Radiol Oncol 2022; 56(1): 111-118. doi: 10.2478/raon-2021-0035 111 research article The learning curve of laparoscopic liver resection utilising a difficulty score Arpad Ivanecz1,2, Irena Plahuta1, Matej Mencinger3,4,5, Iztok Perus2,6, Tomislav Magdalenic1, Spela Turk1, Stojan Potrc1,2 1 Clinical Department of Abdominal and General Surgery, University Medical Centre Maribor, Maribor, Slovenia 2 Department of Surgery, Faculty of Medicine, University of Maribor, Maribor, Slovenia 3 Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Maribor, Slovenia 4 Centre of Applied Mathematics and Theoretical Physics, University of Maribor, Maribor, Slovenia 5 Institute of Mathematics, Physics and Mechanics, Ljubljana, Slovenia 6 Faculty of Natural Science and Engineering, University of Ljubljana, Ljubljana, Slovenia Radiol Oncol 2022; 56(1): 111-118. Received 2 June 2021 Accepted 16 July 2021 Correspondence to: Assist. Prof. Arpad Ivanecz, M.D., Ph.D., Clinical Department of Abdominal and General Surgery, University Medical Centre Maribor, Ljubljanska ulica 5, 2000 Maribor, Slovenia. E-mail: arpad.ivanecz@ukc-mb.si Disclosure: No potential conflicts of interest were disclosed. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Background. This study aimed to quantitatively evaluate the learning curve of laparoscopic liver resection (LLR) of a single surgeon. Patients and methods. A retrospective review of a prospectively maintained database of liver resections was conducted. 171 patients undergoing pure LLRs between April 2008 and April 2021 were analysed. The Halls difficulty score (HDS) for theoretical predictions of intraoperative complications (IOC) during LLR was applied. IOC was defined as blood loss over 775 mL, unintentional damage to the surrounding structures, and conversion to an open approach. Theoretical association between HDS and the predicted probability of IOC was utilised to objectify the shape of the learning curve. Results. The obtained learning curve has resulted from thirteen years of surgical effort of a single surgeon. It consists of an absolute and a relative part in the mathematical description of the additive function described by the loga- rithmic function (absolute complexity) and fifth-degree regression curve (relative complexity). The obtained learning curve determines the functional dependency of the learning outcome versus time and indicates several local ex- treme values (peaks and valleys) in the learning process until proficiency is achieved. Conclusions. This learning curve indicates an ongoing learning process for LLR. The proposed mathematical model can be applied for any surgical procedure with an existing difficulty score and a known theoretically predicted as- sociation between the difficulty score and given outcome (for example, IOC). Key words: learning curve; difficulty score; laparoscopy; hepatectomy; intraoperative complication Introduction Interest in laparoscopic liver resection (LLR) has grown since the publication of the International Louisville Statement on laparoscopic liver sur- gery.1 Since then, the number of LLRs performed worldwide has increased exponentially.2 The laparoscopic approach must not compro- mise the technical quality of the liver resection. The message from the second Morioka consen- sus conference in 2014 was the need for a formal structure of education for those interested in per- forming LLR.3 The need for the organisation of LLR was achieved by the establishment of the Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection112 International Laparoscopic Liver Society in 2016.4 In Southampton, 2017, the third consensus meeting has produced a set of clinical practice guidelines to direct the speciality’s continued safe progres- sion and dissemination.5 A few difficulty scoring systems have been proposed to rate the difficulty of LLR, and the need for validating the existing tools before the clinical application has been high- lighted.6-9 Halls et al.10 developed and internally validated a difficulty score estimating the risk of intraoperative complications (IOC) during LLR, which was externally validated by the authors of the present study.11 Along with the evolution of LLR, its learning curves (LCs) have received increased attention.12-14 The idealised model of the LC has been described, demonstrating continuous result improvement along with experience.15 Recently, the LC has been reported to resemble a true model, in which alter- nating periods of progression and regression oc- curred until mastery was achieved.16 The present study was based on a thirteen-years single-centre experience and was designed to ana- lyse the real LC of LLR. To the best of our knowl- edge, it is the only study quantitatively presenting the LC of LLR. Patients and methods Patients Study subjects were identified from a prospective- ly maintained database of patients who underwent liver resections at the Department of Abdominal and General Surgery, University Medical Centre Maribor, Slovenia. This institution has been a ter- tiary referral centre specialised in hepato-pancre- ato-biliary surgery, where the first LLR was per- formed in April 2008. The study included all the patients in whom a pure laparoscopic liver proce- dure was performed (intention-to-treat analysis) until 31st March 2021. For the present study, pa- tients who underwent laparoscopic cyst fenestra- tion, liver biopsies, and radiofrequency ablation were excluded. Only pure LLR were performed; no hand-assist- ed or hybrid procedures were used. All patients were operated by the same surgeon (AI). He had expertise in open hepato-pancreatico-biliary and laparoscopic surgery but no experience in LLR be- fore this series. Perioperative definitions were pro- vided elsewhere.11 The surgical technique for LLR has been extensively described by others17 and per- formed as reported previously.18-20 At the time of the operation all patients had given their written consent that anonymous data can be used for research purposes. Patient records were anonymized and de-identified before analy- sis. Ethical approval for this study was obtained from the local institutional review board. Statistical analysis IBM SPSS for Windows Version 26.0 (IBM Corp., Armonk, NY, USA) and Wolfram Mathematica for Windows Version 10.4 (Wolfram Research, Inc., Champaign, IL, USA) were used for statistical com- putations. Categorical variables were reported as fre- quency (percentages). Continuous variables were reported as mean and standard deviation when data distribution was normal; otherwise, they were reported as median (minimum-maximum, interquartile range). The chi-square and the paired samples t-test were used. Percentages were list- ed to one decimal place, and a difference in the P-value of <0.05 was considered statistically sig- nificant. Mathematical modelling of the learning curve The Halls difficulty score (HDS)10 was applied. Its parameters (neoadjuvant chemotherapy, previous open liver resection, benign or malignant lesion, lesion size, and classification of resection) were captured from the institutional database. Each LLR was retrospectively scored from 0 to 15. In the proposed model, IOC was used as a sen- sible measure of the complexity of the resection.10 IOC’s key markers were blood loss over 775 mL, unintentional damage to the surrounding struc- tures and conversion to open approach.10 The con- version was defined as the requirement for lapa- rotomy at any time of the procedure, except for the extraction of the resected specimen.10 In11, the authors searched for functorial depend- ence between IOC and HDS using the first 128 pa- tients of the observed cohort. The best-fit-depend- ency was found to be the Weibull cumulative dis- tribution function21 of the form with and . Here x represents the HDS, and represents the predicted probability of IOC occurrence. This functional de- pendence will be referred as the theoretical prob- ability of IOC11 and is graphically represented in Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection 113 Figure 1. This figure is rendered here for the self- sufficiency of this article. The Weibull curve in Figure 1 is monotonically increasing. Regarding the LC, we assume that a procedure with a higher difficulty score must be graded better than a procedure with a lower dif- ficulty score if the resection is done without IOC. Therefore, the difference between the theoretically predicted probability of IOC and obtained IOC is greater if the difficulty score is higher (if IOC = 0). On the other hand, if IOC was detected (if IOC = 1), the difference between the theoretically predicted probability of IOC and obtained IOC is negative (implying a lower grade for a surgeon) if the dif- ficulty score is low. Thus, the learning outcome is proportional to the share of IOC caused by the sur- geon obtained in each of the ten classes. We wanted to test if the time dependency of HDS is (on average) an ascending function. Therefore, resections were divided into three (time) sequen- tial classes (each consisting of 57 patients), and the number of obtained IOC in each class was counted. HDS10 was used in the analysis of LC. Its de- pendency was proven to be (on average) an in- creasing function (Figure 1). The proposed mathematical model of a learning curve The probability (the share of IOC in the time-de- pendent class) of IOC depends on HDS. The share of IOC in a time-dependent class measures the complexity of resections. Therefore, a novel model for presenting the learning outcome in the case of LLR with existing theoretical dependence between HDS and (the probability of) IOC was introduced. We assume that the learning outcome consists of two additive components. The first represents the absolute complexity of the resection according to time (which is proportional to effort). The sec- ond (additive) component is obtained by compar- ing the share of IOC to the theoretically predicted (probability of) IOC depending on the HDS of the patient. Components share the same physical units; therefore, the addition is justified. The sum of com- ponents results in the learning outcome for any patient and finally in the LC. The first component reflects the absolute complexity of the resections within the same class, while the second one reflects the relative complexity (comparing to the theoreti- cally predicted HDS), which can be interpreted as the surgeon’s efficiency. At this point, we mathematically define the ob- jectives determining the learning outcomes, and consequently, the LC. The cohort of 171 patients is divided into ten sequential classes (the last class contains 17+1 patient). By , we denote the sequen- tial number of the patient. By , we denote the sequential number of the class (for every class, its cardinality is equal to 17). Our main assumptions and proposals are the following: 1. Since the resections were listed chronological- ly, we may assume that the sequential num- ber of the patient corresponds to the effort of the surgeon (the correspondence is monotoni- cally increasing). 2. For every class , the absolute complexity of the tasks in the class is proportional to the ratio of the IOC cases. The non-smooth dependency was fitted to smooth logarithmic function . Additionally, it was modified to absolute complexity for each sequential patient 3. For every class , the relative complexity or efficiency of the surgeon is proportional to the sum of differences between the theoreti- cally predicted probabilities of IOC and the obtained probabilities of IOC FIGURE 1. The continuous mean risk curve of intraoperative complication (IOC) as a function of the Halls difficulty score: the theoretical probability of intraoperative complication.11 Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection114 Additionally, was finally modi- fied to relative complexity for each se- quential patient 4. Adding both components, we get the learning curve of the surgeon: Results The presentation of the cohort Between April 2008 and April 2021, 171 patients underwent pure LLR. Their baseline characteristics are presented in Table 1. Perioperative outcomes are given in Table 2. Two patients (1.2%) suffered from unintentional laceration of the transverse colon, sutured laparo- scopically. The procedure was completed laparo- scopically in 147 (86.0%) patients. The reasons for conversion to laparotomy in 24 (14.0%) patients were diffuse parenchymal bleeding (N = 3), inabil- ity to proceed due to the large liver or dense adhe- sions (N = 6), and oncological concern (N = 15). The decision to proceed to conversion was not made upon life-threatening bleeding. The indication for liver resection in converted cases was malignant tumours. Three (1.8%) patients died – one bled out from ruptured oesophageal varices, and two died of liver failure; they all had hepatocellular carci- noma and liver cirrhosis Child-Pugh B. Learning curve analysis results The analysis of the learning curve was motivat- ed by the increasing time dependency of HDS. Therefore, resections were divided into three se- quential classes of 57 resections, and the number of obtained IOC in each class was counted. The re- sults are graphically presented in Figure 2. On significance level the p-value for Chi Square test is slightly above 5% (p = 0.055). However, for linear-by-linear (Mantel Haenszel) test for trend, the p-value is < 0.05. HDS10 was used in the analysis of LC. The risk- of-IOC dependency was proven to be (on average) an increasing function in terms of HDS (Figure 1). A time-dependent and increasing trend can also be seen in Figure 3 (see the red linear trend-line for HDS; the blue chart represents actual data). The sequential number of the patient corre- sponds to the effort of the surgeon (the correspond- ence is monotonically increasing). In the first class, TABLE 1. Baseline characteristics of 171 patients who underwent laparoscopic liver resection Baseline characteristics Na,b Male sexa 104 (60.8%) Age (years)b 64 (20-86, 15) BMI (kg/m2)b 27 (18-50, 4.8) ASA scorea 1 44 (25.7%) 2 73 (42.7%) 3 51 (29.8%) 4 3 (1.8%) Liver cirrhosis Child-Pugh (22)a A 33 (19.3%) B 4 (2.3%) Previous abdominal surgerya 41 (24.0%) Previous liver resectiona 8 (4.6%) Malignant tumoura 128 (74.9%) Neoadjuvant chemotherapya 25 (14.6%) Max. diameter (mm)b 38 (2-160, 33) Number of tumoursa 1 (1-10, 0). Deep location within livera 50 (29.2%) Posterosuperior liver segmentsa 49 (28.7%) a = categorical variables; b = continuous variables have been reported as median (minimum- maximum, interquartile range); ASA = American Society of Anaesthesiologists; BMI = body mass index FIGURE 2. Histogramic time classes dependency of intraoperative complication (IOC) (yes/no) on the observed cohort. Interpolating the data to a polynomial of degree five, one gets a smooth function obtained by Mathematica command SplineFit using option Cubic. Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection 115 the average time difference between sequential surgeries was 117 days (with a standard deviation of 132 days), while in the last class, the time differ- ence was 13 days with a standard deviation of 12 days. The paired samples t-test shows that (at the level of confidence of 95%) the two means are not equal (p < 0.05). The final result of our LC data analysis is pre- sented in Figure 4. Ten consecutive classes of 17 patients are given on abscissa. The height of the columns represents the share of the IOC in the time class. Two types of LCs for the observed cohort and the surgeon under consideration are given. The orange line represents the logarithmic regres- sion curve based on absolute complexity for data . The green line represents the sum of the orange curve and the quintic regression line of relative complexity for data . This green line represents our LC. Discussion Like any other human activity, where individu- als perform more difficult and intricated tasks over time, surgeons have been interested in their LC when performing LLR.16 The obtained learn- ing curve has resulted from thirteen years of sur- gical effort of a single surgeon. It consists of an absolute and a relative part in the mathematical description of the additive function described by the logarithmic function (absolute complexity) and fifth-degree regression curve (relative complexity). The obtained LC determines the functional de- pendency of the learning outcome versus time and indicates several local extreme values (peaks and valleys) in the learning process until proficiency is achieved. A typical LC graphically represents the relation- ship between the learning effort and achievement. LC consists of a measure of learning which usually lies on the ordinate (y-axis), a measure of effort, which usually lies on the abscissa (x-axis) and a mathematical linking function. The shapes of this mathematical (functorial) dependence can vary de- pending on the nature and difficulty of the learn- ing outcomes and difficulty of the task.26,27 It may be assumed that LC should be increasing in time (i.e., with effort). There are several typical LCs for learning different skills whose shape de- pends on the complexity of the task. When learning simple skills, S-shaped or logistic curves appear. The logistic curve admits a single inflexion point (indicating the point when half of the knowledge TABLE 2. Perioperative outcomes of 171 patients who underwent laparoscopic liver resection Intraoperative details and postoperative course Na,b Anatomic resection (23) a 101 (59.1%) Anatomically major resection (23) a 27 (15.8%) Technically major resection (24)a 29 (17.0%) Operation time (min)b 160 (25-450, 90) Blood loss (mL)b 150 (0-2200, 180) Intraoperative complication (10)c 34 (19.9%) Conversion to open approacha 24 (14.0%) Blood loss > 775 mLa 12 (7.0%) Unintentional damage to the surrounding structuresa 2 (1.2%) Hepatic pedicle clampinga 45 (26.3%) Total hepatic pedicle clamping time (min)b 8 (0-75, 10) Transfusion requireda 20 (11.7%) Pathohistological diagnosis Colorectal liver metastases 53 (31%) Hepatocellular carcinoma 46 (29.6%) Intrahepatic cholangiocarcinoma 14 (8.2%) Other metastases 11 (6.4%) Hepatic cysts 10 (5.8%) Hepatic adenoma 6 (4.7%) Focal nodular hyperplasia 8 (4.7%) Haemangioma 6 (3.5%) Other pathology 15 (8.8%) R0 resection 163 (95.3%) Major morbidity CD 3a–4b (25)a 21 (12.3%) Hospital stay (days)b 6 (2-79, 4) a = categorical variables; b = continuous variables have been reported as median (minimum- maximum, interquartile range); c = intraoperative complication was defined as blood loss over 775 mL, unintentional damage to the surrounding structures and conversion to open approach was acquired) and a horizontal asymptote (repre- senting the cap to be acquired). In surgical proce- dures for more complex skills, often a logarithmic LC without a cap appears. However, when inter- preting a paediatric ankle radiograph, the LC turns out to be logarithmic.27 The zig-zag shape can ap- pear as well.27 A steep LC is rare in medicine since the skills are associated with difficult and complex procedures.26,27 We have considered the LC of a single surgeon in a technically demanding LLR. When implement- ing a new surgical procedure, a surgeon already has some fundamental knowledge. The learning outcome is assumed to be proportional to the share of IOC made by the surgeon, i.e. we learn from our Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection116 It is assumed that a higher level of (the sum of) theoretically predicted probability of IOC (within a particular class) reflects a higher level of gained knowledge (higher grade for the LC). This may be justified because the average HDS is also increas- ing with time (Figure 4). Therefore, HDS affects the relative complexity of the case. The orange line represents the basic LC. The relative complexity depends on the subjective decision made by the surgeon according to previously successfully fin- ished cases with no IOC. LLR has been encompass- ing different procedures, each with its own ana- tomic and procedural considerations. Komatsu et al.13 demonstrated an ideal learning curve effect for the left lateral sectionectomy and left hepatectomy, but it was not observed for the right hepatectomy. The more successive cases with no IOC encouraged the surgeon to do more cases with increased HDS. When analysing IOC, the conversion rate of 14% was consistent with the reported ones, counting from 1% to 17%.15,29 An increased risk of conver- sion has been associated with neoadjuvant chemo- therapy, previous open liver resection, malignant tumours, their size, anatomically major and techni- cally major resection.30 Patients who had an elec- tive conversion for an unfavourable intraoperative finding had better outcomes than patients who had an emergency conversion secondary to an adverse intraoperative event.30 All our converted cases oc- curred in malignant tumours. None of the cases was related to life-threatening bleeding. The most common indications for conversion were the in- ability to proceed and oncological concern, respec- tively. A chosen method does not change the prin- ciple of the surgery. Therefore, an oncologically uncompromised resection has been more crucial than the laparoscopic completion of the procedure. The overall major morbidity and mortality rates of 12.3% and 1.8% followed reports in the litera- ture.13,14,16 To sum up, this conversion rate reflected the surgeon’s reliance on the open method when dealing with adverse intraoperative findings.20 Although the first anatomical LLR was per- formed in 199631, the first difficulty score was pub- lished not earlier than 2014.32 Our first LLR was performed in 2008, and the surgeon had to lean on his experience from open liver surgery. It would be riveting to study the results of the surgeon’s trainees who could benefit from the evolution of techniques, learning modules12,16,33, and difficulty scores.6-8,10 The main shortcoming of the presented research is a relatively low number of patients. Therefore, in future research, a larger number of patients should FIGURE 3. Time dependency of the Halls difficulty score on the observed cohort (blue points) and its regression (trend) line (red line). FIGURE 4. Two types of learning curves for observed cohort and the surgeon under consideration. The orange line (AC) represents the logarithmic regression curve based on absolute complexity. The green line (LC) represents the sum of the orange curve and the quintic regression line of relative complexity. This line represents our learning curve. AC = absolute complexity; ac (N) = absolute complexity expressed by the number of intraoperative complications; LC = learning curve mistakes (IOC). However, LLR has not been a sin- gle procedure, and the complexity of operations varies from wedge resections to extended major hepatectomies. This fact contributes to the difficul- ties during learning and assessing the LC.12-16 In the beginning, solitary and peripherally located symp- tomatic benign tumours in anterolateral segments were resected.28 With growing experience, the laparoscopic approach was implemented regard- less of tumour location and its characteristics.1,5 The time difference between sequential surgeries in time classes shortened from 117 days to 13 days. Therefore, one could reasonably assume this was a part of the learning strategy. Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection 117 be involved to show the robustness of the present- ed LC. Furthermore, its retrospective manner is an- other limitation. The propose d LC and used methodology could guide the trainee surgeons and monitor their per- formance. In this sense, practitioners should be provided with a statistically independent set of pa- tients with a constant increase (i.e., a constant gra- dient) of HDS over time. Thus, more difficult cases would be taken over by more qualified surgeons. A newly created application would randomly select patients with the appropriate HDS for each prac- titioner. It would enable control of the (accidental) variability in HDS and its consequences on IOC, which could not be completely avoided in practice. Under the supervision of a qualified operator, the objective evaluation of the LC would avoid deeper valleys in it (higher number of IOCs than theoreti- cally expected) and thus ensure the most optimal learning. Given the basic assumption that we learn from our mistakes (see section A mathematical mod- elling of a learning curve), the maximal acceptable number and type of mistakes in the learning pro- cess should be objectively evaluated through fur- ther research. To conclude, our LC is closer to a true model in which alternating periods of progression and regression occurred until mastery was achieved.16 Furthermore , the method presented in this paper can be applied to any (surgical) procedure with a difficulty score and given outcome (for example IOC), if a theoretically predicted probability de- pendence for the given outcome is available. From this point of view, the method is novel. Acknowledgement This work was supported by University Medical Centre Maribor (grant number IRP-2019/01-03). The funding source has no role in the design, prac- tice, or analysis of this study. References 1. Buell JF, Cherqui D, Geller DA, O’Rourke N, Iannitti D, Dagher I, et al. The in- ternational position on laparoscopic liver surgery: The Louisville Statement, 2008. Ann Surg 2009; 250: 825-30. doi: 10.1097/sla.0b013e3181b3b2d8 2. Ciria R, Cherqui D, Geller DA, Briceno J, Wakabayashi G. Comparative short- term benefits of laparoscopic liver resection: 9000 cases and climbing. Ann Surg 2016; 263: 761-77. doi: 10.1097/sla.0000000000001413 3. Wakabayashi G, Cherqui D, Geller DA, Buell JF, Kaneko H, Han HS, et al. Recommendations for laparoscopic liver resection: a report from the sec- ond international consensus conference held in Morioka. Ann Surg 2015; 261: 619-29. doi: 10.1097/sla.0000000000001184 4. Cherqui D, Wakabayashi G, Geller DA, Buell JF, Han HS, Soubrane O, et al. The need for organization of laparoscopic liver resection. J Hepatobiliary Pancreat Sci 2016; 23: 665-67. doi: 10.1002/jhbp.401 5. Abu Hilal M, Aldrighetti L, Dagher I, Edwin B, Troisi RI, Alikhanov R, et al. The Southampton consensus guidelines for laparoscopic liver surgery: from indication to implementation. Ann Surg 2018; 268: 11-8. doi: 10.1097/ sla.0000000000002524 6. Wakabayashi G. What has changed after the Morioka consensus conference 2014 on laparoscopic liver resection? Hepatobiliary Surg Nutr 2016; 5: 281- 9. doi: 10.21037/hbsn.2016.03.03 7. Hasegawa Y, Wakabayashi G, Nitta H, Takahara T, Katagiri H, Umemura A, et al. A novel model for prediction of pure laparoscopic liver resection surgical difficulty. Surg Endosc 2017; 31: 5356-63. doi: 10.1007/s00464-017-5616-8 8. Kawaguchi Y, Fuks D, Kokudo N, Gayet B. Difficulty of laparoscopic liver resection: proposal for a new classification. Ann Surg 2018; 267: 13-7. doi: 10.1097/sla.0000000000002176 9. Hallet J, Pessaux P, Beyfuss KA, Jayaraman S, Serrano PE, Martel G, et al. Critical appraisal of predictive tools to assess the difficulty of laparoscopic liver resection: a systematic review. Surg Endosc 2019; 33: 366-76. doi: 10.1007/s00464-018-6479-3 10. Halls MC, Berardi G, Cipriani F, Barkhatov L, Lainas P, Harris S, et al. Development and validation of a difficulty score to predict intraoperative complications during laparoscopic liver resection. Br J Surg 2018; 105: 1182- 91. doi: 10.1002/bjs.10821 11. Ivanecz A, Plahuta I, Magdalenić T, Mencinger M, Peruš I, Potrč S, et al. The external validation of a difficulty scoring system for predicting the risk of intraoperative complications during laparoscopic liver resection. BMC Surg 2019; 19: 179. doi: 10.1186/s12893-019-0645-y 12. Guilbaud T, Birnbaum DJ, Berdah S, Farges O, Beyer Berjot L. Learning curve in laparoscopic liver resection, educational value of simulation and train- ing programmes: a systematic review. World J Surg 2019; 43: 2710-9. doi: 10.1007/s00268-019-05111-x 13. Komatsu S, Scatton O, Goumard C, Sepulveda A, Brustia R, Perdigao F, et al. Development process and technical aspects of laparoscopic hepatectomy: learning curve based on 15 years of experience. J Am Coll Surg 2017; 224: 841-50. doi: 10.1016/j.jamcollsurg.2016.12.037 14. van der Poel MJ, Besselink MG, Cipriani F, Armstrong T, Takhar AS, van Dieren S, et al. Outcome and learning curve in 159 consecutive patients undergoing total laparoscopic hemihepatectomy. JAMA Surg 2016; 151: 923-28. doi: 10.1001/jamasurg.2016.1655 15. Vigano L, Laurent A, Tayar C, Tomatis M, Ponti A, Cherqui D. The learning curve in laparoscopic liver resection: improved feasibility and reproducibil- ity. Ann Surg 2009; 250: 772-82. doi: 10.1097/SLA.0b013e3181bd93b2 16. Villani V, Bohnen JD, Torabi R, Sabbatino F, Chang DC, Ferrone CR. “Idealized” vs. “True” learning curves: the case of laparoscopic liver resec- tion. HPB (Oxford) 2016; 18: 504-9. doi: 10.1016/j.hpb.2016.03.610 17. Han HS, Cho JY, Yoon YS. Techniques for performing laparoscopic liver resec- tion in various hepatic locations. J Hepatobiliary Pancreat Surg 2009; 16: 427-32. doi: 10.1007/s00534-009-0118-2 18. Ivanecz A, Krebs B, Stozer A, Jagric T, Plahuta I, Potrc S. Simultaneous pure laparoscopic resection of primary colorectal cancer and synchronous liver metastases: a single institution experience with propensity score matching analysis. Radiol Oncol 2018; 52: 42-53. doi: 10.1515/raon-2017-0047 19. Ivanecz A, Pivec V, Ilijevec B, Rudolf S, Potrč S. Laparoscopic anatomical liver resection after complex blunt liver trauma: a case report. Surg Case Rep 2018; 4: 25. doi: 10.1186/s40792-018-0432-5 20. Ivanecz A, Plahuta I, Magdalenić T, Ilijevec B, Mencinger M, Peruš I, et al. Evaluation of the Iwate model for predicting the difficulty of laparoscopic liver resection: does tumor size matter? J Gastrointest Surg 2021; 25: 1451- 60. doi: 10.1007/s11605-020-04657-9 21. Weibull W. A statistical distribution function of wide applicability. J Appl Mech 1951; 18: 293-97. 22. Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg 1973; 60: 646-9. doi: 10.1002/bjs.1800600817 23. Strasberg SM, Belghiti J, Clavien PA, Gadzijev E, Garden JO, Lau WY, et al. The Brisbane 2000 terminology of liver anatomy and resections. HPB 2000; 2: 333-39. doi: 10.1016/S1365-182X(17)30755-4 Radiol Oncol 2022; 56(1): 111-118. Ivanecz A et al. / Learning curve of laparoscopic liver resection118 24. Kazaryan AM, Røsok BI, Marangos IP, Rosseland AR, Edwin B. Comparative evaluation of laparoscopic liver resection for posterosuperior and antero- lateral segments. Surg Endosc 2011; 25: 3881-9. doi: 10.1007/s00464-011- 1815-x 25. Clavien PA, Barkun J, de Oliveira ML, Vauthey JN, Dindo D, Schulick RD, et al. The Clavien-Dindo classification of surgical complications: five-year experi- ence. Ann Surg 2009; 250: 187-96. doi: 10.1097/SLA.0b013e3181b13ca2 26. Hopper AN, Jamison MH, Lewis WG. Learning curves in surgical practice. Postgrad Med J 2007; 83: 777-9. doi: 10.1136/pgmj.2007.057190 27. Pusic MV, Boutis K, Hatala R, Cook DA. Learning curves in health pro- fessions education. Acad Med 2015; 90: 1034-42. doi: 10.1097/ acm.0000000000000681 28. Nguyen KT, Gamblin TC, Geller DA. World review of laparoscopic liver resection-2,804 patients. Ann Surg 2009; 250: 831-41. doi: 10.1097/ SLA.0b013e3181b0c4df 29. Costi R, Scatton O, Haddad L, Randone B, Andraus W, Massault PP, et al. Lessons learned from the first 100 laparoscopic liver resections: not delaying conversion may allow reduced blood loss and operative time. J Laparoendosc Adv Surg Tech A 2012; 22: 425-31. doi: 10.1089/ lap.2011.0334 30. Halls MC, Cipriani F, Berardi G, Barkhatov L, Lainas P, Alzoubi M, et al. Conversion for unfavorable intraoperative events results in significantly worse outcomes during laparoscopic liver resection: lessons learned from a multicenter review of 2861 cases. Ann Surg 2018; 268: 1051-57. doi: 10.1097/sla.0000000000002332 31. Azagra JS, Goergen M, Gilbart E, Jacobs D. Laparoscopic anatomical (he- patic) left lateral segmentectomy-technical aspects. Surg Endosc 1996; 10: 758-61. doi: 10.1007/bf00193052 32. Ban D, Tanabe M, Ito H, Otsuka Y, Nitta H, Abe Y, et al. A novel difficulty scoring system for laparoscopic liver resection. J Hepatobiliary Pancreat Sci 2014; 21: 745-53. doi: 10.1002/jhbp.166 33. Goh BKP, Prieto M, Syn N, Koh YX, Lim KI. Critical appraisal of the learning curve of minimally invasive hepatectomy: experience with the first 200 cases of a Southeast Asian early adopter. ANZ J Surg 2020; 90: 1092-98. doi: 10.1111/ans.15683