Josue G. Martinez

Four-dimensional deformable image registration using trajectory modeling

A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy.

Ventilation from four-dimensional computed tomography: density versus Jacobian methods

Two calculation methods to produce ventilation images from four-dimensional computed tomography (4DCT) acquired without added contrast have been reported. We reported a method to obtain ventilation images using deformable image registration (DIR) and the underlying CT density information. A second method performs the ventilation image calculation from the DIR result alone, using the Jacobian determinant of the deformation field to estimate the local volume changes resulting from ventilation. For each of these two approaches, there are variations on their implementation. In this study, two implementations of the Jacobian-based methodology are evaluated, as well as a single density change-based model for calculating the physiologic specific ventilation from 4DCT. In clinical practice, (99m)Tc-labeled aerosol single photon emission computed tomography (SPECT) is the standard method used to obtain ventilation images in patients. In this study, the distributions of ventilation obtained from the CT-based ventilation image calculation methods are compared with those obtained from the clinical standard SPECT ventilation imaging. Seven patients with 4DCT imaging and standard (99m)Tc-labeled aerosol SPECT/CT ventilation imaging obtained on the same day as part of a prospective validation study were selected. The results of this work demonstrate the equivalence of the Jacobian-based methodologies for quantifying the specific ventilation on a voxel scale. Additionally, we found that both Jacobian- and density-change-based methods correlate well with global measurements of the resting tidal volume. Finally, correlation with the clinical SPECT was assessed using the Dice similarity coefficient, which showed statistically higher (p-value < 10(-4)) correlation between density-change-based specific ventilation and the clinical reference than did either Jacobian-based implementation.

Reduced rank mixed effects models for spatially correlated hierarchical functional data

Hierarchical functional data are widely seen in complex studies where subunits are nested within units, which in turn are nested within treatment groups. We propose a general framework of functional mixed effects model for such data: within-unit and within-subunit variations are modeled through two separate sets of principal components; the subunit level functions are allowed to be correlated. Penalized splines are used to model both the mean functions and the principal components functions, where roughness penalties are used to regularize the spline fit. An expectation–maximization (EM) algorithm is developed to fit the model, while the specific covariance structure of the model is utilized for computational efficiency to avoid storage and inversion of large matrices. Our dimension reduction with principal components provides an effective solution to the difficult tasks of modeling the covariance kernel of a random function and modeling the correlation between functions. The proposed methodology is illustrated using simulations and an empirical dataset from a colon carcinogenesis study. Supplemental materials are available online.

[18F]-FDG uptake dose-response correlates with radiation pneumonitis in lung cancer patients

PURPOSE To quantify the post-radiotherapy 2-[(18)F]-fluoro-2-deoxyglucose (FDG) pulmonary uptake dose-response in lung cancer patients and determine its relationship with radiation pneumonitis symptoms. METHODS AND MATERIALS The data from 24 patients treated for lung cancer with thoracic radiotherapy who received restaging PET/CT imaging between 4 and 12 weeks after radiotherapy completion were evaluated. Their radiation dose distribution was registered with the post-treatment restaging PET/CT. Using histogram analysis, the voxel average FDG-PET uptake vs. radiation dose was obtained for each case and linear regression was performed. The resulting slope, the pulmonary metabolic radiation response (PMRR), was used to characterize the dose-response. The Common Toxicity Criteria version 3 was used to score clinical pulmonary toxicity symptoms. Receiver operating characteristic (ROC) curves were used to determine the level of FDG uptake vs. dose, MLD, V(5), V(10), V(20), and V(30) that can best predict symptomatic and asymptomatic patients. RESULTS The median time between radiotherapy completion and FDG-PET imaging was 59 days (range, 26-70 days). The median of the mean SUV from lung that received 0-5 Gy was 1.00 (range, 0.37-1.48), 5-10 Gy was 1.01 (range, 0.37-1.77), 10-20 Gy was 1.04 (0.42-1.53), and >20 Gy was 1.29 (range, 0.41-8.01). Using the dose range of 0 Gy to the maximum dose minus 10 Gy, hierarchical linear regression model of the radiation dose and normalized FDG uptake per case found an adequate fit with the linear model. Pneumonitis scores were: Grade 0 for 13, Grade 1 for 5, Grade 2 for 6, and Grade 3, 4 or 5 for none. Using a PMRR threshold of 0.017 yields an associated true positive rate of 0.67 and false positive rate of 0.15 with average error of 30%. A V(5) threshold of 57.6 gives an associated true positive rate of 0.67 and false positive rate of 0.05 with a 20% average error. CONCLUSION The metabolic radiation pneumonitis dose-response was evaluated from post-treatment FDG-PET/CT imaging. Statistical modeling found a linear relationship. The FDG uptake dose-response and V(5) correlated with symptomatic radiation pneumonitis.

Exhaled nitric oxide predicts radiation pneumonitis in esophageal and lung cancer patients receiving thoracic radiation

BACKGROUND AND PURPOSE Radiation pneumonitis is a significant toxicity following thoracic radiotherapy with no method to predict individual risk. MATERIALS AND METHODS Sixty-five patients receiving thoracic radiation for lung or esophageal cancer were enrolled in a phase II study. Each patient received respiratory surveys and exhaled nitric oxide measurements before, on the last day of, and 30-60 days after completing radiotherapy (RT). Pneumonitis toxicity was scored using the common terminology criteria for adverse events, version 4.0. The demographics, dosimetric factors, and nitric oxide ratio (NOR) of end RT/pre-RT were evaluated for correlation with symptomatic patients (Grade ≥ 2). RESULTS Fifty patients completed the trial. The pneumonitis toxicity score was: Grade 3 for 1 patient, Grade 2 for 6 patients, Grade 1 for 18 patients, and Grade 0 for 25 patients. Dosimetric factors were not predictive of symptoms. The NOR was 3.0 ± 1.8 (range 1.47-6.73) for the symptomatic and 0.78 ± 0.29 (range 0.33-1.37) for the asymptomatic patients (p=0.006). A threshold NOR of 1.4 separated symptomatic and asymptomatic patients (p<0.001). The average error was 4%. CONCLUSIONS Elevation in eNO on the last day of radiotherapy predicted subsequent symptomatic radiation pneumonitis weeks to months after treatment.

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use two-dimensional wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible. Supplementary materials for this article are available online.

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

A Note on the Effect on Power of Score Tests via Dimension Reduction by Penalized Regression under the Null

We consider the problem of score testing for certain low dimensional parameters of interest in a model that could include finite but high dimensional secondary covariates and associated nuisance parameters. We investigate the possibility of the potential gain in power by reducing the dimensionality of the secondary variables via oracle estimators such as the Adaptive Lasso. As an application, we use a recently developed framework for score tests of association of a disease outcome with an exposure of interest in the presence of a possible interaction of the exposure with other co-factors of the model. We derive the local power of such tests and show that if the primary and secondary predictors are independent, then having an oracle estimator does not improve the local power of the score test. Conversely, if they are dependent, there is the potential for power gain. Simulations are used to validate the theoretical results and explore the extent of correlation needed between the primary and secondary covariates to observe an improvement of the power of the test by using the oracle estimator. Our conclusions are likely to hold more generally beyond the model of interactions considered here.

Proton therapy radiation pneumonitis local dose–response in esophagus cancer patients

PURPOSE This study quantifies pulmonary radiation toxicity in patients who received proton therapy for esophagus cancer. MATERIALS/METHODS We retrospectively studied 100 esophagus cancer patients treated with proton therapy. The linearity of the enhanced FDG uptake vs. proton dose was evaluated using the Akaike Information Criterion (AIC). Pneumonitis symptoms (RP) were assessed using the Common Toxicity Criteria for Adverse Events version 4.0 (CTCAEv4). The interaction of the imaging response with dosimetric parameters and symptoms was evaluated. RESULTS The RP scores were: 0 grade 4/5, 7 grade 3, 20 grade 2, 37 grade 1, and 36 grade 0. Each dosimetric parameter was significantly higher for the symptomatic group. The AIC winning models were 30 linear, 52 linear quadratic, and 18 linear logarithmic. There was no significant difference in the linear coefficient between models. The slope of the FDG vs. proton dose response was 0.022 for the symptomatic and 0.012 for the asymptomatic (p=0.014). Combining dosimetric parameters with the slope did not improve the sensitivity or accuracy in identifying symptomatic cases. CONCLUSIONS The proton radiation dose response on FDG PET/CT imaging exhibited a predominantly linear dose response on modeling. Symptomatic patients had a higher dose response slope.

Elevation in Exhaled Nitric Oxide Predicts for Radiation Pneumonitis

PURPOSE Radiation pneumonitis is a major toxicity after thoracic radiotherapy (RT), with no method available to accurately predict the individual risk. This was a prospective study to evaluate exhaled nitric oxide as a predictive biomarker for radiation pneumonitis in esophageal cancer patients. PATIENTS AND METHODS A total of 34 patients prescribed neoadjuvant chemoradiotherapy for esophageal cancer were enrolled in the present trial. Each patient underwent respiratory surveys and exhaled nitric oxide (NO) measurements before, at the end of, and 1 to 2 months after completing RT. Pneumonitis toxicity was scored using the Common Terminology Criteria for Adverse Events, version 4.0. The demographics, dosimetric factors, and exhaled NO levels were evaluated for correlation with symptomatic patients (scores ≥ 2). RESULTS Of the 34 patients, 28 were evaluable. All had received 50.4 Gy RT with concurrent chemotherapy. The pneumonitis toxicity score was Grade 3 for 1, Grade 2 for 3, Grade 1 for 7, and Grade 0 for 17. The dosimetric factors were not predictive of symptoms. The mean exhaled NO level measured before, at completion, and at restaging was 17.3 ± 8.5 (range, 5.5-36.7), 16.0 ± 14.2 (range, 5.8-67.7), and 14.7 ± 6.2 (range, 5.5-28.0) parts per billion, respectively. The ratio of exhaled NO at the end of RT vs. before treatment was 3.4 (range, 1.7-6.7) for the symptomatic and 0.8 (range, 0.3-1.3) for the asymptomatic (p = .0017) patients. The elevation in exhaled NO preceded the peak symptoms by 33 days (range, 21-50). The interval to peak symptoms was inversely related to the exhaled NO elevation. CONCLUSIONS Elevations in exhaled NO at the end of RT was found to predict for radiation pneumonitis symptoms.