Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss.

BACKGROUND: Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose-response models that effectively incorporate both local and distant RILI effects. PURPOSE: We develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation-induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways. METHODS: In an IRB-approved prospective study, pre-RT breath-hold CTs (BHCTs) and pre- and one-year post-RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre- and post-RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP-based estimate and predicted additional loss due radiation-induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post-RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k-fold cross-validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post-RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post-RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed. RESULTS: We extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions. CONCLUSIONS: These early findings indicate that airway damage is a crucial factor in RILI that should be included in dose-response modeling to enhance predictions of post-RT lung function.

Automatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimization.

OBJECTIVE: This study aims to develop a fully Automatic Planning framework for Functional Lung Avoidance Radiotherapy (AP-FLART). Approach: The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high-functional lung (HFL) to low-functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection. Main results: Automatic ConvRT plans generated by AP-FALRT exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose, V20, and V5 were significantly reduced by 1.13 Gy (p<.001), 2.01% (p<.001), and 6.66% (p<.001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p<.01), fV20 by 0.90% (p=0.099), and fV5 by 5.07% (p<.01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection. Significance: AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans. Keywords: Functional lung avoidance radiotherapy; Automatic planning; Beam angle selection; Plan optimization.

Metrics of dose to highly ventilated lung are predictive of radiation-induced pneumonitis in lung cancer patients.

PURPOSE: To identify metrics of radiation dose delivered to highly ventilated lung that are predictive of radiation-induced pneumonitis. METHODS AND MATERIALS: A cohort of 90 patients with locally advanced non-small cell lung cancer treated with standard fractionated radiation therapy (RT) (60-66 Gy in 30-33 fractions) were evaluated. Regional lung ventilation was determined from pre-RT 4-dimensional computed tomography (4DCT) using the Jacobian determinant of a B-spline deformable image registration to estimate lung tissue expansion during respiration. Multiple voxel-wise population- and individual-based thresholds for defining high functioning lung were considered. Mean dose and volumes receiving dose ≥ 5-60 Gy were analyzed for both total lung-ITV (MLD,V5-V60) and highly ventilated functional lung-ITV (fMLD,fV5-fV60). The primary endpoint was symptomatic grade 2+ (G2+) pneumonitis. Receiver operator curve (ROC) analyses were used to identify predictors of pneumonitis. RESULTS: G2+ pneumonitis occurred in 22.2% of patients, with no differences between stage, smoking status, COPD, or chemo/immunotherapy use between G<2 and G2+ patients (P≥ 0.18). Highly ventilated lung was defined as voxels exceeding the population-wide median of 18% voxel-level expansion. All total and functional metrics were significantly different between patients with and without pneumonitis (P≤ 0.039). Optimal ROC points predicting pneumonitis from functional lung dose were fMLD ≤ 12.3 Gy, fV5 ≤ 54% and fV20 ≤ 19 %. Patients with fMLD ≤ 12.3 Gy had a 14% risk of developing G2+ pneumonitis whereas risk significantly increased to 35% for those with fMLD > 12.3 Gy (P = 0.035). CONCLUSIONS: Dose to highly ventilated lung is associated with symptomatic pneumonitis and treatment planning strategies should focus on limiting dose to functional regions. These findings provide important metrics to be used in functional lung avoidance RT planning and designing clinical trials.

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients.

Purpose: Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods: SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman's correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results: The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion: For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.

Corrigendum: Investigation of a novel deep learning-based computed tomography perfusion mapping framework for functional lung avoidance radiotherapy.

[This corrects the article DOI: 10.3389/fonc.2021.644703.].

Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy.

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3-5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

Functional lung avoidance and response-adaptive escalation (FLARE) RT: Multimodality plan dosimetry of a precision radiation oncology strategy.

PURPOSE: Nonsmall cell lung cancer (NSCLC) patient radiation therapy (RT) is planned without consideration of spatial heterogeneity in lung function or tumor response. We assessed the dosimetric and clinical feasibility of functional lung avoidance and response-adaptive escalation (FLARE) RT to reduce dose to [99m Tc]MAA-SPECT/CT perfused lung while redistributing an escalated boost dose within [18 F]FDG-PET/CT-defined biological target volumes (BTV). METHODS: Eight stage IIB-IIIB NSCLC patients underwent FDG-PET/CT and MAA-SPECT/CT treatment planning scans. Perfused lung objectives were derived from scatter/collimator/attenuation-corrected MAA-SPECT uptake relative to ITV-subtracted lung to maintain < 20 Gy mean lung dose (MLD). Prescriptions included 60 Gy to the planning target volume (PTV) and concomitant boost of 74 Gy mean to biological target volumes (BTV = GTV + PET gradient segmentation) scaled to each BTV voxel by relative FDG-PET SUV. Dose-painting-by-numbers prescriptions were integrated into commercial treatment planning systems via uptake threshold discretization. Dose constraints for lung, heart, cord, and esophagus were defined. FLARE RT plans were optimized with volumetric modulated arc therapy (VMAT), proton pencil beam scanning (PBS) with 3%-3 mm robust optimization, and combination of PBS (avoidance) plus VMAT (escalation). The high boost dose region was evaluated within a standardized SUVpeak structure. FLARE RT plans were compared to reference VMAT plans. Linear regression between radiation dose to BTV and normalized FDG PET SUV at every voxel was conducted, from which Pearson linear correlation coefficients and regression slopes were extracted. Spearman rank correlation coefficients were estimated between radiation dose to lung and normalized SPECT uptake. Dosimetric differences between treatment modalities were evaluated by Friedman nonparametric paired test with multiple sampling correction. RESULTS: No unacceptable violations of PTV and normal tissue objectives were observed in 24 FLARE RT plans. Compared to reference VMAT plans, FLARE VMAT plans achieved a higher mean dose to BTV (73.7 Gy 98195. 61.3 Gy), higher mean dose to SUVpeak (89.7 Gy vs. 60.8 Gy), and lower mean dose to highly perfused lung (7.3 Gy vs. 14.9 Gy). These dosimetric gains came at the expense of higher mean heart dose (9.4 Gy vs. 5.8 Gy) and higher maximum cord dose (50.1 Gy vs. 44.6 Gy) relative to the reference VMAT plans. Between FLARE plans, FLARE VMAT achieved higher dose to the SUVpeak ROI than FLARE PBS (89.7 Gy vs. 79.2 Gy, P = 0.01), while FLARE PBS delivered lower dose to lung than FLARE VMAT (11.9 Gy vs. 15.6 Gy, P < 0.001). Voxelwise linear dose redistribution slope between BTV dose and FDG PET uptake was higher in magnitude for FLARE PBS + VMAT (0.36 Gy per %SUVmax ) compared to FLARE VMAT (0.27 Gy per %SUVmax ) or FLARE PBS alone (0.17 Gy per %SUVmax ). CONCLUSIONS: FLARE RT is clinically feasible with VMAT and PBS. A combination of PBS for functional lung avoidance and VMAT for FDG PET dose escalation balanced target and normal tissue objective tradeoffs. These results provide a technical platform for testing of FLARE RT safety and efficacy within a precision radiation oncology trial.

Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance.

For patients with lung cancer undergoing curative intent radiotherapy, functional lung imaging can be incorporated into treatment planning to modify the dose distribution within non-target volume lung by differentiation of lung regions that are functionally defective or viable. This concept of functional image-guided lung avoidance treatment planning has been investigated with several imaging modalities, primarily single photon emission computed tomography (SPECT), but also hyperpolarised gas magnetic resonance (MR) imaging, positron emission tomography (PET) and computed tomography (CT)-based measures of lung biomechanics. Here, we review the application of each of these modalities, review practical issues of lung avoidance implementation, including image registration and the role of both ventilation and perfusion imaging, and provide guidelines for reporting of future lung avoidance planning studies.