Ablative margin quantification using deformable versus rigid image registration in colorectal liver metastasis thermal ablation: a retrospective single-center study.

PURPOSE: To investigate the correlation of minimal ablative margin (MAM) quantification using biomechanical deformable (DIR) versus intensity-based rigid image registration (RIR) with local outcomes following colorectal liver metastasis (CLM) thermal ablation. METHODS: This retrospective single-institution study included consecutive patients undergoing thermal ablation between May 2016 and October 2021. Patients who did not have intraprocedural pre- and post-ablation contrast-enhanced CT images for MAM quantification or follow-up period less than 1 year without residual tumor or local tumor progression (LTP) were excluded. DIR and RIR methods were used to quantify the MAM. The registration accuracy was compared using Dice similarity coefficient (DSC). Area under the receiver operating characteristic curve (AUC) was used to test MAM in predicting local tumor outcomes. RESULTS: A total of 72 patients (mean age 57; 44 men) with 139 tumors (mean diameter 1.5 cm ± 0.8 (SD)) were included. During a median follow-up of 29.4 months, there was one residual unablated tumor and the LTP rate was 17% (24/138). The ranges of DSC were 0.96-0.98 and 0.67-0.98 for DIR and RIR, respectively (p < 0.001). When using DIR, 27 (19%) tumors were partially or totally registered outside the liver, compared to 46 (33%) with RIR. Using DIR versus RIR, the corresponding median MAM was 4.7 mm versus 4.0 mm, respectively (p = 0.5). The AUC in predicting residual tumor and 1-year LTP for DIR versus RIR was 0.89 versus 0.72, respectively (p < 0.001). CONCLUSION: Ablative margin quantified on intra-procedural CT imaging using DIR method outperformed RIR for predicting local outcomes of CLM thermal ablation. CLINICAL RELEVANCE STATEMENT: The study supports the role of biomechanical deformable image registration as the preferred image registration method over rigid image registration for quantifying minimal ablative margins using intraprocedural contrast-enhanced CT images. KEY POINTS: • Accurate and reproducible image registration is a prerequisite for clinical application of image-based ablation confirmation methods. • When compared to intensity-based rigid image registration, biomechanical deformable image registration for minimal ablative margin quantification was more accurate for liver registration using intraprocedural contrast-enhanced CT images. • Biomechanical deformable image registration outperformed intensity-based rigid image registration for predicting local tumor outcomes following colorectal liver metastasis thermal ablation.

Targeted exome-based predictors of patterns of progression of colorectal liver metastasis after percutaneous thermal ablation.

BACKGROUND: Percutaneous thermal ablation is a curative-intent locoregional therapy (LRT) for selected patients with unresectable colorectal liver metastasis (CLM). Several factors have been identified that contribute to local tumour control after ablation. However, factors contributing to disease progression outside the ablation zone after ablation are poorly understood. METHODS: In this retrospective study, using next-generation sequencing, we identified genetic biomarkers associated with different patterns of progression following thermal ablation of CLM. RESULTS: A total of 191 ablation naïve patients between January 2011 and March 2020 were included in the analysis, and 101 had genomic profiling available. Alterations in the TGFß pathway were associated with increased risk of development of new intrahepatic tumours (hazard ratio [HR], 2.75, 95% confidence interval [95% CI] 1.39-5.45, P = 0.004); and alterations in the Wnt pathway were associated with increased probability of receiving salvage LRT for any intrahepatic progression (HR, 5.8, 95% CI 1.94-19.5, P = 0.003). CONCLUSIONS: Our findings indicate that genomic alterations in cancer-related signalling pathways can predict different progression patterns and the likelihood of receiving salvage LRT following percutaneous thermal ablation of CLM.

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.

Ablative Margins of Colorectal Liver Metastases Using Deformable CT Image Registration and Autosegmentation.

Background Confirming ablation completeness with sufficient ablative margin is critical for local tumor control following colorectal liver metastasis (CLM) ablation. An image-based confirmation method considering patient- and ablation-related biomechanical deformation is an unmet need. Purpose To evaluate a biomechanical deformable image registration (DIR) method for three-dimensional (3D) minimal ablative margin (MAM) quantification and the association with local disease progression following CT-guided CLM ablation. Materials and Methods This single-institution retrospective study included patients with CLM treated with CT-guided microwave or radiofrequency ablation from October 2015 to March 2020. A biomechanical DIR method with AI-based autosegmentation of liver, tumors, and ablation zones on CT images was applied for MAM quantification retrospectively. The per-tumor incidence of local disease progression was defined as residual tumor or local tumor progression. Factors associated with local disease progression were evaluated using the multivariable Fine-Gray subdistribution hazard model. Local disease progression sites were spatially localized with the tissue at risk for tumor progression (<5 mm) using a 3D ray-tracing method. Results Overall, 213 ablated CLMs (mean diameter, 1.4 cm) in 124 consecutive patients (mean age, 57 years ± 12 [SD]; 69 women) were evaluated, with a median follow-up interval of 25.8 months. In ablated CLMs, an MAM of 0 mm was depicted in 14.6% (31 of 213), from greater than 0 to less than 5 mm in 40.4% (86 of 213), and greater than or equal to 5 mm in 45.1% (96 of 213). The 2-year cumulative incidence of local disease progression was 72% for 0 mm and 12% for greater than 0 to less than 5 mm. No local disease progression was observed for an MAM greater than or equal to 5 mm. Among 117 tumors with an MAM less than 5 mm, 36 had local disease progression and 30 were spatially localized within the tissue at risk for tumor progression. On multivariable analysis, an MAM of 0 mm (subdistribution hazard ratio, 23.3; 95% CI: 10.8, 50.5; P < .001) was independently associated with local disease progression. Conclusion Biomechanical deformable image registration and autosegmentation on CT images enabled identification and spatial localization of colorectal liver metastases at risk for local disease progression following ablation, with a minimal ablative margin greater than or equal to 5 mm as the optimal end point. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sofocleous in this issue.

Bayesian parametric models for survival prediction in medical applications.

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.

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS: Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION: DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

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.

Automatic contouring QA method using a deep learning-based autocontouring system.

PURPOSE: To determine the most accurate similarity metric when using an independent system to verify automatically generated contours. METHODS: A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para-aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans. Eleven similarity metrics (volumetric Dice similarity coefficient (DSC), Hausdorff distance, 95% Hausdorff distance, mean surface distance, and surface DSC with tolerances from 1 to 10 mm) were calculated between the reference and the verification autocontours, and between the manually generated and the verification autocontours. A support vector machine (SVM) was used to determine the threshold that separates clinically acceptable and unacceptable contours for each structure. The 11 metrics were investigated individually and in certain combinations. Linear, radial basis function, sigmoid, and polynomial kernels were tested using the combinations of metrics as inputs for the SVM. RESULTS: The highest contouring error detection accuracies were 0.91 for the UteroCervix, 0.90 for the CTVn, 0.89 for the PAN, 0.92 for the bladder, 0.95 for the rectum, and 0.97 for the kidneys and were achieved using surface DSCs with a thickness of 1, 2, or 3 mm. The linear kernel was the most accurate and consistent when a combination of metrics was used as an input for the SVM. However, the best model accuracy from the combinations of metrics was not better than the best model accuracy from a surface DSC as an input. CONCLUSIONS: We distinguished clinically acceptable contours from clinically unacceptable contours with an accuracy higher than 0.9 for the targets and critical structures in patients with cervical cancer; the most accurate similarity metric was surface DSC with a thickness of 1, 2, or 3 mm.

MRI evaluation of normal tissue deformation and breathing motion under an abdominal compression device.

PURPOSE: Abdominal compression can minimize breathing motion in stereotactic radiotherapy, though it may impact the positioning of dose-limiting normal tissues. This study quantified the reproducibility of abdominal normal tissues and respiratory motion with the use of an abdominal compression device using MR imaging. METHODS: Twenty healthy volunteers had repeat MR over 3 days under an abdominal compression plate device. Normal tissues were delineated on daily axial T2-weighted MR and compared on days 2 and 3 relative to day 1, after adjusting for baseline shifts relative to bony anatomy. Inter-fraction organ deformation was computed using deformable registration of axial T2 images. Deformation > 5 mm was assumed to be clinically relevant. Inter-fraction respiratory amplitude changes and intra-fraction baseline drifts during imaging were quantified on daily orthogonal cine-MR (70 s each), and changes > 3 mm were assumed to be relevant. RESULTS: On axial MR, the mean inter-fraction normal tissue deformation was > 5 mm for all organs (range 5.1-13.4 mm). Inter-fraction compression device misplacements > 5 mm and changes in stomach volume > 50% occurred at a rate of 93% and 38%, respectively, in one or more directions and were associated with larger adjacent organ deformation, in particular for the duodenum. On cine-MR, inter-fraction amplitude changes > 3 mm on day 2 and 3 relative to day 1 occurred at a rate of < 12.5% (mean superior-inferior change was 1.6 mm). Intra-fraction baseline drifts > 3 mm during any cine-MR acquisition occurred at a rate of 23% (mean superior-inferior changes was 2.4 mm). CONCLUSIONS: Respiratory motion under abdominal compression is reproducible in most subjects within 3 mm. However, inter-fraction deformations greater than 5 mm in normal tissues were common and larger than inter- and intra-fraction respiratory changes. Deformations were driven mostly by variable stomach contents and device positioning. The magnitude of this motion may impact normal tissue dosimetry during stereotactic radiotherapy.

Geometric and dosimetric accuracy of deformable image registration between average-intensity images for 4DCT-based adaptive radiotherapy for non-small cell lung cancer.

PURPOSE: Re-planning for four-dimensional computed tomography (4DCT)-based lung adaptive radiotherapy commonly requires deformable dose mapping between the planning average-intensity image (AVG) and the newly acquired AVG. However, such AVG-AVG deformable image registration (DIR) lacks accuracy assessment. The current work quantified and compared geometric accuracies of AVG-AVG DIR and corresponding phase-phase DIRs, and subsequently investigated the clinical impact of such AVG-AVG DIR on deformable dose mapping. METHODS AND MATERIALS: Hybrid intensity-based AVG-AVG and phase-phase DIRs were performed between the planning and mid-treatment 4DCTs of 28 non-small cell lung cancer patients. An automated landmark identification algorithm detected vessel bifurcation pairs in both lungs. Target registration error (TRE) of these landmark pairs was calculated for both DIR types. The correlation between TRE and respiratory-induced landmark motion in the planning 4DCT was analyzed. Global and local dose metrics were used to assess the clinical implications of AVG-AVG deformable dose mapping with both DIR types. RESULTS: TRE of AVG-AVG and phase-phase DIRs averaged 3.2 ± 1.0 and 2.6 ± 0.8 mm respectively (p < 0.001). Using AVG-AVG DIR, TREs for landmarks with <10 mm motion averaged 2.9 ± 2.0 mm, compared to 3.1 ± 1.9 mm for the remaining landmarks (p < 0.01). Comparatively, no significant difference was demonstrated for phase-phase DIRs. Dosimetrically, no significant difference in global dose metrics was observed between doses mapped with AVG-AVG DIR and the phase-phase DIR, but a positive linear relationship existed (p = 0.04) between the TRE of AVG-AVG DIR and local dose difference. CONCLUSIONS: When the region of interest experiences <10 mm respiratory-induced motion, AVG-AVG DIR may provide sufficient geometric accuracy; conversely, extra attention is warranted, and phase-phase DIR is recommended. Dosimetrically, the differences in geometric accuracy between AVG-AVG and phase-phase DIRs did not impact global lung-based metrics. However, as more localized dose metrics are needed for toxicity assessment, phase-phase DIR may be required as its lower mean TRE improved voxel-based dosimetry.

Technical Note: A step-by-step guide to Temporally Feathered Radiation Therapy planning for head and neck cancer.

PURPOSE: Prior in silico simulations propose that Temporally Feathered Radiation Therapy (TFRT) may reduce toxicity related to head and neck radiation therapy. In this study we demonstrate a step-by-step guide to TFRT planning with modern treatment planning systems. METHODS: One patient with oropharyngeal cancer planned for definitive radiation therapy using intensity-modulated radiation therapy (IMRT) techniques was replanned using the TFRT technique. Five organs at risk (OAR) were identified to be feathered. A "base plan" was first created based on desired planning target volumes (PTV) coverage, plan conformality, and OAR constraints. The base plan was then re-optimized by modifying planning objectives, to generate five subplans. All beams from each subplan were imported onto one trial to create the composite TFRT plan. The composite TFRT plan was directly compared with the non-TFRT IMRT plan. During plan assessment, the composite TFRT was first evaluated followed by each subplan to meet preset compliance criteria. RESULTS: The following organs were feathered: oral cavity, right submandibular gland, left submandibular gland, supraglottis, and OAR Pharynx. Prescription dose PTV coverage (>95%) was met in each subplan and the composite TFRT plan. Expected small variations in dose were observed among the plans. The percent variation between the high fractional dose and average low fractional dose was 29%, 28%, 24%, 19%, and 10% for the oral cavity, right submandibular, left submandibular, supraglottis, and OAR pharynx nonoverlapping with the PTV. CONCLUSIONS: Temporally Feathered Radiation Therapy planning is possible with modern treatment planning systems. Modest dosimetric changes are observed with TFRT planning compared with non-TFRT IMRT planning. We await the results of the current prospective trial to seeking to demonstrate the feasibility of TFRT in the modern clinical workflow (NCT03768856). Further studies will be required to demonstrate the potential benefit of TFRT over non-TFRT IMRT Planning.

Quantitative chest CT for subtyping chronic lung allograft dysfunction and its association with survival.

Chronic lung allograft dysfunction (CLAD) is a major cause of mortality in lung transplant recipients. CLAD can be sub-divided into at least 2 subtypes with distinct mortality risk characteristics: restrictive allograft syndrome (RAS), which demonstrates increased overall computed tomography (CT) lung density in contrast with bronchiolitis obliterans syndrome (BOS), which demonstrates reduced overall CT lung density. This study aimed to evaluate a reader-independent quantitative density metric (QDM) derived from CT histograms to associate with CLAD survival. A retrospective study evaluated CT scans corresponding to CLAD onset using pulmonary function tests in 74 patients (23 RAS, 51 BOS). Two different QDM values (QDM1 and QDM2) were calculated using CT lung density histograms. Calculation of QDM1 includes the extreme edges of the histogram. Calculation of QDM2 includes the central region of the histogram. Kaplan-Meier analysis and Cox regression analysis were used for CLAD prognosis. Higher QDM values were significantly associated with decreased survival. The hazard ratio for death was 3.2 times higher at the 75th percentile compared to the 25th percentile using QDM1 in a univariate model. QDM may associate with CLAD patient prognosis.

The role of biomechanical anatomical modeling via computed tomography for identification of restrictive allograft syndrome.

Chronic lung allograft dysfunction (CLAD) reduces long-term graft survival. It is important to distinguish CLAD subtypes: bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS) as RAS has a worse prognosis and accurate subtyping could facilitate targeted treatments. However, the current diagnosis of CLAD subtypes is based on pulmonary function test (PFT) results that reflect global estimates of lung function; anatomical modeling based on computed tomography (CT) has the potential to provide detailed analysis of global and regional lung function. The purpose of this study is to evaluate the utility of CT-based anatomical modeling for the identification of RAS. This retrospective study included 51 patients (CLAD: 17 BOS and 17 RAS, control: 17 No-CLAD). CT data were assessed using a biomechanical model-based platform (MORFEUS) to characterize changes in lung deformation between baseline and disease onset. Lung deformation demonstrated high sensitivity and specificity (>80%) in differentiating RAS from BOS (P<.0001) and No-CLAD (P<.0001). There were matching radiological reading and inward deformation abnormalities in 79% of lung sections in patients with RAS. Anatomical modeling is complementary to conventional assessment in the diagnosis of RAS and potentially provides quantitative data that can help in the characterization and detailed assessment of heterogeneous lung parenchymal disease.

MR-guided prostate biopsy for planning of focal salvage after radiation therapy.

PURPOSE: To determine if the integration of diagnostic magnetic resonance (MR) imaging and MR-guided biopsy would improve target delineation for focal salvage therapy in men with prostate cancer. MATERIALS AND METHODS: Between September 2008 and March 2011, 30 men with biochemical failure after radiation therapy for prostate cancer provided written informed consent and were enrolled in a prospective clinical trial approved by the institutional research ethics board. An integrated diagnostic MR imaging and interventional biopsy procedure was performed with a 1.5-T MR imager by using a prototype table and stereotactic transperineal template. Multiparametric MR imaging (T2-weighted, dynamic contrast material-enhanced, and diffusion-weighted sequences) was followed by targeted biopsy of suspicious regions and systematic sextant sampling. Biopsy needle locations were imaged and registered to diagnostic images. Two observers blinded to clinical data and the results of prior imaging studies delineated tumor boundaries. Area under the receiver operating characteristic curve (Az) was calculated based on generalized linear models by using biopsy as the reference standard to distinguish benign from malignant lesions. RESULTS: Twenty-eight patients were analyzed. Most patients (n = 22) had local recurrence, with 82% (18 of 22) having unifocal disease. When multiparametric volumes from two observers were combined, it increased the apparent overall tumor volume by 30%; however, volumes remained small (mean, 2.9 mL; range, 0.5-8.3 mL). Tumor target boundaries differed between T2-weighted, dynamic contrast-enhanced, and diffusion-weighted sequences (mean Dice coefficient, 0.13-0.35). Diagnostic accuracy in the identification of tumors improved with a multiparametric approach versus a strictly T2-weighted or dynamic contrast-enhanced approach through an improvement in sensitivity (observer 1, 0.65 vs 0.35 and 0.44, respectively; observer 2, 0.82 vs 0.64 and 0.53, respectively; P < .05) and improved further with a 5-mm expansion margin (Az = 0.85 vs 0.91 for observer 2). After excluding three patients with fewer than six informative biopsy cores and six patients with inadequately stained margins, MR-guided biopsy enabled more accurate delineation of the tumor target volume be means of exclusion of false-positive results in 26% (five of 19 patients), false-negative results in 11% (two of 19 patients) and by guiding extension of tumor boundaries in 16% (three of 19 patients). CONCLUSION: The integration of guided biopsy with diagnostic MR imaging is feasible and alters delineation of the tumor target boundary in a substantial proportion of patients considering focal salvage.

Incidental Ring-hyperenhancing Liver Micronodules at CT Hepatic Arteriography-guided Percutaneous Thermal Ablation of Colorectal Liver Metastases.

CT during hepatic arteriography (CTHA) is a highly sensitive imaging method for detecting colorectal liver metastases (CLMs), which supports its use during percutaneous thermal liver ablation. In contrast to its high sensitivity, its specificity for incidental small CLMs not detected at preablation cross-sectional imaging is believed to be low given the absence of specific imaging signatures and the common presence of pseudolesions. In this retrospective study of 22 patients (mean age, 55 years ± 10.6 [SD]; 63.6% male, 36.4% female) with CLMs undergoing CTHA-guided microwave percutaneous thermal ablation between November 2017 and October 2022, the authors provided a definition of incidental ring-hyperenhancing liver micronodules (RHLMs) and investigated whether there is a correlation of RHLMs with histologic analysis or intrahepatic tumor progression at imaging follow-up after applying a biomechanical deformable image registration method. The analysis revealed 25 incidental RHLMs in 41.7% (10 of 24) of the CTHA images from the respective guided ablation sessions. Of those, four RHLMs were ablated. Among the remaining 21 RHLMs, 71.4% (15 of 21) were confirmed to be CLM with either histology (n = 3) or imaging follow-up (n = 12). The remaining 28.6% (six of 21) of RHLMs were not observed at follow-up imaging. This suggests that RHLMs at CTHA may be an early indicator of incidental small CLMs. Keywords: Colorectal Neoplasms, Liver, Angiography, CT, Incidental Findings, Ablation Supplemental material is available for this article. © RSNA, 2024.

Using Patient-Specific 3D Modeling and Simulations to Optimize Microwave Ablation Therapy for Liver Cancer.

Microwave ablation (MWA) of liver tumors presents challenges like under- and over-ablation, potentially leading to inadequate tumor destruction and damage to healthy tissue. This study aims to develop personalized three-dimensional (3D) models to simulate MWA for liver tumors, incorporating patient-specific characteristics. The primary objective is to validate the predicted ablation zones compared to clinical outcomes, offering insights into MWA before therapy to facilitate accurate treatment planning. Contrast-enhanced CT images from three patients were used to create 3D models. The simulations used coupled electromagnetic wave propagation and bioheat transfer to estimate the temperature distribution, predicting tumor destruction and ablation margins. The findings indicate that prolonged ablation does not significantly improve tumor destruction once an adequate margin is achieved, although it increases tissue damage. There was a substantial overlap between the clinical ablation zones and the predicted ablation zones. For patient 1, the Dice score was 0.73, indicating high accuracy, with a sensitivity of 0.72 and a specificity of 0.76. For patient 2, the Dice score was 0.86, with a sensitivity of 0.79 and a specificity of 0.96. For patient 3, the Dice score was 0.8, with a sensitivity of 0.85 and a specificity of 0.74. Patient-specific 3D models demonstrate potential in accurately predicting ablation zones and optimizing MWA treatment strategies.

Intraprocedural Versus Initial Follow-up Minimal Ablative Margin Assessment After Colorectal Liver Metastasis Thermal Ablation: Which One Better Predicts Local Outcomes?

OBJECTIVES: The aim of this study was to investigate the prognostic value of 3-dimensional minimal ablative margin (MAM) quantified by intraprocedural versus initial follow-up computed tomography (CT) in predicting local tumor progression (LTP) after colorectal liver metastasis (CLM) thermal ablation. MATERIALS AND METHODS: This single-institution, patient-clustered, tumor-based retrospective study included patients undergoing microwave and radiofrequency ablation between 2016 and 2021. Patients without intraprocedural and initial follow-up contrast-enhanced CT, residual tumors, or with follow-up less than 1 year without LTP were excluded. Minimal ablative margin was quantified by a biomechanical deformable image registration method with segmentations of CLMs on intraprocedural preablation CT and ablation zones on intraprocedural postablation and initial follow-up CT. Prognostic value of MAM to predict LTP was tested using area under the curve and competing-risk regression model. RESULTS: A total of 68 patients (mean age ± standard deviation, 57 ± 12 years; 43 men) with 133 CLMs were included. During a median follow-up of 30.3 months, LTP rate was 17% (22/133). The median volume of ablation zone was 27 mL and 16 mL segmented on intraprocedural and initial follow-up CT, respectively ( P < 0.001), with corresponding median MAM of 4.7 mm and 0 mm, respectively ( P < 0.001). The area under the curve was higher for MAM quantified on intraprocedural CT (0.89; 95% confidence interval [CI], 0.83-0.94) compared with initial follow-up CT (0.66; 95% CI, 0.54-0.76) in predicting 1-year LTP ( P < 0.001). An MAM of 0 mm on intraprocedural CT was an independent predictor of LTP with a subdistribution hazards ratio of 11.9 (95% CI, 4.9-28.9; P < 0.001), compared with 2.4 (95% CI, 0.9-6.0; P = 0.07) on initial follow-up CT. CONCLUSIONS: Ablative margin quantified on intraprocedural CT significantly outperformed initial follow-up CT in predicting LTP and should be used for ablation endpoint assessment.

Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy.

PURPOSE: The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS: Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS: With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.

Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease Etiologies.

Image segmentation of the liver is an important step in several treatments for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a deep learning model to segment the liver on T1w MR images. We sought to determine the best architecture by training, validating, and testing three different deep learning architectures using a total of 819 T1w MR images gathered from six different datasets, both publicly and internally available. Our experiments compared each architecture's testing performance when trained on data from the same dataset via 5-fold cross validation to its testing performance when trained on all other datasets. Models trained using nnUNet achieved mean Dice-Sorensen similarity coefficients > 90% when tested on each of the six datasets individually. The performance of these models suggests that an nnUNet liver segmentation model trained on a large and diverse collection of T1w MR images would be robust to potential changes in contrast protocol and disease etiology.

Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (CCH) and 20 (CPVE) CECT of LC patients. [Formula: see text] outperformed [Formula: see text] across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03-0.05 (p < 0.05). [Formula: see text], and [Formula: see text] were not statistically different (p > 0.05), however, both were slightly better than [Formula: see text] by DSC up to 0.02. The final model, [Formula: see text], showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5-8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score [Formula: see text] 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.