial markers. An external marker block (RPM) on the patient's abdomen was used for exhale gating in free breathing. This study was based on 5 fractions (patient 1), 1 fraction (patient 2) and 6 fractions (patient 3) where a post-treatment control CBCT was available. After treatment, segmented 2D marker positions in the post-treatment CBCT projections provided the estimated 3D motion trajectory during the CBCT by a probability-based method. An external-internal correlation model (ECM) that estimated the tumor motion from the RPM motion was built from the synchronized RPM signal and marker motion in the CBCT. The ECM was then used to estimate intra-treatment tumor motion. Finally, the motion-including CTV dose was estimated using a dose reconstruction method that emulates tumor motion in beam's eye view as lateral spot shifts and in-depth motion as changes in the proton beam energy. The CTV homogeneity index (HI) The CTV homogeneity index (HI) was calculated as D 2 %  -  D 98 % D 50 %   × 100 % . Results: The tumor position during spot delivery had a root-mean-square error of 1.3 mm in left-right, 2.8 mm in cranio-caudal and 1.7 mm in anterior-posterior directions compared to the planned position. On average, the CTV HI was larger than planned by 3.7%-points (range: 1.0-6.6%-points) for individual fractions and by 0.7%-points (range: 0.3-1.1%-points) for the average dose of 5 or 6 fractions. Conclusions: A method to estimate internal tumor motion and reconstruct the motion-including fraction dose for PBS proton therapy of HCC was developed and demonstrated successfully clinically.

Accuracy and potential improvements of surface-guided breast cancer radiotherapy in deep inspiration breath-hold with daily image-guidance.

Objective.Radiotherapy of left-sided breast cancer in deep inspiration breath-hold (DIBH) reduces the heart dose. Surface guided radiotherapy (SGRT) can guide the DIBH, but the accuracy is subject to variations in the chest wall position relative to the patient surface.Approach.In this study, ten left-sided breast cancer patients received DIBH radiotherapy with tangential fields in 15-18 fractions. After initial SGRT setup in free breathing an orthogonal MV/kV image pair was acquired during SGRT-guided breath-hold. The couch was corrected to align the chest wall during another breath-hold, and a new SGRT reference surface was acquired for the gating. The chest wall position error during treatment was determined from continuous cine MV images in the imager direction perpendicular to the cranio-caudal direction. A treatment error budget was made with individual contributions from the online registration of the setup MV image, the difference in breath-hold level between setup imaging and SGRT reference surface acquisition, the SGRT level during treatment, and intra-fraction shifts of the chest wall relative to the SGRT reference surface. In addition to the original setup protocol (Scenario A), SGRT was also simulated with better integration of image-guidance by capturing either the new reference surface (Scenario B) or the SGRT positional signal (Scenario C) simultaneously with the setup MV image, and accounting for the image-guided couch correction by shifting the SGRT reference surface digitally.Main results.In general, the external SGRT signal correlated well with the internal chest wall position error (correlation coefficient >0.7 for 75% of field deliveries), but external-to-internal target position offsets above 2 mm occasionally occurred (13% of fractions). The PTV margin required to account for the treatment error was 3.5 mm (Scenario A), 3.4 mm (B), and 3.1 mm (C).Significance. Further integration of SGRT with image-guidance may improve treatment accuracy and workflow although the current study did not show large accuracy improvements of scenario B and C compared to scenario A.

Geometric and dosimetric comparison of four intrafraction motion adaptation strategies for stereotactic liver radiotherapy.

The accuracy of stereotactic body radiotherapy (SBRT) in the liver is limited by tumor motion. Selection of the most suitable motion mitigation strategy requires good understanding of the geometric and dosimetric consequences. This study compares the geometric and dosimetric accuracy of actually delivered respiratory gated SBRT treatments for 15 patients with liver tumors with three simulated alternative motion adaptation strategies. The simulated alternatives are MLC tracking, baseline shift adaptation by inter-field couch corrections and no intrafraction motion adaptation. The patients received electromagnetic transponder-guided respiratory gated IMRT or conformal treatments in three fractions with a 3-4 mm gating window around the full exhale position. The CTV-PTV margin was 5 mm axially and 7-10 mm cranio-caudally. The CTV and PTV were covered with 95% and 67% of the prescribed mean CTV dose, respectively. The time-resolved target position error during treatments with the four investigated motion adaptation strategies was used to calculate motion margins and the motion-induced reduction in CTV D 95 relative to the planned dose (ΔD 95). The mean (range) number of couch corrections per treatment session to compensate for tumor drift was 2.8 (0-7) with gating, 1.4 (0-5) with baseline shift adaptation, and zero for the other strategies. The motion margins were 3.5 mm (left-right), 9.4 mm (cranio-caudal) and 3.9 mm (anterior-posterior) without intrafraction motion adaptation, approximately half of that with baseline shift adaptation, and 1-2 mm with MLC tracking and gating. With 7 mm CC margins the mean (range) of ΔD 95 for the CTV was 8.1 (0.6-29.4)%-points (no intrafraction motion adaptation), 4.0 (0.4-13.3)%-points (baseline shift adaptation), 1.0 (0.3-2.2)%-points (MLC tracking) and 0.8 (0.1-1.8)%-points (gating). With 10 mm CC margins ΔD 95 was instead 4.8 (0.3-14.8)%-points (no intrafraction motion adaptation) and 2.9 (0.2-9.8)%-points (baseline shift adaptation). In conclusion, baseline shift adaptation can mitigate gross dose deficits without the requirement of real-time motion adaptation. MLC tracking and gating, however, more effectively ensure high similarity between planned and delivered doses.

A Feasibility Study on Ribs as Anatomical Landmarks for Motion Tracking of Lung and Liver Tumors at External Beam Radiotherapy.

At external beam radiotherapy for some tumors located at thorax region due to lack of information in gray scale fluoroscopic images tumor position determination is problematic. One of the clinical strategies is to implant clip as internal fiducial marker inside or near tumor to represent tumor position while the contrast of implanted clip is highly observable rather than tumor. As alternative, using natural anatomical landmarks located at thorax region of patient body is proposed to extract tumor position information without implanting clips that is invasive method with possible side effect. Among natural landmarks, ribs of rib-cage structure that result proper visualization at X-ray images may be optimal as representative for tumor motion. In this study, we investigated the existence of possible correlation between ribs as natural anatomical landmarks and various lung and liver tumors located at different sites as challenging issue. A simulation study was performed using data extracted from 4-dimensional extended cardiac-torso anthropomorphic phantom that is able to simulate motion effect of dynamic organs, as well. Several tumor sites with predefined distances originated from chosen ribs at anterior-posterior direction were simulated at 3 upper, middle, and lower parts of chest. Correlation coefficient between ribs and tumors was calculated to investigate the robustness of ribs as anatomical landmarks for tumor motion tracking. Moreover, a consistent correlation model was taken into account to track tumor motion with a rib as best candidate among selected ribs. Final results represent availability of using rib cage as anatomical landmark to track lung and liver tumors in a noninvasive way. Observations of our calculations showed a proper correlation between tumors and ribs while the degree of this correlation is changing depends on tumor site while lung tumors are more varied and complex with less correlation with ribs motion against liver tumors.

Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy.

In external beam radiotherapy, one of the most common and reliable methods for patient geometrical setup and/or predicting the tumor location is use of external markers. In this study, the main challenging issue is increasing the accuracy of patient setup by investigating external markers location. Since the location of each external marker may yield different patient setup accuracy, it is important to assess different locations of external markers using appropriate selective algorithms. To do this, two commercially available algorithms entitled a) canonical correlation analysis (CCA) and b) principal component analysis (PCA) were proposed as input selection algorithms. They work on the basis of maximum correlation coefficient and minimum variance between given datasets. The proposed input selection algorithms work in combination with an adaptive neuro-fuzzy inference system (ANFIS) as a correlation model to give patient positioning information as output. Our proposed algorithms provide input file of ANFIS correlation model accurately. The required dataset for this study was prepared by means of a NURBS-based 4D XCAT anthropomorphic phantom that can model the shape and structure of complex organs in human body along with motion information of dynamic organs. Moreover, a database of four real patients undergoing radiation therapy for lung cancers was utilized in this study for validation of proposed strategy. Final analyzed results demonstrate that input selection algorithms can reasonably select specific external markers from those areas of the thorax region where root mean square error (RMSE) of ANFIS model has minimum values at that given area. It is also found that the selected marker locations lie closely in those areas where surface point motion has a large amplitude and a high correlation.

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers.