Classification of changes occurring in lung patient during radiotherapy using relative γ analysis and hidden Markov models

PURPOSE

To present a new automated patient classification method based on relative gamma analysis and hidden Markov models (HMM) to identify patients undergoing important anatomical changes during radiation therapy.

METHODS

Daily EPID images of every treatment field were acquired for 52 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis. Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g., average gamma value, standard deviation, percent above 1). These parameters formed patient-specific time series. Data from the first 24 patients were used as a training set for the HMM. The trained HMM was then applied to the remaining 28 patients and compared to manual clinical evaluation and fixed thresholds.

RESULTS

A three-category system was used for patient classification ranging from minor deviations (category 1) to severe deviations (category 3) from the treatment plan. Patient classified using the HMM lead to the same result as the classification made by a human expert 83% of the time. The HMM overestimate the category 10% of the time and underestimate 7% of the time. Both methods never disagree by more than one category. In addition, the information provided by the HMM is richer than the simple threshold-based approach. HMM provides information on the likelihood that a patient will improve or deteriorate as well as the expected time the patient will remain in that state.

CONCLUSION

We showed a method to classify patients during the course of radiotherapy based on relative changes in EPID images and a hidden Markov model. Information obtained through this automated classification can complement the clinical information collected during treatment and help identify patients in need of a plan adaptation.

4DCT and CBCT based PTV margin in Stereotactic Body Radiotherapy(SBRT) of non-small cell lung tumor adhered to chest wall or diaphragm

Background

Large tumor motion often leads to larger treatment volumes, especially the lung tumor located in lower lobe and adhered to chest wall or diaphragm. The purpose of this work is to investigate the impacts of planning target volume (PTV) margin on Stereotactic Body Radiotherapy (SBRT) in non-small cell lung cancer (NSCLC).

Methods

Subjects include 20 patients with the lung tumor located in lower lobe and adhered to chest wall or diaphragm who underwent SBRT. Four-dimensional computed tomography (4DCT) were acquired at simulation to evaluate the tumor intra-fractional centroid and boundary changes, and Cone-beam Computer Tomography (CBCT) were acquired during each treatment to evaluate the tumor inter-fractional set-up displacement. The margin to compensate for tumor variations uncertainties was calculated with various margin calculated recipes published in the exiting literatures.

Results

The means (±standard deviation) of tumor centroid changes were 0.16 (±0.13) cm, 0.22 (±0.15) cm, and 1.37 (±0.81) cm in RL, AP, and SI directions, respectively. The means (±standard deviation) of tumor edge changes were 0.21 (±0.18) cm, 0.50 (±0.23) cm, and 0.19 (±0.44) cm in RL, AP, and SI directions, respectively. The means (±standard deviation) of tumor set-up displacement were 0.03 (±0.24) cm, 0.02 (±0.26) cm, and 0.02 (±0.43) cm in RL, AP, and SI directions, respectively. The PTV margin to compensate for lung cancer tumor variations uncertainties were 0.88, 0.98 and 2.68 cm in RL, AP and SI directions, which were maximal among all margin recipes.

Conclusions

4DCT and CBCT imaging are appropriate to account for the tumor intra-fractional centroid, boundary variations and inter-fractional set-up displacement. The PTV margin to compensate for lung cancer tumor variations uncertainties can be obtained. Our results show that a conventional 1.0 cm margin in the SI plane dose not suffice to compensate the geometrical variety of the tumor located in lower lobe and adhered to chest wall and diaphragm.

Management of independent motion between multiple targets in lung cancer radiation therapy

PURPOSE

To quantify interfractional independent motions between multiple primary targets in radiation therapy (RT) of lung cancer and to study the dosimetric benefits of an online adaptive replanning method to account for these variations.

METHODS AND MATERIALS

Ninety-five on-treatment diagnostic-quality computed tomography (CT) scans acquired for 9 lung cancer patients treated with image-guided RT (IGRT) using a CT-on-rails (CTVision, Siemens) were analyzed. On each on-treatment CT set, contours of the targets (gross tumor volume, clinical target volume, or involved nodes), and organs at risk were generated by populating the planning contours using an autosegmentation tool (ABAS, Elekta) with manual editing. For each patient, an intensity modulated RT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2 Gy per fraction. Three plans were generated and compared for each on-treatment CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes, and segment weights of the original plan to conform to the on-treatment anatomy and a new fully reoptimized plan based on the on-treatment CT.

RESULTS

The interfractional deviations of the distance between centers of masses of the targets from the planning CTs varied from -1.0 to 0.8 cm with an average -0.09 ± 0.41 cm (1 standard deviation). The average combined CTV receiving at least 100% of the prescribed dose (V100) were 99.0 ± 0.7%, 97.8 ± 2.8%, 99.0 ± 0.6%, and 99.1 ± 0.6%, and the lung V_20Gy 928 ± 332 cm³, 944 ± 315 cm³, 917 ± 300 cm³, and 891 ± 295 cm³ for the original, repositioning, adaptive, and reoptimized plans, respectively. Wilcoxon signed-rank tests showed that the adaptive plans were statistically significantly better than the repositioning plans and comparable with the reoptimized plans.

CONCLUSION

Interfractional, relative volume changes and independent motions between multiple primary targets during lung cancer RT, which cannot be accounted for by the current IGRT repositioning exist, but can be corrected by the online adaptive replanning method.

High-quality t2-weighted 4-dimensional magnetic resonance imaging for radiation therapy applications

PURPOSE

The purpose of this study was to improve triggering efficiency of the prospective respiratory amplitude-triggered 4-dimensional magnetic resonance imaging (4DMRI) method and to develop a 4DMRI imaging protocol that could offer T2 weighting for better tumor visualization, good spatial coverage and spatial resolution, and respiratory motion sampling within a reasonable amount of time for radiation therapy applications.

METHODS AND MATERIALS

The respiratory state splitting (RSS) and multi-shot acquisition (MSA) methods were analytically compared and validated in a simulation study by using the respiratory signals from 10 healthy human subjects. The RSS method was more effective in improving triggering efficiency. It was implemented in prospective respiratory amplitude-triggered 4DMRI. 4DMRI image datasets were acquired from 5 healthy human subjects. Liver motion was estimated using the acquired 4DMRI image datasets.

RESULTS

The simulation study showed the RSS method was more effective for improving triggering efficiency than the MSA method. The average reductions in 4DMRI acquisition times were 36% and 10% for the RSS and MSA methods, respectively. The human subject study showed that T2-weighted 4DMRI with 10 respiratory states, 60 slices at a spatial resolution of 1.5 × 1.5 × 3.0 mm(3) could be acquired in 9 to 18 minutes, depending on the individual's breath pattern. Based on the acquired 4DMRI image datasets, the ranges of peak-to-peak liver displacements among 5 human subjects were 9.0 to 12.9 mm, 2.5 to 3.9 mm, and 0.5 to 2.3 mm in superior-inferior, anterior-posterior, and left-right directions, respectively.

CONCLUSIONS

We demonstrated that with the RSS method, it was feasible to acquire high-quality T2-weighted 4DMRI within a reasonable amount of time for radiation therapy applications.