Navigator channel adaptation to reconstruct three dimensional heart volumes from two dimensional radiotherapy planning data

BACKGROUND: Biologically-based models that utilize 3D radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients. A major challenge arises from having only 2D treatment planning data for patients with long-term follow-up. In this study, we evaluate the accuracy of an advanced deformable image registration (DIR) and navigator channels (NC) adaptation technique to reconstruct 3D heart volumes from 2D radiotherapy planning images for Hodgkin's Lymphoma (HL) patients. METHODS: Planning CT images were obtained for 50 HL patients who underwent mediastinal radiotherapy. Twelve image sets (6 male, 6 female) were used to construct a male and a female population heart model, which was registered to 23 HL "Reference" patients' CT images using a DIR algorithm, MORFEUS. This generated a series of population-to-Reference patient specific 3D deformation maps. The technique was independently tested on 15 additional "Test" patients by reconstructing their 3D heart volumes using 2D digitally reconstructed radiographs (DRR). The technique involved: 1) identifying a matching Reference patient for each Test patient using thorax measurements, 2) placement of six NCs on matching Reference and Test patients' DRRs to capture differences in significant heart curvatures, 3) adapting the population-to-Reference patient-specific deformation maps to generate population-to-Test patient-specific deformation maps using linear and bilinear interpolation methods, 4) applying population-to-Test patient specific deformation to the population model to reconstruct Test-patient specific 3D heart models. The percentage volume overlap between the NC-adapted reconstruction and actual Test patient's true heart volume was calculated using the Dice coefficient. RESULTS: The average Dice coefficient expressed as a percentage between the NC-adapted and actual Test model was 89.4 ± 2.8%. The modified NC adaptation technique made significant improvements to the population deformation heart models (p = 0.01). As standard evaluation, the residual Dice error after adaptation was comparable to the volumetric differences observed in free-breathing heart volumes (p = 0.62). CONCLUSIONS: The reconstruction technique described generates accurate 3D heart models from limited 2D planning data. This development could potentially be used to retrospectively calculate delivered dose to the heart for historically treated patients and thereby provide a better understanding of late radiation-related cardiac effects

Effect of deformable registration uncertainty on lung SBRT dose accumulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134767/1/mp8412.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134767/2/mp8412_am.pd

Contemporary Evidence on Colorectal Liver Metastases Ablation: Toward a Paradigm Shift in Locoregional Treatment

Image-guided percutaneous ablation techniques represent an attractive local therapy for the treatment of colorectal liver metastases (CLM) given its low risk of severe complications, which allows for early initiation of adjuvant therapies and spare functional liver parenchyma, allowing repeated treatments at the time of recurrence. However, ablation does not consistently achieve similar oncological outcomes to surgery, with the latter being currently considered the first-line local treatment modality in international guidelines. Recent application of computer-assisted ablation planning, guidance, and intra-procedural response assessment has improved percutaneous ablation outcomes. In addition, the evolving understanding of tumor molecular profiling has brought to light several biological factors associated with oncological outcomes following local therapies. The standardization of ablation procedures, the understanding of previously unknown biological factors affecting ablation outcomes, and the evidence by ongoing prospective clinical trials are poised to change the current perspective and indications on the use of ablation for CLM

StyleGAN2-based Out-of-Distribution Detection for Medical Imaging

One barrier to the clinical deployment of deep learning-based models is the presence of images at runtime that lie far outside the training distribution of a given model. We aim to detect these out-of-distribution (OOD) images with a generative adversarial network (GAN). Our training dataset was comprised of 3,234 liver-containing computed tomography (CT) scans from 456 patients. Our OOD test data consisted of CT images of the brain, head and neck, lung, cervix, and abnormal livers. A StyleGAN2-ADA architecture was employed to model the training distribution. Images were reconstructed using backpropagation. Reconstructions were evaluated using the Wasserstein distance, mean squared error, and the structural similarity index measure. OOD detection was evaluated with the area under the receiver operating characteristic curve (AUROC). Our paradigm distinguished between liver and non-liver CT with greater than 90% AUROC. It was also completely unable to reconstruct liver artifacts, such as needles and ascites.Comment: Extended abstract published in the "Medical Imaging Meets NeurIPS" workshop at NeurIPS 2022. Original abstract can be found at http://www.cse.cuhk.edu.hk/~qdou/public/medneurips2022/125.pd

Imaging in Interventional Radiology: 2043 and Beyond

Since its inception in the early 20th century, interventional radiology (IR) has evolved tremendously and is now a distinct clinical discipline with its own training pathway. The arsenal of modalities at work in IR includes x-ray radiography and fluoroscopy, CT, MRI, US, and molecular and multimodality imaging within hybrid interventional environments. This article briefly reviews the major developments in imaging technology in IR over the past century, summarizes technologies now representative of the standard of care, and reflects on emerging advances in imaging technology that could shape the field in the century ahead. The role of emergent imaging technologies in enabling high-precision interventions is also briefly reviewed, including image-guided ablative therapies

Use of Contrast Media During CT-guided Thermal Ablation of Colorectal Liver Metastasis for Procedure Planning is Associated with Improved Immediate Outcomes

PURPOSE: The aim of this study was to analyze the impact of using intra-procedural pre-ablation contrast-enhanced CT prior to percutaneous thermal ablation (pre-ablation CECT) of colorectal liver metastases (CLM) on local outcomes. MATERIALS AND METHODS: This retrospective analysis of a prospectively collected liver ablation registry included 144 consecutive patients (median age 57 years IQR [49, 65], 60% men) who underwent 173 CT-guided ablation sessions for 250 CLM between October 2015 and March 2020. In addition to oncologic outcomes, technical success was retrospectively evaluated using a biomechanical deformable image registration software for 3D-minimal ablative margin (3D-MAM) quantification. Bayesian regression was used to estimate effects of pre-ablation CECT on residual unablated tumor, 3D-MAM, and local tumor progression-free survival (LTPFS). RESULTS: Pre-ablation CECT was acquired in 71/173 (41%) sessions. Residual unablated tumor was present in one (0.9%) versus nine tumors (6.6%) ablated with versus without using pre-ablation CECT, respectively (p = 0.024). Pre-ablation CECT use decreased the odds of residual disease on first follow-up by 78% (CI CONCLUSIONS: Pre-ablation CECT is associated with improved immediate outcomes by significantly reducing the incidence of residual unablated tumor and by mitigating the risk of incomplete ablation for larger CLM. We recommend performing baseline intra-procedural pre-ablation CECT as a standard imaging protocol. LEVEL OF EVIDENCE: Level 3 (retrospective cohort study)

Incidental Ring-Hyperenhancing Liver Micronodules at CT Hepatic Arteriography-Guided Percutaneous Thermal Ablation of Colorectal Liver Metastases

BACKGROUND: Evidence-based treatment decisions in medicine are made founded on population-level evidence obtained during randomized clinical trials. In an era of personalized medicine, these decisions should be based on the predicted benefit of a treatment on a patient-level. Survival prediction models play a central role as they incorporate the time-to-event and censoring. In medical applications uncertainty is critical especially when treatments differ in their side effect profiles or costs. Additionally, models must be adapted to local populations without diminishing performance and often without the original training data available due to privacy concern. Both points are supported by Bayesian models-yet they are rarely used. The aim of this work is to evaluate Bayesian parametric survival models on public datasets including cardiology, infectious diseases, and oncology. MATERIALS AND METHODS: Bayesian parametric survival models based on the Exponential and Weibull distribution were implemented as a Python package. A linear combination and a neural network were used for predicting the parameters of the distributions. A superiority design was used to assess whether Bayesian models are better than commonly used models such as Cox Proportional Hazards, Random Survival Forest, and Neural Network-based Cox Proportional Hazards. In a secondary analysis, overfitting was compared between these models. An equivalence design was used to assess whether the prediction performance of Bayesian models after model updating using Bayes rule is equivalent to retraining on the full dataset. RESULTS: In this study, we found that Bayesian parametric survival models perform as good as state-of-the art models while requiring less hyperparameters to be tuned and providing a measure of the uncertainty of the predictions. In addition, these models were less prone to overfitting. Furthermore, we show that updating these models using Bayes rule yields equivalent performance compared to models trained on combined original and new datasets. CONCLUSIONS: Bayesian parametric survival models are non-inferior to conventional survival models while requiring less hyperparameter tuning, being less prone to overfitting, and allowing model updating using Bayes rule. Further, the Bayesian models provide a measure of the uncertainty on the statistical inference, and, in particular, on the prediction

Analysis and Prediction of Liver Volume Change Maps Derived From Computational Tomography Scans Acquired Pre- And Post-radiation Therapy

External beam radiation therapy (EBRT) of liver cancers can cause local liver atrophy as a result of tissue damage or hypertrophy as a result of liver regeneration. Predicting those volumetric changes would enable new strategies for liver function preservation during treatment planning. However, understanding of the spatial dose/volume relationship is still limited. This study leverages the use of deep learning-based segmentation and biomechanical deformable image registration (DIR) to analyze and predict this relationship. Pre- and Post-EBRT imaging data were collected for 100 patients treated for hepatocellular carcinomas, cholangiocarcinoma or CRC with intensity-modulated radiotherapy (IMRT) with prescription doses ranging from 50 to 100 Gy delivered in 10-28 fractions. For each patient, DIR between the portal and venous (PV) phase of a diagnostic computed tomography (CT) scan acquired before radiation therapy (RT) planning, and a PV phase of a diagnostic CT scan acquired after the end of RT (on average 147 ± 36 d) was performed to calculate Jacobian maps representing volume changes in the liver. These volume change maps were used: (i): to analyze the dose/volume relationship in the whole liver and individual Couinaud\u27s segments; and (ii): to investigate the use of deep-learning to predict a Jacobian map solely based on the pre-RT diagnostic CT and planned dose distribution. Moderate correlations between mean equivalent dose in 2 Gy fractions (EQD2) and volume change was observed for all liver sub-regions analyzed individually with Pearson correlatio

Dose Accumulation with CBCT Conversion in Head and Neck and Prostate

https://openworks.mdanderson.org/sumexp22/1052/thumbnail.jp

Automated Segmentation of Colorectal Liver Metastasis and Liver Ablation on Contrast-Enhanced CT Images

Objectives: Colorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones. Methods: Four FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from the liver tumor segmentation (LiTS) challenge. For the evaluation, we acquired 15 patients from the 3D-IRCADb database, 18 patients from our institution (CRLM = 24, ablation-zone = 19), and those submitted to the LiTS challenge (n = 70). Qualitative evaluations of our institutional data were performed by two board-certified radiologists (interventional and diagnostic) and a radiology-trained physician fellow, using a Likert scale of 1-5. Results: The most accurate model was the Hybrid-WNet. On a patient-by-patient basis in the 3D-IRCADb dataset, the median (min-max) Dice similarity coefficient (DSC) was 0.73 (0.41-0.88), the median surface distance was 1.75 mm (0.57-7.63 mm), and the number of false positives was 1 (0-4). In the LiTS challenge (n = 70), the global DSC was 0.810. The model sensitivity was 98% (47/48) for sites ≥15 mm in diameter. Qualitatively, 100% (24/24; minority vote) of the CRLM and 84% (16/19; majority vote) of the ablation zones had Likert scores ≥4. Conclusion: The Hybrid-WNet model provided fast (\u3c30 s) and accurate segmentations of CRLM and ablation zones on contrast-enhanced CT scans, with positive physician reviews