Study Protocol PROMETHEUS:Prospective Multicenter Study to Evaluate the Correlation Between Safety Margin and Local Recurrence After Thermal Ablation Using Image Co-registration in Patients with Hepatocellular Carcinoma

Purpose: The primary objective is to determine the minimal ablation margin required to achieve a local recurrence rate of 18 years with Barcelona Clinic Liver Cancer stage 0/A hepatocellular carcinoma (or B with a maximum of two lesions < 5 cm each) are eligible. Patients will undergo dual-phase contrast-enhanced computed tomography directly before and after ablation. Ablation margins will be quantitatively assessed using co-registration software, blinding assessors (i.e. two experienced radiologists) for outcome. Presence and location of recurrence are evaluated independently on follow-up scans by two other experienced radiologists, blinded for the quantitative margin analysis. A sample size of 189 tumors (~ 145 patients) is required to show with 80% power that the risk of local recurrence is confidently below 10%. A two-sided binomial z-test will be used to test the null hypothesis that the local recurrence rate is ≥ 10% for patients with a minimal ablation margin ≥ 2 mm. Logistic regression will be used to find the relationship between minimal ablation margins and local recurrence. Kaplan–Meier estimates are used to assess local and overall recurrence, disease-free and overall survival. Discussion: It is expected that this study will result in a clear understanding of the correlation between ablation margins and local recurrence. Using co-registration software in future patients undergoing ablation for hepatocellular carcinoma may improve intraprocedural evaluation of technical success. Trial registration The Netherlands Trial Register (NL9713), https://www.trialregister.nl/trial/9713

Computational Modeling of Thermal Ablation Zones in the Liver: A Systematic Review

Purpose: This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for radiofrequency and microwave thermal liver ablation planning and compare their accuracy. Methods: A systematic literature search was performed in the MEDLINE and Web of Science databases. Characteristics of the computational model and validation method of the included articles were retrieved. Results: The literature search identified 780 articles, of which 35 were included. A total of 19 articles focused on simulating radiofrequency ablation (RFA) zones, and 16 focused on microwave ablation (MWA) zones. Out of the 16 articles simulating MWA, only 2 used in vivo experiments to validate their simulations. Out of the 19 articles simulating RFA, 10 articles used in vivo validation. Dice similarity coefficients describing the overlap between in vivo experiments and simulated RFA zones varied between 0.418 and 0.728, with mean surface deviations varying between 1.1 mm and 8.67 mm. Conclusion: Computational models to simulate ablation zones of MWA and RFA show considerable heterogeneity in model type and validation methods. It is currently unknown which model is most accurate and best suitable for use in clinical practice

Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning

Segmentations are crucial in medical imaging to obtain morphological, volumetric, andradiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist'sclinical workflow, while automatic segmentation generally obtains sub-par performance. Wetherefore developed a minimally interactive deep learning-based segmentation method forsoft-tissue tumors (STTs) on CT and MRI. The method requires the user to click six points nearthe tumor's extreme boundaries. These six points are transformed into a distance map andserve, with the image, as input for a Convolutional Neural Network. For training andvalidation, a multicenter dataset containing 514 patients and nine STT types in sevenanatomical locations was used, resulting in a Dice Similarity Coefficient (DSC) of 0.85±0.11(mean ± standard deviation (SD)) for CT and 0.84±0.12 for T1-weighted MRI, when comparedto manual segmentations made by expert radiologists. Next, the method was externallyvalidated on a dataset including five unseen STT phenotypes in extremities, achieving0.81±0.08 for CT, 0.84±0.09 for T1-weighted MRI, and 0.88±0.08 for previously unseen T2-weighted fat-saturated (FS) MRI. In conclusion, our minimally interactive segmentationmethod effectively segments different types of STTs on CT and MRI, with robustgeneralization to previously unseen phenotypes and imaging modalities

Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning

Segmentations are crucial in medical imaging to obtain morphological, volumetric, andradiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist'sclinical workflow, while automatic segmentation generally obtains sub-par performance. Wetherefore developed a minimally interactive deep learning-based segmentation method forsoft-tissue tumors (STTs) on CT and MRI. The method requires the user to click six points nearthe tumor's extreme boundaries. These six points are transformed into a distance map andserve, with the image, as input for a Convolutional Neural Network. For training andvalidation, a multicenter dataset containing 514 patients and nine STT types in sevenanatomical locations was used, resulting in a Dice Similarity Coefficient (DSC) of 0.85±0.11(mean ± standard deviation (SD)) for CT and 0.84±0.12 for T1-weighted MRI, when comparedto manual segmentations made by expert radiologists. Next, the method was externallyvalidated on a dataset including five unseen STT phenotypes in extremities, achieving0.81±0.08 for CT, 0.84±0.09 for T1-weighted MRI, and 0.88±0.08 for previously unseen T2-weighted fat-saturated (FS) MRI. In conclusion, our minimally interactive segmentationmethod effectively segments different types of STTs on CT and MRI, with robustgeneralization to previously unseen phenotypes and imaging modalities

Study Protocol PROMETHEUS: Prospective Multicenter Study to Evaluate the Correlation Between Safety Margin and Local Recurrence After Thermal Ablation Using Image Co-registration in Patients with Hepatocellular Carcinoma

Purpose: The primary objective is to determine the minimal ablation margin required to achieve a local recurrence rate of 18 years with Barcelona Clinic Liver Cancer stage 0/A hepatocellular carcinoma (or B with a maximum of two lesions < 5 cm each) are eligible. Patients will undergo dual-phase contrast-enhanced computed tomography directly before and after ablation. Ablation margins will be quantitatively assessed using co-registration software, blinding assessors (i.e. two experienced radiologists) for outcome. Presence and location of recurrence are evaluated independently on follow-up scans by two other experienced radiologists, blinded for the quantitative margin analysis. A sample size of 189 tumors (~ 145 patients) is required to show with 80% power that the risk of local recurrence is confidently below 10%. A two-sided binomial z-test will be used to test the null hypothesis that the local recurrence rate is ≥ 10% for patients with a minimal ablation margin ≥ 2 mm. Logistic regression will be used to find the relationship between minimal ablation margins and local recurrence. Kaplan–Meier estimates are used to assess local and overall recurrence, disease-free and overall survival. Discussion: It is expected that this study will result in a clear understanding of the correlation between ablation margins and local recurrence. Using co-registration software in future patients undergoing ablation for hepatocellular carcinoma may improve intraprocedural evaluation of technical success. Trial registration The Netherlands Trial Register (NL9713), https://www.trialregister.nl/trial/9713