Marrow-ablative chemotherapy followed by tandem autologous hematopoietic cell transplantation in pediatric patients with malignant brain tumors

To improve survival in young children with malignant brain tumors, irradiation-avoiding or -minimizing marrow-ablative chemotherapy (HDCx) with autologous hematopoietic cell transplantation (AuHCT) has been investigated. We evaluated the outcome of 44 children with malignant brain tumors treated with HDCx and tandem AuHCT at Children's Hospital Los Angeles between June 1999 and July 2012. Forty-four children with malignant brain tumors were studied. Twenty-one had medulloblastoma/primitive neuro-ectodermal tumor, eight atypical teratoid/rhabdoid tumor (ATRT), five high-grade glioma, four malignant germ cell tumor, three ependymoma and three choroid plexus carcinoma. Twenty-nine patients received three tandem transplants and 15 received two tandem transplants, respectively. The 5-year PFS and overall survivals (OS) for all patients were 46.3±8.2% and 51.7±8.5%, respectively. The PFS and OS for 27 newly diagnosed patients were 68.9±9.9% and 73.5±9.3%, respectively, compared with 17 transplanted at relapse 11.8±9.8% (P<0.001) and 15.1±12.3% (P=0.0231), respectively. The 5-year PFS and OS in 13 previously unirradiated patients were 74±13% and 74±13% versus 33.2±9.8% and 40.2±10.6% in 31 irradiated patients (P=0.11 and P=0.239), respectively. One patient died of transplant-related toxicity. HDCx with tandem AuHCT is feasible and safe in children with malignant brain tumors with encouraging irradiation-free survival in newly diagnosed children.

Regional Lung Function Profiles of Stage I and III Lung Cancer Patients: An Evaluation for Functional Avoidance Radiation Therapy

PURPOSE

The development of clinical trials is underway to use 4-dimensional computed tomography (4DCT) ventilation imaging to preferentially spare functional lung in patients undergoing radiation therapy. The purpose of this work was to generate data to aide with clinical trial design by retrospectively characterizing dosimetric and functional profiles for patients with different stages of lung cancer.

METHODS AND MATERIALS

A total of 118 lung cancer patients (36% stage I and 64% stage III) from 2 institutions were used for the study. A 4DCT-ventilation map was calculated using the patient's 4DCT imaging, deformable image registration, and a density-change-based algorithm. To assess each patient's spatial ventilation profile both quantitative and qualitative metrics were developed, including an observer-based defect observation and metrics based on the ventilation in each lung third. For each patient we used the clinical doses to calculate functionally weighted mean lung doses and metrics that assessed the interplay between the spatial location of the dose and high-functioning lung.

RESULTS

Both qualitative and quantitative metrics revealed a significant difference in functional profiles between the 2 stage groups (P<.01). We determined that 65% of stage III and 28% of stage I patients had ventilation defects. Average functionally weighted mean lung dose was 19.6 Gy and 5.4 Gy for stage III and I patients, respectively, with both groups containing patients with large spatial overlap between dose and high-function regions.

CONCLUSION

Our 118-patient retrospective study found that 65% of stage III patients have regionally variant ventilation profiles that are suitable for functional avoidance. Our results suggest that regardless of disease stage, it is possible to have unique spatial interplay between dose and high-functional lung, highlighting the importance of evaluating the function of each patient and developing a personalized functional avoidance treatment approach.

Evaluating the Toxicity Reduction With Computed Tomographic Ventilation Functional Avoidance Radiation Therapy

PURPOSE

Computed tomographic (CT) ventilation imaging is a new modality that uses 4-dimensional (4D) CT information to calculate lung ventilation. Although retrospective studies have reported on the reduction in dose to functional lung, no work to our knowledge has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT ventilation-based functional avoidance planning.

METHODS AND MATERIALS

Seventy previously treated lung cancer patients who underwent 4DCT imaging were used for the study. CT ventilation maps were calculated with 4DCT deformable image registration and a density change-based algorithm. Pneumonitis was graded on the basis of imaging and clinical presentation. Maximum likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5 Gy, V10 Gy, V20 Gy, V30 Gy, and mean dose) to functional lung. For 30 patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting Radiation Therapy Oncology Group 0617 constraints. The NTCP models were applied to the functional plans and the clinically used plans to calculate toxicity reduction.

RESULTS

By the use of functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8%, and 4.8% were achieved based on the mean fV20 Gy, fV30 Gy, and mean dose to functional lung metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20 Gy, fV30 Gy, and mean dose to functional lung. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients.

CONCLUSION

Our study quantifies the possible toxicity reduction from CT ventilation-based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics, with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT ventilation into thoracic treatment planning.

A complete 4DCT-ventilation functional avoidance virtual trial: Developing strategies for prospective clinical trials

Introduction

4DCT‐ventilation is an exciting new imaging modality that uses 4DCT data to calculate lung‐function maps. Because 4DCTs are acquired as standard of care for lung cancer patients undergoing radiotherapy, 4DCT‐ventiltation provides functional information at no extra dosimetric or monetary cost to the patient. The development of clinical trials is underway to use 4DCT‐ventilation imaging to spare functional lung in patients undergoing radiotherapy. The purpose of this work was to perform a virtual trial using retrospective data to develop the practical aspects of a 4DCT‐ventilation functional avoidance clinical trial.

Methods

The study included 96 stage III lung cancer patients. A 4DCT‐ventilation map was calculated using the patient's 4DCT‐imaging, deformable registration, and a density‐change‐based algorithm. Clinical trial inclusion assessment used quantitative and qualitative metrics based on the patient's spatial ventilation profile. Clinical and functional plans were generated for 25 patients. The functional plan aimed to reduce dose to functional lung while meeting standard target and critical structure constraints. Standard and dose‐function metrics were compared between the clinical and functional plans.

Results

Our data showed that 69% and 59% of stage III patients have regional variability in function based on qualitative and quantitative metrics, respectively. Functional planning demonstrated an average reduction of 2.8 Gy (maximum 8.2 Gy) in the mean dose to functional lung.

Conclusions

Our work demonstrated that 60–70% of stage III patients would be eligible for functional planning and that a typical functional lung mean dose reduction of 2.8 Gy can be expected relative to standard clinical plans. These findings provide salient data for the development of functional clinical trials.

Evaluating Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy

PURPOSE

Four-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP).

METHODS AND MATERIALS

Seventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics.

RESULTS

The most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20 Gy (AUC, 0.70). Probabilities of grade 3+ RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20 Gy) to the functional subvolumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20 Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55).

CONCLUSIONS

A full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiation therapy process.

Assessing the use of 4DCT-ventilation in pre-operative surgical lung cancer evaluation

PURPOSE

A primary treatment option for lung cancer patients is surgical resection. Patients who have poor lung function prior to surgery are at increased risk of developing serious and life-threatening complications after surgical resection. Surgeons use nuclear medicine ventilation-perfusion (VQ) scans along with pulmonary function test (PFT) information to assess a patient's pre-surgical lung function. The nuclear medicine images and pre-surgery PFTs are used to calculate percent predicted postoperative (%PPO) PFT values by estimating the amount of functioning lung tissue that would be lost with surgical resection. Nuclear medicine imaging is currently considered the standard of care when evaluating the amount of ventilation that would be lost due to surgery. A novel lung function imaging modality has been developed in radiation oncology that uses 4-Dimensional computed tomography data to calculate ventilation maps (4DCT-ventilation). Compared to nuclear medicine, 4DCT-ventilation is cheaper, does not require a radioactive contrast agent, provides a faster imaging procedure, and has improved spatial resolution. In this work we perform a retrospective study to assess the use of 4DCT-ventilation as a pre-operative surgical lung function evaluation tool. Specifically, the purpose of our study was to compare %PPO PFT values calculated with 4DCT-ventilation and %PPO PFT values calculated with nuclear medicine ventilation-perfusion imaging.

METHODS

The study included 16 lung cancer patients that had undergone 4DCT imaging, nuclear medicine imaging, and had Forced Expiratory Volume in 1 second (FEV₁ ) acquired as part of a standard PFT. The 4DCT datasets, spatial registration, and a density-change-based model were used to compute 4DCT-ventilation maps. Both 4DCT-ventilation and nuclear medicine images were used to calculate %PPO FEV₁ . The %PPO FEV₁ was calculated by scaling the pre-surgical FEV₁ by (1-fraction of total resected ventilation); where the resected ventilation was determined using either the 4DCT-ventilation or nuclear medicine imaging. Calculations were done assuming both lobectomy and pneumonectomy resections. The %PPO FEV₁ values were compared between the 4DCT-ventilation-based calculations and the nuclear medicine-based calculations using correlation coefficients, average differences, and Receiver Operating Characteristic (ROC) analysis.

RESULTS

Overall the 4DCT-ventilation derived %PPO FEV₁ values agreed well with nuclear medicine-derived %PPO FEV₁ data with correlations of 0.99 and 0.81 for lobectomy and pneumonectomy, respectively. The average differences between the 4DCT-ventilation and nuclear medicine-based calculation for %PPO FEV₁ were less than 5%. ROC analysis revealed predictive accuracy that ranged from 87.5% to 100% when assessing the ability of 4DCT-ventilation to predict for nuclear medicine-based %PPO FEV₁ values.

CONCLUSIONS

4DCT-ventilation is an innovative technology developed in radiation oncology that has great potential to translate to the surgical domain. The high correlation results when comparing 4DCT-ventilation to the current standard of care provide a strong rationale for a prospective clinical trial assessing 4DCT-ventilation in the clinical setting. 4DCT-ventilation can reduce the cost and imaging time for patients while providing improved spatial accuracy and quantitative results for surgeons.

Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model

PURPOSE

Four-dimensional computed tomography (4DCT) images are often marred by artifacts that substantially degrade image quality and confound image interpretation. Human observation remains the standard method of 4DCT artifact evaluation, which is time-consuming and subjective. We develop a method to automatically identify and reduce artifacts in cine 4DCT images.

METHODS

We proposed an algorithm that consisted of two main stages: deformable image registration and respiratory motion simulation. Specifically, each 4DCT phase image was registered to the breath-holding CT image using the block-matching method, with erroneous spatial matches removed by the least median of squares filter and the full displacement vector field generated by the moving least squares interpolation. The lung's respiratory motion trajectory was then recovered from the displacement vector field using the parameterized polynomial function, with fitting parameters estimated by combinatorial optimization. In this way, artifacts were located according to deviations between image points and their motion trajectories and further corrected based on position prediction.

RESULTS

The mean spatial error (standard deviation) was 1.00 (0.85) mm after registration as opposed to 6.96 (4.61) mm before registration. In addition, we took human observation conducted by medical experts as the gold standard. The average sensitivity, specificity, and accuracy of the proposed method in artifact identification were 0.97, 0.84, and 0.89, respectively.

CONCLUSIONS

The proposed method identified and reduced artifacts accurately and automatically, providing an alternative way to analyze 4DCT image quality and to correct problematic images for radiation therapy.

The numerical stability of transformation-based CT ventilation

Abstract

Computed tomography (CT)-derived ventilation imaging utilizes deformable image registration (DIR) to recover respiratory-induced tissue volume changes from inhale/exhale 4DCT phases. While current strategies for validating CT ventilation rely on analyzing its correlation with existing functional imaging modalities, the numerical stability of the CT ventilation calculation has not been characterized.

Purpose

The purpose of this study is to examine how small changes in the DIR displacement field can affect the calculation of transformation-based CT ventilation.

Methods

First, we derive a mathematical theorem, which states that the change in ventilation metric induced by a perturbation to single displacement vector is bounded by the perturbation magnitude. Second, we introduce a novel Jacobian constrained optimization method for computing user-defined CT ventilation images.

Results

Using the Jacobian constrained method, we demonstrate that for the same inhale/exhale CT pair, it is possible to compute two DIR transformations that have similar spatial accuracies, but generate ventilation images with significantly different physical characteristics. In particular, we compute a CT ventilation image that perfectly correlates with a single-photon emission CT perfusion scan.

Conclusion

The analysis and experiments indicate that while transformation-based CT ventilation is a promising modality, small changes in the DIR displacement field can result in large relative changes in the ventilation image. As such, approaches for improving the reproducibility of CT ventilation are still needed.

GPU-accelerated Block Matching Algorithm for Deformable Registration of Lung CT Images

Deformable registration (DR) is a key technology in the medical field. However, many of the existing DR methods are time-consuming and the registration accuracy needs to be improved, which prevents their clinical applications. In this study, we propose a parallel block matching algorithm for lung CT image registration, in which the sum of squared difference metric is modified as the cost function and the moving least squares approach is used to generate the full displacement field. The algorithm is implemented on Graphic Processing Unit (GPU) with the Compute Unified Device Architecture (CUDA). Results show that the proposed parallel block matching method achieves a fast runtime while maintaining an average registration error (standard deviation) of 1.08 (0.69) mm.