Impact of COVID-19 on the management of hepatocellular carcinoma in a high-prevalence area: What's new 12 months later?

INTRODUCTION AND OBJECTIVES: The lockdown policy introduced in 2020 to minimize the spread of the COVID-19 pandemic, significantly affected the management and care of patients affected by hepatocellular carcinoma (HCC). The aim of this follow-up study was to determine the 12 months impact of the COVID-19 pandemic on the cohort of patients affected by HCC during the lockdown, within six French academic referral centers in the metropolitan area of Paris. MATERIALS AND METHODS: We performed a 12 months follow-up of the cross-sectional study cohort included in 2020 on the management of patients affected by HCC during the first six weeks of the COVID-19 pandemic (exposed), compared to the same period in 2019 (unexposed). Overall survival were compared between the groups. Predictors of mortality were analysed with Cox regression. RESULTS: From the initial cohort, 575 patients were included (n = 263 Exposed_COVID, n = 312 Unexposed_COVID). Overall and disease free survival at 12 months were 59.9 ± 3.2% vs 74.3 ± 2.5% (p<0.001) and 40.2 ± 3.5% vs 63.5 ± 3.1% (p<0.001) according to the period of exposure (Exposed_COVID vs Unexposed_COVID, respectively). Adjusted Cox regression revealed that the period of exposure (Exposed_COVID HR: 1.79, 95%CI (1.36, 2.35) p<0.001) and BCLC stage B, C and D (BCLC B HR: 1.82, 95%CI (1.07, 3.08) p = 0.027 - BCLC C HR: 1.96, 95%CI (1.14, 3.38) p = 0.015 - BCLC D HR: 3.21, 95%CI (1.76, 5.85) p<0.001) were predictors of death. CONCLUSIONS: Disruption of routine healthcare services because of the pandemic translated to reduced 1 year overall and disease-free survival among patients affected by HCC, in the metropolitan area of Paris, France.

Impact of COVID-19 on the management of hepatocellular carcinoma in a high-prevalence area.

BACKGROUND & AIMS: Patients affected by hepatocellular carcinoma (HCC) represent a vulnerable population during the COVID-19 pandemic and may suffer from altered allocation of healthcare resources. The aim of this study was to determine the impact of the COVID-19 pandemic on the management of patients with HCC within 6 referral centres in the metropolitan area of Paris, France. METHODS: We performed a multicentre, retrospective, cross-sectional study on the management of patients with HCC during the first 6 weeks of the COVID-19 pandemic (exposed group), compared with the same period in 2019 (unexposed group). We included all patients discussed in multidisciplinary tumour board (MTB) meetings and/or patients undergoing a radiological or surgical programmed procedure during the study period, with curative or palliative intent. Endpoints were the number of patients with a modification in the treatment strategy, or a delay in decision-to-treat. RESULTS: After screening, n = 670 patients were included (n = 293 exposed to COVID, n = 377 unexposed to COVID). Fewer patients with HCC presented to the MTB in 2020 (p = 0.034) and fewer had a first diagnosis of HCC (n = 104 exposed to COVID, n = 143 unexposed to COVID, p = 0.083). Treatment strategy was modified in 13.1% of patients, with no differences between the 2 periods. Nevertheless, 21.5% vs. 9.5% of patients experienced a treatment delay longer than 1 month in 2020 compared with 2019 (p <0.001). In 2020, 7.1% (21/293) of patients had a diagnosis of an active COVID-19 infection: 11 (52.4%) patients were hospitalised and 4 (19.1%) patients died. CONCLUSIONS: In a metropolitan area highly impacted by the COVID-19 pandemic, we observed fewer patients with HCC, and similar rates of treatment modification, but with a significantly longer treatment delay in 2020 vs. 2019. LAY SUMMARY: During the coronavirus disease 2019 (COVID-19) pandemic era, fewer patients with hepatocellular carcinoma (HCC) presented to the multidisciplinary tumour board, especially with a first diagnosis of HCC. Patients with HCC had a treatment delay that was longer in the COVID-19 period than in 2019.

Use of Artificial Intelligence as an Innovative Method for Liver Graft Macrosteatosis Assessment.

The worldwide implementation of a liver graft pool using marginal livers (ie, grafts with a high risk of technical complications and impaired function or with a risk of transmitting infection or malignancy to the recipient) has led to a growing interest in developing methods for accurate evaluation of graft quality. Liver steatosis is associated with a higher risk of primary nonfunction, early graft dysfunction, and poor graft survival rate. The present study aimed to analyze the value of artificial intelligence (AI) in the assessment of liver steatosis during procurement compared with liver biopsy evaluation. A total of 117 consecutive liver grafts from brain-dead donors were included and classified into 2 cohorts: ≥30 versus <30% hepatic steatosis. AI analysis required the presence of an intraoperative smartphone liver picture as well as a graft biopsy and donor data. First, a new algorithm arising from current visual recognition methods was developed, trained, and validated to obtain automatic liver graft segmentation from smartphone images. Second, a fully automated texture analysis and classification of the liver graft was performed by machine-learning algorithms. Automatic liver graft segmentation from smartphone images achieved an accuracy (Acc) of 98%, whereas the analysis of the liver graft features (cropped picture and donor data) showed an Acc of 89% in graft classification (≥30 versus <30%). This study demonstrates that AI has the potential to assess steatosis in a handy and noninvasive way to reliably identify potential nontransplantable liver grafts and to avoid improper graft utilization.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

PURPOSE: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. METHODS: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. RESULTS: With the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. CONCLUSIONS: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.

Testing feasibility of an accurate microscopic assessment of macrovesicular steatosis in liver allograft biopsies by smartphone add-on lenses.

Light microscopy is an essential tool in histological examination of tissue samples. However, the required equipment for a correct and rapid diagnosis is sometimes unavailable. Smartphones and mobile phone networks are widespread, and could be used for diagnostic imaging and telemedicine. Macrovesicular steatosis (MS) is a major risk factor for liver graft failure, and is only assessable by microscopic examination of a frozen tissue section. The aim of this study was to compare the microscopic assessment of MS in liver allograft biopsies by a smartphone with eyepiece adaptor (BLIPS device) to standard light microscopy. Forty liver graft biopsies were evaluated in transmitted light, using an Iphone 5s and 4 different mini-objective, add-on lenses. A significant correlation was reported between the two different approaches for graft MS assessment (Spearman's correlation coefficient: rs = 0.946; p < .001). Smartphone with eyepiece adaptor had similar discriminatory power to identify MS in liver grafts than standard light microscopy. Based on these findings, a smartphone integrated with a low-cost eyepiece adaptor can achieve adequate accuracy in the assessment of MS in liver graft, and could be used as an alternative to standard light microscope when unavailable.