Evidence map›Paper›PMID 39133389›Full record

ArticleAnnals of biomedical engineering2024

Intraoperative Ablation Control Based on Real-Time Necrosis Monitoring Feedback: Numerical Evaluation.

Ryo Murakami, Satoshi Mori, Haichong K Zhang

Abstract read
In one paragraph

Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Ryo MurakamiRobotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.ORCID http://orcid.org/0009-0005-5770-5866
Satoshi Mori, Ehime, Japan.
Haichong K ZhangRobotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA. hzhang10@wpi.edu.ORCID http://orcid.org/0000-0002-1314-8456

Funding

PSMA-based Cancer Imaging AgentsR01CA134675 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI POMPER, MARTIN G · 2009 to 2025
$6.2M
Physician Assistance Technology in Image-guided Robotic Intervention of ProstateR01EB030539 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI HATA, NOBUHIKO, ZHANG, HAICHONG · 2021 to 2024
$2.7M
Robotic Lung Ultrasound for Triage of COVID-19 Patients in a Resource-Limited EnvironmentDP5OD028162 · OD · WORCESTER POLYTECHNIC INSTITUTE · PI ZHANG, HAICHONG · 2019 to 2023
$2.3M
Foundation for the National Institutes of Health CA134675Foundation for the National Institutes of Health OD028162NCI NIH HHS R01 CA134675NIBIB NIH HHS R01 EB030539NIH HHS DP5 OD028162
6 · The paper itself

Abstract

Ablation therapy is a type of minimally invasive treatment, utilized for various organs including the brain, heart, and kidneys. The accuracy of the ablation process is critically important to avoid both insufficient and excessive ablation, which may result in compromised efficacy or complications. The thermal ablation is formulated by two theoretical models: the heat transfer (HT) and necrosis formation (NF) models. In modern medical practices, feed-forward (FF) and temperature feedback (TFB) controls are primarily used as ablation control methodologies. FF involves pre-therapy procedure planning based on previous experiences and theoretical knowledge without monitoring the intraoperative tissue response, hence, it can't compensate for discrepancies in the assumed HT or NF models. These discrepancies can arise due to individual patient's tissue characteristic differences and specific environmental conditions. Conversely, TFB control is based on the intraoperative temperature profile. It estimates the resulting heat damage based on the monitored temperature distribution and assumed NF model. Therefore, TFB can make necessary adjustments even if there is an error in the assumed HT model. TFB is thus seen as a more robust control method against modeling errors in the HT model. Still, TFB is limited as it assumes a fixed NF model, irrespective of the patient or the ablation technique used. An ideal solution to these limitations would be to actively monitor heat damage to the tissue during the operation and utilize this data to control ablation. This strategy is defined as necrosis feedback (NFB) in this study. Such real-time necrosis monitoring modalities making NFB possible are emerging, however, there is an absence of a generalized study that discusses the integration and quantifies the significance of the real-time necrosis monitor techniques for ablation therapy. Such an investigation is expected to clarify the universal principles of how these techniques would improve ablation therapy. In this study, we examine the potential of NFB in suppressing errors associated with the NF model as NFB is theoretically capable of monitoring and suppressing the errors associated with the NF models in its closed control loop. We simulate and compare the performances of TFB and NFB with artificially generated modeling errors using the finite element method (FEM). The results show that NFB provides more accurate ablation control than TFB when NF-oriented errors are applied, indicating NFB's potential to improve the ablation control accuracy and highlighting the value of the ongoing research to make real-time necrosis monitoring a clinically viable option.

Indexed as

NecrosisAblation TechniquesFeedbackHumansModels, BiologicalAblation therapyClosed-loop controlControl engineeringGlioblastomaGliomaNecrosis feedback (NFB) controlNecrosis monitoringPhotoacoustic imaging

Identifiers

PMID39133389
PMCPMC12515391

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.