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.

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.

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.

Study Protocol STEREOLAB: Stereotactic Liver Ablation Assisted with Intra-Arterial CT Hepatic Arteriography and Ablation Confirmation Software Assessment.

PURPOSE: This study aims to evaluate the technical efficacy and local tumor progression-free survival (LTPFS) of a standardized workflow for thermal ablation of colorectal liver metastases (CRLM) consisting of CT during hepatic arteriography (CTHA)-based imaging analysis, stereotactic thermal ablation, and computer-based software assessment of ablation margins. MATERIALS AND METHODS: This investigator initiated, single-center, single-arm prospective trial will enroll up to 50 patients (≤ 5 CRLM, Measuring ≤ 5 cm). Procedures will be performed in an angio-CT suite under general anesthesia. The primary objective is to estimate LTPFS with a follow-up of up to 2 years and secondary objectives are analysis of the impact of minimal ablative margins on LTPFS, adverse events, contrast media utilization and radiation exposure, overall oncological outcomes, and anesthesia/procedural time. Adverse events (AE) will be recorded by CTCAE (Common Toxicity Criteria for Adverse Events), and Bayesian optimal phase-2 design will be applied for major intraprocedural AE stop boundaries. The institutional CRLM ablation registry will be used as benchmark for comparative analysis with the historical cohort. DISCUSSION: The STEREOLAB trial will introduce a high-precision and standardized thermal ablation workflow for CRLM consisting of CT during hepatic arteriography imaging, stereotactic guidance, and ablation confirmation. Trial Registration ClinicalTrials.gov identifier: (NCT05361551).