Myocarditis Associated with Immune Checkpoint Inhibitors: An Expert Consensus on Data Gaps and a Call to Action.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for cancer. Due to the mechanism of action of ICIs, inflammatory reactions against normal tissue were an anticipated side effect of these agents; these immune-related adverse events have been documented and are typically low grade and manageable. Myocarditis has emerged as an uncommon but potentially life-threatening adverse reaction in patients treated with ICIs. Assessment and characterization of ICI-associated myocarditis is challenging because of its low incidence and protean manifestations. Nevertheless, the seriousness of ICI-associated myocarditis justifies a coordinated effort to increase awareness of this syndrome, identify patients who may be at risk, and enable early diagnosis and appropriate treatment. The "Checkpoint Inhibitor Safety Working Group," a multidisciplinary committee of academic, industry, and regulatory partners, convened at a workshop hosted by Project Data Sphere, LLC, on December 15, 2017. This meeting aimed to evaluate the current information on ICI-associated myocarditis, determine methods to collect and share data on this adverse reaction, and establish task forces to close the identified knowledge gaps. In this report, we summarize the workshop findings and proposed steps to address the impact of ICI-associated myocarditis in patients with cancer.

Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis.

BACKGROUND: We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer. METHODS: In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials. FINDINGS: Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times. INTERPRETATION: The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes. FUNDING: None.

Depressive Symptomatology and Priming Effects Among Younger and Older Adults.

Background/Study Context: This study examined the potential impact of self-reported depressive symptoms on the age-related capacity for inhibition and suppression, utilizing a negative priming paradigm. METHODS: One hundred eighty-five community-residing adults varying in age (98 younger adults, Mage = 22; 87 older adults, Mage = 69) completed a nonconscious priming task, the Geriatric Depression Scale (GDS), the White Bear Suppression Inventory (WBSI), the Depression Sensitivity Scale (DSS), a free thought suppression task, as well as several measures indexing overall cognitive ability and psychomotor speed. Hierarchical regressions investigated the interaction of depressive symptoms with age and its effect on both positive and negative priming performance, indexing both facilitation and inhibition effects, respectively. RESULTS: Results support the hypothesis that noncognitive factors affect effortful performance among older adults, although this influence varied with the specific component of the GDS, i.e., Dysphoria, Social Withdrawal, and Cognitive Control, and with the measure of depressive symptoms, i.e., GDS versus DSS. CONCLUSION: These data suggest that aging's impact on both facilitation and inhibition, e.g., positive and negative priming, are to an extent, a function of individual differences in depressive symptoms that interact with age in influencing the necessity to reallocate one's cognitive resources to deal with depressive thoughts and feelings.

Tribute to Donald Metcalf.

A collection of tributes and remembrances from esteemed colleagues, mentees, and friends on the life and work of "the father of hematopoietic cytokines".

Novel Virtual Reality App for Training Patients on MRI-guided Radiation Therapy.

Purpose: Patients receiving respiratory gated magnetic resonance imaging-guided radiation therapy (MRIgRT) for abdominal targets must hold their breath for ≥25 seconds at a time. Virtual reality (VR) has shown promise for improving patient education and experience for diagnostic MRI scan acquisition. We aimed to develop and pilot-test the first VR app to educate, train, and reduce anxiety and discomfort in patients preparing to receive MRIgRT. Methods and Materials: A multidisciplinary team iteratively developed a new VR app with patient input. The app begins with minigames to help orient patients to using the VR device and to train patients on breath-holding. Next, app users are introduced to the MRI linear accelerator vault and practice breath-holding during MRIgRT. In this quality improvement project, clinic personnel and MRIgRT-eligible patients with pancreatic cancer tested the VR app for feasibility, acceptability, and potential efficacy for training patients on using breath-holding during MRIgRT. Results: The new VR app experience was tested by 19 patients and 67 clinic personnel. The experience was completed on average in 18.6 minutes (SD = 5.4) by patients and in 14.9 (SD = 3.5) minutes by clinic personnel. Patients reported the app was "extremely helpful" (58%) or "very helpful" (32%) for learning breath-holding used in MRIgRT and "extremely helpful" (28%) or "very helpful (50%) for reducing anxiety. Patients and clinic personnel also provided qualitative feedback on improving future versions of the VR app. Conclusion: The VR app was feasible and acceptable for training patients on breath-holding for MRIgRT. Patients eligible for MRIgRT for pancreatic cancer and clinic personnel reported on future improvements to the app to enhance its usability and efficacy.

Editorial: Our top 10 developments in stem cell biology over the last 30 years.

To celebrate 30 years of peer-reviewed publication of cutting edge stem cell research in Stem Cells, the first journal devoted to this promising field, we pause to review how far we have come in the three-decade lifetime of the Journal. To do this, we will present our views of the 10 most significant developments that have advanced stem cell biology where it is today. With the increasing rate of new data, it is natural that the bulk of these developments would have occurred in recent years, but we must not think that stem cell biology is a young science. The idea of a stem cell has actually been around for quite a long time having appeared in the scientific literature as early as 1868 with Haeckels' concept of a stamzelle as an uncommitted or undifferentiated cell responsible for producing many types of new cells to repair the body [Naturliche Schopfungsgeschichte, 1868; Berlin: Georg Reimer] but it took many years to obtain hard evidence in support of this theory. Not until the work of James Till and Ernest McCulloch in the 1960s did we have proof of the existence of stem cells and until the derivation of embryonal carcinoma cells in the 1960s-1970s and the first embryonic stem cell in 1981, such adult or tissue-specific stem cells were the only known class. The first issue of Stem Cells was published in 1981; no small wonder that most of its papers were devoted to hematopoietic progenitors. More recently, induced pluripotent stem cells (iPSCs) have been developed, and this is proving to be a fertile area of investigation as shown by the volume of publications appearing not only in Stem Cells but also in other journals over the last 5 years. The reader will note that many of the articles in this special issue are concerned with iPSC; however, this reflects the current surge of interest in the topic rather than any deliberate attempt to ignore other areas of stem cell investigation.

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping.

PURPOSE: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. METHODS: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. RESULTS: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. CONCLUSIONS: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties.

Optimized knot placement for B-splines in deformable image registration.

PURPOSE: To develop an automatic knot placement algorithm to enable the use of NonUniform Rational B-Splines (NURBS) in deformable image registration. METHODS: The authors developed a two-step approach to fit a known displacement vector field (DVF). An initial fit was made with uniform knot spacing. The error generated by this fit was then assigned as an attractive force pulling on the knots, acting against a resistive spring force in an iterative equilibration scheme. To demonstrate the accuracy gain of knot optimization over uniform knot placement, we compared the sum of the squared errors and the frequency of large errors. RESULTS: Fits were made to a one-dimensional DVF using 1-20 free knots. Given the same number of free knots, the optimized, nonuniform B-spline fit produced a smaller error than the uniform B-spline fit. The accuracy was improved by a mean factor of 4.02. The optimized B-spline was found to greatly reduce the number of errors more than 1 standard deviation from the mean error of the uniform fit. The uniform B-spline had 15 such errors, while the optimized B-spline had only two. The algorithm was extended to fit a two-dimensional DVF using control point grid sizes ranging from 8 x 8 to 15 x 15. Compared with uniform fits, the optimized B-spline fits were again found to reduce the sum of squared errors (mean ratio = 2.61) and number of large errors (mean ratio = 4.50). CONCLUSIONS: Nonuniform B-splines offer an attractive alternative to uniform B-splines in modeling the DVF. They carry forward the mathematical compactness of B-splines while simultaneously introducing new degrees of freedom. The increased adaptability of knot placement gained from the generalization to NURBS offers increased local control as well as the ability to explicitly represent topological discontinuities.

Reconstruction of brachytherapy seed positions and orientations from cone-beam CT x-ray projections via a novel iterative forward projection matching method.

PURPOSE: To generalize and experimentally validate a novel algorithm for reconstructing the 3D pose (position and orientation) of implanted brachytherapy seeds from a set of a few measured 2D cone-beam CT (CBCT) x-ray projections. METHODS: The iterative forward projection matching (IFPM) algorithm was generalized to reconstruct the 3D pose, as well as the centroid, of brachytherapy seeds from three to ten measured 2D projections. The gIFPM algorithm finds the set of seed poses that minimizes the sum-of-squared-difference of the pixel-by-pixel intensities between computed and measured autosegmented radiographic projections of the implant. Numerical simulations of clinically realistic brachytherapy seed configurations were performed to demonstrate the proof of principle. An in-house machined brachytherapy phantom, which supports precise specification of seed position and orientation at known values for simulated implant geometries, was used to experimentally validate this algorithm. The phantom was scanned on an ACUITY CBCT digital simulator over a full 660 sinogram projections. Three to ten x-ray images were selected from the full set of CBCT sinogram projections and postprocessed to create binary seed-only images. RESULTS: In the numerical simulations, seed reconstruction position and orientation errors were approximately 0.6 mm and 5 degrees, respectively. The physical phantom measurements demonstrated an absolute positional accuracy of (0.78 +/- 0.57) mm or less. The theta and phi angle errors were found to be (5.7 +/- 4.9) degrees and (6.0 +/- 4.1) degrees, respectively, or less when using three projections; with six projections, results were slightly better. The mean registration error was better than 1 mm/6 degrees compared to the measured seed projections. Each test trial converged in 10-20 iterations with computation time of 12-18 min/iteration on a 1 GHz processor. CONCLUSIONS: This work describes a novel, accurate, and completely automatic method for reconstructing seed orientations, as well as centroids, from a small number of radiographic projections, in support of intraoperative planning and adaptive replanning. Unlike standard back-projection methods, gIFPM avoids the need to match corresponding seed images on the projections. This algorithm also successfully reconstructs overlapping clustered and highly migrated seeds in the implant. The accuracy of better than 1 mm and 6 degrees demonstrates that gIFPM has the potential to support 2D Task Group 43 calculations in clinical practice.

Localizing intracavitary brachytherapy applicators from cone-beam CT x-ray projections via a novel iterative forward projection matching algorithm.

PURPOSE: To present a novel method for reconstructing the 3D pose (position and orientation) of radio-opaque applicators of known but arbitrary shape from a small set of 2D x-ray projections in support of intraoperative brachytherapy planning. METHODS: The generalized iterative forward projection matching (gIFPM) algorithm finds the six degree-of-freedom pose of an arbitrary rigid object by minimizing the sum-of-squared-intensity differences (SSQD) between the computed and experimentally acquired autosegmented projection of the objects. Starting with an initial estimate of the object's pose, gIFPM iteratively refines the pose parameters (3D position and three Euler angles) until the SSQD converges. The object, here specialized to a Fletcher-Weeks intracavitary brachytherapy (ICB) applicator, is represented by a fine mesh of discrete points derived from complex combinatorial geometric models of the actual applicators. Three pairs of computed and measured projection images with known imaging geometry are used. Projection images of an intrauterine tandem and colpostats were acquired from an ACUITY cone-beam CT digital simulator. An image postprocessing step was performed to create blurred binary applicators only images. To quantify gIFPM accuracy, the reconstructed 3D pose of the applicator model was forward projected and overlaid with the measured images and empirically calculated the nearest-neighbor applicator positional difference for each image pair. RESULTS: In the numerical simulations, the tandem and colpostats positions (x,y,z) and orientations (alpha, beta, gamma) were estimated with accuracies of 0.6 mm and 2 degrees, respectively. For experimentally acquired images of actual applicators, the residual 2D registration error was less than 1.8 mm for each image pair, corresponding to about 1 mm positioning accuracy at isocenter, with a total computation time of less than 1.5 min on a 1 GHz processor. CONCLUSIONS: This work describes a novel, accurate, fast, and completely automatic method to localize radio-opaque applicators of arbitrary shape from measured 2D x-ray projections. The results demonstrate approximately 1 mm accuracy while compared against the measured applicator projections. No lateral film is needed. By localizing the applicator internal structure as well as radioactive sources, the effect of intra-applicator and interapplicator attenuation can be included in the resultant dose calculations. Further validation tests using clinically acquired tandem and colpostats images will be performed for the accurate and robust applicator/sources localization in ICB patients.

4D Cone-beam CT reconstruction using a motion model based on principal component analysis.

PURPOSE: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. METHODS: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. RESULTS: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. CONCLUSIONS: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine.

A neural network based 3D/3D image registration quality evaluator for the head-and-neck patient setup in the absence of a ground truth.

PURPOSE: To develop a neural network based registration quality evaluator (RQE) that can identify unsuccessful 3D/3D image registrations for the head-and-neck patient setup in radiotherapy. METHODS: A two-layer feed-forward neural network was used as a RQE to classify 3D/3D rigid registration solutions as successful or unsuccessful based on the features of the similarity surface near the point-of-solution. The supervised training and test data sets were generated by rigidly registering daily cone-beam CTs to the treatment planning fan-beam CTs of six patients with head-and-neck tumors. Two different similarity metrics (mutual information and mean-squared intensity difference) and two different types of image content (entire image versus bony landmarks) were used. The best solution for each registration pair was selected from 50 optimizing attempts that differed only by the initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error threshold to determine whether that solution was successful or not. The supervised training was then used to train the RQE. The performance of the RQE was evaluated using the test data set that consisted of registration results that were not used in training. RESULTS: The RQE constructed using the mutual information had very good performance when tested using the test data sets, yielding the sensitivity, the specificity, the positive predictive value, and the negative predictive value in the ranges of 0.960-1.000, 0.993-1.000, 0.983-1.000, and 0.909-1.000, respectively. Adding a RQE into a conventional 3D/3D image registration system incurs only about 10%-20% increase of the overall processing time. CONCLUSIONS: The authors' patient study has demonstrated very good performance of the proposed RQE when used with the mutual information in identifying unsuccessful 3D/3D registrations for daily patient setup. The classifier had very good generality and required only to be trained once for each implementation. When the RQE is incorporated with an automated 3D/3D image registration system, it can improve the robustness of the system.

Assessing the intrinsic precision of 3D/3D rigid image registration results for patient setup in the absence of a ground truth.

PURPOSE: To assess the precision and robustness of patient setup corrections computed from 3D/3D rigid registration methods using image intensity, when no ground truth validation is possible. METHODS: Fifteen pairs of male pelvic CTs were rigidly registered using four different in-house registration methods. Registration results were compared for different resolutions and image content by varying the image down-sampling ratio and by thresholding out soft tissue to isolate bony landmarks. Intrinsic registration precision was investigated by comparing the different methods and by reversing the source and the target roles of the two images being registered. RESULTS: The translational reversibility errors for successful registrations ranged from 0.0 to 1.69 mm. Rotations were less than 1 degrees. Mutual information failed in most registrations that used only bony landmarks. The magnitude of the reversibility error was strongly correlated with the success/ failure of each algorithm to find the global minimum. CONCLUSIONS: Rigid image registrations have an intrinsic uncertainty and robustness that depends on the imaging modality, the registration algorithm, the image resolution, and the image content. In the absence of an absolute ground truth, the variation in the shifts calculated by several different methods provides a useful estimate of that uncertainty. The difference observed by reversing the source and target images can be used as an indication of robust convergence.

Reconstruction of a cone-beam CT image via forward iterative projection matching.

PURPOSE: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set. METHODS: A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections. RESULTS: When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining CT image and deformation errors of less than 4% and 13%, respectively. The method is sensitive to contrast mismatch between the simulated projections and the target projections when the soft-tissue contrast in the projections is low. CONCLUSIONS: By using prior knowledge available in a FBCT image, the authors show that a CBCT image can be iteratively reconstructed from a comparatively small number of projection images, thus saving acquisition time and reducing imaging dose. This will enable more frequent daily imaging during radiation therapy. Because the process preserves the CT numbers of the FBCT image, the resulting 3D image intensities will be more accurate than a CBCT image reconstructed via conventional backprojection methods. Reconstruction errors are insensitive to noise at levels beyond what would typically be found in CBCT projection data, but are sensitive to contrast mismatch errors between the CBCT projection data and the DRRs.

A method to map errors in the deformable registration of 4DCT images.

PURPOSE: To present a new approach to the problem of estimating errors in deformable image registration (DIR) applied to sequential phases of a 4DCT data set. METHODS: A set of displacement vector fields (DVFs) are made by registering a sequence of 4DCT phases. The DVFs are assumed to display anatomical movement, with the addition of errors due to the imaging and registration processes. The positions of physical landmarks in each CT phase are measured as ground truth for the physical movement in the DVF. Principal component analysis of the DVFs and the landmarks is used to identify and separate the eigenmodes of physical movement from the error eigenmodes. By subtracting the physical modes from the principal components of the DVFs, the registration errors are exposed and reconstructed as DIR error maps. The method is demonstrated via a simple numerical model of 4DCT DVFs that combines breathing movement with simulated maps of spatially correlated DIR errors. RESULTS: The principal components of the simulated DVFs were observed to share the basic properties of principal components for actual 4DCT data. The simulated error maps were accurately recovered by the estimation method. CONCLUSIONS: Deformable image registration errors can have complex spatial distributions. Consequently, point-by-point landmark validation can give unrepresentative results that do not accurately reflect the registration uncertainties away from the landmarks. The authors are developing a method for mapping the complete spatial distribution of DIR errors using only a small number of ground truth validation landmarks.