Morphometry-based measurements of the structural response to whole-brain radiation

Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients

Background and purpose

Stereotactic body radiation therapy (SBRT) of locally advanced pancreatic cancer (LAPC) is challenging due to significant motion of gastrointestinal (GI) organs. The goal of our study was to quantify inter and intrafraction deformations and dose accumulation of upper GI organs in LAPC patients.

Materials and methods

Five LAPC patients undergoing five-fraction magnetic resonance-guided radiation therapy (MRgRT) using abdominal compression and daily online plan adaptation to 50 Gy were analyzed. A pre-treatment, verification, and post-treatment MR imaging (MRI) for each of the five fractions (75 total) were used to calculate intra and interfraction motion. The MRIs were registered using Large Deformation Diffeomorphic Metric Mapping (LDDMM) deformable image registration (DIR) method and total dose delivered to stomach_duodenum, small bowel (SB) and large bowel (LB) were accumulated. Deformations were quantified using gradient magnitude and Jacobian integral of the Deformation Vector Fields (DVF). Registration DVFs were geometrically assessed using Dice and 95th percentile Hausdorff distance (HD95) between the deformed and physician’s contours. Accumulated doses were then calculated from the DVFs.

Results

Median Dice and HD95 were: Stomach_duodenum (0.9, 1.0 mm), SB (0.9, 3.6 mm), and LB (0.9, 2.0 mm). Median (max) interfraction deformation for stomach_duodenum, SB and LB was 6.4 (25.8) mm, 7.9 (40.5) mm and 7.6 (35.9) mm. Median intrafraction deformation was 5.5 (22.6) mm, 8.2 (37.8) mm and 7.2 (26.5) mm. Accumulated doses for two patients exceeded institutional constraints for stomach_duodenum, one of whom experienced Grade1 acute and late abdominal toxicity.

Conclusion

LDDMM method indicates feasibility to measure large GI motion and accumulate dose. Further validation on larger cohort will allow quantitative dose accumulation to more reliably optimize online MRgRT.

Neuroanatomical changes seen in MRI in patients with cerebral metastasized breast cancer after radiotherapy

Purpose:

To quantify neuroanatomical changes using magnetic resonance imaging (MRI) in patients with cerebral metastasized breast cancer after brain radiotherapy (RT).

Methods:

Fifteen patients with breast cancer with brain metastases who underwent whole brain RT (WBR), radiosurgery (RS), and/or hypofractionated stereotactic treatment (STX) were examined at four time points (TPs). A total of 48 MRIs were available: prior to RT (TP1), 5–8 months after RT (TP2), 9–11 months after RT (TP3), and >20 months after RT (TP4). Using automatic segmentation, 25 subcortical structures were analyzed. Patients were split into three groups: STX (receiving STX and RS), RS (receiving RS only), and WBR (receiving WBR at least once). After testing for a normal distribution for all values using the Kolmogorov-Smirnov test, a two-sided paired t test was used to analyze volumetric changes. For those values that were not normally distributed, the nonparametric Mann-Whitney test was employed.

Results:

The left cerebellum white matter (p = 0.028), the right pallidum (p = 0.038), and the left thalamus (p = 0.039) significantly increased at TP2 compared to TP1. The third ventricle increased at all TPs (p = 0.034–0.046). The left choroid plexus increased at TP3 (p = 0.037) compared to TP1. The left lateral ventricle increased at TP3 (p = 0.012) and TP4 (p = 0.027). Total gray matter showed a trend of volume decline in STX and WBR groups.

Conclusions:

These findings indicate that alterations in the volume of subcortical structures may act as a sensitive parameter when evaluating neuroanatomical changes and brain atrophy due to radiotherapy. Differences observed for patients who received STX and WBR, but not those treated with RS, need to be validated further.

Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy

PURPOSE

Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy.

METHODS

To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs).

RESULTS

The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83 ± 0.09, 0.82 ± 0.08, and 0.81 ± 0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81 ± 0.06 and 0.85 ± 0.02.

CONCLUSION

We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.

Early postnatal irradiation-induced age-dependent changes in adult mouse brain: MRI based characterization

Background

Brain radiation exposure, in particular, radiotherapy, can induce cognitive impairment in patients, with significant effects persisting for the rest of their life. However, the main mechanisms leading to this adverse event remain largely unknown. A study of radiation-induced injury to multiple brain regions, focused on the hippocampus, may shed light on neuroanatomic bases of neurocognitive impairments in patients. Hence, we irradiated BALB/c mice (male and female) at postnatal day 3 (P3), day 10 (P10), and day 21 (P21) and investigated the long-term radiation effect on brain MRI changes and hippocampal neurogenesis.

Results

We found characteristic brain volume reductions in the hippocampus, olfactory bulbs, the cerebellar hemisphere, cerebellar white matter (WM) and cerebellar vermis WM, cingulate, occipital and frontal cortices, cerebellar flocculonodular WM, parietal region, endopiriform claustrum, and entorhinal cortex after irradiation with 5 Gy at P3. Irradiation at P10 induced significant volume reduction in the cerebellum, parietal region, cingulate region, and olfactory bulbs, whereas the reduction of the volume in the entorhinal, parietal, insular, and frontal cortices was demonstrated after irradiation at P21. Immunohistochemical study with cell division marker Ki67 and immature marker doublecortin (DCX) indicated the reduced cell division and genesis of new neurons in the subgranular zone of the dentate gyrus in the hippocampus after irradiation at all three postnatal days, but the reduction of total granule cells in the stratum granulosun was found after irradiation at P3 and P10.

Conclusions

The early life radiation exposure during different developmental stages induces varied brain pathophysiological changes which may be related to the development of neurological and neuropsychological disorders later in life.

Quadratic penalty method for intensity-based deformable image registration and 4DCT lung motion recovery

Intensity-based deformable image registration (DIR) requires minimizing an image dissimilarity metric. Imaged anatomy, such as bones and vasculature, as well as the resolution of the digital grid, can often cause discontinuities in the corresponding objective function. Consequently, the application of a gradient-based optimization algorithm requires a preprocessing image smoothing to ensure the existence of necessary image derivatives. Simple block matching (exhaustive search) methods do not require image derivative approximations, but their general effectiveness is often hindered by erroneous solutions (outliers). Block match methods are therefore often coupled with a statistical outlier detection method to improve results.

PURPOSE

The purpose of this work is to present a spatially accurate, intensity-based DIR optimization formulation that can be solved with a straightforward gradient-free quadratic penalty algorithm and is suitable for 4D thoracic computed tomography (4DCT) registration. Additionally, a novel regularization strategy based on the well-known leave-one-out robust statistical model cross-validation method is introduced.

METHODS

The proposed Quadratic Penalty DIR (QPDIR) method minimizes both an image dissimilarity term, which is separable with respect to individual voxel displacements, and a regularization term derived from the classical leave-one-out cross-validation statistical method. The resulting DIR problem lends itself to a quadratic penalty function optimization approach, where each subproblem can be solved by straightforward block coordinate descent iteration.

RESULTS

The spatial accuracy of the method was assessed using expert-determined landmarks on ten 4DCT datasets available on www.dir-lab.com. The QPDIR algorithm achieved average millimeter spatial errors between 0.69 (0.91) and 1.19 (1.26) on the ten test cases. On all ten 4DCT test cases, the QPDIR method produced spatial accuracies that are superior or equivalent to those produced by current state-of-the-art methods. Moreover, QPDIR achieved accuracies at the resolution of the landmark error assessment (i.e., the interobserver error) on six of the ten cases.

CONCLUSION

The QPDIR algorithm is based on a simple quadratic penalty function formulation and a regularization term inspired by leave-one-out cross validation. The formulation lends itself to a parallelizable, gradient-free, block coordinate descent numerical optimization method. Numerical results indicate that the method achieves a high spatial accuracy on 4DCT inhale/exhale phases.

Brain volume reduction after whole-brain radiotherapy: quantification and prognostic relevance

Background

Recent studies have questioned the value of adding whole-brain radiotherapy (WBRT) to stereotactic radiosurgery (SRS) for brain metastasis treatment. Neurotoxicity, including radiation-induced brain volume reduction, could be one reason why not all patients benefit from the addition of WBRT. In this study, we quantified brain volume reduction after WBRT and assessed its prognostic significance.

Methods

Brain volumes of 91 patients with cerebral metastases were measured during a 150-day period after commencing WBRT and were compared with their pretreatment volumes. The average daily relative change in brain volume of each patient, referred to as the "brain volume reduction rate," was calculated. Univariate and multivariate Cox regression analyses were performed to assess the prognostic significance of the brain volume reduction rate, as well as of 3 treatment-related and 9 pretreatment factors. A one-way analysis of variance was used to compare the brain volume reduction rate across recursive partitioning analysis (RPA) classes.

Results

On multivariate Cox regression analysis, the brain volume reduction rate was a significant predictor of overall survival after WBRT (P < 0.001), as well as the number of brain metastases (P = 0.002) and age (P = 0.008). Patients with a relatively favorable prognosis (RPA classes 1 and 2) experienced significantly less brain volume decrease after WBRT than patients with a poor prognosis (RPA class 3) (P = 0.001). There was no significant correlation between delivered radiation dose and brain volume reduction rate (P = 0.147).

Conclusion

In this retrospective study, a smaller decrease in brain volume after WBRT was an independent predictor of longer overall survival.

Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer

We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using a Jacobian map and to extract quantitative radiomic features from the Jacobian map to predict the pathologic tumor response in locally advanced esophageal cancer patients. In 20 patients who underwent CRT, a multi-resolution BSpline deformable registration was performed to register the follow-up (post-CRT) CT to the baseline CT image. The Jacobian map (J) was computed as the determinant of the gradient of the deformation vector field. The Jacobian map measured the ratio of local tumor volume change where J  <  1 indicated tumor shrinkage and J  >  1 denoted expansion. The tumor was manually delineated and corresponding anatomical landmarks were generated on the baseline and follow-up images. Intensity, texture and geometry features were then extracted from the Jacobian map of the tumor to quantify tumor morphological changes. The importance of each Jacobian feature in predicting pathologic tumor response was evaluated by both univariate and multivariate analysis. We constructed a multivariate prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO) for feature selection. The SVM-LASSO model was evaluated using ten-times repeated 10-fold cross-validation (10  ×  10-fold CV). After registration, the average target registration error was 4.30  ±  1.09 mm (LR:1.63 mm AP:1.59 mm SI:3.05 mm) indicating registration error was within two voxels and close to 4 mm slice thickness. Visually, the Jacobian map showed smoothly-varying local shrinkage and expansion regions in a tumor. Quantitatively, the average median Jacobian was 0.80  ±  0.10 and 1.05  ±  0.15 for responder and non-responder tumors, respectively. These indicated that on average responder tumors had 20% median volume shrinkage while non-responder tumors had 5% median volume expansion. In univariate analysis, the minimum Jacobian (p  =  0.009, AUC  =  0.98) and median Jacobian (p  =  0.004, AUC  =  0.95) were the most significant predictors. The SVM-LASSO model achieved the highest accuracy when these two features were selected (sensitivity  =  94.4%, specificity  =  91.8%, AUC  =  0.94). Novel features extracted from the Jacobian map quantified local tumor morphological changes using only baseline tumor contour without post-treatment tumor segmentation. The SVM-LASSO model using the median Jacobian and minimum Jacobian achieved high accuracy in predicting pathologic tumor response. The Jacobian map showed great potential for longitudinal evaluation of tumor response.

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field

BACKGROUND AND PURPOSE

Accurate follow-up of metastatic brain tumors has important implications for patient prognosis and management. The aim of this study was to develop and evaluate the accuracy of a semiautomated algorithm in detecting growing or shrinking metastatic brain tumors on longitudinal brain MRIs.

MATERIALS AND METHODS

We used 50 pairs of successive MR imaging datasets, 30 on 1.5T and 20 on 3T, containing contrast-enhanced 3D T1-weighted sequences. These yielded 150 growing or shrinking metastatic brain tumors. To detect them, we completed 2 major steps: 1) spatial normalization and calculation of the Jacobian operator field to quantify changes between scans, and 2) metastatic brain tumor candidate segmentation and detection of volume-changing metastatic brain tumors with the Jacobian operator field. Receiver operating characteristic analysis was used to assess the detection accuracy of the algorithm, and it was verified with jackknife resampling. The reference standard was based on detections by a neuroradiologist.

RESULTS

The areas under the receiver operating characteristic curves were 0.925 for 1.5T and 0.965 for 3T. Furthermore, at its optimal performance, the algorithm achieved a sensitivity of 85.1% and 92.1% and specificity of 86.7% and 91.3% for 1.5T and 3T, respectively. Vessels were responsible for most false-positives. Newly developed or resolved metastatic brain tumors were a major source of false-negatives.

CONCLUSIONS

The proposed algorithm could detect volume-changing metastatic brain tumors on longitudinal brain MRIs with statistically high accuracy, demonstrating its potential as a computer-aided change-detection tool for complementing the performance of radiologists, decreasing inter- and intraobserver variability, and improving efficacy.