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Journal of Medical Physics logoLink to Journal of Medical Physics
. 2025 Dec 31;50(4):685–692. doi: 10.4103/jmp.jmp_74_25

Detection of Intrafractional Set-Up Errors Using Electronic Portal Imaging Device-based in vivo Dosimetry in Deep-inspiration Breath-hold Irradiation for Left Breast Cancer

Mai Moritani 1, Yoshihiro Ueda 1,, Shoki Inui 1, Hikari Minami 1, Yuya Nitta 1, Sayaka Kihara 1, Asako Hirose 1, Masaru Isono 1, Koji Konishi 1
PMCID: PMC12893369  PMID: 41684481

Abstract

Aim:

This study aimed to clarify the relationship between the results of in vivo dosimetry (IVD) analysis and intrafractional set-up errors (SEs) to determine the criteria for detecting intra-fractional SEs of 3 mm.

Methods:

Fifteen patients undergoing deep-inspiration breath-hold (DIBH) irradiation for left breast cancer were included in the study. The prescribed dose and fraction size were 50 Gy for six patients, and 42.56 Gy for nine patients. Visual coaching devices were used to improve the reproducibility of deep inspiration states. For IVD, integrated electronic portal imaging device (EPID) images were obtained using treatment beams. Intra-fractional SEs were detected, and gamma analysis was performed on these images. Receiver-operating characteristic curves were calculated to assess the accuracy of the detection of the intrafractional SEs for each criterion.

Results:

The mean values for two-dimensional vectors, absolute Z-direction, and three dimensional (3D) vectors of intra-fractional SEs were 1.9 ± 1.5 mm, 1.8 ± 1.6 mm, and 2.9 ± 1.8 mm, respectively. The mean γ-pass rates in each criterion were 90.6% ± 10.4%, 89.6% ± 10.8%, 92.7% ± 9.7%, 94.8% ± 8.3%, and 94.4% ± 8.3% for 2% 3 mm, 3% 2 mm, 3% 3 mm, 3% 4 mm, and 4% 3 mm, respectively. The correlation coefficients between the SEs in the 3D vector and each γ value ranged from 0.6 to 0.8.

Conclusions:

In IVD with EPIDs for DIBH irradiation, the optimal γ-analysis index for intrafractional SEs detection >3 mm is 3% 3 mm.

Keywords: Deep-inspiration breath-hold irradiation, electronic portal imaging device, in vivo dosimetry, intrafractional set-up errors

INTRODUCTION

Breast cancer is the most commonly diagnosed cancer in women worldwide and the leading cause of cancer-related deaths.[1] It can be cured with early detection and treatment, and the development of treatments for breast cancer is ongoing.[2] One treatment option for breast cancer is postoperative radiotherapy, which is associated with certain adverse effects.

In postoperative radiotherapy for left breast cancer, there is a concern regarding the increased risk of cardiac disease because of the higher cardiac dose in postoperative radiotherapy of the left breast than that of the right breast.[3] A previous study demonstrated that the risk increased by 7.4% for every 1-Gy increase in cardiac dose, following a linear pattern.[4] The deep-inspiration breath-hold (DIBH) technique, which only delivers radiation when the patient is holding a deep inspiration breath, has been adapted to reduce the cardiac dose. A previous study stated that postoperative radiotherapy with DIBH could reduce the average cardiac dose by 25%–67% compared with free breathing.[5]

However, the ability to maintain a breath-hold varies from patient to patient and day to day.[6] Specifically, Wu et al. reported that set-up errors (SEs) were within 3 mm in all dorsoventral, cephalad, and lateral directions in DIBH.[7] These cause intrafractional SEs in the left breast during treatment. Detecting intrafractional SEs is crucial to ensure safe DIBH. One method used to evaluate intrafractional SEs is to acquire and analyse cine images using an electronic portal imaging device (EPID). However, no existing systems commonly used in clinical practice have any software to analyze EPID-cine images, and in-house software is required.[8] To analyze EPID-cine images using in-house software, EPID-cine image files must be output from the treatment system and imported into the analysis software. It is nearly impossible to detect intrafractional SEs in real-time during daily treatment for multiple patients.

One method for assessing intrafractional SEs during the daily treatment of patients is in vivo dosimetry (IVD). There are various methods for IVD measurements, with diode- and EPID-based measurements being the most common. Diode-based IVD measurements are not suitable for DIBH because of their sensitivity to small changes in the direction and position of the diode.[9] Lee et al. reported that the maximum diode differences were 20% when the angle varied from 0° to 80°.[10]

Recently, an IVD system (PerFRACTION v1.7.3; Sun Nuclear Corporation, Melbourne, FL, “USA”) was applied clinically to detect intrafractional SEs for multiple patients daily.[11] After treatment, the system automatically analyses the integrated EPID images, which can detect SEs in the treatment beam[12] and can output some errors as γ-values by comparison with a reference image. For example, Olch et al. reported the clinical utility of IVD with EPID for the detection of intrafractional SEs.[13] Inui et al. demonstrated the effectiveness of an EPID-based IVD for detecting gases in the body.[11] However, no criteria have been reported to assess the number of intrafractional SEs occurring in DIBH based on the results of IVD analysis. In this study, we aimed to investigate the relationship between the analysis results of IVD using EPID during left-breast DIBH irradiation and intrafractional SEs during treatment, and to clarify optimal criteria for detecting SEs exceeding 3 mm.

METHODS

Participants

This study comprised 15 patients who had applied DIBH in postoperative radiotherapy for left breast cancer at the Osaka International Cancer Institute from June 2023 to November 2023. The participants’ age ranged from 35 to 72 (median, 55.5) years. The size of the left breast of the 15 patients in this study was calculated by mammography in the cranial–caudal direction for each patient using the Centricity Universal Viewer Zero Footprint (GE Healthcare, Chicago, IL, USA). The mean breast surface area was 71.04 cm2 (maximum, 219.41 cm2; minimum, 20.27 cm2; median, 66.71 cm2).

Treatment planning

The patients were placed on their backs in a raised position using a wing board (Civco Medical Solutions, Kalona, IA, USA). Two 2-mm-diameter metal balls were affixed: One at the level of the clavicle incision and the other approximately 2 cm below the inframammary groove, to guide the most apical and caudal parts of the left breast. During computed tomography (CT) simulation, the respiratory movement of an infrared reflective marker (IRM) placed over the abdomen was detected using the respiratory gating of the scanner system (Varian Medical Systems, Palo Alto, CA, USA). The patient posture is shown in Figure 1a. Radiographers explained the DIBH method to patients before CT imaging. The DIBH methods are described as follows. First, the patients performed a breath-hold with maximal inhalation. Next, a visual coaching device (VCD) was set up to maintain the inhaled state at 80% of the average maximum inhalation position. The VCD monitor screen shown to the patients is shown in Figure 1b. On the VCD, a green band centred 80% from maximum inspiration to ± 3 mm was displayed, and the patients were instructed to stop the white band, the position of the IRM, at the center of the green band during CT simulation.

Figure 1.

Figure 1

Visual coaching device (VCD) system setting for deep-inspiration breath-hold (DIBH) reproducibility, (a) Patient setup using wing board, VCD and infrared reflective marker (IRM), (b) VCD monitor screen shown to the patients. The white band is the position of IRM, and the green band is defined at the 80% position of the patient’s maximum inspiration. The black arrow indicates the range of ±3mm from maximum inspiration. VCD: Visual coaching device, IRM: Infrared reflective marker

One CT image set was acquired, whereas the patients held their breath for 20 s in DIBH using a 64-channel multi-detector CT scanner (Revolution HD, GE Healthcare). This was repeated thrice to acquire three CT image sets. The CT slice thickness and field of view were 2.0 mm and 50 cm, respectively. The reason for acquiring three sets of CT images was to assess how much the irradiation target shifted with respiration. Out of these three scans, those where respiration could not be stably suppressed were excluded. The case showing the least lung expansion among the remaining images was then used for treatment planning.

The CT image set was transferred to the radiation treatment planning system (Eclipse v15.6; Varian Medical Systems). The radiation oncologists outlined the left breast, lungs, heart, and humerus based on CT images. The treatment machine and beam energy of the main field used for planning were TrueBeam (Varian Medical Systems) equipped with a Millennium multi-leaf collimator (MLC) and 4 MV or 6 MV X-rays at dose rates of 250 MU/min and 600 MU/min. The beam angles, collimator angles, and irradiation fields were determined by the radiation oncologists. The calculation algorithm was an anisotropic analytical algorithm. The prescribed dose and fraction size were 50 Gy and 25 fractions, respectively, for six patients and 42.56 Gy and 16 fractions, respectively, for nine patients at the reference points. To reduce the maximum dose to ≤107%, enhanced dynamic wedges or field-in-field techniques were used for planning. The beam energies for small fields in the field-in-field techniques were 10 MV and 6 MV, with a dose rate of 600 MU/min.

The flow of patient positioning at treatment

The flow of patient positioning and breath-hold training before treatment was as follows. First, the patient’s position was adjusted by matching skin marks on the patient’s chest with lasers projected from the walls and ceiling of the treatment room. An IRM was placed on each patient’s abdomen. The patients were briefed by radiographers about the DIBH procedure as in the CT simulation. The treatment couch was then moved after matching using X-ray images taken from the frontal and lateral views in the DIBH state, focusing on the ribs, body contours, and humeral head. Finally, a linac graph was obtained with the treatment field, and position corrections were made in the anterior–posterior and superior–inferior directions to determine the treatment position. When a shift of >3 mm in the AP direction was observed, the green band was determined again by detecting 80% of the position from the maximum inhalation in the same manner as described above, with the VCD switched off.

During treatment, the source-to-image distance for the EPID was set to 160 cm to obtain an integrated image with the treatment beam. DIBH was continued for 20 s for each irradiation during treatment. After treatment, the acquired EPID-integrated images were automatically imported using the PerFRACTION and ARIA systems (Varian Medical Systems).

In vivo dosimetry

PerFRACTION is an automated patient quality assurance software for radiotherapy that automatically collects and analyses EPID images per beam and log files output from the treatment device. The analysis uses a graphics processing unit-accelerated convolution/superposition algorithm (sun nuclear dose calculator) to assess treatment accuracy based on user-defined thresholds. In the log file, irradiation parameters such as MU, dose rate, gantry angle, couch angle, and MLC position are recorded over time. Zhuang and Olch showed that positional and angular errors, as well as dose fluctuations, could be identified with high accuracy using PerFRACTION.[14] Specifically, positional errors of <1 mm, rotational errors of 0.5°, and power fluctuations of up to 0.2% can be detected. PerFRACTION has two verification methods: “Fraction 0” for treatment plan verification and “Fraction N” for in vivo patient monitoring during treatment.[15] The Fraction N mode was used in this study. Using the integrated EPID images acquired in fraction 1 as a baseline, the EPID images of each fraction were compared using relative ratings in two-dimensional (2D) analysis. In this study, γ analyses were performed for the main field of each plan at the criteria, such as 3% 3 mm, 2% 3 mm, 4% 3 mm, 3% 2 mm, and 3% 4 mm.

Intra-fractional set-up errors

Intra-fractional SEs were calculated using the Offline Review function in the ARIA system as follows. The digital reconstructed radiograph (DRR) [Figure 2a] and the acquired integrated image [Figure 2b] for each patient were fused. This fused image [Figure 2c] was used to measure the displacements in the dorsoventral (y-axis), cephalad (z-axis), and lateral (x-axis) directions with reference to the ribs and body lines. The intra-fractional SEs in the x-, y-, and z-axes were then calculated by subtracting the reference, SEs of the first-time treatment, from each of the measured second and subsequent values. The horizontal (x-and y-axes) movement of the image was calculated as a 2D vector, as it could not be separated, whereas the overall horizontal and vertical movements were calculated as three-dimensional (3D) vectors from the square root of the sum of squares of the differences in each direction.

Figure 2.

Figure 2

How to measure intra-fractional SEs on the integrated electronic portal imaging device (EPID) image, (a) The digital reconstructed radiograph of the patient’s left breast, (b) The acquired the integrated EPID image, (c) The fused image of (a and b) was used for measuring intra-fractional set-up errors. 3D: Three dimensional. The white arrow labelled ‘Z’ indicates displacement in the patient’s cephalad direction; the white arrow labelled ‘√(x² + y²) ’ indicates displacement in the horizontal plane (x-axis and y-axis) of the image; the white arrow labelled ‘3D’ visually represents the overall horizontal and vertical displacement.

Statistical analyses

Statistical analyses were performed using SPSS Statistics for Windows v24 (IBM, Armonk, NY, USA). First, Pearson correlations were calculated to derive the relationship between intra-fractional SEs and γ-analysis results. In the present study, a correlation ≥0.3 indicates a weak correlation, and a correlation ≥0.7 indicates a strong correlation.[16] P value less than <0.05 was considered significant. The Wilcoxon test was performed to test for significant differences in the intra-fractional SEs for 2D vectors and the z-axis direction.

Next, receiver operating characteristic (ROC) curves were calculated to assess the accuracy of the SE detection for each criterion. The obtained intrafractional SEs of the 3D vectors were binarised to 3 mm. Measurements ≥3 mm and <3 mm were assigned values of 1 and 0, respectively. Sensitivity and specificity were calculated from the correlation between the binarised intra-fractional SE data and the γ-pass rates for each criterion. The area under the curve (AUC) was calculated using the ROC curve. The interpretation of AUCs was good for 0.8≤ AUC <0.9 and excellent for AUC ≥0.9.[17] In this study, the AUC cutoff value was determined using “the point at which the product of sensitivity and specificity is maximised”.[17]

RESULTS

The mean, standard deviation, and range of the intra-fractional SEs for the 2D vectors, absolute z-directions, and 3D vectors are summarized in Table 1. No significant differences were observed between the intra-fractional SEs for the 2D and Z-directions (P = 0.114). The difference between the two was also small, and the SEs were not biased in any particular direction. The mean γ-pass rates in each criterion were 90.6% ± 10.4%, 89.6% ± 10.8%, 92.7% ± 9.7%, 94.8% ± 8.3%, and 94.4% ± 8.3% for 2% 3 mm, 3% 2 mm, 3% 3 mm, 3% 4 mm, and 4% 3 mm, respectively.

Table 1.

Intra-fractional set-up errors

Statistic (mm) 2D z 3D
Mean±SD 1.9±1.5 1.8±1.6 2.9±1.8
Maximum 8.4 8.4 10.5
Minimum 0.0 0.0 0.2

2D: Two-dimensional, 3D: Three-dimensional, SD: Standard deviation

Figure 3 shows the relationship between the intra-fractional SEs of the 2D vectors and γ-pass rate for each criterion. The γ-pass rate decreased as the SE increased for all the criteria. The correlation coefficients between the intrafractional SEs of the 2D vectors and the γ-pass rate for each criterion are listed in Table 2. A negative correlation was observed for all criteria (P < 0.001). The highest correlation coefficient was observed when the criterion was 3% 3 mm.

Figure 3.

Figure 3

The relationship between the intra-fractional set-up errors of two-dimensional vectors and the γ-pass rate. Solid lines in each graph represent approximate straight lines. (a) The relationship between the intra-fractional set-up errors of the two-dimensional vectors and γ-pass rate for 2%3 mm (b) The relationship between the intra-fractional set-up errors of the two-dimensional vectors and γ-pass rate for 3%2 mm (c) The relationship between the intra-fractional set-up errors of the two-dimensional vectors and γ-pass rate for 3%3 mm (d) The relationship between the intra-fractional set-up errors of the two-dimensional vectors and γ-pass rate for 3%4 mm (e) The relationship between the intra-fractional set-up errors of the two-dimensional vectors and γ-pass rate for 4%3 mm

Table 2.

Correlation coefficients between intra-fractional set-up errors of two-dimensional vectors and the γ-pass rate in each criterion

Directions 2% 3 mm 3% 2 mm 3% 3 mm 3% 4 mm 4% 3 mm
2D −0.668 −0.579 −0.681 −0.647 −0.621
z −0.318 −0.317 −0.316 −0.307 −0.296
3D −0.653 −0.595 −0.655 −0.633 −0.610

2D: Two-dimensional, 3D: Three-dimensional

Figure 4 shows the relationship between the intrafractional SEs in the z-axis direction and the γ-pass rate for each criterion. A loose decreasing trend in the γ-pass rate with increasing SE was observed for all criteria. The correlation coefficients between the intrafractional SEs in the z-axis direction and the γ-pass rate for each criterion are listed in Table 2. A weak negative correlation was observed for all the criteria (P < 0.001).

Figure 4.

Figure 4

The relationship between intra-fractional set-up errors in the Z-axis direction and the γ-pass rate. Solid lines in each graph represent approximate straight lines. (a) The relationship between the intra-fractional set-up errors of the Z-axis and γ-pass rate for 2%3 mm (b) The relationship between the intra-fractional set-up errors of the Z-axis and γ-pass rate for 3%2 mm (c) The relationship between the intra-fractional set-up errors of the Z-axis and γ-pass rate for 3%3 mm (d) The relationship between the intra-fractional set-up errors of the Z-axis and γ-pass rate for 3%4 mm (e) The relationship between the intra-fractional set-up errors of the Z-axis and γ-pass rate for 4%3 mm

Figure 5 shows the relationship between the intra-fractional SEs of the 3D vectors and the γ-pass rate for each criterion. The γ-pass rate decreased as the SE increased for all the criteria. The correlation coefficients between the intrafractional SEs of the 3D vectors and the γ-pass rate for each criterion are listed in Table 2. A negative correlation was observed for all criteria (P < 0.001). The highest correlation coefficient was observed when the criterion was 3% 3 mm.

Figure 5.

Figure 5

The relationship between the intra-fractional set-up errors of the three-dimensional vectors and the γ-pass rate. Solid lines in each graph represent approximate straight lines. (a) The relationship between the intra-fractional set-up errors of the three-dimensional vectors and γ-pass rate for 2%3 mm (b) The relationship between the intra-fractional set-up errors of the three-dimensional vectors and γ-pass rate for 3%2 mm (c) The relationship between the intra-fractional set-up errors of the three-dimensional vectors and γ-pass rate for 3%3 mm (d) The relationship between the intra-fractional set-up errors of the three-dimensional vectors and γ-pass rate for 3%4 mm (e) The relationship between the intra-fractional set-up errors of the three-dimensional vectors and γ-pass rate for 4%3 mm

Figure 6 shows the ROC curves for all criteria. The 3% 3 mm curve passed through the top left-hand corner. The AUCs, sensitivity, specificity, maximum products of sensitivity and specificity, cutoff values, and 95% confidence intervals are summarized in Table 3. The AUC was >0.8 for all criteria, with a maximum of 3% 3 mm. The sensitivity was the highest at 3% 2 mm, 3% 3 mm, and 3% 4 mm. The specificity was highest at 3% 4 mm.

Figure 6.

Figure 6

Receiver operating characteristic curves in all criteria at 3 mm setup error in three-dimensional vector. Two percent 3 mm Light blue, 3% 2 mm Sky blue, 3% 3 mm dark blue, 3% 4 mm yellow, and 4% 3 mm orange

Table 3.

Area under the curve, maximum product of sensitivity and specificity, cut-off value, and 95% confidence interval

2% 3 mm 3% 2 mm 3% 3 mm 3% 4 mm 4% 3 mm
AUC 0.838 0.808 0.852 0.843 0.833
Maximum product 0.589 0.565 0.606 0.606 0.598
Sensitivity 0.6957 0.7115 0.7115 0.7115 0.7081
Specificity 0.8466 0.7945 0.8519 0.8523 0.8440
Cut-off value 91 91 94 97 96
95% CI 0.803–0.874 0.768–0.848 0.818–0.886 0.808–0.879 0.796–0.870

CI: Confidence interval, AUC: Area under the curve

DISCUSSION

This study focused on determining the relationship between the results of EPID-based IVD using PerFRACTION and intrafractional SEs to improve the safety of postoperative radiotherapy in patients with left breast cancer applying the DIBH technique. The γ-pass rate decreased as the intrafractional SEs increased, and the 3% 3 mm criterion for IVD to detect a 3-mm SE showed the strongest correlation and was the optimal threshold value. This is the first study on the application of IVD in DIBH, which is crucial for ensuring safe irradiation during DIBH.

According to the data on the accuracy assessment of PerFRACTION reported by Olch et al., the proportion of fractions with a γ-pass rate of <93% was 8.3% for 3% 3 mm compared with 20.5% for 2% 2 mm.[18] Furthermore, in the thoracic region, 13.5% of patients at 3% 3 mm and 53.9% at 2% 2 mm had a γ-pass rate of <93%. The rate at 2% 2 mm was the largest of all treatment regions. In this study, the proportion of fractions with pass rates <95% at each criterion was 30.1%, 31.6%, 28.2%, 24.8%, and 24.8% at 2% 3 mm, 3% 2 mm, 3% 3 mm, 3% 4 mm, and 4% 3 mm, respectively. These data suggest that 2% 2 mm, 2% 3 mm, and 3% 2 mm are significantly sensitive to detect SEs and are therefore unsuitable as references for DIBH.

The γ-pass rate in the data of Olch et al. was higher than that in this study,[13] which can be due to the following reason. The tangential irradiation field included the breast and air areas. When the breast shifts, the ratio of tissue to air in the beam path changes. Zhuang and Olch showed that EPID-based IVD is highly sensitive to changes in patient thickness.[14] Air and tissue have remarkably different capacities to absorb X-rays, and these differences can be observed in the integrated EPID images, resulting in changes in the γ-pass rate. Therefore, the γ-pass rate of PerFRACTION is sensitive to SEs during the tangential irradiation of the breast. In the study by Olch et al., most irradiation regions were in the body and head, suggesting that there were fewer changes in the body material along the beam path. Even if SEs occur, the change in the degree of X-ray absorption along the beam path may be smaller than the change in tangential irradiation of the breast. Therefore, the γ-pass rates observed in this study were lower than those reported by Olch et al.

Regarding the correlation between SEs and γ-pass rate, the results in Figure 2 were insensitive to errors in the z-axis direction. When an intrafractional SE occurs in the z-direction, the flatter the breast, the smaller the change in the breast shape in the integrated EPID image. Therefore, even with a large SE in the z-axis direction, the γ-pass rate did not significantly decrease. The accuracy of detection of SEs with PerFRACTION in postoperative radiotherapy with DIBH may be dependent on breast size. This is because the γ-value in the IVD is thought to be different for flat and raised breasts, even with the same SEs. According to Maskarinec et al., the breasts of Caucasian and Native Hawaiian women were approximately 50% larger than those of Japanese and Chinese women.[18] Therefore, it is unknown whether the data from this study apply equally well to Caucasian women.

Ueda et al. reported that the length of the heart in the irradiated field was strongly correlated with the dose delivered.[19] They reported that a 5-mm change in maximum heart distance resulted in an approximate 3% change in average dose. These findings indicate that intra-fractional SEs exceeding 5 mm could compromise the adequacy of treatment in DIBH. When SEs are detected continuously using the present system, breathing instructions for DIBH may be provided to patients during the treatment period to improve DIBH accuracy. Therefore, we considered that a system with intrafractional SEs of at least 3 mm was required to detect anomalies, and 3 mm was used as the criterion for assessing the detection accuracy using ROC analysis. The present system had an AUC of 0.852 at 3% 3 mm when detecting 3-mm SEs. This suggests that at 3% 3 mm in this study, intrafractional SEs >3 mm in IVD by EPID are sufficiently detectable.

A limitation of this study is that the left breast position can shift gently while holding one’s breath. Estoesta et al. reported that the difference between the distance from the central axis to the internal chest wall in DRR and the equivalent distance (at treatment) in EPID-cine images acquired during treatment was 0.28 cm on average in 20 patients with DIBH.[20] However, as this study uses integrated images, it is not possible to check whether the γ-pass rate changes with changes in breast position during treatment.

CONCLUSIONS

This study investigated the relationship between intrafractional SEs during DIBH irradiation for left breast cancer and the results of EPID-based IVD using PerFRACTION. A significant negative correlation was observed between γ-pass rates and intrafractional SEs, and the 3%/3 mm criterion showed the strongest correlation. The results of this study show that the 3%/3 mm criterion for γ-analysis is the most suitable for the detection of intrafractional SEs exceeding 3 mm in IVD using EPID. It can therefore be concluded that applying the 3%/3 mm criterion in the clinical implementation of IVD during DIBH irradiation is useful for improving the safety and accuracy of treatment. EPID-based IVD for DIBH is expected to enhance reliability in clinical settings and contribute to the safe and reproducible treatment by advancing the generalisation of detection criteria and international standardisation through collaborative research with other institutions in the future.

Conflicts of interest

There are no conflicts of interest.

Acknowledgment

We thank Dr. Matthias, from Editage (https://www.editage.jp/) for editing a draft of this manuscript.

Funding Statement

Nil.

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