Evaluation of Learning Curves for US-CT/MR and US-US Fusion Imaging in the Liver

Evaluation of Learning Curves for US-CT/MR and US-US Fusion Imaging in the Liver | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of Learning Curves for US-CT/MR and US-US Fusion Imaging in the Liver yinglin Long, qingyang Kong, yvxuan Wu, rui Ma, erjiao Xu, rongqin Zheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7480136/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this retrospective study was to investigate the learning curve of ultrasound (US)–computed tomography (CT)/magnetic resonance (MR) as well as US–three-dimensional (3D) US fusion imaging for liver tumors. Patients intending to undergo liver tumor thermal ablation in our institution were enrolled. Two operators were investigated, one senior and one junior US doctor. Both operators practiced on the same patients for each US fusion imaging technique, respectively. The results of registration were blindly judged by two senior sonographers with > 5-year experience on US fusion imaging. The registration time and success rate of registration were recorded. The trend of registration time with practice attempts was analyzed using CUSUM analysis. Both operators reached the criteria to terminate the learning in both two types of fusion imaging after 15 practices. The CUSUM analysis of the registration time showed that phases A and B were divided by the 10–11th practices for US–CT/MR fusion imaging and 5th practices for US–3D US fusion imaging for both operators. In the US–CT/MR fusion imaging, the success rates of registration for the senior operator in both phases were high (91–100%), whereas that for the junior operator in the phase A was low (50%). By contrast, in US–US fusion imaging, the success rates of registration for the senior and the junior operator in both phases were high (100%). In conclusion, US–3D US fusion imaging has a shorter learning curve than US–CT/MR fusion imaging and is less dependent on experience. Fusion imaging Ultrasound Learning curve CUSUM analysis Liver Figures Figure 1 Introduction Locoregional ablation therapy has been recommended as the first-line treatment for hepatocellular carcinoma (HCC) by several international guidelines 1 – 2 . It is considered efficient and safe for the treatment of early and very-early stage HCC. Among the various imaging modalities, ultrasound (US) guidance was the most common imaging modality in the clinical practice due to its superiority in real-time, convenience, and radiation-free performances 3 . However, it still has some limitations, such as detection, puncture guidance, and precise evaluation of the treatment effect 4 . Ultrasound fusion imaging has been reported as a helpful technique during the thermal ablation procedure in liver cancer 5 – 9 . Ultrasound fusion imaging is a new technique to fuse real-time US with pre-ablation computed tomography (CT)/magnetic resonance (MR) or three-dimensional (3D) US, allowing alignment of different imaging modalities 10 – 12 . We have reported that both US–CT/MR and US–3D US fusion imaging are playing important roles in improving the efficacy and safety of thermal ablation for liver cancers 13 – 16 . Moreover, the comparison of the application between these two types of US fusion imaging has already been reported 17 . However, according to our own experiences 18 and researches in other centers 19 – 21 , US fusion imaging is a highly experience-dependent technique wherein an effective operation often requires a certain degree of practice to master this technique. Current researches are mainly based on the results of operators with adequate experience in US fusion imaging. The learning curve of US fusion imaging in clinical practice is still unclear, and the training protocol is lacking. Therefore, this study aimed to investigate the learning curve of US–CT/MR and US–3D US fusion imaging for liver tumor and provide some suggestions for the training and learning process of US fusion imaging. Materials and Methods Inclusion and exclusion criteria The present study was approved by the institutional ethics review board and complied with the Declaration of Helsinki. Informed consent was obtained from each participant. This prospective study enrolled patients scheduled to undergo liver tumor thermal ablation in our institute from May 2016 to August 2016. Inclusion criteria were as follows: (1) patients with a conspicuous liver tumor on US image and (2) those with definite liver tumor on contrast-enhanced CT/MRI images in DICOM format (within 2 weeks). Patients with pacemaker implantation were excluded. Equipment A MyLab Twice US machine (Esoate, Genoa, Italy) with A convex array probe CA541 (frequency range, 1–8 MHz) that equipped the US fusion imaging system (Virtual Navigator) was used in this study. The US fusion imaging system included a magnetic field generator positioned beside the patient and an electromagnetic tracker on the probe. Operators Two operators with different experiences were investigated in this study. One was a senior sonographer with > 10-years of experiences in US scanning and good CT/MRI film reading ability. The other one was a junior sonographer with < 2 years of experiences in US scanning and considerable CT/MRI film reading ability. Both two operators were required to have a basic knowledge of US fusion imaging but without practical experience. Fusion imaging steps The steps of CT/MR–US fusion imaging included importation of CT/MR DICOM image into the US machine, reconstruction of CT/MR volume images, primary registration to have a primary alignment, and fine tuning to acquire a precise alignment. The steps of US–3D US fusion imaging included acquisition of 3D US volume image by a free-hand scanning of the target tumor zone and fine tuning to acquire a precise alignment. (To simulate the primary registration process of US–CT/MR fusion imaging, the electromagnetic generator was manually moved approximately 2 cm after acquiring a 3D US volume image.) For the detailed steps, refer to our previous reports 15 . 17 . The learning process The reference registration time was performed by an experienced operator of US fusion imaging. Both US–CT/MR and US–3D US fusion imaging were carried out five times, and the average value was calculated and considered as the reference experienced registration time. Before the study, two operators received a lecture about the basic knowledge on US fusion imaging. Then, they were required to familiarize the US fusion imaging system on a self-made phantom for at least ten times in 1 week before performing the fusion on actual patients. After that, both US–CT/MR and US–3D US fusion imaging were practiced on the same patients by both operators. The sequences of two operators and two types of US fusion imaging were randomly assigned. The timing started when the operators entered the Virtual Navigator system. When the operator considered the registration error to be within 3 mm or after 3 attempts, the timing would be terminated. An image in the maximum section of the tumor as well as a video loop while scanning the tumor would be saved at the end of each practice. When both operators reached the reference experienced registration time and had consecutive successful registration 3 times in each type of US fusion imaging, the learning process would be terminated. Judgment of registration results The registration results of each practice were judged blindly by two other senior doctors with > 5-year experiences of US fusion imaging using the images and clips saved during each practice. When registration errors of the target tumor were found, adjacent registration markers were < 3 mm, and the organ outline was almost completely overlapped, registration was considered successful. Otherwise, it would be considered unsuccessful. When judgments between these two doctors were different, consensus would be reached through discussion. 5 Statistical analysis If the operator finally considered the registration error to be within 3 mm in 3 attempts, the registration time was defined as the duration time from entering the Virtual Navigator system for the first time to the completion of registration. If the operator failed to control the registration error within 3 mm after 3 attempts, the registration time was defined as the duration time from entering the Virtual Navigator system for the first time to the termination of the third attempt.The success rate of registration is defined as the percentage of cases that the registration was judged to be successful. CUSUM analysis was employed to analyze the learning process of fusion imaging to determine different stages of the learning curve. The detailed statistical analysis was as follows. All cases were listed according to the time sequence. The CUSUM value of the first practice (CUSUM 1 ) was defined as the difference in the registration time of the first practice (t 1 ) and the mean registration time of all practices (t mean ). The CUSUM value at the second practice and the following ones was defined as the previous CUSUM value ( \(\:{CUSUM}_{n-1}\) ) added to the difference between the registration time and the mean registration time. This process continued, and the following formula was created: \(\:{CUSUM}_{n}=\stackrel{}{\int\:}[\left({t}_{n}-{t}_{mean}\right)+{CUSUM}_{n-1}]\) . The CUSUM learning curve of the registration time was depicted, and curve fitting was performed. Curve fitting was considered successful if P is < 0.05. Goodness of fit was judged according to the coefficient R 2 to select the proper regression curve. The learning curve can be divided into learning and experienced periods by the peak value. The registration time and the success rate of registration were calculated based on different periods. The measurement data were presented as mean ± standard deviation, and categorical data were presented as percentages. The registration times between groups were compared using two independent t -test. Homogeneity of variance test was performed using Levene test. The success rates of registration were compared between groups using Fisher’s exact test. The difference was considered significant when P -value was < 0.05. Results Enrollment The reference experienced registration time for US–CT/MR and US–3D US was 365s and 200 s, respectively. When the operator reached the reference experienced registration time and succeeded three times consecutively in each type of US fusion imaging, the learning process could be terminated. After 15 practices, both operators reached this criterion to terminate the learning process for both types of US fusion imaging. The baseline characteristics of 15 cases are listed in Table 1 . Table 1 Baseline Characteristics of Enrolled Patients and Lesions Characteristic Value Age (years) 52 (32–70) Sex (male/female) 13 /2 Diagnosis (HCC/FNH/others) 11 /2 /2 Cirrhosis (yes/no) 11 /4 Ascites (yes/no) 13/2 Interval between CT/MR and current examination (d) 4 (0–15) Maximum diameter (mm) 20 (10–53) Location (left/right lobe) 3 /12 HCC, hepatocellular carcinoma; FNH, focal nodular hyperplasia; CT, computed tomography; MR, magnetic resonance The mean registration time The mean registration time of US–CT/MR and US–3D US fusion imaging for two operators were 522 ± 214 s (senior operator, US–CT/MR), 156 ± 66 s (senior operator, US–3D US), 560 ± 237 s (junior operator, US–CT/MR) 183 ± 122 s (junior operator, US–3D US). CUSUM learning curve evaluation The CUSUM learning curve of US–CT/MR and US–3D US fusion imaging for two operators is depicted in Fig. 1 . Quadratic and cubic regression were performed, and P -value as well as goodness of fit coefficient ( R 2 ) are shown in Table 2 . The R 2 for cubic regression was superior to that for quadratic regression, and thus cubic regression was considered the best regression model for US–CT/MR and US–3D US fusion imaging for two operators and shown in Fig. 1 . Table 2 Regression of the Registration Time Using the CUSUM Value for Two Operators Learning Two Fusion Imaging The senior operator The junior operator US–CT/MR US–3D US US–CT/MR US–3D US Quadratic curve R 2 0.430 0.926 0.818 0.955 Quadratic curve P 0.034 0.000 0.000 0.000 Cubic curve R 2 0.709 0.950 0.840 0.960 Cubie curve P 0.003 0.000 0.000 0.000 The peak value of the CUSUM learning curve divided the curve into learning (phase A) and experienced (phase B) periods. The practice number at peak value in two types of US fusion imaging for two operators is shown in Table 3 . Table 3 Case Numbers of Phases A and B for Two Operators Learning Two Fusion Imaging The senior operator The junior operator US–CT/MR US–3D US US–CT/MR US–3D US A period 11 5 10 5 B period 4 10 5 10 The CUSUM analysis of the registration time for US–CT/MR fusion imaging showed that phase A and phase B was divided according to the 10–11th practices for both operators. By contrast, the CUSUM analysis of the registration time for US–3D US fusion imaging showed that the learning and experienced phases were divided according to the 5th practices for both operators. Comparison of the registration time According the phase A and phase B in the learning process, the registration time was calculated in different periods for the senior and junior operators, respectively, and shown in Table 4 . The registration time in phase B was significantly shorter than that in phase A. Table 4 The Registration Time of Phases A and B for Two Operators The senior operator The junior operator US–CT/MR US–3D US P US–CT/MR US–3D US P Total(s) 522 ± 214 156 ± 67 <0.001 560 ± 237 183 ± 122 <0.001 A period (s) 600 ± 178 229 ± 59 0.001 671 ± 209 282 ± 147 0.003 B period (s) 310 ± 165 120 ± 31 0.001 340 ± 92 134 ± 76 <0.001 P 0.014 <0.001 0.005 0.021 For both the senior and junior operators, the registration times of US–3D US fusion imaging were shorter than those of US–CT/MRI in phases A and B (P<0.05). Comparison of the success rate of registration The success rate of registration was calculated in different periods for the senior and junior operators, respectively, as shown in Table 5 . Table 5 Success Rate of Registration in Phases A and B for Two Operators The senior operator The junior operator US–CT/MR US–3D US P US–CT/MR US–3D US P Total 93%(14/15) 93%(14/15) 1.000 67%(10/15) 93%(14/15) 0.169 A period 91%(10/11) 100%(5/5) 0.580 50%(5/10) 80%(4/5) 0.580 B period 100%(4/4) 90%(9/10) 1.000 100%(5/5) 100%(10/10) 1.000 P 1.000 1.000 0.101 0.333 For the senior operator, the success rates of US–CT/MRI and US–3D 3D US fusion imaging in both phases A and B were higher than 90%. By contrast, for the junior operator, the success rate of US–CT/MRI fusion imaging in phase A was relatively lower than that in phase B, and the success rate of US–3D US fusion imaging in both phases A and B was 100%. Conclusion US–3D US fusion imaging has a shorter learning curve than US–CT/MR fusion imaging and is less dependent on experience. Discussion The learning curve was used to describe the process of decreased operation time as the practice was repeated 22 – 24 . Generally, it can be divided into a learning period with gradually decreasing operation time and an experienced period with a relatively stable operation time 25 – 27 . Nowadays, the learning curve theory is widely used in economy, psychology, and medicine 28 – 31 . In medicine, the most common parameters used to describe the learning process are the operation time, treatment response, and complication rate 26 , 32 – 33 . As the practice was repeated, the operation time decreased, treatment response improved, and complication decreased. As for the US fusion imaging technique, the registration time represents the degree of experience, whereas the success rate of registration represents the learning effect. Therefore, this study evaluated the learning curve of US fusion imaging mainly using these two parameters. CUSUM analysis is a method used to visualize the slight variations of continuous data through the cumulative sum of differences between every raw data and average value 34 – 36 . Therefore, this method was commonly employed in the present study to determine the learning and experienced periods. With CUSUM analysis at the registration time, approximately 10 practices could be used to divide these two periods for US–CT/MR fusion imaging for both the senior and junior operators, indicating that after approximately 10 practices, the registration time had become stable and the operators had become familiar with this type of US fusion imaging. We noticed that the learning period of experienced operator to learn US–CT/MR fusion imaging was 11 practices, whereas the novice was only 10 practices, which was possibly caused by the inadequate sample size and individual differences. Conversely, for US–3D US fusion imaging, both operators entered into the experienced period after 5 practices, which indicated that US–3D US fusion imaging was easier to learn and grasp regardless of experience. The comparison of the registration time between the learning and experienced periods for US–CT/MR and US–3D US fusion imaging validated that the division of these two periods in the CUSUM analysis was proper and reliable. Moreover, the registration time of US–CT/MR fusion imaging was longer than US–3D US fusion imaging, consistent with our previous clinical comparative study 37 that indicated the duration time of US–3D US fusion imaging was shorter than that of US–CT/MR fusion imaging. This result was generally due to the relatively complex steps in US–CT/MR fusion imaging. Besides, the senior operator reached a higher success rate of registration at both learning and experienced periods during the learning process of US–CT/MR fusion imaging, whereas the junior operator reached a lower success rate at the learning period. Differently, during the learning process of US–3D US fusion imaging, the success rates of registration were high in the learning period either for the experienced or the novice operator. This demonstrated that experience did affect the registration in US–CT/MR fusion imaging to a certain extent, although it did not significantly influence the registration time. By contrast, experience did not affect the registration significantly for US–3D US fusion imaging. This is probably due to the fact that the registration between CT/MR and US images requires the operator to recognize the identical vascular structures in two different images, with the process requiring a higher level of CT/MR and US images reading ability than US–3D US fusion imaging. The possible reasons for the above differences between US–CT/MR and US–3D US fusion imaging were as follows. First, the registration of US–CT/MR fusion imaging requires the operator to distinguish the anatomical structure between CT/MRI and US images; therefore, the operator should familiarize the CT/MRI film reading as well as US image scanning. By contrast, the anatomical landmarks in US volume images were similar to that in real-time US images; therefore, the registration of US–3D US fusion imaging needs the operator to be familiar with US images only. Second, there were inevitable errors between CT/MR images and US images because of the difference in imaging principles and time interval of image acquisition, making the registration procedure more complicated. As the 3D US volume image was acquired from real-time US image, the similarity between these two images was high. As a result, the registration process of US–CT/MR fusion imaging required more time and practice than that of US–3D US fusion imaging. In the present study, US–3D US fusion imaging only required 5 practices to enter the experienced period for two operators with different experiences, whereas US–CT/MR fusion imaging required about 10 practices to enter the experienced period for both senior and junior operators. The learning curve could be affected by several factors, such as the operating steps of the technique, experience of the operator, and effective training. The results of this study indicated that compared to US–CT/MR fusion imaging, the operating steps of US–3D US fusion imaging were easier to grasp with fewer practices and less influenced by experience. According to our results, advises about the learning and training of fusion imaging are listed as follows. For operators with or without experience, at least 10 clinical practices on patients are recommended for US–CT/MR fusion imaging and 5 clinical practices for US–3D US fusion imaging. If an operator needs to grasp US–CT/MR fusion imaging, not only US scanning skills but also the ability of CT/MR film reading is required. However, this study has some limitations. First, this study did not consider the liver background condition and the classification of lesions as factors of the learning curve, which would be further investigated in the future research. Second, the present study enrolled a junior operator with a considerable knowledge on US and CT/MR instead of a novice without experience, since the application of US fusion imaging, especially for the thermal ablation procedure, was concerned with a doctor with considerable knowledge on US and CT/MR. If the operator’s knowledge about US and CT/MR images is limited, he/she may require more practices to master the operation skills in US fusion imaging. In conclusion, US–3D US fusion imaging has a shorter learning curve than US–CT/MR fusion imaging and is less dependent on experience. Declarations Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Declaration: This study was supported by the Clinical Research Special Fund of The Third Affiliated Hospital of Sun Yat-sen University (No. QHJH202302), the Guangzhou Science and Technology Plan Project (No. 202201011075) and the National Natural Science Foundation of China Cultivation Special Project (No. 2023GZRPYQN06). Ethics approval: The study protocol was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (Approval No. 中山附三医伦[2011]2-33号). Consent to publish: All participants provided written consent for the publication of anonymised data collected during the study. No identifiable information about participants will be disclosed in this publication. Consent to participate: Written informed consent was obtained from all participants prior to enrollment, which included consent for both study participation and publication of anonymized data. 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Ultrasonography. 2023 Jan;42(1):147-153. doi: 10.14366/usg.22078. Epub 2022 Aug 19. PMID: 36414247; PMCID: PMC9816701. Schutz HM, Quispel R, Veldt BJ, Smedts FMM, Anten MGF, Hoogduin KJ, Honkoop P, van Nederveen FH, Hol L, Kliffen M, Fitzpatrick CE, Erler NS, Bruno MJ, van Driel LMJW; QUEST. Cumulative sum learning curves guiding multicenter multidisciplinary quality improvement of EUS-guided tissue acquisition of solid pancreatic lesions. Endosc Int Open. 2022 Apr 14;10(4):E549-E557. doi: 10.1055/a-1766-5259. PMID: 35433206; PMCID: PMC9010081. Koç MA. Cumulative sum analysis of the learning curve for laparoscopic complete mesocolic excision with central vascular ligation for right sided colon cancer. Cukurova Medical Journal. 2022 Dec;47(3): 1359-1365. doi: 10.17826/cumj.1162953. Xu E, Long Y, Li K, Zeng Q, Tan L, Luo L, Huang Q, Zheng R. Comparison of CT/MRI-CEUS and US-CEUS fusion imaging techniques in the assessment of the thermal ablation of liver tumors. Int J Hyperthermia. 2019 Jan 1;35(1):159-167. doi: 10.1080/02656736.2018.1487591. Epub 2018 Oct 9. PMID: 30300032. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":false,"prefix":"","firstName":"qingyang","middleName":"","lastName":"Kong","suffix":""},{"id":544159647,"identity":"aca89ef9-ae9c-4f7a-8346-478010009211","order_by":2,"name":"yvxuan Wu","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"yvxuan","middleName":"","lastName":"Wu","suffix":""},{"id":544159648,"identity":"e6a352b0-25ee-4826-99a2-35714ca70569","order_by":3,"name":"rui Ma","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"rui","middleName":"","lastName":"Ma","suffix":""},{"id":544159649,"identity":"1a48cc69-1474-4d62-b315-780827a67987","order_by":4,"name":"erjiao Xu","email":"","orcid":"","institution":"Eighth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"erjiao","middleName":"","lastName":"Xu","suffix":""},{"id":544159650,"identity":"f37ee220-5948-4661-a2f9-2c00317576d2","order_by":5,"name":"rongqin Zheng","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"rongqin","middleName":"","lastName":"Zheng","suffix":""},{"id":544159651,"identity":"d87bd70d-ae0c-4d00-b7d9-c0ee4f3b7edf","order_by":6,"name":"kai 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07:11:11","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104726,"visible":true,"origin":"","legend":"","description":"","filename":"15dd99dc35434a0c949f426e6f7a4a681structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7480136/v1/789ee44de406d03bf6c1e05b.xml"},{"id":96243720,"identity":"0b60e599-e264-44b0-8b12-bfcadf33aa43","added_by":"auto","created_at":"2025-11-19 07:16:53","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112777,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7480136/v1/3f6ec7943899cb0baa3bdefd.html"},{"id":95895214,"identity":"9c526e93-5d79-4f54-ba1b-6a1a42f0cf05","added_by":"auto","created_at":"2025-11-14 07:11:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158050,"visible":true,"origin":"","legend":"\u003cp\u003eThe CUSUM learning curve of the registration time for two operators learning two fusion imaging. \u003cstrong\u003ea.\u003c/strong\u003e The CUSUM learning curve of the registration time for the senior operator to learn US–CT/MRI fusion imaging (t1). \u003cstrong\u003eb.\u003c/strong\u003e The CUSUM learning curve of the registration time for the senior operator to learn US–3D US (t2). \u003cstrong\u003ec.\u003c/strong\u003e The CUSUM learning curve of the registration time for the junior operator to learn US–CT/MRI fusion imaging (t3). \u003cstrong\u003ed.\u003c/strong\u003e The CUSUM learning curve of the registration time for the junior operator to learn US–3D US (t4).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7480136/v1/9370bf0bd6159f79483d35da.png"},{"id":98432204,"identity":"04be2d32-35ed-4347-901b-e65b54642a4e","added_by":"auto","created_at":"2025-12-17 16:49:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":834550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7480136/v1/49a10b84-cc65-4220-8c37-015ecc59e5e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Learning Curves for US-CT/MR and US-US Fusion Imaging in the Liver","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLocoregional ablation therapy has been recommended as the first-line treatment for hepatocellular carcinoma (HCC) by several international guidelines\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is considered efficient and safe for the treatment of early and very-early stage HCC. Among the various imaging modalities, ultrasound (US) guidance was the most common imaging modality in the clinical practice due to its superiority in real-time, convenience, and radiation-free performances\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, it still has some limitations, such as detection, puncture guidance, and precise evaluation of the treatment effect \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUltrasound fusion imaging has been reported as a helpful technique during the thermal ablation procedure in liver cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Ultrasound fusion imaging is a new technique to fuse real-time US with pre-ablation computed tomography (CT)/magnetic resonance (MR) or three-dimensional (3D) US, allowing alignment of different imaging modalities\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. We have reported that both US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging are playing important roles in improving the efficacy and safety of thermal ablation for liver cancers\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Moreover, the comparison of the application between these two types of US fusion imaging has already been reported\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, according to our own experiences\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and researches in other centers\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, US fusion imaging is a highly experience-dependent technique wherein an effective operation often requires a certain degree of practice to master this technique. Current researches are mainly based on the results of operators with adequate experience in US fusion imaging. The learning curve of US fusion imaging in clinical practice is still unclear, and the training protocol is lacking.\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to investigate the learning curve of US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging for liver tumor and provide some suggestions for the training and learning process of US fusion imaging.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e\u003cp\u003e The present study was approved by the institutional ethics review board and complied with the Declaration of Helsinki. Informed consent was obtained from each participant.\u003c/p\u003e\u003cp\u003eThis prospective study enrolled patients scheduled to undergo liver tumor thermal ablation in our institute from May 2016 to August 2016. Inclusion criteria were as follows: (1) patients with a conspicuous liver tumor on US image and (2) those with definite liver tumor on contrast-enhanced CT/MRI images in DICOM format (within 2 weeks). Patients with pacemaker implantation were excluded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEquipment\u003c/h3\u003e\n\u003cp\u003eA MyLab Twice US machine (Esoate, Genoa, Italy) with A convex array probe CA541 (frequency range, 1\u0026ndash;8 MHz) that equipped the US fusion imaging system (Virtual Navigator) was used in this study. The US fusion imaging system included a magnetic field generator positioned beside the patient and an electromagnetic tracker on the probe.\u003c/p\u003e\n\u003ch3\u003eOperators\u003c/h3\u003e\n\u003cp\u003eTwo operators with different experiences were investigated in this study. One was a senior sonographer with \u0026gt;\u0026thinsp;10-years of experiences in US scanning and good CT/MRI film reading ability. The other one was a junior sonographer with \u0026lt;\u0026thinsp;2 years of experiences in US scanning and considerable CT/MRI film reading ability. Both two operators were required to have a basic knowledge of US fusion imaging but without practical experience.\u003c/p\u003e\n\u003ch3\u003eFusion imaging steps\u003c/h3\u003e\n\u003cp\u003eThe steps of CT/MR\u0026ndash;US fusion imaging included importation of CT/MR DICOM image into the US machine, reconstruction of CT/MR volume images, primary registration to have a primary alignment, and fine tuning to acquire a precise alignment.\u003c/p\u003e\u003cp\u003eThe steps of US\u0026ndash;3D US fusion imaging included acquisition of 3D US volume image by a free-hand scanning of the target tumor zone and fine tuning to acquire a precise alignment. (To simulate the primary registration process of US\u0026ndash;CT/MR fusion imaging, the electromagnetic generator was manually moved approximately 2 cm after acquiring a 3D US volume image.)\u003c/p\u003e\u003cp\u003eFor the detailed steps, refer to our previous reports\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e.\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eThe learning process\u003c/h3\u003e\n\u003cp\u003eThe reference registration time was performed by an experienced operator of US fusion imaging. Both US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging were carried out five times, and the average value was calculated and considered as the reference experienced registration time.\u003c/p\u003e\u003cp\u003eBefore the study, two operators received a lecture about the basic knowledge on US fusion imaging. Then, they were required to familiarize the US fusion imaging system on a self-made phantom for at least ten times in 1 week before performing the fusion on actual patients.\u003c/p\u003e\u003cp\u003eAfter that, both US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging were practiced on the same patients by both operators. The sequences of two operators and two types of US fusion imaging were randomly assigned. The timing started when the operators entered the Virtual Navigator system. When the operator considered the registration error to be within 3 mm or after 3 attempts, the timing would be terminated. An image in the maximum section of the tumor as well as a video loop while scanning the tumor would be saved at the end of each practice. When both operators reached the reference experienced registration time and had consecutive successful registration 3 times in each type of US fusion imaging, the learning process would be terminated.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eJudgment of registration results\u003c/h2\u003e\u003cp\u003eThe registration results of each practice were judged blindly by two other senior doctors with \u0026gt;\u0026thinsp;5-year experiences of US fusion imaging using the images and clips saved during each practice. When registration errors of the target tumor were found, adjacent registration markers were \u0026lt;\u0026thinsp;3 mm, and the organ outline was almost completely overlapped, registration was considered successful. Otherwise, it would be considered unsuccessful. When judgments between these two doctors were different, consensus would be reached through discussion.\u003c/p\u003e\u003cp\u003e\u003cb\u003e5 Statistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIf the operator finally considered the registration error to be within 3 mm in 3 attempts, the registration time was defined as the duration time from entering the Virtual Navigator system for the first time to the completion of registration. If the operator failed to control the registration error within 3 mm after 3 attempts, the registration time was defined as the duration time from entering the Virtual Navigator system for the first time to the termination of the third attempt.The success rate of registration is defined as the percentage of cases that the registration was judged to be successful.\u003c/p\u003e\u003cp\u003eCUSUM analysis was employed to analyze the learning process of fusion imaging to determine different stages of the learning curve.\u003c/p\u003e\u003cp\u003eThe detailed statistical analysis was as follows. All cases were listed according to the time sequence. The CUSUM value of the first practice (CUSUM\u003csub\u003e1\u003c/sub\u003e) was defined as the difference in the registration time of the first practice (t\u003csub\u003e1\u003c/sub\u003e) and the mean registration time of all practices (t\u003csub\u003emean\u003c/sub\u003e). The CUSUM value at the second practice and the following ones was defined as the previous CUSUM value (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CUSUM}_{n-1}\\)\u003c/span\u003e\u003c/span\u003e) added to the difference between the registration time and the mean registration time. This process continued, and the following formula was created:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CUSUM}_{n}=\\stackrel{}{\\int\\:}[\\left({t}_{n}-{t}_{mean}\\right)+{CUSUM}_{n-1}]\\)\u003c/span\u003e\u003c/span\u003e. The CUSUM learning curve of the registration time was depicted, and curve fitting was performed. Curve fitting was considered successful if P is \u0026lt;\u0026thinsp;0.05. Goodness of fit was judged according to the coefficient \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e to select the proper regression curve. The learning curve can be divided into learning and experienced periods by the peak value. The registration time and the success rate of registration were calculated based on different periods.\u003c/p\u003e\u003cp\u003eThe measurement data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical data were presented as percentages. The registration times between groups were compared using two independent \u003cem\u003et\u003c/em\u003e-test. Homogeneity of variance test was performed using Levene test. The success rates of registration were compared between groups using Fisher\u0026rsquo;s exact test. The difference was considered significant when \u003cem\u003eP\u003c/em\u003e-value was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eEnrollment\u003c/h2\u003e\u003cp\u003eThe reference experienced registration time for US\u0026ndash;CT/MR and US\u0026ndash;3D US was 365s and 200 s, respectively.\u003c/p\u003e\u003cp\u003eWhen the operator reached the reference experienced registration time and succeeded three times consecutively in each type of US fusion imaging, the learning process could be terminated. After 15 practices, both operators reached this criterion to terminate the learning process for both types of US fusion imaging. The baseline characteristics of 15 cases are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of Enrolled Patients and Lesions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (32\u0026ndash;70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male/female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 /2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis (HCC/FNH/others)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 /2 /2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirrhosis (yes/no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 /4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscites (yes/no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13/2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterval between CT/MR and current examination (d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (0\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum diameter (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (10\u0026ndash;53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation (left/right lobe)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 /12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eHCC, hepatocellular carcinoma; FNH, focal nodular hyperplasia; CT, computed tomography; MR, magnetic resonance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eThe mean registration time\u003c/h2\u003e\u003cp\u003eThe mean registration time of US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging for two operators were 522\u0026thinsp;\u0026plusmn;\u0026thinsp;214 s (senior operator, US\u0026ndash;CT/MR), 156\u0026thinsp;\u0026plusmn;\u0026thinsp;66 s (senior operator, US\u0026ndash;3D US), 560\u0026thinsp;\u0026plusmn;\u0026thinsp;237 s (junior operator, US\u0026ndash;CT/MR) 183\u0026thinsp;\u0026plusmn;\u0026thinsp;122 s (junior operator, US\u0026ndash;3D US).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCUSUM learning curve evaluation\u003c/h2\u003e\u003cp\u003eThe CUSUM learning curve of US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging for two operators is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Quadratic and cubic regression were performed, and \u003cem\u003eP\u003c/em\u003e-value as well as goodness of fit coefficient (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e for cubic regression was superior to that for quadratic regression, and thus cubic regression was considered the best regression model for US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging for two operators and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression of the Registration Time Using the CUSUM Value for Two Operators Learning Two Fusion Imaging\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eThe senior operator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eThe junior operator\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuadratic curve \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuadratic curve \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCubic curve \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCubie curve \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe peak value of the CUSUM learning curve divided the curve into learning (phase A) and experienced (phase B) periods. The practice number at peak value in two types of US fusion imaging for two operators is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCase Numbers of Phases A and B for Two Operators Learning Two Fusion Imaging\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eThe senior operator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eThe junior operator\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe CUSUM analysis of the registration time for US\u0026ndash;CT/MR fusion imaging showed that phase A and phase B was divided according to the 10\u0026ndash;11th practices for both operators. By contrast, the CUSUM analysis of the registration time for US\u0026ndash;3D US fusion imaging showed that the learning and experienced phases were divided according to the 5th practices for both operators.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eComparison of the registration time\u003c/h2\u003e\u003cp\u003eAccording the phase A and phase B in the learning process, the registration time was calculated in different periods for the senior and junior operators, respectively, and shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The registration time in phase B was significantly shorter than that in phase A.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Registration Time of Phases A and B for Two Operators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eThe senior operator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eThe junior operator\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e522\u0026thinsp;\u0026plusmn;\u0026thinsp;214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156\u0026thinsp;\u0026plusmn;\u0026thinsp;67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e560\u0026thinsp;\u0026plusmn;\u0026thinsp;237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e183\u0026thinsp;\u0026plusmn;\u0026thinsp;122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA period (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e600\u0026thinsp;\u0026plusmn;\u0026thinsp;178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e229\u0026thinsp;\u0026plusmn;\u0026thinsp;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e671\u0026thinsp;\u0026plusmn;\u0026thinsp;209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e282\u0026thinsp;\u0026plusmn;\u0026thinsp;147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB period (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310\u0026thinsp;\u0026plusmn;\u0026thinsp;165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340\u0026thinsp;\u0026plusmn;\u0026thinsp;92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e134\u0026thinsp;\u0026plusmn;\u0026thinsp;76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor both the senior and junior operators, the registration times of US\u0026ndash;3D US fusion imaging were shorter than those of US\u0026ndash;CT/MRI in phases A and B (P\u0026lt;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eComparison of the success rate of registration\u003c/h2\u003e\u003cp\u003eThe success rate of registration was calculated in different periods for the senior and junior operators, respectively, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSuccess Rate of Registration in Phases A and B for Two Operators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eThe senior operator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eThe junior operator\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUS\u0026ndash;CT/MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUS\u0026ndash;3D US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93%(14/15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93%(14/15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67%(10/15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93%(14/15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91%(10/11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%(5/5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50%(5/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80%(4/5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%(4/4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%(9/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%(5/5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%(10/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor the senior operator, the success rates of US\u0026ndash;CT/MRI and US\u0026ndash;3D 3D US fusion imaging in both phases A and B were higher than 90%. By contrast, for the junior operator, the success rate of US\u0026ndash;CT/MRI fusion imaging in phase A was relatively lower than that in phase B, and the success rate of US\u0026ndash;3D US fusion imaging in both phases A and B was 100%.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUS\u0026ndash;3D US fusion imaging has a shorter learning curve than US\u0026ndash;CT/MR fusion imaging and is less dependent on experience.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe learning curve was used to describe the process of decreased operation time as the practice was repeated\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Generally, it can be divided into a learning period with gradually decreasing operation time and an experienced period with a relatively stable operation time\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Nowadays, the learning curve theory is widely used in economy, psychology, and medicine\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In medicine, the most common parameters used to describe the learning process are the operation time, treatment response, and complication rate\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. As the practice was repeated, the operation time decreased, treatment response improved, and complication decreased. As for the US fusion imaging technique, the registration time represents the degree of experience, whereas the success rate of registration represents the learning effect. Therefore, this study evaluated the learning curve of US fusion imaging mainly using these two parameters. CUSUM analysis is a method used to visualize the slight variations of continuous data through the cumulative sum of differences between every raw data and average value\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Therefore, this method was commonly employed in the present study to determine the learning and experienced periods.\u003c/p\u003e\u003cp\u003eWith CUSUM analysis at the registration time, approximately 10 practices could be used to divide these two periods for US\u0026ndash;CT/MR fusion imaging for both the senior and junior operators, indicating that after approximately 10 practices, the registration time had become stable and the operators had become familiar with this type of US fusion imaging. We noticed that the learning period of experienced operator to learn US\u0026ndash;CT/MR fusion imaging was 11 practices, whereas the novice was only 10 practices, which was possibly caused by the inadequate sample size and individual differences. Conversely, for US\u0026ndash;3D US fusion imaging, both operators entered into the experienced period after 5 practices, which indicated that US\u0026ndash;3D US fusion imaging was easier to learn and grasp regardless of experience.\u003c/p\u003e\u003cp\u003eThe comparison of the registration time between the learning and experienced periods for US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging validated that the division of these two periods in the CUSUM analysis was proper and reliable. Moreover, the registration time of US\u0026ndash;CT/MR fusion imaging was longer than US\u0026ndash;3D US fusion imaging, consistent with our previous clinical comparative study\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e that indicated the duration time of US\u0026ndash;3D US fusion imaging was shorter than that of US\u0026ndash;CT/MR fusion imaging. This result was generally due to the relatively complex steps in US\u0026ndash;CT/MR fusion imaging. Besides, the senior operator reached a higher success rate of registration at both learning and experienced periods during the learning process of US\u0026ndash;CT/MR fusion imaging, whereas the junior operator reached a lower success rate at the learning period. Differently, during the learning process of US\u0026ndash;3D US fusion imaging, the success rates of registration were high in the learning period either for the experienced or the novice operator. This demonstrated that experience did affect the registration in US\u0026ndash;CT/MR fusion imaging to a certain extent, although it did not significantly influence the registration time. By contrast, experience did not affect the registration significantly for US\u0026ndash;3D US fusion imaging. This is probably due to the fact that the registration between CT/MR and US images requires the operator to recognize the identical vascular structures in two different images, with the process requiring a higher level of CT/MR and US images reading ability than US\u0026ndash;3D US fusion imaging.\u003c/p\u003e\u003cp\u003eThe possible reasons for the above differences between US\u0026ndash;CT/MR and US\u0026ndash;3D US fusion imaging were as follows. First, the registration of US\u0026ndash;CT/MR fusion imaging requires the operator to distinguish the anatomical structure between CT/MRI and US images; therefore, the operator should familiarize the CT/MRI film reading as well as US image scanning. By contrast, the anatomical landmarks in US volume images were similar to that in real-time US images; therefore, the registration of US\u0026ndash;3D US fusion imaging needs the operator to be familiar with US images only. Second, there were inevitable errors between CT/MR images and US images because of the difference in imaging principles and time interval of image acquisition, making the registration procedure more complicated. As the 3D US volume image was acquired from real-time US image, the similarity between these two images was high. As a result, the registration process of US\u0026ndash;CT/MR fusion imaging required more time and practice than that of US\u0026ndash;3D US fusion imaging. In the present study, US\u0026ndash;3D US fusion imaging only required 5 practices to enter the experienced period for two operators with different experiences, whereas US\u0026ndash;CT/MR fusion imaging required about 10 practices to enter the experienced period for both senior and junior operators.\u003c/p\u003e\u003cp\u003eThe learning curve could be affected by several factors, such as the operating steps of the technique, experience of the operator, and effective training. The results of this study indicated that compared to US\u0026ndash;CT/MR fusion imaging, the operating steps of US\u0026ndash;3D US fusion imaging were easier to grasp with fewer practices and less influenced by experience. According to our results, advises about the learning and training of fusion imaging are listed as follows. For operators with or without experience, at least 10 clinical practices on patients are recommended for US\u0026ndash;CT/MR fusion imaging and 5 clinical practices for US\u0026ndash;3D US fusion imaging. If an operator needs to grasp US\u0026ndash;CT/MR fusion imaging, not only US scanning skills but also the ability of CT/MR film reading is required.\u003c/p\u003e\u003cp\u003eHowever, this study has some limitations. First, this study did not consider the liver background condition and the classification of lesions as factors of the learning curve, which would be further investigated in the future research. Second, the present study enrolled a junior operator with a considerable knowledge on US and CT/MR instead of a novice without experience, since the application of US fusion imaging, especially for the thermal ablation procedure, was concerned with a doctor with considerable knowledge on US and CT/MR. If the operator\u0026rsquo;s knowledge about US and CT/MR images is limited, he/she may require more practices to master the operation skills in US fusion imaging.\u003c/p\u003e\u003cp\u003eIn conclusion, US\u0026ndash;3D US fusion imaging has a shorter learning curve than US\u0026ndash;CT/MR fusion imaging and is less dependent on experience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThis study was supported by the Clinical Research Special Fund of The Third Affiliated Hospital of Sun Yat-sen University (No. QHJH202302), the Guangzhou Science and Technology Plan Project (No. 202201011075) and the National Natural Science Foundation of China Cultivation Special Project (No. 2023GZRPYQN06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study protocol was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (Approval No.\u0026nbsp;中山附三医伦[2011]2-33号).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eAll participants provided written consent for the publication of anonymised data collected during the study. No identifiable information about participants will be disclosed in this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from all participants prior to enrollment, which included consent for both study participation and publication of anonymized data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The datasets generated during this study are fully available within the article and supplementary materials. \u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEuropean Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182-236. \u003c/li\u003e\n\u003cli\u003eBenson AB, D\u0026rsquo;angelica MI, Abrams I, et al. NCCN clinical practice guidelines in oncology( NCCN guidelines) [EB /OL]. Hepatobiliary Cancers. 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PMID: 30300032.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fusion imaging, Ultrasound, Learning curve, CUSUM analysis, Liver","lastPublishedDoi":"10.21203/rs.3.rs-7480136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7480136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this retrospective study was to investigate the learning curve of ultrasound (US)\u0026ndash;computed tomography (CT)/magnetic resonance (MR) as well as US\u0026ndash;three-dimensional (3D) US fusion imaging for liver tumors. Patients intending to undergo liver tumor thermal ablation in our institution were enrolled. Two operators were investigated, one senior and one junior US doctor. Both operators practiced on the same patients for each US fusion imaging technique, respectively. The results of registration were blindly judged by two senior sonographers with \u0026gt;\u0026thinsp;5-year experience on US fusion imaging. The registration time and success rate of registration were recorded. The trend of registration time with practice attempts was analyzed using CUSUM analysis. Both operators reached the criteria to terminate the learning in both two types of fusion imaging after 15 practices. The CUSUM analysis of the registration time showed that phases A and B were divided by the 10\u0026ndash;11th practices for US\u0026ndash;CT/MR fusion imaging and 5th practices for US\u0026ndash;3D US fusion imaging for both operators. In the US\u0026ndash;CT/MR fusion imaging, the success rates of registration for the senior operator in both phases were high (91\u0026ndash;100%), whereas that for the junior operator in the phase A was low (50%). By contrast, in US\u0026ndash;US fusion imaging, the success rates of registration for the senior and the junior operator in both phases were high (100%). In conclusion, US\u0026ndash;3D US fusion imaging has a shorter learning curve than US\u0026ndash;CT/MR fusion imaging and is less dependent on experience.\u003c/p\u003e","manuscriptTitle":"Evaluation of Learning Curves for US-CT/MR and US-US Fusion Imaging in the Liver","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 07:11:06","doi":"10.21203/rs.3.rs-7480136/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8300f20-b9af-40ba-b87e-2b7b5cc7dd5e","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T05:24:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 07:11:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7480136","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7480136","identity":"rs-7480136","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}