Prospective Trial of Functional Lung Avoidance Radiation Therapy for Lung Cancer: Quality of Life Report.

PURPOSE: A novel form of lung function imaging has been developed that uses 4-dimensional computed tomography (4DCT) data to generate lung ventilation images (4DCT-ventilation). Functional avoidance uses 4DCT-ventilation to reduce doses to functional lung with the aim of reducing pulmonary side effects. A phase 2, multicenter 4DCT-ventilation functional avoidance clinical trial was completed. The purpose of this work was to quantify changes in patient-reported outcomes (PROs) for patients treated with functional avoidance and determine which metrics are predictive of PRO changes. MATERIALS AND METHODS: Patients with locally advanced lung cancer receiving curative-intent radiation therapy were accrued. Each patient had a 4DCT-ventilation image generated using 4DCT data and image processing. PRO instruments included the Functional Assessment of Cancer Therapy-Lung (FACT-L) questionnaire administered pretreatment; at the end of treatment; and at 3, 6, and 12 months posttreatment. Using the FACT-Trial Outcome Index and the FACT-Lung Cancer Subscale results, the percentage of clinically meaningful declines (CMDs) were determined. A linear mixed-effects model was used to determine which patient, clinical, dose, and dose-function metrics were predictive of PRO decline. RESULTS: Of the 59 patients who completed baseline PRO surveys. 83% had non-small cell lung cancer, with 75% having stage 3 disease. The median dose was 60 Gy in 30 fractions. CMD FACT-Trial Outcome Index decline was 46.3%, 38.5%, and 26.8%, at 3, 6, and 12 months, respectively. CMD FACT-Lung Cancer Subscale decline was 33.3%, 33.3%, and 29.3%, at 3, 6, and 12 months, respectively. Although an increase in most dose and dose-function parameters was associated with a modest decline in PROs, none of the results were significant (all P > .053). CONCLUSIONS: The current work presents an innovative combination of use of functional avoidance and PRO assessment and is the first report of PROs for patients treated with prospective 4DCT-ventilation functional avoidance. Approximately 30% of patients had clinically significant decline in PROs at 12 months posttreatment. The study provides additional data on outcomes with 4DCT-ventilation functional avoidance.

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.

Is individual perfusion dose-response different than ventilation dose-response for lung cancer patients treated with radiotherapy?

OBJECTIVE: Current ventilation and perfusion dose-response studies focus on single-modalities (ventilation or perfusion) and perform pulmonary-toxicity assessment related to radiotherapy on a population-based basis. This study aims at quantitative and clinical evaluation of intrapatient differences between ventilation and perfusion dose-responses among lung cancer patients treated with radiotherapy. METHODS: 20 patients enrolled on a prospective functional avoidance protocol underwent single photon emission computed tomography-CT ventilation and perfusion scans pre- and post-radiotherapy. Relative changes in pre- to post-treatment ventilation and perfusion in lung regions receiving ≥20 Gy were calculated. In addition, the slopes of the linear fit to the relative ventilation and perfusion changes in regions receiving 0-60 Gy were calculated. A radiologist read and assigned a functional defect score to pre- and post-treatment ventilation/perfusion scans. RESULTS: 25% of patients had a difference >35% between ventilation and perfusion pre- to post-treatment changes and 20-30% of patients had opposite directions for ventilation and perfusion pre- to post-treatment changes. Using a semi-quantitative scale, radiologist assessment showed that 20% of patients had different pre- to post-treatment ventilation changes when compared to pre- to post-treatment perfusion changes. CONCLUSION: Our data showed that ventilation dose-response can differ from perfusion dose-response for 20-30% of patients. Therefore, when performing thoracic dose-response in cancer patients, it is insufficient to look at ventilation or perfusion alone; but rather both modes of functional imaging may be needed when predicting for clinical outcomes. ADVANCES IN KNOWLEDGE: The significance of this study can be highlighted by the differences between the intrapatient dose-response assessments of this analysis compared to existing population-based dose-response analyses. Elucidating intrapatient ventilation and perfusion dose-response differences may be valuable in predicting pulmonary toxicity in lung cancer patients post-radiotherapy.

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.

Trimodality therapy for patients with stage III non-small-cell lung cancer: A comprehensive surveillance, epidemiology, and end results analysis.

PURPOSE: Debate exists regarding the optimal management for patients with stage III non-small-cell lung cancer (NSCLC). Recent inclusion of chemotherapeutic data in the Surveillance, Epidemiology, and End Results (SEER) database has made it possible to identify patients with NSCLC who received chemotherapy. We hypothesized that patients with stage III NSCLC experience improved overall survival from trimodality therapy (TMT) versus definitive chemoradiation therapy (CRT) alone. MATERIALS AND METHODS: We analyzed the overall survival of stage III NSCLC patients based on the receipt of TMT versus CRT alone. This included crude and adjusted univariate models as well as crude and doubly robust adjusted multivariable analyses, both utilizing propensity score matching and inverse probability of treatment weighting. Factors included in the multivariable analyses included: age, sex, marital status, income, date of diagnosis, primary site, histology, grade, T stage, N stage, and intended treatment. Planned subset analyses were performed for stage III(N2) patients. RESULTS: Adult patients with stage III NSCLC (N = 9008) from the SEER database were included in our analyses. In our univariate analyses, an overall survival benefit was observed for TMT versus CRT (CrudeHR = 0.58, 95% CI = 0.55-0.61, p < 0.001; AdjHR = 0.58, 95% CI = 0.54-0.61, p < 0.001). This persisted in both crude and doubly robust multivariable analyses (CrudeHR = 0.57, 95% CI = 0.53-0.61, p < 0.001; AdjHR = 0.56, 95% CI = 0.53-0.59, p < 0.001). Patients with stage III(N2) disease also demonstrated a significant benefit to OS with TMT versus CRT alone. CONCLUSION: The significant difference in overall survival seen with TMT suggests this may be an effective treatment approach for select patients.

Changes in post-treatment cardiac PET avidity predict overall survival in lung cancer patients treated with chemoradiation: Secondary analysis of the ACRIN 6668/RTOG 0235 clinical trial.

The purpose of this work was to use data from a large co-operative group trial to evaluate whether metabolic FDG-PET changes in the heart for lung cancer patients can predict for clinical outcomes. The study found that cardiac SUV changes following definitive chemoradiation are significantly (HR 0.811, 95% CI 0.68-0.96, p = 0.017) associated with overall survival in locally advanced NSCLC patients. If validated in a prospective cohort, our data show the potential for cardiac metabolic changes to be an early predictor for clinical outcomes.

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy.

PURPOSE: In our conventional image registration workflow, the four-dimensional (4D) CBCT was directly registered to the reference helical CT (HCT) using a dual registration approach within the Elekta XVI software. In this study, we proposed a new HCT-CBCT auto-registration strategy using a previously registered CBCT (CBCTpre) as the reference image and tested its clinical feasibility. METHODS: From a previous CBCT session, the registered average 4D CBCT was selected as CBCTpre and the HCT-CBCTpre registration vector from the clinician's manual registration result was recorded. In the new CBCT session, auto-registration was performed between the new average 4D CBCT (CBCTtx) and CBCTpre (CBCTpre-CBCTtx). The overall HCT-CBCTtx registration result was then derived by combing the results from two registrations (i.e., HCT-CBCTpre + CBCTpre-CBCTtx). The results from the proposed method were compared with clinician's manually adjusted HCT-CBCTtx registration results ("ground truth") to evaluate its accuracy using a test dataset consisting of 32 challenging registration cases. RESULTS: The uncertainty of the proposed auto-registration method was -0.1 ± 0.5, 0.1 ± 1.0, and -0.1 ± 0.7 mm in three translational directions (lateral, longitudinal, and vertical) and 0.0° ± 0.9°, 0.3° ± 0.9°, and 0.4° ± 0.7° in three rotation directions, respectively. Two patients (6.3%) had translational uncertainty > 2 mm (max = 3.1 mm) and both occurred in the longitudinal direction. Meanwhile, the uncertainty of the conventional direct HCT-CBCTtx auto-registration was -0.4 ± 2.6, -0.2 ± 7.4, -1.4 ± 3.6 mm for translations and -0.3° ± 1.2°, 0.0° ± 1.6°, and 0.1 ± 1.1° for rotations. Eleven patients (34.4%) had translation uncertainty > 2 mm (max = 26.2 mm) in at least one direction. Accuracy in translation was improved with the new method, while rotation accuracy stayed in the same order. CONCLUSION: We demonstrated the feasibility of incorporating prior clinical registration knowledge into the online HCT-CBCT registration process. The proposed auto-registration method provides a quick and reliable starting solution for online HCT-CBCT registration.

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.

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-radiotherapy99mTc-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.

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.

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.

A prospective study to validate pulmonary blood mass changes on non-contrast 4DCT in pulmonary embolism patients.

PURPOSE: Limited diagnostic options exist for patients with suspected pulmonary embolism (PE) who cannot undergo CT-angiogram (CTA). CT-ventilation methods recover respiratory motion-induced lung volume changes as a surrogate for ventilation. We recently demonstrated that pulmonary blood mass change, induced by tidal respiratory motion, is a potential surrogate for pulmonary perfusion. In this study, we examine blood mass and volume change in patients with PE and parenchymal lung abnormalities (PLA). METHODS: A cross-sectional analysis was conducted on a prospective, cohort-study with 129 consecutive PE suspected patients. Patients received 4DCT within 48 h of CTA and were classified as having PLA and/or PE. Global volume change (VC) and percent global pulmonary blood mass change (PBM) were calculated for each patient. Associations with disease type were evaluated using quantile regression. RESULTS: 68 of 129 patients were PE positive on CTA. Median change in PBM for PE-positive patients (0.056; 95% CI: 0.045, 0.068; IQR: 0.051) was smaller than that of PE-negative patients (0.077; 95% CI: 0.064, 0.089; IQR: 0.056), with an estimated difference of 0.021 (95% CI: 0.003, 0.038; p = 0.0190). PLA was detected in 57 (44.2%) patients. Median VC for PLA-positive patients (1.26; 95% CI: 1.22, 1.30; IQR: 0.15) showed no significant difference from PLA-negative VC (1.25; 95% CI: 1.21, 1.28; IQR: 0.15). CONCLUSIONS: We demonstrate that pulmonary blood mass change is significantly lower in PE-positive patients compared to PE-negative patients, indicating that PBM derived from dynamic non-contrast CT is a potentially useful surrogate for pulmonary perfusion.

Higher biologically effective dose is associated with improved survival in patients with squamous cell carcinoma of the lung treated with stereotactic body radiation therapy.

BACKGROUND: Multiple studies have suggested that patients with early-stage SCC of the lung treated with SBRT are more susceptible to local failure compared to other NSCLC histologies. It is unknown if higher BED leads to improved outcomes in this patient population. We evaluated the effect of "high" BED versus "low" BED SBRT on overall survival (OS) in SCC and non-SCC NSCLC patients. METHODS: The National Cancer Database was used to identify patients with cT1-2N0M0 NSCLC diagnosed between 2006-2016 treated with 3-5 fraction SBRT. Patients were grouped by BEDhigh (>150 Gy) and BEDlow (≤132 Gy). Univariate and multivariable analysis using Kaplan-Meier and Cox proportional hazards regression modeling were performed. Propensity-score matched analysis with inverse probability of treatment (IPTW) weighting was used to account for selection bias. RESULTS: We identified 4,717 eligible SCC patients and 8,807 eligible non-SCC NSCLC patients. In SCC patients, BEDhigh was associated with improved OS in both univariate and multivariate analysis (MVA HR 0.84 95% CI 0.76-0.92, p < 0.001), with estimated IPTW-adjusted 3-year OS of 49% compared to 41% for the BEDlow group. In contrast, BEDhigh was not associated with improved OS compared to BEDlow for non-SCC NSCLC patients (MVA HR 0.94 95% CI 0.86-1.04, p = 0.23), with estimated IPTW-adjusted 3-year OS of 54% and 53%, respectively. CONCLUSIONS: Our analysis suggests that in patients with early-stage NSCLC, SBRT regimens with BED > 150 Gy may confer a survival benefit in patients with SCC histology. Histology-based dose modification should be considered, and prospective validation may be warranted.

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.

The Role of Postoperative Radiation Therapy for pN2 Non-small-cell Lung Cancer.

BACKGROUND: The role for postoperative radiation therapy (PORT) for patients with non-small-cell lung cancer (NSCLC) with mediastinal lymph node (LN) involvement (pN2 disease) is controversial. We compared surgery alone with PORT among patients with pN2 NSCLC. We then performed subset analyses to better delineate patients that might benefit from PORT. PATIENTS AND METHODS: We conducted a propensity score (PS)-matched, inverse probability of treatment weighting (IPTW) Surveillance, Epidemiology, and End Results (SEER) analysis of patients with pN2 disease from 1989 to 2016 with surgery alone or PORT. Multiple imputation with chained equations was used for missing LN data. RESULTS: A total of 8631 patients were included in this analysis; 4579 underwent surgery alone, and 4052 underwent PORT. Following PS matching and IPTW, there was no difference in overall survival (OS) (hazard ratio [HR], 0.99; P = .76). However, PORT improved OS among a subset of patients with a LN positive to sampled ratio ≥ 50% (HR, 0.90; P = .01). Moreover, there was a trend towards improved OS among this subset, even with chemotherapy (HR, 0.91; P = .09). CONCLUSION: PORT is not associated with an improvement or detriment in OS for all patients with pN2 NSCLC. However, patients with a positive to sampled LN ratio ≥ 50% may benefit, regardless of chemotherapy status. Nevertheless, PORT will remain the standard of care as we await the results of the ongoing LUNG ART trial.

Technical Note: On the spatial correlation between robust CT-ventilation methods and SPECT ventilation.

PURPOSE: The computed tomography (CT)-derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion-induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT-ventilation images and single photon emission CT-ventilation (SPECT-V). METHODS: Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ , was conducted using the 4DCT and SPECT-V images acquired for 15 non-small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT-ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0.01 , 0.25 . Voxel-wise Spearman correlation between the SPECT-V and the resulting CT-ventilation images was computed. RESULTS: The median correlations between MCVC and SPECT-V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT-V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ = 0.07 , the IJF and SPECT-V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ = 0.03 , the MCVC and SPECT-V correlations across all 15 test cases ranged between -0.06 and 0.84. CONCLUSION: The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT-V, with the transformation-based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ ∈ [ 0.03 , 0.15 ] , indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.

A feasibility study to estimate optimal rigid-body registration using combinatorial rigid registration optimization (CORRO).

PURPOSE: Clinical image pairs provide the most realistic test data for image registration evaluation. However, the optimal registration is unknown. Using combinatorial rigid registration optimization (CORRO) we demonstrate a method to estimate the optimal alignment for rigid-registration of clinical image pairs. METHODS: Expert selected landmark pairs were selected for each CT/CBCT image pair for six cases representing head and neck, thoracic, and pelvic anatomic regions. Combination subsets of a k number of landmark pairs (k-combination set) were generated without repeat to form a large set of k-combination sets (k-set) for k = 4,8,12. The rigid transformation between the image pairs was calculated for each k-combination set. The mean and standard deviation of these transformations were used to derive final registration for each k-set. RESULTS: The standard deviation of registration output decreased as the k-size increased for all cases. The joint entropy evaluated for each k-set of each case was smaller than those from two commercially available registration programs indicating a stronger correlation between the image pair after CORRO was used. A joint histogram plot of all three algorithms showed high correlation between them. As further proof of the efficacy of CORRO the joint entropy of each member of 30 000 k-combination sets in k = 4 were calculated for one of the thoracic cases. The minimum joint entropy was found to exist at the estimated mean of registration indicating CORRO converges to the optimal rigid-registration results. CONCLUSIONS: We have developed a methodology called CORRO that allows us to estimate optimal alignment for rigid-registration of clinical image pairs using a large set landmark point. The results for the rigid-body registration have been shown to be comparable to results from commercially available algorithms for all six cases. CORRO can serve as an excellent tool that can be used to test and validate rigid registration algorithms.

Severity of radiation pneumonitis, from clinical, dosimetric and biological features: a pilot study.

BACKGROUND AND OBJECTIVE: Radiation pneumonitis (RP) could be a lethal complication of lung cancer treatment. No reliable predictors of RP severity have been recognized. This prospective pilot study was performed to identify early predictors of high grade lung toxicity and to evaluate clinical, biological or dosimetric features associated with different grades of toxicity. METHOD: Sixteen patients with non-small cell lung cancer with indication of concurrent chemoradiotherapy using 60 Gy/2 Gy/fraction starting at cycle one of platinum based chemotherapy were included. Bronchoalveolar lavage (BAL), pulmonary function testing (PFT), and 18F-2-fluoro-2-deoxy-D-glucose positron-emission tomography was performed before radiotherapy (RT), after three weeks of treatment, and two months post-RT. For analysis, patients were grouped by grade (low [G1-G2] vs. high [G3-G5]). The two groups were compared to identify predictors of RP. Protein expression BAL and lung tissue metabolism was evaluated in two patients (RP-G1 vs. RP-G3). Categorical variables such as comorbidities, stages and locations were summarized as percentages. Radiation doses, pulmonary function values and time to RP were summarized by medians with ranges or as means with standard deviation. Longitudinal analysis PFT was performed by a T-test. RESULTS: All 16 patients developed RP, as follows: G1 (5 pts; 31.3%); G2 (5 pts; 31.3%); G3 (5 pts; 31.3%); and G5 (1 pts; 6.1%). Patients with high grade RP presented significant decrease (p = 0.02) in diffusing lung capacity for carbon monoxide (DLCO) after three weeks of RT. No correlation between dosimetric values and RP grades was observed. BAL analysis of the selected patients showed that CXCL-1, CD154, IL-1ra, IL-23, MIF, PAI-1 and IFN-γ were overexpressed in the lungs of the RP-G3 patient, even before treatment. The pre-RT SUVmax value in the RP-G3 patient was non-significantly higher than in the patient with RP-G1. CONCLUSIONS: RT induces some degree of RP. Our data suggest that decrease in DLCO% is the most sensitive parameter for the early detection of RP. Moreover, we detect biological differences between the two grades of pneumonitis, highlighting the potential value of some cytokines as a prognostic marker for developing high grade lung toxicity. Further multicenter studies with larger sample size are essential to validate these findings.

Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning.

PURPOSE: Segmentation of organs-at-risk (OARs) is an essential component of the radiation oncology workflow. Commonly segmented thoracic OARs include the heart, esophagus, spinal cord, and lungs. This study evaluated a convolutional neural network (CNN) for automatic segmentation of these OARs. METHODS: The dataset was created retrospectively from consecutive radiotherapy plans containing all five OARs of interest, including 22,411 CT slices from 168 patients. Patients were divided into training, validation, and test datasets according to a 66%/17%/17% split. We trained a modified U-Net, applying transfer learning from a VGG16 image classification model trained on ImageNet. The Dice coefficient and 95% Hausdorff distance on the test set for each organ was compared to a commercial atlas-based segmentation model using the Wilcoxon signed-rank test. RESULTS: On the test dataset, the median Dice coefficients for the CNN model vs. the multi-atlas model were 71% vs. 67% for the spinal cord, 96% vs. 94% for the right lung, 96%vs. 94% for the left lung, 91% vs. 85% for the heart, and 63% vs. 37% for the esophagus. The median 95% Hausdorff distances were 9.5  mm vs. 25.3 mm, 5.1  mm vs. 8.1 mm, 4.0  mm vs. 8.0 mm, 9.8  mm vs. 15.8 mm, and 9.2 mm vs. 20.0 mm for the respective organs. The results all favored the CNN model (P < 0.05). CONCLUSIONS: A 2D CNN can achieve superior results to commercial atlas-based software for OAR segmentation utilizing non-domain transfer learning, which has potential utility for quality assurance and expediting patient care.

Hippocampus segmentation on noncontrast CT using deep learning.

PURPOSE: Accurate segmentation of the hippocampus for hippocampal avoidance whole-brain radiotherapy currently requires high-resolution magnetic resonance imaging (MRI) in addition to neuroanatomic expertise for manual segmentation. Removing the need for MR images to identify the hippocampus would reduce planning complexity, the need for a treatment planning MR imaging session, potential uncertainties associated with MRI-computed tomography (CT) image registration, and cost. Three-dimensional (3D) deep convolutional network models have the potential to automate hippocampal segmentation. In this study, we investigate the accuracy and reliability of hippocampal segmentation by automated deep learning models from CT alone and compare the accuracy to experts using MRI fusion. METHODS: Retrospectively, 390 Gamma Knife patients with high-resolution CT and MR images were collected. Following the RTOG 0933 guidelines, images were rigidly fused, and a neuroanatomic expert contoured the hippocampus on the MR, then transferred the contours to CT. Using a calculated cranial centroid, the image volumes were cropped to 200 × 200 × 35 voxels, which were used to train four models, including our proposed Attention-Gated 3D ResNet (AG-3D ResNet). These models were then compared with results from a nested tenfold validation. From the predicted test set volumes, we calculated the 100% Hausdorff distance (HD). Acceptability was assessed using the RTOG 0933 protocol criteria, and contours were considered passing with HD ≤ 7 mm. RESULTS: The bilateral hippocampus passing rate across all 90 models trained in the nested cross-fold validation was 80.2% for AG-3D ResNet, which performs with a comparable pass rate (P = 0.3345) to physicians during centralized review for the RTOG 0933 Phase II clinical trial. CONCLUSIONS: Our proposed AG-3D ResNet's segmentation of the hippocampus from noncontrast CT images alone are comparable to those obtained by participating physicians from the RTOG 0933 Phase II clinical trial.