Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Safety and efficacy of absorbable and non-absorbable fixation systems for intraperitoneal mesh fixation: an experimental study in swine

PURPOSE

Choice of the best possible fixation system in terms of safety and effectiveness for intraperitoneal mesh placement in hernia surgery remains controversial. The aim of the present study was to compare the performance of four fixation systems in a swine model of intraperitoneal mesh fixation.

METHODS

Fourteen Landrace swine were utilized in the study. The experiment included two stages. Initially, four pieces of mesh (Ventralight ™ ST) sizing 10 × 5 cm were placed and fixed intraperitoneally to reinforce 4 small full thickness abdominal wall defects created with diathermy. These defects were repaired primarily with absorbable suture before mesh implantation. Each mesh was anchored with a different tack device between Absorbatack™, Protack™, Capsure™, or Optifix™. The second stage took place after 60 days and included euthanasia, laparoscopy, and laparotomy via U-shaped incision to obtain the measurements for the outcome parameters. The primary endpoint of the study was to compare the peel strength of the compound tack/mesh from the abdominal wall. Secondary parameters were the extent and quality of visceral adhesions to the mesh, the degree of mesh shrinkage and the histological response around the tacks.

RESULTS

Thirteen out of 14 animals survived the experiment and 10 were included in the final analysis. Capsure™ tacks had higher peel strength when compared to Absorbatack™ (p = 0.028); Protack™ (p = 0.043); and Optifix™ (p = 0.009). No significant differences were noted regarding the extent of visceral adhesions (Friedman's test p value 0.854), the adhesion quality (Friedman's test p value 0.506), or the mesh shrinkage (Friedman's test p value = 0.827). Four out of the ten animals developed no adhesions at all 2 months after implantation.

CONCLUSION

Capsure™ fixation system provided higher peel strength that the other tested devices in our swine model of intraperitoneal mesh fixation. Our findings generate the hypothesis that this type of fixation may be superior in a clinical setting. Clinical trials with long-term follow-up are required to assess the safety and efficacy of mesh fixation systems in hernia surgery.

Liver Transplantation for Pediatric Hepatocellular Carcinoma: A Systematic Review

Simple Summary

Hepatocellular carcinoma is a type of primary liver cancer and the second most common type of liver cancer in children. Although partial hepatectomy can be curative, many children present with tumors that are not amenable to resection and thus the only potentially curative option is liver transplantation. In this systematic review, we have pooled the data from the worldwide literature and showed that survival after liver transplantation for pediatric hepatocellular carcinoma is favorable and many children do well even if their tumors exceed certain potentially restrictive criteria originally developed to select adults with hepatocellular carcinoma for liver transplantation.

Abstract

Liver transplantation (LT) is the only potentially curative option for children with unresectable hepatocellular carcinoma (HCC). We performed a systematic review of the MEDLINE, Scopus, Cochrane Library, and Web of Science databases (end-of-search date: 31 July 2020). Our outcomes were overall survival (OS) and disease-free survival (DFS). We evaluated the effect of clinically relevant variables on outcomes using the Kaplan–Meier method and log-rank test. Sixty-seven studies reporting on 245 children undergoing LT for HCC were included. DFS data were available for 150 patients and the 1-, 3-, and 5-year DFS rates were 92.3%, 89.1%, and 84.5%, respectively. Sixty of the two hundred and thirty-eight patients (25.2%) died over a mean follow up of 46.8 ± 47.4 months. OS data were available for 222 patients and the 1-, 3-, and 5-year OS rates were 87.9%, 78.8%, and 74.3%, respectively. Although no difference was observed between children transplanted within vs. beyond Milan criteria (p = 0.15), superior OS was observed in children transplanted within vs. beyond UCSF criteria (p = 0.02). LT can yield favorable outcomes for pediatric HCC beyond Milan but not beyond UCSF criteria. Further research is required to determine appropriate LT selection criteria for pediatric HCC.

The process of estimating the cost of surgery: Providing a practical framework for surgeons

Over the last decades, health care costs have been increasing at an alarming, exponential rate which is considered unsustainable. Surgical care utilizes one-third of health care costs. Estimating, evaluating, and understanding the cost of surgery is a vital step towards cost management and reduction. Current cost estimation studies and cost-effectiveness studies have vast disparities in their methodology, with published costs of Operating Room varying from as low as $7 and as high as $113 per minute. Costs in surgery are distinguished as direct and indirect. Allocation of direct costs involves identification, measurement, and valuation processes. Allocation of indirect costs involves the allocation of capital and overhead costs and of indirect department costs. Annualised capital costs and overhead hospital costs are then allocated to surgery by either the cost-centre allocation or the activity-based allocation frameworks. Indirect department costs are allocated to a specific surgery by weighted service allocation or hourly rate allocation or inpatient day allocation, or marginal markup allocation. The growing societal, financial and political pressure for cost reduction has brought cost analysis to the forefront of healthcare discussions. Thus, we believe that almost every single surgeon will eventually enter the field of healthcare economics by necessity. This review aims to provide surgeons with a practical framework for engaging in cost estimation studies.

Role of liver transplantation in the management of colorectal liver metastases: Challenges and opportunities

The liver is the most common site of colorectal cancer metastasis. Complete resection of the metastatic tumor is currently the only treatment modality available with a potential for cure. However, only 20% of colorectal liver metastases (CRLM) are considered resectable at the time of presentation. Liver transplantation (LT) has been proposed as an alternative oncologic treatment for patients with unresectable CRLM. This review summarizes the published experiences of LT in the setting of unresectable CRLM from the previous decades and discusses the challenges and future horizons in the field. Contemporary experiences that come mostly from countries with broader access to liver grafts are also explored and their promising findings in terms of overall survival (OS) and disease-free survival (DFS) are outlined along with their study design and methods. The rationale of establishing specific patient selection criteria and the dilemmas around immunosuppressive regimens in patients undergoing LT for CRLM are also highlighted. Additionally, this review describes the findings of studies comparing LT vs chemotherapy alone and LT vs portal vein embolization plus resection for CRLM in terms of OS and DFS. Last but not least, we present current perspectives and ongoing prospective trials that try to elucidate the role of LT for CRLM.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint^TM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Death following pulmonary complications of surgery before and during the SARS-CoV-2 pandemic

BACKGROUND

This study aimed to determine the impact of pulmonary complications on death after surgery both before and during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic.

METHODS

This was a patient-level, comparative analysis of two, international prospective cohort studies: one before the pandemic (January-October 2019) and the second during the SARS-CoV-2 pandemic (local emergence of COVID-19 up to 19 April 2020). Both included patients undergoing elective resection of an intra-abdominal cancer with curative intent across five surgical oncology disciplines. Patient selection and rates of 30-day postoperative pulmonary complications were compared. The primary outcome was 30-day postoperative mortality. Mediation analysis using a natural-effects model was used to estimate the proportion of deaths during the pandemic attributable to SARS-CoV-2 infection.

RESULTS

This study included 7402 patients from 50 countries; 3031 (40.9 per cent) underwent surgery before and 4371 (59.1 per cent) during the pandemic. Overall, 4.3 per cent (187 of 4371) developed postoperative SARS-CoV-2 in the pandemic cohort. The pulmonary complication rate was similar (7.1 per cent (216 of 3031) versus 6.3 per cent (274 of 4371); P = 0.158) but the mortality rate was significantly higher (0.7 per cent (20 of 3031) versus 2.0 per cent (87 of 4371); P < 0.001) among patients who had surgery during the pandemic. The adjusted odds of death were higher during than before the pandemic (odds ratio (OR) 2.72, 95 per cent c.i. 1.58 to 4.67; P < 0.001). In mediation analysis, 54.8 per cent of excess postoperative deaths during the pandemic were estimated to be attributable to SARS-CoV-2 (OR 1.73, 1.40 to 2.13; P < 0.001).

CONCLUSION

Although providers may have selected patients with a lower risk profile for surgery during the pandemic, this did not mitigate the likelihood of death through SARS-CoV-2 infection. Care providers must act urgently to protect surgical patients from SARS-CoV-2 infection.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.

Effect of COVID-19 pandemic lockdowns on planned cancer surgery for 15 tumour types in 61 countries: an international, prospective, cohort study

BACKGROUND

Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19 outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restriction.

METHODS

This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in 61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19 Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the period they awaited surgery, and classified into light restrictions (index <20), moderate lockdowns (20-60), and full lockdowns (>60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government response index groups. This study was registered at ClinicalTrials.gov, NCT04384926.

FINDINGS

Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up of 23 weeks (IQR 16-30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted hazard ratio [HR] 0·81, 95% CI 0·77-0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51, 0·50-0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate lockdowns (HR 0·84, 95% CI 0·80-0·88; p<0·001), and full lockdowns (0·57, 0·54-0·60; p<0·001), remained independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in moderate lockdowns, 2001 [23·8%] of 11 827 in full lockdowns), although there were no differences in resectability rates observed with longer delays.

INTERPRETATION

Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience of elective surgery systems requires strengthening, which might include protected elective surgical pathways and long-term investment in surge capacity for acute care during public health emergencies to protect elective staff and services.

FUNDING

National Institute for Health Research Global Health Research Unit, Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, Medtronic, Sarcoma UK, The Urology Foundation, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Management of biliary atresia: To transplant or not to transplant

Kasai procedure (KP) and liver transplantation (LT) represent the only therapeutic options for patients with biliary atresia (BA), the most common indication for LT in the pediatric population. However, KP represents by no means a radical option but rather a bridging one, as nearly all patients will finally require a liver graft. More and more experts in the field of transplant surgery propose that maybe it is time for a paradigm change in BA treatment and abandon KP as transplantation seems inevitable. Inadequacy of organs yet makes this option currently not feasible, so it seems useful to find ways to maximize the efficacy of KP. In previous decades, multiple studies tried to identify these factors which opt for better results, but in general, outcomes of KP have not improved to the level that was anticipated. This review provides the framework of conditions which favor native liver survival after KP and the ones which optimize a positive LT outcome. Strategies of transition of care at the right time are also presented, as transplantation plays a key role in the surgical treatment of BA. Future studies and further organization in the transplant field will allow for greater organ availability and better outcomes to be achieved for BA patients.

In vivo Simulation-Based Learning for Undergraduate Medical Students: Teaching and Assessment

An increasing emphasis on simulation has become evident in the last three decades following fundamental shifts in the medical profession. Simulation-based learning (SBL) is a wide term that encompasses several means for imitating a skill, attitude, or procedure to train personnel in a safe and adaptive environment. A classic example has been the use of live animal tissue, named in vivo SBL. We aimed to review all published evidence on in vivo SBL for undergraduate medical students; this includes both teaching concepts as well as focused assessment of students on those concepts. We performed a systematic review of published evidence on MEDLINE. We also incorporated evidence from a series of systematic reviews (eviCORE) focused on undergraduate education which have been outputs from our dedicated research network (eMERG). In vivo SBL has been shown to be valuable at undergraduate level and should be considered as a potential educational tool. Strict adherence to 3R (Reduce, Refine, Replace) principles in order to reduce animal tissue usage, should always be the basis of any curriculum. In vivo SBL could potentially grant an extra mile towards medical students' inspiration and aspiration to become safe surgeons; however, it should be optimised and supported by a well-designed curriculum which enhances learning via multi-level fidelity SBL.

SARS-CoV-2 infection and venous thromboembolism after surgery: an international prospective cohort study

SARS‐CoV‐2 has been associated with an increased rate of venous thromboembolism in critically ill patients. Since surgical patients are already at higher risk of venous thromboembolism than general populations, this study aimed to determine if patients with peri‐operative or prior SARS‐CoV‐2 were at further increased risk of venous thromboembolism. We conducted a planned sub‐study and analysis from an international, multicentre, prospective cohort study of elective and emergency patients undergoing surgery during October 2020. Patients from all surgical specialties were included. The primary outcome measure was venous thromboembolism (pulmonary embolism or deep vein thrombosis) within 30 days of surgery. SARS‐CoV‐2 diagnosis was defined as peri‐operative (7 days before to 30 days after surgery); recent (1–6 weeks before surgery); previous (≥7 weeks before surgery); or none. Information on prophylaxis regimens or pre‐operative anti‐coagulation for baseline comorbidities was not available. Postoperative venous thromboembolism rate was 0.5% (666/123,591) in patients without SARS‐CoV‐2; 2.2% (50/2317) in patients with peri‐operative SARS‐CoV‐2; 1.6% (15/953) in patients with recent SARS‐CoV‐2; and 1.0% (11/1148) in patients with previous SARS‐CoV‐2. After adjustment for confounding factors, patients with peri‐operative (adjusted odds ratio 1.5 (95%CI 1.1–2.0)) and recent SARS‐CoV‐2 (1.9 (95%CI 1.2–3.3)) remained at higher risk of venous thromboembolism, with a borderline finding in previous SARS‐CoV‐2 (1.7 (95%CI 0.9–3.0)). Overall, venous thromboembolism was independently associated with 30‐day mortality (5.4 (95%CI 4.3–6.7)). In patients with SARS‐CoV‐2, mortality without venous thromboembolism was 7.4% (319/4342) and with venous thromboembolism was 40.8% (31/76). Patients undergoing surgery with peri‐operative or recent SARS‐CoV‐2 appear to be at increased risk of postoperative venous thromboembolism compared with patients with no history of SARS‐CoV‐2 infection. Optimal venous thromboembolism prophylaxis and treatment are unknown in this cohort of patients, and these data should be interpreted accordingly.

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

BACKGROUND

COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation.

METHODOLOGY

The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT.

RESULTS

Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image.

CONCLUSIONS

The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Validation of the Emergency Surgery Score (ESS) in a Greek patient population: a prospective bi-institutional cohort study

Purpose

The Emergency Surgery Score (ESS) is a reliable point-based score that predicts mortality and morbidity in emergency surgery patients. However, it has been validated only in the U.S. patients. We aimed to prospectively validate ESS in a Greek patient population.

Methods

All patients who underwent an emergent laparotomy were prospectively included over a 15-month period. A systematic chart review was performed to collect relevant preoperative, intraoperative, and postoperative variables based on which the ESS was calculated for each patient. The relationship between ESS and 30-day mortality, morbidity (i.e., the occurrence of at least one complication), and the need for intensive care unit (ICU) admission was evaluated and compared between the Greek and U.S. patients using the c-statistics methodology. The study was registered on "Research Registry" with the unique identifying number 5901.

Results

A total of 214 patients (102 Greek) were included. The mean age was 64 years, 44% were female, and the median ESS was 7. The most common indication for surgery was hollow viscus perforation (25%). The ESS reliably and incrementally predicted mortality (c-statistics = 0.79 [95% CI 0.67–0.90] and 0.83 [95% CI 0.74–0.92]), morbidity (c-statistics = 0.83 [95% CI 0.76–0.91] and 0.79 [95% CI 0.69–0.88]), and ICU admission (c-statistics = 0.88 [95% CI 0.81–0.96] and 0.84 [95% CI 0.77–0.91]) in both Greek and U.S. patients.

Conclusion

The correlation between the ESS and the surgical outcomes was statistically significant in both Greek and U.S. patients undergoing emergency laparotomy. ESS could prove globally useful for preoperative patient counseling and quality-of-care benchmarking.

Head and neck cancer surgery during the COVID-19 pandemic: An international, multicenter, observational cohort study

BACKGROUND

The aims of this study were to provide data on the safety of head and neck cancer surgery currently being undertaken during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

This international, observational cohort study comprised 1137 consecutive patients with head and neck cancer undergoing primary surgery with curative intent in 26 countries. Factors associated with severe pulmonary complications in COVID-19-positive patients and infections in the surgical team were determined by univariate analysis.

RESULTS

Among the 1137 patients, the commonest sites were the oral cavity (38%) and the thyroid (21%). For oropharynx and larynx tumors, nonsurgical therapy was favored in most cases. There was evidence of surgical de-escalation of neck management and reconstruction. Overall 30-day mortality was 1.2%. Twenty-nine patients (3%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 30 days of surgery; 13 of these patients (44.8%) developed severe respiratory complications, and 3.51 (10.3%) died. There were significant correlations with an advanced tumor stage and admission to critical care. Members of the surgical team tested positive within 30 days of surgery in 40 cases (3%). There were significant associations with operations in which the patients also tested positive for SARS-CoV-2 within 30 days, with a high community incidence of SARS-CoV-2, with screened patients, with oral tumor sites, and with tracheostomy.

CONCLUSIONS

Head and neck cancer surgery in the COVID-19 era appears safe even when surgery is prolonged and complex. The overlap in COVID-19 between patients and members of the surgical team raises the suspicion of failures in cross-infection measures or the use of personal protective equipment.

LAY SUMMARY

Head and neck surgery is safe for patients during the coronavirus disease 2019 pandemic even when it is lengthy and complex. This is significant because concerns over patient safety raised in many guidelines appear not to be reflected by outcomes, even for those who have other serious illnesses or require complex reconstructions. Patients subjected to suboptimal or nonstandard treatments should be carefully followed up to optimize their cancer outcomes. The overlap between patients and surgeons testing positive for severe acute respiratory syndrome coronavirus 2 is notable and emphasizes the need for fastidious cross-infection controls and effective personal protective equipment.

Laparoscopic versus open hepatectomy for intrahepatic cholangiocarcinoma: An individual patient data survival meta-analysis

BACKGROUND

We compared the outcomes of laparoscopic hepatectomy (LH) vs. open hepatectomy (OH) for intrahepatic cholangiocarcinoma (iCCA).

METHODS

A systematic review of the MEDLINE, EMBASE, Scopus, and Cochrane Library databases was performed using PRISMA guidelines (end-of-search date: 08-June-2020). Individual patient data on overall survival (OS) and recurrence-free survival (RFS) were extracted. Random-effects meta-analyses, and one- and two-stage survival analyses were conducted.

RESULTS

Eight retrospective cohort studies comparing LH (n = 544) vs. OH (n = 2256) were identified. LH demonstrated lower overall complication (Risk ratio [RR] = 0.64, 95% confidence interval [CI]: 0.46-0.90; p = 0.01), surgical lymphadenectomy (RR = 0.74, 95% CI: 0.58-0.93; p = 0.01) and margin-positive resection (RR = 0.78, 95% CI: 0.62-0.99; p = 0.04) rates, and higher recurrence-free rate (RR = 1.24, 95% CI: 1.01-1.51; p = 0.04) vs. OH. In Cox regression, no difference was observed regarding OS (Hazard Ratio [HR] = 1.11, 95% CI: 0.65-1.91; p = 0.70) and RFS (HR = 1.19, 95% CI: 0.74-1.90; p = 0.47).

CONCLUSION

The use of LH should be considered when feasible in well-selected iCCA patients by hepatobiliary surgeons with experience in minimally-invasive surgery.

Economic analysis of open versus laparoscopic versus robotic hepatectomy: a systematic review and meta-analysis

BACKGROUND

Following the publication of reports from landmark international consensuses (Louisville 2008 and Morioka 2014), minimally invasive hepatectomy became widely accepted as a legitimate alternative to open surgery. We aimed to compare the operative, hospitalization, and total economic costs of open (OLR) vs. laparoscopic (LLR) vs. robotic liver resection (RLR).

METHODS

We performed a systematic literature review (end-of-search date: July 3, 2020) according to the PRISMA statement. Random-effects meta-analyses were conducted. Quality assessment was performed with the Cochrane Risk of Bias tool for randomized controlled trials, and the Newcastle-Ottawa Scale for non-randomized studies.

RESULTS

Thirty-eight studies reporting on 3847 patients (1783 OLR; 1674 LLR; 390 RLR) were included. The operative costs of LLR were significantly higher than those of OLR, while subgroup analysis also showed higher operative costs in the LLR group for major hepatectomy, but no statistically significant difference for minor hepatectomy. Hospitalization costs were significantly lower in the LLR group, with subgroup analyses indicating lower costs for LLR in both major and minor hepatectomy series. No statistically significant difference was observed regarding total costs between LLR and OLR both overall and on subgroup analyses in either major or minor hepatectomy series. Meta-analyses showed higher operative, hospitalization, and total costs for RLR vs. LLR, but no statistically significant difference regarding total costs for RLR vs. OLR.

CONCLUSION

LLR's higher operative costs are offset by lower hospitalization costs compared to OLR leading to no statistically significant difference in total costs, while RLR appears to be a more expensive alternative approach.