An analysis of virtual reality in abdominal surgery-A scoping review.

BACKGROUND: The integration of virtual reality (VR) in surgery has gained prominence as VR applications have increased in popularity. METHODS: A scoping review was undertaken, gathering the most relevant sources, utilising a detailed literature search of medical and academic databases including EMBASE, PubMed, Cochrane, IEEE, Google Scholar, and the Google search engine. RESULTS: Of the 18 articles included, 7 focused on VR in colon surgery, 5 addressed VR in pancreas surgery, and the remaining 6 concentrated on VR in liver surgery. All the articles concluded that VR has a promising future in abdominal surgery by facilitating precision, visualisation, and surgeon training. CONCLUSIONS: Adopting VR technology in abdominal surgery has the potential to improve preoperative planning, decrease perioperative anxiety among patients, and facilitate the training of surgeons, residents, and medical students. Additional supporting studies are necessary before VR can be widely implemented in surgical care delivery.

Analysis of artificial intelligence in thyroid diagnostics and surgery: A scoping review.

BACKGROUND: Artificial Intelligence provides numerous applications in the healthcare sector. The main aim of this study is to evaluate the extent of the current application of artificial intelligence in thyroid diagnostics. METHODS: Our protocol was based on the Scoping Reviews extension of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-ScR). Information was gathered from PubMed, Cochrane, and EMBASE databases and Google Scholar. Eligible studies were published between 2017 and 2022. RESULTS: The search identified 133 records, after which 18 articles were included in the scoping review. All the publications were journal articles and discussed various ways that specialists in thyroid diagnostics and surgery have utilized artificial intelligence in their practice. CONCLUSIONS: The development and incorporation of Artificial Intelligence applications in thyroid diagnostics and surgery has been moderate yet promising. However, applications are currently inconsistent and further research is needed to delineate the true benefit and limitations in this field.

Low serum iPTH at the end of surgery is the earliest predictor of postoperative hypocalcemia after total thyroidectomy.

BACKGROUND: The most frequent complication of total thyroidectomy remains hypocalcemia due to low postoperative levels of serum intact parathyroid hormone (iPTH). The purpose of this study was to investigate the role of decreased iPTH at the end of surgery in predicting hypocalcemia. In addition, we examined the percentage decrease of iPTH as potential indicator of hypocalcemia. METHODS: We retrospectively collected the data of patients who underwent total thyroidectomy for benign and malignant diseases at our institution between 2010 and 2022. The iPTH level was measured before and at the end of surgery, and serum calcium levels on the first postoperative day. Demographic, clinical, and biochemical characteristics of patients with low iPTH were compared with patients with normal iPTH levels using ANOVA for continuous variables and χ2-tests for categorical variables. Multivariable logistic regression analysis evaluated the association of iPTH at the end of surgery and the relative reduction of iPTH with postoperative hypocalcemia. RESULTS: The mean age of the 607 patients in this study was 55.6 years, and the female-to-male ratio was 5:1. Goiter was the most common indication for surgery (N = 382, 62.9%), followed by Graves' disease (N = 135, 22.2%). The mean preoperative iPTH was 49.0 pg/ml, while the mean postoperative iPTH was 29.3 pg/ml. A total of 197 patients (32.5%) had an iPTH level below normal, 77 patients (39%), had iPTH levels of 10-15.0 pg/ml and 120 patients (61%) of < 10.0 pg/ml at the end of surgery. Among all patients, 124 (20.4%) developed hypocalcemia on the first postoperative day. The mean percentage of decrease of iPTH was highest among patients with iPTH < 10 pg/ml (76.9%, p < 0.01); this group of patients had also the highest rate of postoperative hypocalcemia on day one (45.0% vs. 26.0% vs 12.2%, p < 0.01). CONCLUSIONS: Measurement of iPTH at the end of total thyroidectomy predicts patients who are at risk for postoperative hypocalcemia. The combination of low serum iPTH with a decrease in iPTH level of ≥ 50% may improve prediction of hypocalcemia compared to iPTH levels alone allowing for early calcium substitution in these patients at high risk of developing postoperative hypocalcemia.

Development and validation of a predictive model of the hospital cost associated with bariatric surgery.

Hospitals are facing difficulties in predicting, evaluating, and managing cost-affecting parameters in patient treatments. Inaccurate cost prediction leads to a deficit in operational revenue. This study aims to determine the ability of Machine Learning (ML) algorithms to predict the cost of care in bariatric and metabolic surgery and develop a predictive tool for improved cost analysis. 602 patients who underwent bariatric and metabolic surgery at Wetzikon hospital from 2013 to 2019 were included in the study. Multiple variables including patient factors, surgical factors, and post-operative complications were tested using a number of predictive modeling strategies. The study was registered under Req 2022-00659 and approved by an institutional review board. The cost was defined as the sum of all costs incurred during the hospital stay, expressed in CHF (Swiss Francs). The data was preprocessed and split into a training set (80%) and a test set (20%) to build and validate models. The final model was selected based on the mean absolute percentage error (MAPE). The Random Forest model was found to be the most accurate in predicting the overall cost of bariatric surgery with a mean absolute percentage error of 12.7. The study provides evidence that the Random Forest model could be used by hospitals to help with financial calculations and cost-efficient operation. However, further research is needed to improve its accuracy. This study serves as a proof of principle for an efficient ML-based prediction tool to be tested on multi-center data in future phases of the study.

Second Primary Cancer Among Patients With Papillary Thyroid Carcinoma Following the Chernobyl Disaster.

Importance: To our knowledge, there are no complete population-based studies of the risks of developing second malignant tumors after papillary thyroid carcinoma (PTC) in patients following the Chernobyl nuclear accident. Objective: To study the risk of second primary cancers in patients with PTC after the Chernobyl disaster. Design, Setting, and Participants: This was a retrospective cohort study conducted in the Republic of Belarus over a 31-year time frame evaluating patients with primary PTC and second malignant tumors. Personal data from the Belarussian Cancer Registry were used in the investigation, and only second primary cancers were included in the analysis. Patients were observed from January 1, 1990, to December 31, 2021, for the establishment of second primary malignant tumors. Main Outcomes and Measures: For analysis, synchronous and metachronous tumors were grouped into 1 group (second primary cancer group). If the patient had more than 2 cancers, they were observed until development of a second tumor and, subsequently, the development of a third tumor. The starting point for calculating the number of person-years was the date of thyroid cancer diagnosis. The end point for calculating the number of person-years was the date of diagnosis of the second primary malignant tumor, the date of death, the date of the last visit of the patient, or December 31, 2021 (the end the of study period). The incidence of a second primary malignant tumor with PTC was calculated for the study groups using standardized incidence ratios. Results: Of the 30 568 patients with a primary PTC included in this study, 2820 (9.2%) developed a second malignant tumor (2204 women and 616 men); the mean (SD) age of all patients at time of the primary cancer was 53.9 (12.6) years and at time of the secondary cancer was 61.5 (11.8) years. Overall, the standardized incidence ratio was statistically significant for all types of cancer (1.25; 95% CI, 1.21-1.30), including solid malignant tumors (1.20; 95% CI, 1.15-1.25) and all leukemias (1.61; 95% CI, 2.17-2.13). Cancers of the digestive system (466 cases [21.1%]), genital organs (376 cases [17.1%]), and breasts (603 cases [27.4%]) were the most prevalent second primary tumors in women following PTC. Second primary tumors of the gastrointestinal tract (146 cases [27.7%]), genitourinary system (139 cases [22.6%]), and urinary tract (139 cases [22.6%]) were the most prevalent in men. Urinary tract cancers (307 cases [10.9%]) and gastrointestinal tumors (612 cases [21.4%]) were the most prevalent second primary tumors overall. Conclusions and Relevance: This cohort study reports the increased incidence of solid secondary tumors in men and women over a 31-year time frame after the Chernobyl disaster. Moreover, there was a statistically significant increased risk of second tumors of the breast, colon, rectum, mesothelium, eye, adnexa, meninges, and adrenal glands as well as Kaposi sarcoma. These data might have an effect on the follow-up of this cohort of patients to detect secondary malignant tumors at an early stage.

Safety and efficacy of LA-ERCP procedure following Roux-en-Y gastric bypass: a systematic review and meta-analysis.

INTRODUCTION: Rapid weight loss following Roux-en-Y gastric bypass surgery (RYGB) translates to an increased need for endoscopic retrograde cholangiopancreatography (ERCP) intervention. Laparoscopically Assisted Transgastric ERCP (LA-ERCP) has emerged to address the issue of accessing the excluded stomach. This study aims to evaluate the safety and efficacy of LA-ERCP procedure following RYGB. METHODS: The Cochrane, EMBASE, SCOPUS, MEDLINE, Daily and Epub databases were searched from inception to May 2022 using the PRISMA guidelines. Eligible studies reported participants older than 18 years who underwent the LA-ERCP procedure, following RYGB, and outcomes of patients. RESULTS: 27 unique studies met the inclusion criteria with 1283 patients undergoing 1303 LA-ERCP procedures. 81.9% of the patients were female and the mean age was 52.18 ± 13.38 years. The rate of concurrent cholecystectomy was 33.6%. 90.9% of procedures were undertaken for a biliary indication. The mean time between RYGB and LA-ERCP was 89.19 months. The most common intervention performed during the LA-ERCP was a sphincterotomy (94.3%). Mean total operative time was 130.48 min. Mean hospital length of stay was 2.697 days. Technical success was 95.3%, while clinical success was 93.8%. 294 complications were recorded with a 20.6% complication rate. The most frequent complications encountered were pancreatitis (6.8%), infection (6.1%), bleeding (3.4%), and perforation (2.5%). Rate of conversion to open laparotomy was 7%. CONCLUSION: This meta-analysis presents preliminary evidence to suggest the safety and efficacy of LA-ERCP procedure following RYGB. Further investigations are warranted to evaluate the long-term efficacy of this procedure using studies with long-term patient follow-up.

Machine learning in pancreas surgery, what is new? literature review.

Background: Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery. Methods: We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included. Results: A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022. Conclusion: The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.

Corrigendum: The development of machine learning in bariatric surgery.

[This corrects the article DOI: 10.3389/fsurg.2023.1102711.].

Gastrointestinal Stromal Tumor (GIST): A Remarkable Case Report and Literature Review.

Gastrointestinal stromal tumor (GIST) makes up less than 1% of all gastrointestinal tumors, but it is the most common mesenchymal tumor of the digestive system. It is commonly found in the stomach and the small intestine and rarely seen in the colon and the esophagus. Additionally, sigmoid GIST is quite rare since colorectal GIST often occurs in the rectum. A total of 21 patients (including the study case) were looked at for this study, of which 14 (66.6%) were males and seven (33.3%) were females. We focused on GIST and conducted an online search and systematic analysis of all case presentations.

The development of machine learning in bariatric surgery.

Background: Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results: A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions: This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.

Robotic colorectal surgery: quality assessment of patient information available on the internet using webscraping.

The primary goal of this study is to assess current patient information available on the internet concerning robotic colorectal surgery. Acquiring this information will aid in patients understanding of robotic colorectal surgery. Data was acquired through a web-scraping algorithm. The algorithm used two Python packages: Beautiful Soup and Selenium. The long-chain keywords incorporated into Google, Bing and Yahoo search engines were 'Da Vinci Colon-Rectal Surgery', 'Colorectal Robotic Surgery' and 'Robotic Bowel Surgery'. 207 websites resulted, were sorted and evaluated according to the ensuring quality information for patients (EQIP) score. Of the 207 websites visited, 49 belonged to the subgroup of hospital websites (23.6%), 46 to medical centers (22.2%), 45 to practitioners (21.7%), 42 to health care systems (20,2%), 11 to news services (5.3%), 7 to web portals (3.3%), 5 to industry (2.4%), and 2 to patient groups (0.9%). Only 52 of the 207 websites received a high rating. The quality of available information on the internet concerning robotic colorectal surgery is low. The majority of information was inaccurate. Medical facilities involved in robotic colorectal surgery, robotic bowel surgery and related robotic procedures should develop websites with credible information to guide patient decisions.

Is pain control for chronic neuropathic pain after inguinal hernia repair using endoscopic retroperitoneal neurectomy effective? A meta-analysis of 142 patients from 1995 to 2022.

PURPOSE: Neuropathic pain is a complication after groin hernia surgery. Triple neurectomy of the iliohypogastric nerve, ilioinguinal nerve and genitofemoral nerve is an efficient treatment modality, with several surgical approaches. The minimally invasive endoscopic method to neurectomy was specifically investigated in this meta-analysis. Our aim is to determine the efficacy of this method in the treatment of chronic neuropathic pain posthernia repair surgery. METHODS: A systematic review was conducted using four databases to search for the keywords ("endoscopic retroperitoneal neurectomy" and "laparoscopic retroperitoneal neurectomy"). The NCBI National Library of Medicine, Cochrane Library, MEDLINE Complete and BioMed Central were last searched on 26 May 2022. Randomised control trials and retrospective or prospective papers involving endoscopic retroperitoneal neurectomy operations after inguinal hernia repair were included. All other surgeries, procedures and study designs were excluded. The internal quality of included studies was assessed using the Newcastle-Ottawa Scale. The percentage of patients who had reduction in pain ("positive treatment outcome") was used to assess the procedure's effectiveness in each analysis. RESULTS: Five comparable endoscopic retroperitoneal neurectomy studies with a total of 142 patients were analysed. Both the Wald test (Q (6) = 1.79, = .775) and the probability ratio test (Q (6) = 4.24, = .374) provide similar findings (0.000, 0.0% [0.0%; 78%]). The meta-analysis' key finding is that the intervention was up to 78% effective (95% confidence interval, 71%; 84%). CONCLUSION: Endoscopic retroperitoneal neurectomy can be an effective treatment option for postoperative neuropathic pain relief following surgical hernia repair. Although there is limited reported experience with this technique, it may provide a clinical benefit to the patient. We recommend further prospective data and long-term follow-up studies be conducted to confirm and expand on these outcomes.

Developing and validating a multivariable prediction model for predicting the cost of colon surgery.

Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%-25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs.

Cost and Quality Comparison of Hernia Surgery in Stationary, Day-Patient and Outpatient Care.

BACKGROUND: Medical progress is increasingly enabling more and more stationary treatment to be provided in the outpatient sector. This development should be welcomed, as healthcare costs have been rising for years. The design of efficient processes and a needs-based infrastructure enable further savings. According to international recommendations (EHS/IEHS), outpatient treatment of unilateral inguinal hernias is recommended. METHOD: Data from patients in GZO Hospital Wetzikon/Zurich between 2019 and 2021 for unilateral inguinal hernia repair was included in this study (n = 234). Any over- or under-coverage correlated with one of the three treatment groups: stationary, partially stationary and patients treated in outpatients clinic. Complications and 30-day readmissions were also monitored. RESULTS: Final revenue for all patients is -95.36 CHF. For stationary treatments, the mean shifts down to -575.01 CHF, for partially stationary treatments the mean shifts up to -24.73 CHF, and for patients in outpatient clinic final revenue is 793.12 CHF. This result is also consistent with the operation times, which are lowest in the outpatient clinic with a mean of 36 min, significantly longer in the partially stationary setting with 58 min, and longest in the stationary setting with 76 min. The same applies to the anesthesia times and the relevant care times by the nurses as the most important cost factors in addition to the supply and allocation costs. CONCLUSIONS: We show that cost-effective elective unilateral inguinal hernia care in the outpatient clinic with profit (mean 793.12 CHF) is possible. Stationary unilateral hernia care (mean -575.01 CHF) is loss-making. Crucial factors for cost efficiency are optimized processes in the operating room (anesthesia, surgical technique and quality, operating time), as well as optimized care processes with minimal preoperative services and care times for the patient. However, at the same time, these optimizations pose a challenge to surgical and anesthesiology training and structures with high levels of preoperative and Postoperative services and pay-as-you-go costs. The complication rate is 0.91% lower than in a comparable study. The readmission within 30 days post-operation results with a positive deviation of -3.53% (stationary) and with a negative deviation of +2.29% (outpatient clinic) compared to a comparative study.

The development of machine learning in lung surgery: A narrative review.

Background: Machine learning reflects an artificial intelligence that allows applications to improve their accuracy to predict outcomes, eliminating the need to conduct explicit programming on them. The medical field has increased its focus on establishing tools for integrating machine learning algorithms in laboratory and clinical settings. Despite their importance, their incorporation is minimal in the medical sector yet. The primary goal of this study is to review the development of machine learning in the field of thoracic surgery, especially lung surgery. Methods: This article used the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA). The sources used to gather data are the PubMed, Cochrane, and CINAHL databases and the Google Scholar search engine. Results: The study included 19 articles, where ten concentrated on the application of machine learning in especially lung surgery, six focused on the benefits and limitations of machine learning algorithms in lung surgery, and three provided an overview of the future of machine learning in lung surgery. Conclusion: The outcome of this study indicates that the field of lung surgery has attempted to integrate machine learning algorithms. However, the implementation rate is low, owing to the newness of the concept and the various challenges it encompasses. Also, this study reveals the absence of sufficient literature discussing the application of machine learning in lung surgery. The necessity for future research on the topic area remains evident.

Suprapubic Cholecystectomy Improves Cosmetic Outcome Compared to Classic Cholecystectomy.

Currently, cholecystectomy is performed laparoscopically. While the conventional approach (CA) with four access ports persists, other methods seek to reduce trauma or to optimize cosmetic results. In this study, the safety and cosmetic outcome of a suprapubic approach (SA) were evaluated. Between 2015 and 2016, patients undergoing elective cholecystectomy either by CA or by a suprapubic approach (SA) at our institution were included. The cosmetic outcome, postoperative morbidity, operative time and length of stay were evaluated. Pictures of the site of intervention were taken 6−12 months postoperatively and rated on a scale from 1 (unsatisfying aesthetic result) to 5 (excellent aesthetic result). Five "non-medical" and five "medical" raters as well as one board-certified plastic surgeon performed the ratings. A total of 70 patients were included (n = 28 SA, n = 42 CA). The two groups did not differ in baseline characteristics (age, gender, BMI). The SA group showed a significantly better aesthetic outcome compared to the CA group 4.8 (4.8−4.9) vs. 4.2 (3.8−4.4), (p > 0.001). Medical raters: 4.0 (3.8−4.2) vs. 4.8 (4.6−5.0), (p < 0.001); non-medical raters: 4.2 (3.8−4.6) vs. 5.0 (4.8−5.0), (p < 0.001); plastic surgeon: 4.0 (4.0−4.0) vs. 5.0 (5.0−5.0), (p < 0.001). Fair interrater consistency was demonstrated with an ICC of 0.47 (95% CI = 0.38−0.57). No significant difference in the complication rate (1 (3.5%) in SA vs. 6 (14%) in CA, (p = 0.3)), or the operating time 66 (50−86) vs. 70 (65−82) min, (p = 0.3), were observed. Patients stayed for a median of three (3−3) days in the SA group and 3 (3−4) days in the CA group (p = 0.08). This study demonstrated that the suprapubic approach is an appropriate alternative to conventional laparoscopic cholecystectomy, presenting a better cosmetic outcome with a similar complication rate.

The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review.

Background: Artificial intelligence simulates human intelligence in machines that have undergone programming to make them think like human beings and imitate their activities. Artificial intelligence has dominated the medical sector to perform various patient diagnosis activities and improve communication between professionals and patients. The main goal of this study is to perform a scoping review to evaluate the development of artificial intelligence in all forms of hernia surgery except the diaphragm and upside-down hernia. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to guide the structuring of the manuscript and fulfill all the requirements of every subheading. The sources used to gather data are the PubMed, Cochrane, and EMBASE databases, IEEE and Google and Google Scholar search engines. AMSTAR tool is the most appropriate for assessing the methodological quality of the included studies. Results: The study exclusively included twenty articles, whereby seven focused on artificial intelligence in inguinal hernia surgery, six focused on abdominal hernia surgery, five on incisional hernia surgery, and two on AI in medical imaging and robotics in hernia surgery. Conclusion: The outcomes of this study reveal a significant literature gap on artificial intelligence in hernia surgery. The results also indicate that studies focus on inguinal hernia surgery more than any other types of hernia surgery since the articles addressing the topic are more. The study implies that more research is necessary for the field to develop and enjoy the benefits associated with AI. Thus, this situation will allow the integration of AI in activities like medical imaging and surgeon training.

Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review.

OBJECTIVE: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on "ML in colorectal surgery", with its viable applications. METHODS: PubMed, Google Scholar, Medline, and Cochrane library were searched. RESULTS: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. CONCLUSIONS: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients' perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients' end. Confidence can only be developed within society if more research with precise results is carried out.

Genomic analysis of focal nodular hyperplasia with associated hepatocellular carcinoma unveils its malignant potential: a case report.

Background: Focal nodular hyperplasia (FNH) is typically considered a benign tumor of the liver without malignant potential. The co-occurrence of FNH and hepatocellular carcinoma (HCC) has been reported in rare cases. In this study we sought to investigate the clonal relationship between these lesions in a patient with FNH-HCC co-occurrence. Methods: A 74-year-old female patient underwent liver tumor resection. The resected nodule was subjected to histologic analyses using hematoxylin and eosin stain and immunohistochemistry. DNA extracted from microdissected FNH and HCC regions was subjected to whole exome sequencing. Clonality analysis were performed using PyClone. Results: Histologic analysis reveals that the nodule consists of an FNH and two adjoining HCC components with distinct histopathological features. Immunophenotypic characterization and genomic analyses suggest that the FNH is clonally related to the HCC components, and is composed of multiple clones at diagnosis, that are likely to have progressed to HCC through clonal selection and/or the acquisition of additional genetic events. Conclusion: To the best of our knowledge, our work is the first study showing a clonal relationship between FNH and HCC. We show that FNH may possess the capability to undergo malignant transformation and to progress to HCC in very rare cases.