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Background: Precision surgery for liver tumors favors laparoscopic anatomical liver resection (LALR), involving the removal of specific liver segments or subsegments. Indocyanine green (ICG)-negative staining is a commonly used method for defining resection boundaries but may be prone to failure. The challenge arises when ICG staining fails, as it cannot be repeated during surgery. In this study, we employed the virtual liver segment projection (VLSP) technology as a salvage approach for precise boundary determination. Our aim was to assess the feasibility of the VLSP to be used for the determination of the boundaries of the liver resection in this situation. Methods: Between January 2021 and June 2023, 12 consecutive patients undergoing subsegment-oriented LALR were included in this pilot series. The VLSP technology was utilized to define the resection boundaries at the time of ICG-negative staining failure. Routine surgical parameters and short-term outcomes were evaluated to assess the safety of VLSP in this procedure. In addition, its feasibility was assessed by analyzing the accuracy between the predicted resected liver volume (PRLV) and actual resected liver volume (ARLV). Results: Of the 12 enrolled patients, the mean operation time was 444.58 ± 101.70 min (range 290–570 min), with a mean blood loss of 125.00 ± 96.53 ml (range 50–400 mL). One patient (8.3%) was converted to laparotomy for subsequent parenchymal transection, four (33.3%) received blood transfusions and four (33.3%) had postoperative complications. All patients received an R0 resection. The Pearson correlation coefficient (r) between PRLV and ARLV was 0.98 (R2 = 0.96, p < 0.05), and the relative error (RE) was 8.62 ± 6.66% in the 12 patients, indicating agreement. Conclusion: Failure of intraoperative ICG-negative staining during subsegment-oriented LALR is possible, and VLSP may be an alternative to define the resection boundaries in such cases. [ABSTRACT FROM AUTHOR]

Anatomical variations of the right hepatic vein, especially large variant right hepatic veins (≥5 mm), have important clinical implications in liver transplantation and resection. This study aimed to evaluate anatomical variations of the right hepatic vein using quantitative three‐dimensional visualization analysis. Computed tomography images of 650 patients were retrospectively analyzed, and three‐dimensional visualization was applied using the derived data to analyze large variant right hepatic veins. The proportion of the large variant right hepatic vein was 16.92% (110/650). According to the location and number of the variant right hepatic veins, the configuration of the right hepatic venous system was divided into seven subtypes. The length of the retrohepatic inferior vena cava had a positive correlation with the diameter of the right hepatic vein (rs = 0.266, p = 0.001) and the variant right hepatic veins (rs = 0.211, p = 0.027). The diameter of the right hepatic vein was positively correlated with that of the middle hepatic vein (rs = 0.361, p < 0.001), while it was inversely correlated with that of the variant right hepatic veins (rs = −0.267, p = 0.005). The right hepatic vein diameter was positively correlated with the drainage volume (rs = 0.489, p < 0.001), while the correlation with the variant right hepatic veins drainage volume was negative (rs = −0.460, p < 0.001). The number of the variant right hepatic veins and their relative diameters were positively correlated (p < 0.001). The volume and percentage of the drainage area of the right hepatic vein decreased significantly as the number of the variant right hepatic vein increased (p < 0.001). The findings of this study concerning the variations of the hepatic venous system may be useful for the surgical planning of liver resection or transplantation. [ABSTRACT FROM AUTHOR]

Background: Most existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions, different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study. Methods: The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3) One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder. Results: Model trained on different datasets has different segmentation performance. The prediction accuracy between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets. Conclusions: (1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets. [ABSTRACT FROM AUTHOR]

Effective and noninvasive diagnosis and prompt treatment of early-stage hepatocellular carcinoma (HCC) are urgently needed to reduce its mortality rate. Herein, the integration of high-resolution diagnostic second near-infrared (NIR-II) photoacoustic computed tomography (PACT) and imaging-guided targeted photothermal ablation of orthotopic small HCC (SHCC) is presented for the first time, which was enabled by a plasmonic platinum (Pt)-doped polydopamine melanin-mimic nanoagent. As designed, an antibody-modified nanoagent (designated Pt@PDA-c) with a plasmonic blackbody-like NIR absorption and superior photothermal conversion efficiency (71.3%) selectively targeted and killed CXCR4-overexpressing HCC (HepG2) cells, which was validated in in vitro experiments. The targeted accumulation properties of Pt@PDA-c in vivo were previously recognized by demonstrating effective NIR-II PA imaging and photothermal ablation in a subcutaneous HCC mouse model. Subsequently, with real-time quantitative guidance by PACT for the accurate diagnosis of intraabdominal SHCC (approximately 4 mm depth), the effective and noninvasive photothermal ablation of SHCCs was successfully demonstrated in an orthotopic tumor-bearing mouse model without damaging adjacent liver tissues. These results show a great potential of NIR-II PACT-guided noninvasive photothermal therapy as an innovative phototheranostic approach and expand the biomedical applications of melanin-mimic materials. In this paper, we report the first diagnostic NIR-II photoacoustic computed tomography (PACT)-guided noninvasive photothermal ablation of small hepatocellular carcinoma (SHCC) located in deep tissues in orthotopic tumor-bearing mice; this process is empowered by a polydopamine-based melanin-mimic tumor-targeting nanoagent doped with plasmonic platinum that provides superior NIR-II (1064 nm) absorption and photothermal conversion efficiency of 71.3%. Following surface modification with anti-CXCR4 antibodies, the nanoagent (namely Pt@PDA-c) can selectively target CXCR4-overexpressed HepG2 carcinoma cells and tumor lesions, and serve as the theranostic agent for both NIR-II PACT-based diagnosis of orthotopic SHCC (diameter less than 5 mm) and efficient NIR-II PTT in vivo. This study may also extend the potential of melanin-derived blackbody materials for optical-biomedical and water distillation applications. [Display omitted] [ABSTRACT FROM AUTHOR]