Evidence map›Paper›PMID 41455866›Full record

ReviewAnnals of biomedical engineering2026

Computer-assisted Treatment Planning and Mathematical Modeling for Percutaneous Liver Tumor Ablation: An Updated Survey.

Feifei Ding, Weiwei Wu, Wujun Jiang, Zhuhuang Zhou, Shuicai Wu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Feifei DingDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.ORCID http://orcid.org/0009-0004-7356-8884
Weiwei WuCollege of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Wujun JiangDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Zhuhuang ZhouDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China. zhouzh@bjut.edu.cn.
Shuicai WuDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China. wushuicai@bjut.edu.cn.

Funding

Beijing Higher Education Young Elite Teacher Project No. KM202310025019
6 · The paper itself

Abstract

The computer-assisted automatic planning (CAAP) technology for percutaneous liver tumor ablation plays a crucial role in enhancing the precision and safety of minimally invasive treatments. CAAP reduces the manual workload of clinicians through path planning and treatment parameter optimization, while simultaneously improving planning efficiency and clinical reliability. This paper first reviews the basic principles of thermal ablation, including path planning, treatment parameter optimization, and temperature field modeling, and outlines the theoretical foundations of CAAP's key technologies. The existing methods are then classified according to their technical characteristics, covering traditional optimization algorithms, intelligent optimization methods, and deep reinforcement learning techniques. The focus is on single-needle and multi-needle path planning, multi-objective optimization, and dynamic environmental adaptability technologies. Furthermore, the performance of these methods in automation, real-time processing, and multi-physics modeling is evaluated. Finally, potential future developments are proposed.

Indexed as

Ablation TechniquesLiver NeoplasmsModels, BiologicalSurgery, Computer-AssistedAlgorithmsHumansAutomatic planningComputer-assisted technologyLiver tumorPercutaneous thermal ablationTemperature field simulation

Identifiers

PMID41455866

What OpenQuestion holds

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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.