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Bini F, Pica A, Marinozzi F, Giusti A, Leoncini A, Trimboli P. Model-Optimizing Radiofrequency Parameters of 3D Finite Element Analysis for Ablation of Benign Thyroid Nodules. Bioengineering (Basel) 2023; 10:1210. [PMID: 37892940 PMCID: PMC10604455 DOI: 10.3390/bioengineering10101210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Radiofrequency (RF) ablation represents an efficient strategy to reduce the volume of thyroid nodules. In this study, a finite element model was developed with the aim of optimizing RF parameters, e.g., input power and treatment duration, in order to achieve the target volume reduction rate (VRR) for a thyroid nodule. RF ablation is modelled as a coupled electro-thermal problem wherein the electric field is applied to induce tissue heating. The electric problem is solved with the Laplace equation, the temperature distribution is estimated with the Pennes bioheat equation, and the thermal damage is evaluated using the Arrhenius equation. The optimization model is applied to RF electrode with different active tip lengths in the interval from 5 mm to 40 mm at the 5 mm step. For each case, we also explored the influence of tumour blood perfusion rate on RF ablation outcomes. The model highlights that longer active tips are more efficient as they require lesser power and shorter treatment time to reach the target VRR. Moreover, this condition is characterized by a reduced transversal ablation zone. In addition, a higher blood perfusion increases the heat dispersion, requiring a different combination of RF power and time treatment to achieve the target VRR. The model may contribute to an improvement in patient-specific RF ablation treatment.
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Affiliation(s)
- Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (A.P.); (F.M.)
| | - Andrada Pica
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (A.P.); (F.M.)
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (A.P.); (F.M.)
| | - Alessandro Giusti
- Dalle Mole Institute for Artificial Intelligence (IDSIA), Università della Svizzera Italiana (USI), The University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6900 Lugano, Switzerland;
| | - Andrea Leoncini
- Servizio di Radiologia e Radiologia Interventistica, Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), 6900 Lugano, Switzerland;
| | - Pierpaolo Trimboli
- Clinic of Endocrinology and Diabetology, Lugano Regional Hospital, Ente Ospedaliero Cantonale (EOC), 6500 Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
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Wu LF, Zhuang GH, Hu QL, Zhang L, Luo ZM, Lv YJ, Tang J. Using information technology to optimize the identification process for outpatients having blood drawn and improve patient satisfaction. BMC Med Inform Decis Mak 2022; 22:61. [PMID: 35272653 PMCID: PMC8915497 DOI: 10.1186/s12911-022-01799-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This study explored the application effect of information technology in optimizing the patient identification process. METHODS The method for optimizing the identification process involved in drawing blood among outpatients using information technology was executed from July 2020. In this paper, 959 patients who had blood drawn from January to June 2020 were included as the pre-optimization group, and 1011 patients who had blood drawn from July to December 2019 were included as the post-optimization group. The correct rate of patient identification, waiting time, and patient satisfaction before and after the optimization were statistically analyzed. The changes in these three indexes before and after the optimization implementation, as well as the application effects, were compared. RESULTS The correct rate of patient identification after optimization (99.80%) was higher than before optimization (98.02%) (X2 = 13.120; P < 0.001), and the waiting time for having blood drawn was also significantly shortened (t = 8.046; P < 0.001). The satisfaction of patients was also significantly improved (X2 = 20.973; P < 0.001). CONCLUSIONS By combining information technology with the characteristics of blood collection in our hospital, using the call system to obtain patient information, then scan the QR code of the guide sheet for automatic verification, and finally manually reconfirm patient information, which can significantly reduce the occurrence of identification errors, improve work efficiency and improve patients' satisfaction.
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Affiliation(s)
- Li-Feng Wu
- Department of Clinical Laboratory, First People's Hospital of Linping District, Hangzhou, Hangzhou, 311100, Zhejiang, China
| | - Guo-Hua Zhuang
- Department of Clinical Laboratory, First People's Hospital of Linping District, Hangzhou, Hangzhou, 311100, Zhejiang, China
| | - Qi-Lei Hu
- Department of Clinical Laboratory, First People's Hospital of Linping District, Hangzhou, Hangzhou, 311100, Zhejiang, China.,Quality Management Section, First People's Hospital of Linping District, Hangzhou, No. 369 of Yingbin Road, Linping District, Hangzhou, 311100, Zhejiang, China
| | - Liang Zhang
- Department of Clinical Laboratory, First People's Hospital of Linping District, Hangzhou, Hangzhou, 311100, Zhejiang, China
| | - Zhang-Mei Luo
- Department of Clinical Laboratory, First People's Hospital of Linping District, Hangzhou, Hangzhou, 311100, Zhejiang, China
| | - Yin-Jiang Lv
- Department of Clinical Laboratory, First People's Hospital of Linping District, Hangzhou, Hangzhou, 311100, Zhejiang, China
| | - Jian Tang
- Quality Management Section, First People's Hospital of Linping District, Hangzhou, No. 369 of Yingbin Road, Linping District, Hangzhou, 311100, Zhejiang, China.
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