Interim Analysis of a Two-Institution, Prospective Clinical Trial of 4DCT-Ventilation-based Functional Avoidance Radiation Therapy

A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning

PURPOSE

Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning.

MATERIALS AND METHODS

Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model.

RESULTS

CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05).

CONCLUSION

Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.

Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database

PURPOSE

A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods.

METHODS AND MATERIALS

The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]).

RESULTS

Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively.

CONCLUSIONS

Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image

BACKGROUND

The main application of [18F] FDG-PET (18 FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging.

PURPOSE

To develop a deep-learning-based (DL) method to combine 18 FDG-PET and CT images for producing pulmonary perfusion images (PPI).

METHODS

Pulmonary technetium-99 m-labeled macroaggregated albumin SPECT (PPISPECT ), 18 FDG-PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18 FDG-PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi-modality 18 FDG-PET and CT images for producing PPI (PPIDLM ). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single-channel to a dual-channel to combine multi-modality images. For comparative evaluation, 18 FDG-PET images were also used alone to generate PPIDLPET . Sixty-seven samples were randomly selected for training and cross-validation, and 36 were used for testing. The Spearman correlation coefficient (rs ) and multi-scale structural similarity index measure (MS-SSIM) between PPIDLM /PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high-/low- functional lung (HFL/LFL) volumes.

RESULTS

The voxel-wise rs and MS-SSIM of PPIDLM /PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross-validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM /PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS-SSIM with PPISPECT than PPIDLPET (p < 0.001).

CONCLUSIONS

The DL-based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization.

Evaluation of variables predicting PFT changes for lung cancer patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial

PURPOSE

Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional regions of the lung. A phase-II, multi-center, prospective study of 4DCT-ventilation functional avoidance was completed. Pre and post-treatment pulmonary function tests (PFTs) were acquired and assessed pulmonary function change. This study aims to evaluate which clinical, dose and dose-function factors predict PFT changes for patients treated with 4DCT-ventilation functional avoidance radiotherapy.

MATERIALS AND METHODS

56 patients with locally advanced lung cancer receiving radiotherapy were accrued. PFTs were obtained at baseline and three months following radiotherapy and included forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The ability of patient, clinical, dose (lung and heart), and dose-function metrics (metrics that combine dose and 4DCT-ventilation-based function) to predict PFT changes were evaluated using univariate and multivariate linear regression.

RESULTS

Univariate analysis showed that only dose-function metrics and the presence of chronic obstructive pulmonary disease (COPD) were significant (p<0.05) in predicting FEV1 decline. Multivariate analysis identified a combination of clinical (immunotherapy status, presence of thoracic comorbidities, smoking status, and age), along with lung dose, heart dose, and dose-function metrics in predicting FEV1 and FEV1/FVC changes.

CONCLUSION

The current work evaluated factors predicting PFT changes for patients treated in a prospective functional avoidance radiotherapy study. The data revealed that lung dose- function metrics could predict PFT changes, validating the significance of reducing the dose to the functional lung to mitigate the decline in pulmonary function and providing guidance for future clinical trials.

An analysis of the regional heterogeneity in tissue elasticity in lung cancer patients with COPD

Purpose

Recent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity.

Methods

We retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe.

Results

The mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups.

Conclusion

We observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.

Characterizing Pulmonary Function Test Changes for Patients With Lung Cancer Treated on a 2-Institution, 4-Dimensional Computed Tomography-Ventilation Functional Avoidance Prospective Clinical Trial

Purpose

Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy.

Methods and Materials

Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEV1), and forced vital capacity (FVC). PFT metrics were compared for patients who did and did not experience grade 2 or higher pneumonitis.

Results

Fifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEV1, and FVC was -11.6% ± 14.2%, -5.6% ± 16.9%, and -9.0% ± 20.1%, respectively. The mean change in DLCO was -15.4% ± 14.4% for patients with grade 2 or higher radiation pneumonitis and -10.8% ± 14.1% for patients with grade <2 radiation pneumonitis (P = .37). The mean change in FEV1 was -14.3% ± 22.1% for patients with grade 2 or higher radiation pneumonitis and -3.9% ± 15.4% for patients with grade <2 radiation pneumonitis (P = .09).

Conclusions

The current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV1. The presented data can help elucidate the potential pulmonary function improvement with functional avoidance radiation therapy.

Xenon-Enhanced Ventilation Computed Tomography for Functional Lung Avoidance Radiation Therapy in Patients With Lung Cancer

PURPOSE

This phase 2 trial aimed to determine whether xenon-enhanced ventilation computed tomography (XeCT)-guided functional-lung-avoidance radiation therapy could reduce the radiation pneumonitis (RP) rate in patients with lung cancer undergoing definitive chemoradiation therapy.

METHODS AND MATERIALS

Functional lung ventilation was measured via pulmonary function testing (PFT) and XeCT. A standard plan (SP) without reference to XeCT and a functional-lung-avoidance plan (fAP) optimized for lowering the radiation dose to the functional lung at the guidance of XeCT were designed. Dosimetric parameters and predicted RP risks modeled by biological evaluation were compared between the 2 plans in a treatment planning system (TPS). All patients received the approved fAP. The primary endpoint was the rate of grade ≥2 RP, and the secondary endpoints were the survival outcomes. The study hypothesis was that fAP could reduce the rate of grade ≥2 RP to 12% compared with a 30% historical rate.

RESULTS

Thirty-six patients were evaluated. Xenon-enhanced total functional lung volumes positively correlated with PFT ventilation parameters (forced vital capacity, P = .012; forced expiratory volume in 1 second, P = .035), whereas they were not correlated with the diffusion capacity parameter. We observed a 17% rate of grade ≥2 RP (6 of 36 patients), which was significantly different (P = .040) compared with the historical control. Compared with the SP, the fAP significantly spared the total ventilated lung, leading to a reduction in predicted grade ≥2 RP (P = .001) by TPS biological evaluation. The median follow-up was 15.2 months. The 1-year local control (LC), disseminated failure-free survival (DFFS), and overall survival (OS) rates were 88%, 66%, and 91%, respectively. The median LC and OS were not reached, and the median DFFS was 24.0 months (95% confidence interval, 15.7-32.3 months).

CONCLUSIONS

This report of XeCT-guided functional-lung-avoidance radiation therapy provided evidence showing its feasibility in clinical practice. Its benefit should be assessed in a broader multicenter trial setting.

Different functional lung-sparing strategies and radiotherapy techniques for patients with esophageal cancer

Background

Integration of 4D-CT ventilation function images into esophageal cancer radiation treatment planning aimed to assess dosimetric differences between different functional lung (FL) protection strategies and radiotherapy techniques.

Methods

A total of 15 patients with esophageal cancer who had 4D-CT scans were included. Lung ventilation function images based on Jacobian values were obtained by deformation image registration and ventilation imaging algorithm. Several different plans were designed for each patient: clinical treatment planning (non-sparing planning), the same beam distribution to FL-sparing planning, three fixed-beams FL-sparing intensity-modulated radiation therapy (IMRT) planning (5F-IMRT, 7F-IMRT, 9F-IMRT), and two FL-sparing volumetric modulated arc therapy (VMAT) planning [1F-VMAT (1-Arc), 2F-VMAT (2-Arc)]. The dosimetric parameters of the planning target volume (PTV) and organs at risk (OARs) were compared and focused on dosimetric differences in FL.

Results

The FL-sparing planning compared with the non-sparing planning significantly decreased the FL-Dmean, V5-30 and Lungs-Dmean, V10-30 (Vx: volume of receiving ≥X Gy), although it slightly compromised PTV conformability and increased Heart-V40 (P&lt; 0.05). The 5F-IMRT had the lowest PTV-conformability index (CI) but had a lower Lungs and Heart irradiation dose compared with those of the 7F-IMRT and 9F-IMRT (P&lt; 0.05). The 2F-VMAT had higher PTV-homogeneity index (HI) and reduced irradiation dose to FL, Lungs, and Heart compared to those of the 1F-VMAT planning (P&lt; 0.05). The 2F-VMAT had higher PTV conformability and homogeneity and decreased FL-Dmean, V5-20 and Lungs-Dmean, V5-10 but correspondingly increased spinal cord-Dmean compared with those of the 5F-IMRT planning (P&lt; 0.05).

Conclusion

In this study, 4D-CT ventilation function image-based FL-sparing planning for esophageal cancer can effectively reduce the dose of the FL. The 2F-VMAT planning is better than the 5F-IMRT planning in reducing the dose of FL.

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model

Purpose

The current algorithms for measuring ventilation images from 4D cone-beam computed tomography (CBCT) are affected by the accuracy of deformable image registration (DIR). This study proposes a new deep learning (DL) method that does not rely on DIR to derive ventilation images from 4D-CBCT (CBCT-VI), which was validated with the gold-standard single-photon emission-computed tomography ventilation image (SPECT-VI).

Materials and Methods

This study consists of 4D-CBCT and 99mTc-Technegas SPECT/CT scans of 28 esophagus or lung cancer patients. The scans were rigidly registered for each patient. Using these data, CBCT-VI was derived using a deep learning-based model. Two types of model input data are studied, namely, (a) 10 phases of 4D-CBCT and (b) two phases of peak-exhalation and peak-inhalation of 4D-CBCT. A sevenfold cross-validation was applied to train and evaluate the model. The DIR-dependent methods (density-change-based and Jacobian-based methods) were used to measure the CBCT-VIs for comparison. The correlation was calculated between each CBCT-VI and SPECT-VI using voxel-wise Spearman's correlation. The ventilation images were divided into high, medium, and low functional lung regions. The similarity of different functional lung regions between SPECT-VI and each CBCT-VI was evaluated using the dice similarity coefficient (DSC). One-factor ANONA model was used for statistical analysis of the averaged DSC for the different methods of generating ventilation images.

Results

The correlation values were 0.02 ± 0.10, 0.02 ± 0.09, and 0.65 ± 0.13/0.65 ± 0.15, and the averaged DSC values were 0.34 ± 0.04, 0.34 ± 0.03, and 0.59 ± 0.08/0.58 ± 0.09 for the density change, Jacobian, and deep learning methods, respectively. The strongest correlation and the highest similarity with SPECT-VI were observed for the deep learning method compared to the density change and Jacobian methods.

Conclusion

The results showed that the deep learning method improved the accuracy of correlation and similarity significantly, and the derived CBCT-VIs have the potential to monitor the lung function dynamic changes during radiotherapy.

Normal Lung Tissue CT Density Changes after Volumetric-Arc Radiotherapy (VMAT) for Lung Cancer

Radiation-induced lung injury remains a significant toxicity in thoracic radiotherapy. Because a precise diagnosis is difficult and commonly used assessment scales are unclear and subjective, there is a need to establish quantitative and sensitive grading methods. The lung tissue density change expressed in Hounsfield units (HUs) derived from CT scans seems a useful numeric surrogate. The study aimed to confirm a dose-response effect on HU value changes (ΔHU), their evolution in time, and the impact of selected clinical and demographic factors. We used dedicated, self-developed software to register and analyze 120 pairs of initial and follow-up CT scans of 47 lung cancer patients treated with dynamic arc radiotherapy. The differences in HU values between CT scans were calculated within discretized dose-bins limited by isodose lines. We have proved the dose-effect relationship, which is well described with a sigmoid model. We found the time evolution of HU changes to suit a typical clinical presentation of radiation-induced toxicity. Some clinical factors were found to correlate with ΔHU degree: planning target volume (PTV), V35 in the lung, patient’s age and a history of arterial hypertension, and initial lung ventilation intensity. Lung density change assessment turned out to be a sensitive and valuable method of grading post-RT lung toxicity.

Results of a Multi-Institutional Phase 2 Clinical Trial for 4DCT-Ventilation Functional Avoidance Thoracic Radiation Therapy

PURPOSE

Radiation pneumonitis remains a major limitation in the radiation therapy treatment of patients with lung cancer. Functional avoidance radiation therapy uses functional imaging to reduce pulmonary toxic effects by designing radiation therapy plans that reduce doses to functional regions of the lung. Lung functional imaging has been developed that uses 4-dimensional computed tomography (4DCT) imaging to calculate 4DCT-based lung ventilation (4DCT-ventilation). A phase 2 multicenter study was initiated to evaluate 4DCT-ventilation functional avoidance radiation therapy. The study hypothesis was that functional avoidance radiation therapy could reduce the rate of grade ≥2 radiation pneumonitis to 12% compared with a 25% historical rate, with the trial being positive if ≤16.4% of patients experienced grade ≥2 pneumonitis.

METHODS AND MATERIALS

Lung cancer patients receiving curative-intent radiation therapy (prescription doses of 45-75 Gy) and chemotherapy were accrued. Patient 4DCT scans were used to generate 4DCT-ventilation images. The 4DCT-ventilation images were used to generate functional avoidance plans that reduced doses to functional portions of the lung while delivering the prescribed tumor dose. Pneumonitis was evaluated by a clinician at 3, 6, and 12 months after radiation therapy.

RESULTS

Sixty-seven evaluable patients were accrued between April 2015 and December 2019. The median prescription dose was 60 Gy (range, 45-66 Gy) delivered in 30 fractions (range, 15-33 fractions). The average reduction in the functional volume of lung receiving ≥20 Gy with functional avoidance was 3.5% (range, 0%-12.8%). The median follow-up was 312 days. The rate of grade ≥2 radiation pneumonitis was 10 of 67 patients (14.9%; 95% upper CI, 24.0%), meeting the phase 2 criteria.

CONCLUSIONS

4DCT-ventilation offers an imaging modality that is convenient and provides functional imaging without an extra procedure necessary. This first report of a multicenter study of 4DCT-ventilation functional avoidance radiation therapy provided data showing that the trial met phase 2 criteria and that evaluation in a phase 3 study is warranted.

Dynamic lung compliance imaging from 4DCT-derived volume change estimation

Background. Lung compliance (LC) is the ability of the lung to expand with changes in pressure and is one of the earliest physiological measurements to be altered in patients with parenchymal lung disease. Therefore, compliance monitoring could potentially identify patients at risk for disease progression. However, in clinical practice, compliance measurements are prohibitively invasive for use as a routine monitoring tool.Purpose. We propose a novel method for computing dynamic lung compliance imaging (LCI) from non-contrast computed tomography (CT) scans. LCI applies image processing methods to free-breathing 4DCT images, acquired under two different continuous positive airway pressures (CPAP) applied using a full-face mask, in order to compute the lung volume change induced by the pressure change. LCI provides a quantitative volumetric map of lung stiffness.Methods. We compared mean LCI values computed for 10 patients with idiopathic pulmonary fibrosis (IPF) and 7 non-IPF patients who were screened for lung nodules. 4DCTs were acquired for each patient at 5 cm and 10 cm H₂0 CPAP, as the patients were free breathing at functional residual capacity. LCI was computed from the two 4DCTs. Mean LCI intensities, which represent relative voxel volume change induced by the change in CPAP pressure, were computed.Results.The mean LCI values for patients with IPF ranged between [0.0309, 0.1165], whereas the values ranged between [0.0704, 0.2185] for the lung nodule cohort. Two-sided Wilcoxon rank sum test indicated that the difference in medians is statistically significant (pvalue = 0.009) and that LCI -measured compliance is overall lower in the IPF patient cohort.Conclusion. There is considerable difference in LC scores between patients with IPF compared to controls. Future longitudinal studies should look for LC alterations in areas of lung prior to radiographic detection of fibrosis to further characterize LCI's potential utility as an image marker for disease progression.

Feasibility of function-guided lung treatment planning with parametric response mapping

Purpose

Recent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning.

Methods and materials

A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient.

Results

PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM‐guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity.

Conclusions

PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM‐guided treatment planning.

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning

Purpose.To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.Methods. A clinical data set of 58 pre- and post-radiotherapy^99mTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-residual network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. The training process was repeated for a 50-imaging study, five-fold validation with twenty model instances trained per fold. The highest performing model instance from each fold was selected for inference upon the eight-study test set. A manual lung segmentation was used to compute correlation metrics constrained to the voxels within the lungs. From the pre-treatment test cases (N = 5), 50th percentile contours of well-perfused lung were generated from both the clinical and synthetic perfusion images and the agreement was quantified.Results. Across the hold-out test set, our deep learning model predicted perfusion with a Spearman correlation coefficient of 0.70 (IQR: 0.61-0.76) and a Pearson correlation coefficient of 0.66 (IQR: 0.49-0.73). The agreement of the functional avoidance contour pairs was Dice of 0.803 (IQR: 0.750-0.810) and average surface distance of 5.92 mm (IQR: 5.68-7.55).Conclusion. We demonstrate that from 4DCT alone, a deep learning model can generate synthetic perfusion images with potential application in functional avoidance treatment planning.

Preliminary Study on the Effect of 4DCT-Ventilation-Weighted Dose on the Radiation Induced Pneumonia Probability (RIPP)

Purpose

The purpose of the present study was to evaluate the feasibility of using 4-dimensional computed tomography (4DCT)-ventilation-weighted dose analysis to predict radiation-induced pneumonia probability (RIPP).

Methods and Materials

The study population for this retrospective analysis included 16 patients with stage III lung cancer. Each patient's 4DCT images, including end-inhale and end-exhale sequences, were used for the deformable image registration, and the Hounsfield units (HU) density-change was used to calculate the ventilation. A previously established equation was used to convert the original dose (OD) D _{0, i} in the lungs in the original plan (OP) to the weighted-dose (WD) D _{w, i} in the weighted plan (WP). The patients were divided into 2 groups, one with radiation-induced pneumonia (RIP), and one without. The Spearman correlation analysis was used to analyze the correlation of RIP with ΔV₂₀ (ΔV _x = V _{w, x} in the WP - V _{0, x} in the OP), ΔMLD (ΔMLD = mean lung dose (MLD) in the WP - MLD in the OP), and ΔV₅.

Results

The results showed that 5 of the 16 patients were suffering from acute RIP, 4 of which had higher ΔV₂₀ and ΔMLD values than the rest of the patients. The results of the Spearman correlation analysis for those 4 patients were as follows: RIP vs. ΔV₂₀, r = 0.5123; RIP vs. ΔMLD, r = 0.5119; RIP vs. ΔV₅, r = 0.1904.

Conclusions

The 4DCT-ventilation-based weighted-dose analysis showed some correlation between RIPP and both ΔV₂₀ and ΔMLD, when comparing the weighted-dose and the conventional dose-volume histogram (DVH) analyses.

Hyperpolarized 129Xe Magnetic Resonance Imaging for Functional Avoidance Treatment Planning in Thoracic Radiation Therapy: A Comparison of Ventilation- and Gas Exchange-Guided Treatment Plans

PURPOSE

To present a methodology to use pulmonary gas exchange maps to guide functional avoidance treatment planning in radiation therapy (RT) and evaluate its efficacy compared with ventilation-guided treatment planning.

METHODS AND MATERIALS

Before receiving conventional RT for non-small cell lung cancer, 11 patients underwent hyperpolarized ¹²⁹Xe gas exchange magnetic resonance imaging to map the distribution of xenon in its gas phase (ventilation) and transiently bound to red blood cells in the alveolar capillaries (gas exchange). Both ventilation and gas exchange maps were independently used to guide development of new functional avoidance treatment plans for every patient, while adhering to institutional dose-volume constraints for normal tissues and target coverage. Furthermore, dose-volume histogram (DVH)-based reoptimizations of the clinical plan, with reductions in mean lung dose (MLD) equal to the functional avoidance plans, were created to serve as the control group. To evaluate each plan (regardless of type), gas exchange maps, representing end-to-end lung function, were used to calculate gas exchange-weighted MLD (fMLD), gas exchange-weighted volume receiving ≥20 Gy (fV20), and mean dose in the highest gas exchanging 33% and 50% volumes of lung (MLD-f33% and MLD-f50%). Using each clinically approved plan as a baseline, the reductions in functional metrics were compared for ventilation-optimization, gas exchange optimization, and DVH-based reoptimization. Statistical significance was determined using the Freidman test, with subsequent subdivision when indicated by P values less than .10 and post hoc testing with Wilcoxon signed rank tests to determine significant differences (P < .05). Toxicity modeling was performed using an established function-based model to estimate clinical significance of the results.

RESULTS

Compared with DVH-based reoptimization of the clinically approved plans, gas exchange-guided functional avoidance planning more effectively reduced the gas exchange-weighted metrics fMLD (average ± SD, -78 ± 79 cGy for gas exchange, compared with -45 ± 34 cGy for DVH-based; P = .03), MLD-f33% (-135 ± 136 cGy, compared with -52 ± 47 cGy; P = .004), and MLD-f50% (-96 ± 95 cGy, compared with -47 ± 40 cGy; P = .01). Comparing the 2 functional planning types, gas exchange-guided planning more effectively reduced MLD-f33% compared with ventilation-guided planning (-64 ± 95; P = .009). For some patients, gas exchange-guided functional avoidance plans demonstrated clinically significant reductions in model-predicted toxicity, more so than the accompanying ventilation-guided plans and DVH-based reoptimizations.

CONCLUSION

Gas exchange-guided planning effectively reduced dose to high gas exchanging regions of lung while maintaining clinically acceptable plan quality. In many patients, ventilation-guided planning incidentally reduced dose to higher gas exchange regions, to a lesser extent. This methodology enables future prospective trials to examine patient outcomes.

VMAT Planning With Xe-CT Functional Images Enables Radiotherapy Planning With Consideration of Lung Function

BACKGROUND/AIM

The most severe adverse event of radiotherapy in lung cancer is radiation pneumonitis (RP). Some indices commonly used to prevent RP are evaluated based on the anatomical lung volume. The irradiation dose may be more accurately assessed by using functional lung volume. We evaluated the usefulness of computed tomography (CT) incorporating functional ventilation images acquired by the inhalation of xenon (Xe) gas (Xe-CT functional images).

PATIENTS AND METHODS

Two plans were created for twelve patients: volumetric modulated arc therapy (VMAT) planning using conventional chest CT images (anatomical plans) and VMAT planning using Xe-CT functional images (functional plans), and the dosimetric parameters were compared.

RESULTS

Compared to the anatomical plans, the functional plans had significantly reduced V _20Gy in the high-functional lungs (p=0.005), but significant differences were not seen in the moderate-functional and low-functional lungs.

CONCLUSION

The incorporation of Xe-CT functional images into VMAT plans enables radiotherapy planning with consideration of lung function.

Characterizing spatial differences between SPECT-ventilation and SPECT-perfusion in patients with lung cancer undergoing radiotherapy

This study investigates agreement between ventilation and perfusion for lung cancer patients undergoing radiotherapy. Ventilation-perfusion scans of nineteen patients with stage III lung cancer from a prospective protocol were compared using voxel-wise Spearman correlation-coefficients. The presented results show in about 25% of patients, ventilation and perfusion exhibit lower agreement.

Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model

To analyze radiation induced changes in Hounsfield units and determine their correlation with changes in perfusion and ventilation. Additionally, to compare the post-RT changes in human subjects to those measured in a swine model used to quantify perfusion changes and validate their use as a preclinical model. A cohort of 5 Wisconsin Miniature Swine (WMS) were studied. Additionally, 19 human subjects were recruited as part of an IRB approved clinical trial studying functional avoidance radiation therapy for lung cancer and were treated with SBRT. Imaging (a contrast enhanced dynamic perfusion CT in the swine and 4DCT in the humans) was performed prior to and post-RT. Jacobian elasticity maps were calculated on all 4DCT images. Contours were created from the isodose lines to discretize analysis into 10 Gy dose bins. B-spline deformable image registration allowed for voxel-by-voxel comparative analysis in these contours between timepoints. The WMS underwent a research course of 60 Gy in 5 fractions delivered locally to a target in the lung using an MRI-LINAC system. In the WMS subjects, the dose-bin contours were copied onto the contralateral lung, which received < 5 Gy for comparison. Changes in HU and changes in Jacobian were analyzed in these contours. Statistically significant (p < 0.05) changes in the mean HU value post-RT compared to pre-RT were observed in both the human and WMS groups at all timepoints analyzed. The HU increased linearly with dose for both groups. Strong linear correlation was observed between the changes seen in the swine and humans (Pearson coefficient > 0.97, p < 0.05) at all timepoints. Changes seen in the swine closely modeled the changes seen in the humans at 12 months post RT (slope = 0.95). Jacobian analysis showed between 30 and 60% of voxels were damaged post-RT. Perfusion analysis in the swine showed a statistically significant (p < 0.05) reduction in contrast inside the vasculature 3 months post-RT compared to pre-RT. The increases in contrast outside the vasculature was strongly correlated (Pearson Correlation 0.88) with the reduction in HU inside the vasculature but were not correlated with the changes in Jacobians. Radiation induces changes in pulmonary anatomy at 3 months post-RT, with a strong linear correlation with dose. The change in HU seen in the non-vessel lung parenchyma suggests this metric is a potential biomarker for change in perfusion. Finally, this work suggests that the WMS swine model is a promising pre-clinical model for analyzing radiation-induced changes in humans and poses several benefits over conventional swine models.

Functional avoidance-based intensity modulated proton therapy with 4DCT derived ventilation imaging for lung cancer

The primary objective is to evaluate the potential dosimetric gains of performing functional avoidance‐based proton treatment planning using 4DCT derived ventilation imaging. 4DCT data of 31 patients from a prospective functional avoidance clinical trial were evaluated with intensity modulated proton therapy (IMPT) plans and compared with clinical volumetric modulated arc therapy (VMAT) plans. Dosimetric parameters were compared between standard and functional plans with IMPT and VMAT with one‐way analysis of variance and post hoc paired student t‐test. Normal Tissue Complication Probability (NTCP) models were employed to estimate the risk of two toxicity endpoints for healthy lung tissues. Dose degradation due to proton motion interplay effect was evaluated. Functional IMPT plans led to significant dose reduction to functional lung structures when compared with functional VMAT without significant dose increase to Organ at Risk (OAR) structures. When interplay effect is considered, no significant dose degradation was observed for the OARs or the clinical target volume (CTV) volumes for functional IMPT. Using fV20 as the dose metric and Grade 2+ pneumonitis as toxicity endpoint, there is a mean 5.7% reduction in Grade 2+ RP with the functional IMPT and as high as 26% in reduction for individual patient when compared to the standard IMPT planning. Functional IMPT was able to spare healthy lung tissue to avoid excess dose to normal structures while maintaining satisfying target coverage. NTCP calculation also shows that the risk of pulmonary complications can be further reduced with functional based IMPT.

Quantifying pulmonary perfusion from noncontrast computed tomography

Purpose

Computed tomography (CT)‐derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT‐Perfusion (CT‐P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.

Methods

CT‐Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT‐P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT‐P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT‐ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ. Spatial Spearman correlation between single photon emission CT perfusion (SPECT‐P) and the proposed CT‐P method was assessed in two patient cohorts via a parameter sweep of τ. The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT‐P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT‐P and 4DCT images acquired prior to radiotherapy. For each test case, CT‐P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT‐P and the resulting CT‐P images were computed.

Results

The median correlations between CT‐P and SPECT‐P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT‐P and SPECT‐P correlations across all 30 test cases ranged between 0.02 and 0.82. A one‐sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two‐sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One‐sample sign test was statistically significant with 96.5 % confidence interval: 0.20–0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one‐sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45–0.71, P < 0.00001.

Conclusion

CT‐Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT‐P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT‐P imaging.

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy

Purpose

Dose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT).

Methods and Materials

SPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving ≥20 Gy and mean functional intensity receiving ≥20 Gy (iV₂₀) were calculated. RILT was defined as grade 2+ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dose-function metrics and risk of RILT.

Results

By analyzing V/Q normalized intensities and functional distributions across the population, a wide range in functional capability (especially in the ipsilateral lung) was observed in patients with NSCLC before RT. Through multivariable regression models, global lung average dose to the lower lung was found to be significantly associated with RILT, and Q and V iV₂₀ were correlated with RILT when using ipsilateral lung metrics. Through a receiver operating characteristic analysis, combined V/Q low-function receiving ≥20 Gy (low-functioning V/Q₂₀) in the ipsilateral lung was found to be the best predictor (area under the curce: 0.79) of RILT risk.

Conclusions

Irradiation of the inferior lung appears to be a locational sensitivity for RILT risk. The multivariable correlation between ipsilateral lung iV₂₀ and RILT, as well as the association of low-functioning V/Q₂₀ and RILT, suggest that irradiating low-functioning regions in the lung may lead to higher toxicity rates.

An assessment of the correlation between robust CT-derived ventilation and pulmonary function test in a cohort with no respiratory symptoms

OBJECTIVE

To evaluate CT-ventilation imaging (CTVI) within a well-characterized, healthy cohort with no respiratory symptoms and examine the correlation between CTVI and concurrent pulmonary function test (PFT).

METHODS

CT scans and PFTs from 77 Caucasian participants in the NORM dataset (clinicaltrials.gov NCT00848406) were analyzed. CTVI was generated using the robust Integrated Jacobian Formulation (IJF) method. IJF estimated total lung capacity (TLC) was computed from CTVI. Bias-adjusted Pearson's correlation between PFT and IJF-based TLC was computed.

RESULTS

IJF- and PFT-measured TLC showed a good correlation for both males and females [males: 0.657, 95% CI (0.438-0.797); females: 0.667, 95% CI (0.416-0.817)]. When adjusting for age, height, smoking, and abnormal CT scan, correlation moderated [males: 0.432, 95% CI (0.129-0.655); females: 0.540, 95% CI (0.207-0.753)]. Visual inspection of CTVI revealed participants who had functional defects, despite the fact that all participant had normal high-resolution CT scan.

CONCLUSION

In this study, we demonstrate that IJF computed CTVI has good correlation with concurrent PFT in a well-validated patient cohort with no respiratory symptoms.

ADVANCES IN KNOWLEDGE

IJF-computed CTVI's overall numerical robustness and consistency with PFT support its potential as a method for providing spatiotemporal assessment of high and low function areas on volumetric non-contrast CT scan.

Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy

Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.

A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation

PURPOSE

The purpose of this study is to develop a deep learning (DL) method for producing four-dimensional computed tomography (4DCT) ventilation imaging and to evaluate the accuracy of the DL-based ventilation imaging against single-photon emission-computed tomography (SPECT) ventilation imaging (SPECT-VI). The performance of the DL-based method is assessed by comparing with density change- and Jacobian-based (HU and JAC) methods.

MATERIALS AND METHODS

Fifty patients with esophagus or lung cancer who underwent thoracic radiotherapy were enrolled in this study. For each patient, 4DCT scans paired with 99mTc-Technegas SPECT/CT were acquired before the first radiotherapy treatment. 4DCT and SPECT/CT were first rigidly registered using MIMvista and converted to data matrix using MATLAB, and then transferred to a DL model based on U-net for correlating 4DCT features and SPECT-VI. Two forms of 4DCT dataset [(a) ten phases and (b) two phases of peak-exhalation and peak-inhalation] as input are studied. Tenfold cross-validation procedure was used to evaluate the performance of the DL model. For comparative evaluation, HU and JAC methodologies are used to calculate specific ventilation imaging based on 4DCT (CTVI) for each patient. The voxel-wise Spearman's correlation was evaluated over the whole lung between each of CTVI and corresponding SPECT-VI. The SPECT-VI and produced CTVIs were segmented into high, median, and low functional lung (HFL, MFL, and LFL) regions. The spatial overlap of corresponding HFL, MFL, and LFL for each CTVI against SPECT-VI was also evaluated using the dice similarity coefficient (DSC). The averaged DSC of functional lung regions was calculated and statistically analyzed with a one-factor ANONA model among different methods.

RESULTS

The voxel-wise Spearman r_s values were (0.22 ± 0.31), (-0.09 ± 0.18), and (0.73 ± 0.16)/(0.71 ± 0.17) for the CTVI_HU , CTVI_JAC , and CTVI_DL(1) /CTVI_DL(2) . These results showed the DL method yielded the strongest correlation with SPECT-VI. Using the DSC as the spatial overlap metric, we found that the CTVI_HU , CTVI_JAC , and CTVI_DL(1) /CTVI_DL(2) methods achieved averaged DSC values for all patients to be (0.45 ± 0.08), (0.33 ± 0.04), and (0.73 ± 0.09)/(0.71 ± 0.09), respectively. The results demonstrated that the DL method yielded the highest similarity with SPECT-VI with the prominently significant difference (P < 10^-7 ).

CONCLUSIONS

This study developed a DL method for producing CTVI and performed a validation against SPECT-VI. The results demonstrated that DL method can derive CTVI with greatly improved accuracy in comparison to HU and JAC methods. The produced ventilation images can be more accurate and useful for lung functional avoidance radiotherapy and treatment response modeling.

Evaluating Positron Emission Tomography-Based Functional Imaging Changes in the Heart After Chemo-Radiation for Patients With Lung Cancer

PURPOSE

Studies have noted a link between radiation dose to the heart and overall survival (OS) for patients with lung cancer treated with chemoradiation. The purpose of this study was to characterize pre- to posttreatment cardiac metabolic changes using fluorodeoxyglucose/positron emission tomography (FDG-PET) images and to evaluate whether changes in cardiac metabolism predict for OS.

METHODS AND MATERIALS

Thirty-nine patients enrolled in a functional avoidance prospective study who had undergone pre- and postchemoradiation FDG-PET imaging were evaluated. For each patient, the pretreatment and posttreatment PET/CTs were rigidly registered to the planning CT, dose, and structure set. PET-based metabolic dose-response was assessed by comparing pretreatment to posttreatment mean standardized uptake values (SUVmean) in the heart as a function of dose-bin. OS analysis was performed by comparing SUVmean changes for patients who were alive or had died at last follow-up and by using a multivariate model to assess whether pre- to posttreatment SUVmean changes were a predictor of OS.

RESULTS

The dose-response curve revealed increasing changes in SUV as a function of cardiac dose with an average SUVmean increase of 1.7% per 10 Gy. Patients were followed for a median of 437 days (range, 201-1131 days). SUVmean change was significantly predictive of OS on multivariate analysis with a hazard ratio of 0.541 (95% confidence intervals, 0.312-0.937). Patients alive at follow-up had an average increase of 17.2% in cardiac SUVmean while patients that died had an average decrease in SUVmean decrease of 13.5% (P = .048).

CONCLUSIONS

Our data demonstrated that posttreatment SUV changes in the heart were significant indicators of dose-response and predictors of OS. The present work is hypothesis generating and must be validated in an independent cohort. If validated, our data show the potential for cardiac metabolic changes to be an early predictor for clinical outcomes.

Accounting for respiratory motion in small serial structures during radiotherapy planning: proof of concept in virtual bronchoscopy-guided lung functional avoidance radiotherapy

Respiratory motion management techniques in radiotherapy (RT) planning are primarily focused on maintaining tumor target coverage. An inadequately addressed need is accounting for motion in dosimetric estimations in smaller serial structures. Accurate dose estimations in such structures are more sensitive to motion because respiration can cause them to move completely in or out of a high dose-gradient field. In this work, we study three motion management strategies (m1-m3) to find an accurate method to estimate the dosimetry in airways. To validate these methods, we generated a 'ground truth' digital breathing model based on a 4DCT scan from a lung stereotactic ablative radiotherapy (SAbR) patient. We simulated 225 breathing cycles with  ±10% perturbations in amplitude, respiratory period, and time per respiratory phase. A high-resolution breath-hold CT (BHCT) was also acquired and used with a research virtual bronchoscopy software to autosegment 239 airways. Contours for planning target volume (PTV) and organs at risk (OARs) were defined on the maximum intensity projection of the 4DCT (CT_MIP) and transferred to the average of the 10 4DCT phases (CT_AVG). To design the motion management methods, the RT plan was recreated using different images and structure definitions. Methods m1 and m2 recreated the plan using the CT_AVG image. In method m1, airways were deformed to the CT_AVG. In m2, airways were deformed to each of the 4DCT phases, and union structures were transferred onto the CT_AVG. In m3, the RT plan was recreated on each of the 10 phases, and the dose distribution from each phase was deformed to the BHCT and summed. Dose errors (mean [min, max]) in airways were: m1: 21% (0.001%, 93%); m2: 45% (0.1%, 179%); and m3: 4% (0.006%, 14%). Our work suggests that accurate dose estimation in moving small serial structures requires customized motion management techniques (like m3 in this work) rather than current clinical and investigational approaches.

Robust HU-based CT ventilation from an integrated mass conservation formulation

Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel.

PURPOSE

We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing.

METHODS

Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method.

RESULTS

The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11).

CONCLUSION

While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms.

Functional Adaptation in Radiation Therapy

The promise of adaptive therapy to improve outcomes in radiation oncology has been an area of interest and research in the community for many years. One of the sources of data that can be used to drive adaptive therapy is functional information about the tumor or normal tissues. This avenue of adaptation includes many potential sources of data including global markers and functional imaging. Global markers can be assessments derived from blood measurements, patient functional testing, and circulating tumor material and functional imaging data comprises spatial physiological information from various imaging studies such as positron emission tomography, magnetic resonance imaging, and single photon emission computed tomography. The goal of functional adaptation is to use these functional data to adapt radiation therapy to improve patient outcomes. While functional adaptation holds a lot of promise, there are challenges such as quantifying and minimizing uncertainties, streamlining clinical implementation, determining the ideal way to incorporate information within treatment plan optimization, and proving the clinical benefit through trials. This paper will discuss the types of functional information currently being used for adaptation, highlight several areas where functional adaptation has been studied, and introduce some of the barriers to more widespread clinical implementation.

CT-based ventilation imaging in radiation oncology

A form of lung function imaging is emerging that uses phase-resolved four-dimensional CT (4DCT or breath-hold CT) images along with image processing techniques to generate lung function maps that provide a surrogate of lung ventilation. CT-based ventilation (referred to as CT-ventilation) research has gained momentum in Radiation Oncology because many lung cancer patients undergo four-dimensional CT simulation as part of the standard treatment planning process. Therefore, generating CT-ventilation images provides functional information without burdening the patient with an extra imaging procedure. CT-ventilation has progressed from an image processing calculation methodology, to validation efforts, to retrospective demonstration of clinical utility in Radiation Oncology. In particular, CT-ventilation has been proposed for two main clinical applications: functional avoidance radiation therapy and thoracic dose-response assessment. The idea of functional avoidance radiation therapy is to preferentially spare functional portions of the lung (as measured by CT-ventilation) during radiation therapy with the hypothesis that reducing dose to functional portions of the lung will lead to reduced rates of radiation-related thoracic toxicity. The idea of imaging-based dose-response assessment is to evaluate pre- to post-treatment CT-ventilation-based imaging changes. The hypothesis is that early, imaging-change-based response can be an early predictor of subsequent thoracic toxicity. Based on the retrospective evidence, the clinical applications of CT-ventilation have progressed from the retrospective setting to on-going prospective clinical trials. This review will cover basic CT-ventilation calculation methodologies, validation efforts, presentation of clinical applications, summarize on-going clinical trials, review potential uncertainties and shortcomings of CT-ventilation, and discuss future directions of CT-ventilation research.