Versatile multi-constrained planning for thermal ablation of large liver tumors

Li, Ruotong; Shi, Yangyang; Si, Weixin; Huang, Li; Zhuang, Bowen; Weinmann, Michael; Klein, Reinhard; Heng, Pheng‐Ann

doi:10.1016/j.compmedimag.2021.101993

Cited by 5 publications

(3 citation statements)

References 77 publications

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“…76,217,218,253 Although various ablation techniques have their unique advantages in the field of tumor treatment, they also face great challenges and dilemmas. 219,220 Firstly, almost all current tumor ablation techniques have the problem of residual tumor due to incomplete ablation, thus the problem of residual tumor after ablation, and consequently tumor recurrence and distant metastasis has become an urgent clinical problem. Secondly, as tumor ablation is a localised treatment, systemic treatment is not possible for patients with tumors.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…76,217,218,253 Although various ablation techniques have their unique advantages in the field of tumor treatment, they also face great challenges and dilemmas. 219,220 The ease of binding to negatively charged non-specific cells or proteins leads to short retention times and poor passive targeting in vivo, some of the synthetic materials are cytotoxic and gene transfection efficiency is low…”

Section: Discussion and Outlookmentioning

confidence: 99%

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Present and future of metal nanoparticles in tumor ablation therapy

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et al. 2023

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Section: Discussion and Outlookmentioning

confidence: 99%

Section: Discussion and Outlookmentioning

confidence: 99%

Present and future of metal nanoparticles in tumor ablation therapy

Lou,

Xie,

et al. 2023

Nanoscale

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“…This problem is not only of importance in the travel industry, but also has a wide range of applications in everyday life, such as planning a weekend city exploration, vacation trip, or business trip. In this field, the application of algorithms and artificial intelligence technologies has provided travelers with more intelligent and convenient travel planning services [2] .…”

Section: Forewordmentioning

confidence: 99%

Combine deep learning and LKH algorithm to solve multiconstraint tourism planning

Zhong,

Lin

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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Multi-constraint travel route planning is a challenging problem, which involves multiple constraints such as transportation, time, cost and traveler preference. In recent years, the development of deep learning technology has provided new possibilities to solve this problem. In this paper, a method combining deep learning and LKH algorithm is proposed to solve the multi-constraint tourism planning. First, we collect tourism data, including scenic spot information, transportation information and traveler preferences. Then, we trained the tourism data using deep learning models, such as neural networks, to learn the correlations between attractions and traveler preferences. We designed a multi-task learning model that can simultaneously predict multiple constraints such as travel time, distance, cost, etc. Finally, we apply the LKH algorithm to the output of the deep learning model to obtain the optimal travel route. Experimental results show that our method can efficiently generate travel routes that meet travelers' needs under multiple constraints. This study provides a new solution for multi-constraint travel planning, and also provides a practical application case for the combination of deep learning and traditional algorithms.

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Multi‐stage automatic and rapid ablation and needle trajectory planning method for CT‐guided percutaneous liver tumor ablation

Li,

Zhou,

Zhang

et al. 2024

Medical Physics

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BackgroundComputer‐assisted planning methods have increasingly contributed to preoperative ablation planning; however, these methods cannot automatically obtain the final optimal solution within a short time and are rarely validated in practice, greatly limiting their clinical applicability.PurposeWe aimed to propose a full‐automatic multi‐stage ablation and needle trajectory planning method for CT‐guided percutaneous liver ablation to attain the final optimal plans under multiple clinical constraints rapidly.MethodsOur proposed method integrates the ablation zone planning fulfilling complete tumor coverage and critical structure avoidance while reaching a trade‐off between ablation number and healthy tissue damage, and needle trajectory planning under multiple clinical constraints. Our needle trajectory planning determines feasible skin entry regions based on hard constraints, where the multi‐objective optimization (MOO) considering soft constraints is performed using the Pareto Optimality and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the final optimal solution. The performance of our proposed method was evaluated on 30 tumors of various characteristics from 23 patients and clinically validated in five clinical cases.ResultsOur proposed method achieved 99.8% treatment zone coverage and 40.5% ablation efficiency without involving critical structures, and completely satisfied multiple clinical constraints in all needle trajectory planning results. The average planning time was 23.6 s for tumors of different sizes. All the plans were considered clinically acceptable by the doctors’ evaluation. Our method achieved complete tumor coverage without complications in clinical case validation.ConclusionOur proposed planning method can generate a final optimal plan satisfying multiple clinical constraints within a short time, potentially facilitating preoperative planning for hepatic tumor ablation.

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Versatile multi-constrained planning for thermal ablation of large liver tumors

Cited by 5 publications

References 77 publications

Present and future of metal nanoparticles in tumor ablation therapy

Present and future of metal nanoparticles in tumor ablation therapy

Combine deep learning and LKH algorithm to solve multiconstraint tourism planning

Multi‐stage automatic and rapid ablation and needle trajectory planning method for CT‐guided percutaneous liver tumor ablation

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