Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.

A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor's deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images

PURPOSE

To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images.

METHODS

Nineteen patients with lung cancer underwent orthogonal rotational kV x-ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression-based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A: Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PMCL ). Scenario B: The short-arc 4D-CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A. The waveform was repeated for 60 s (W4D-CBCT ) by adapting to the respiratory phase of the external surrogate. W4D-CBCT was used as the target positions (PM4D-CBCT ). Scenario C: The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior-inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W4D-CBCT were corrected based on the AS signal. The AS-corrected W4D-CBCT was used as the target positions (PMAS-4D-CBCT ). Scenario D: The AS signal was extracted from single projection images. Other processes were the same as in Scenario C. The prediction errors were calculated for the remaining 10 s.

RESULTS

The 3D prediction error within 3 mm was 77.3% for PM4D-CBCT , which was 12.8% lower than that for PMCL . Using the diaphragm waveforms, the percentage of errors within 3 mm improved by approximately 7% to 84.0%-85.3% for PMAS-4D-CBCT in Scenarios C and D, respectively. Statistically significant differences were observed between the prediction errors of PM4D-CBCT and PMAS-4D-CBCT .

CONCLUSION

PMAS-4D-CBCT outperformed PM4D-CBCT , proving the efficacy of the AS signal-based correction. PMAS-4D-CBCT would make it possible to predict target positions from 4D-CBCT images without gold markers.

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

PURPOSE

The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning.

METHODS

The dataset consisted of 600 cases of clinical VMAT plans from a single institution. The predictor variables (n = 28) for each plan included complexity parameters, machine type, and photon beam energy. Dosimetric measurements were performed using a helical diode array (ArcCHECK), and the dosimetric accuracy of the passing rates for a 5% dose difference (DD5%) and gamma index of 3%/3 mm (γ3%/3 mm) were predicted using three machine learning models: regression tree analysis (RTA), multiple regression analysis (MRA), and neural networks (NNs). First, the prediction models were applied to 500 cases of the VMAT plans. Then, the dosimetric accuracy was predicted using each model for the remaining 100 cases (evaluation dataset). The error between the predicted and measured passing rates was evaluated.

RESULTS

For the 600 cases, the mean ± standard deviation of the measured passing rates was 92.3% ± 9.1% and 96.8% ± 3.1% for DD5% and γ3%/3 mm, respectively. For the evaluation dataset, the mean ± standard deviation of the prediction errors for DD5% and γ3%/3 mm was 0.5% ± 3.0% and 0.6% ± 2.4% for RTA, 0.0% ± 2.9% and 0.5% ± 2.4% for MRA, and -0.2% ± 2.7% and -0.2% ± 2.1% for NN, respectively.

CONCLUSIONS

NNs performed slightly better than RTA and MRA in terms of prediction error. These findings may contribute to increasing the efficiency of patient-specific quality-assurance procedures.

Effects of respiratory motion on volumetric and positional difference of GTV in lung cancer based on 3DCT and 4DCT scanning

Differences in gross target volume (GTV) and central point positions among moving lung cancer models constructed by CT scanning at different frequencies were compared, in order to explore the effect of different respiratory frequencies on the GTV constructions in moving lung tumors. Eight models in different shapes and sizes were established to stimulate lung tumors. The three-dimensional computed tomography (3DCT) and four-dimensional computed tomography (4DCT) scanning were performed at 10, 15 and 20 times/min in different models. Differences in GTV volumes and central point positions at different motion frequencies were compared by means of GTV3Ds (GTV_3D-10, GTV_3D-15, GTV_3D-20) and IGTV4Ds (IGTV_4D-10, IGTV_4D-15, IGTV_4D-20). Volumes of GTV_3D-10, GTV_3D-15, GTV_3D-20 were 12.41±14.26, 10.38±11.18 and 12.50±15.23 cm³ respectively (P=0.687). Central point coordinates in the x-axis direction were −8.16±96.21, −8.57±96.08 and −8.56±95.73 respectively (P=0.968). Central point coordinates in the y-axis direction were 108.22±25.03, 110.41±22.47 and 109.04±24.24 (P=0.028). Central point coordinates in the z-axis direction were 65.19±13.68, 65.43±13.40 and 65.38±13.17 (P=0.902). The difference was significant in the y-axis direction (P=0.028). Volumes of IGTV_4D-10, IGTV_4D-15, IGTV_4D-20 were 17.78±19.42, 17.43±19.56 and 17.44±18.80 cm³ (P=0.417). Central point coordinates in the x-axis direction were −7.73±95.93, −7.86±95.56 and −7.92±95.14 (P=0.325). Central point coordinates in the y-axis direction were 109.41±24.54, 109.60±24.13 and 109.16±24.28 (P=0.525). Central point coordinates in the z-axis direction were 65.52±13.31, 65.59±13.39 and 65.51±13.34 (P=0.093). However, the central point position of GTV in the head and foot direction by 3DCT scanning was severely affected by the respiratory frequency.