Endoscopic ultrasound-guided tissue acquisition for focal liver lesions in patients with a history of multiple primary malignant neoplasms.

Objective: This study aimed to investigate the usefulness of endoscopic ultrasound-guided tissue acquisition (EUS-TA) for diagnosing focal liver lesions in patients with a history of multiple primary malignant neoplasms. Methods: Among patients who underwent EUS-TA for focal liver lesions between 2016 and 2022, those with a history of multiple malignant neoplasms were included. A histologically confirmed malignant tumor within the past 5 years before EUS-TA was defined as a history of malignant neoplasm. The primary outcomes were diagnostic ability and adverse events of EUS-TA. Results: This study included 16 patients (median age, 73 [33-90] years), the median tumor size was 32 (6-51) mm, 14 had a history of double malignant neoplasms, whereas two had triple malignant neoplasms. Malignant neoplasms were detected histologically or cytologically in all cases. Immunohistochemistry was performed in 75% (12/16), and the final diagnosis of EUS-TA was metastatic liver tumor in 12 patients, and primary malignant liver tumor in four patients. The primary site could be identified in 11 of 12 metastatic tumor cases. The diagnostic yield of EUS-TA was 100% (16/16) for differentiating benign and malignant tumors and 94% (15/16) for confirming the histological type including the primary site of metastatic lesions. No adverse events were associated with the procedure. Conclusion: EUS-TA is a useful diagnostic modality for focal liver lesions in patients with a history of multiple malignant neoplasms, allowing for the differential diagnosis of primary and metastatic tumors and identification of the primary site of metastatic lesions.

Comparison of laparoscopic liver resection for the ventral versus the dorsal areas of segment 8.

PURPOSE: The technical difficulties of laparoscopic liver resection (LLR) are greatly associated with the location of liver tumors. Since segment 8 (S8) contains a wide area, the difficulty of LLR for S8 tumors may vary depending on the location within the segment, such as the ventral (S8v) and dorsal (S8d) area, but the difference is unclear. METHODS: We retrospectively investigated 30 patients who underwent primary laparoscopic partial liver resection for liver tumors in S8 at Kobe University Hospital between January 2018 and June 2023. RESULTS: Thirteen and 17 patients underwent LLR for S8v and S8d, respectively. The operation time was significantly longer (S8v 203[135-259] vs. S8d 261[186-415] min, P = 0.002) and the amount of blood loss was significantly higher (10[10-150] vs. 10[10-200] mL, P = 0.034) in the S8d group than in the S8v group. No significant differences were observed in postoperative complications or postoperative length of hospital stay. Additionally, intraoperative findings revealed that the rate at which the case performed partial liver mobilization in the S8d group was higher (2[15.4%] vs. 8[47.1%], P = 0.060) and the median parenchymal transection time of the S8d group was longer (102[27-148] vs. 129[37-175] min, P = 0.097) than those in the S8v group, but there were no significant differences. CONCLUSION: The safety of LLR for the S8d was comparable to that of LLR for S8v, although LLR for S8d resulted in longer operative time and more blood loss. THE TRIAL REGISTRATION NUMBER: B230165 (approved at December 26, 2023).

Ultrasomics in liver cancer: Developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound.

BACKGROUND: Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) represent the predominant histological types of primary liver cancer, comprising over 99% of cases. Given their differing biological behaviors, prognoses, and treatment strategies, accurately differentiating between HCC and ICC is crucial for effective clinical management. Radiomics, an emerging image processing technology, can automatically extract various quantitative image features that may elude the human eye. Reports on the application of ultrasound (US)-based radiomics methods in distinguishing HCC from ICC are limited. AIM: To develop and validate an ultrasomics model to accurately differentiate between HCC and ICC. METHODS: In our retrospective study, we included a total of 280 patients who were diagnosed with ICC (n = 140) and HCC (n = 140) between 1999 and 2019. These patients were divided into training (n = 224) and testing (n = 56) groups for analysis. US images and relevant clinical characteristics were collected. We utilized the XGBoost method to extract and select radiomics features and further employed a random forest algorithm to establish ultrasomics models. We compared the diagnostic performances of these ultrasomics models with that of radiologists. RESULTS: Four distinct ultrasomics models were constructed, with the number of selected features varying between models: 13 features for the US model; 15 for the contrast-enhanced ultrasound (CEUS) model; 13 for the combined US + CEUS model; and 21 for the US + CEUS + clinical data model. The US + CEUS + clinical data model yielded the highest area under the receiver operating characteristic curve (AUC) among all models, achieving an AUC of 0.973 in the validation cohort and 0.971 in the test cohort. This performance exceeded even the most experienced radiologist (AUC = 0.964). The AUC for the US + CEUS model (training cohort AUC = 0.964, test cohort AUC = 0.955) was significantly higher than that of the US model alone (training cohort AUC = 0.822, test cohort AUC = 0.816). This finding underscored the significant benefit of incorporating CEUS information in accurately distinguishing ICC from HCC. CONCLUSION: We developed a radiomics diagnostic model based on CEUS images capable of quickly distinguishing HCC from ICC, which outperformed experienced radiologists.

Attention-guided context asymmetric fusion networks for the liver tumor segmentation of computed tomography images.

Background: Liver tumor segmentation based on medical imaging is playing an increasingly important role in liver tumor research and individualized therapeutic decision-making. However, it remains a challenging in terms of the accuracy of automatic segmentation of liver tumors. Therefore, we aimed to develop a novel deep neural network for improving the results from the automatic segmentation of liver tumors. Methods: This paper proposes the attention-guided context asymmetric fusion network (AGCAF-Net), combining attention guidance and fusion context modules on the basis of a residual neural network for the automatic segmentation of liver tumors. According to the attention-guided context block (AGCB), the feature map is first divided into multiple small blocks, the local correlation between features is calculated, and then the global nonlocal fusion module (GNFM) is used to obtain the global information between pixels. Additionally, the context pyramid module (CPM) and asymmetric semantic fusion module (AFM) are used to obtain multiscale features and resolve the feature mismatch during feature fusion, respectively. Finally, we used the liver tumor segmentation benchmark (LiTS) dataset to verify the efficiency of our designed network. Results: Our results showed that AGCAF-Net with AFM and CPM is effective in improving the accuracy of liver tumor segmentation, with the Dice coefficient increasing from 82.5% to 84.1%. The segmentation results of liver tumors by AGCAF-Net were superior to those of several state-of-the-art U-net methods, with a Dice coefficient of 84.1%, a sensitivity of 91.7%, and an average symmetric surface distance of 3.52. Conclusions: AGCAF-Net can obtain better matched and accurate segmentation in liver tumor segmentation, thus effectively improving the accuracy of liver tumor segmentation.

Unmasking Carcinoid Syndrome in a Chronic Obstructive Pulmonary Disease (COPD) Patient: A Rare Presentation with Wheezing and Angioedema.

Carcinoid syndrome is a rare condition resulting from neuroendocrine tumors (NETs) that secrete vasoactive substances like serotonin. This report describes the case of a 61-year-old man with a history of chronic obstructive pulmonary disease (COPD) and hypertension who presented with new-onset angioedema, loss of consciousness, and a fall. He had been treated for COPD exacerbations during ER visits without improvement and was unaware of a prior mesenteric carcinoid tumor diagnosis from 2012. The next emergency evaluation revealed significant airway and facial edema necessitating intubation. Imaging and biopsy identified a well-differentiated grade 1 NET with extensive liver metastases. Laboratory tests showed elevated levels of serum serotonin, chromogranin A, and 24-hour urine 5-hydroxyindoleacetic acid (5-HIAA). Post-discharge, a PET scan confirmed metastatic lesions primarily in the liver and small bowel, with an unresectable mesenteric mass. The patient was treated with lanreotide and became symptom-free. This case underscores the need to consider carcinoid syndrome in patients with COPD presenting with unexplained respiratory symptoms, as timely diagnosis and treatment can significantly enhance patient outcomes.

Mode of action of dieldrin-induced liver tumors: application to human risk assessment.

Dieldrin is an organochlorine insecticide that was widely used until 1970 when its use was banned because of its liver carcinogenicity in mice. Several long-term rodent bioassays have reported dieldrin to induce liver tumors in in several strains of mice, but not in rats. This article reviews the available information on dieldrin liver effects and performs an analysis of mode of action (MOA) and human relevance of these liver findings. Scientific evidence strongly supports a MOA based on CAR activation, leading to alterations in gene expression, which result in increased hepatocellular proliferation, clonal expansion leading to altered hepatic foci, and ultimately the formation of hepatocellular adenomas and carcinomas. Associative events include increased liver weight, centrilobular hypertrophy, increased expression of Cyp2b10 and its resulting increased enzymatic activity. Other associative events include alterations of intercellular gap junction communication and oxidative stress. Alternative MOAs are evaluated and shown not to be related to dieldrin administration. Weight of evidence shows that dieldrin is not DNA reactive, it is not mutagenic, and it is not genotoxic in general. Furthermore, activation of other pertinent nuclear receptors, including PXR, PPARα, AhR, and estrogen are not related to dieldrin-induced liver tumors nor is there liver cytotoxicity. In previous studies, rats, dogs, and non-human primates did not show increased cell proliferation or production of pre-neoplastic or neoplastic lesions following dieldrin treatment. Thus, the evidence strongly indicates that dieldrin-induced mouse liver tumors are due to CAR activation and are specific to the mouse, which are qualitatively not relevant to human hepatocarcinogenesis. Thus, there is no carcinogenic risk to humans. This conclusion is also supported by a lack of positive epidemiologic findings for evidence of liver carcinogenicity. Based on current understanding of the mode of action of dieldrin-induced liver tumors in mice, the appropriate conclusion is that dieldrin is a mouse specific liver carcinogen and it does not pose a cancer risk to humans.

Ultrasound-MR fusion imaging combined with intraductal cooling via PTCD during microwave ablation of perihilar liver tumors: a retrospective pilot study.

PURPOSE: To explore the feasibility and safety of a microwave ablation (MWA) strategy involving intraductal chilled saline perfusion (ICSP) via percutaneous transhepatic cholangial drainage (PTCD) combined with ultrasound-magnetic resonance (US-MR) fusion imaging for liver tumors proximal to the hilar bile ducts (HBDs). METHODS: Patients with liver tumors proximal to the HBDs (≤5 mm) who underwent MWA at our hospital between June 2020 and April 2023 were retrospectively analyzed. The strategy of US-MR fusion imaging combined with PTCD-ICSP was used to assist the MWA procedures. The technical success, technique efficacy, local tumor progression, intrahepatic distant recurrence and complications were recorded and analyzed. RESULTS: In total, 12 patients with 12 liver tumors were retrospectively enrolled in this study. US-MR fusion imaging was utilized in all patients, and PTCD-ICSP assistance was successfully used for 4 nodules abutting HBDs (0 mm). The rates of technical success, technique efficacy, local tumor progression and intrahepatic distant recurrence were 91.7%, 83.3%, 0% and 8.3%, respectively. The major complication of biliary infection occurred in only one patient who had previously undergone left hemihepatectomy and bile-intestinal anastomosis. CONCLUSIONS: MWA for liver tumors proximal to HBDs assisted by US-MR fusion imaging combined with PTCD-ICSP was feasible and safe. This strategy made MWA of liver tumors abutting HBDs possible.

Outcomes of radiofrequency ablation for liver tumors in patients on hemodialysis: Results from the US Nationwide Inpatient Sample 2005-2020.

OBJECTIVE: Few studies have examined the outcomes of radiofrequency ablation (RFA) for liver tumors in patients on hemodialysis. This study aimed to investigate short-term outcomes following RFA for liver tumors in patients on hemodialysis. METHODS: Data of patients ≥ 20 years old diagnosed with liver tumors who underwent RFA were extracted from the Nationwide Inpatient Sample (NIS) database 2005-2020. The study population was divided into two groups: patients on hemodialysis and those not on hemodialysis. Propensity score matching (PSM) was employed to address baseline differences. Associations between hemodialysis and in-hospital outcomes, including prolonged length of stay (LOS), in-hospital mortality, unfavorable discharge, and complications were determined using logistic regression analyses. RESULTS: After applying the inclusion and exclusion criteria, a total of 12,749 patients constituted the study population, with 550 remaining after 1:4 PSM (110 on hemodialysis and 440 without hemodialysis). After adjustment in the multivariable analyses, patients on maintenance hemodialysis showed significantly higher risks of prolonged LOS (adjusted odds ratio [aOR] = 2.88, 95 % confidence interval [CI]: 1.78-4.65), in-hospital mortality (aOR=31.90, 95 % CI: 17.68-57.58), unfavorable discharge (aOR=3.79, 95 % CI: 2.05-7.01), at least one complications (aOR=3.68, 95 % CI: 2.49-5.44), and greater total hospital costs (adjusted Beta [aBeta] = 126.75, 95 % CI: 113.68-139.82). CONCLUSIONS: Patients on hemodialysis undergoing RFA for liver tumors have greater risks of adverse short-term outcomes including in-hospital mortality, prolonged LOS, complications, and unfavorable discharge. Careful consideration and close monitoring are warranted for patients on hemodialysis when planning for RFA.3.

Accurate segmentation of liver tumor from multi-modality non-contrast images using a dual-stream multi-level fusion framework.

The use of multi-modality non-contrast images (i.e., T1FS, T2FS and DWI) for segmenting liver tumors provides a solution by eliminating the use of contrast agents and is crucial for clinical diagnosis. However, this remains a challenging task to discover the most useful information to fuse multi-modality images for accurate segmentation due to inter-modal interference. In this paper, we propose a dual-stream multi-level fusion framework (DM-FF) to, for the first time, accurately segment liver tumors from non-contrast multi-modality images directly. Our DM-FF first designs an attention-based encoder-decoder to effectively extract multi-level feature maps corresponding to a specified representation of each modality. Then, DM-FF creates two types of fusion modules, in which a module fuses learned features to obtain a shared representation across multi-modality images to exploit commonalities and improve the performance, and a module fuses the decision evidence of segment to discover differences between modalities to prevent interference caused by modality's conflict. By integrating these three components, DM-FF enables multi-modality non-contrast images to cooperate with each other and enables an accurate segmentation. Evaluation on 250 patients including different types of tumors from two MRI scanners, DM-FF achieves a Dice of 81.20%, and improves performance (Dice by at least 11%) when comparing the eight state-of-the-art segmentation architectures. The results indicate that our DM-FF significantly promotes the development and deployment of non-contrast liver tumor technology.

Malignant myopericytoma originating from the colon: A case report.

BACKGROUND: Myopericytoma is a benign tumor that typically occurs within subcutaneous tissue and most often involves the distal extremities, followed by the proximal extremities, neck, thoracic vertebrae and oral cavity. Complete resection is often curative. Malignant myopericytoma is extremely rare and has a poor prognosis. Here, we report for the first time a case of malignant myopericytoma originating from the colon. CASE SUMMARY: A 69-year-old male was admitted to our hospital with right upper quadrant pain for five days. Imaging suggested a liver mass with hemorrhage. A malignant hepatic tumor was the initial diagnosis. Surgical resection was performed after a complete preoperative work up. Initial postoperative pathology suggested that the mass was a malignant myoblastoma unrelated to the liver. Four months after the first surgery, an enhanced computed tomography (CT) scan revealed a recurrence of the tumor. The diagnosis of malignant myopericytoma derived from the colon was confirmed on histopathological examination of the specimen from the second surgery. The patient did not return to the hospital regularly for surveillance. The first postoperative abdominal CT examination six months after the second surgery demonstrated multiple liver metastases. Survival time between the diagnosis of the tumor to death was approximately one year. CONCLUSION: Malignant myopericytoma is a rare cancer. Preoperative diagnosis may be difficult. Due to a lack of treatment options, prognosis is poor.

Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy.

Purpose: Addressing the challenges of unclear tumor boundaries and the confusion between cysts and tumors in liver tumor segmentation, this study aims to develop an auto-segmentation method utilizing Gaussian filter with the nnUNet architecture to effectively distinguish between tumors and cysts, enhancing the accuracy of liver tumor auto-segmentation. Methods: Firstly, 130 cases of liver tumorsegmentation challenge 2017 (LiTS2017) were used for training and validating nnU-Net-based auto-segmentation model. Then, 14 cases of 3D-IRCADb dataset and 25 liver cancer cases retrospectively collected in our hospital were used for testing. The dice similarity coefficient (DSC) was used to evaluate the accuracy of auto-segmentation model by comparing with manual contours. Results: The nnU-Net achieved an average DSC value of 0.86 for validation set (20 LiTS cases) and 0.82 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.75, which increased to 0.81 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation. Conclusion: Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.

Light&fast generative adversarial network for high-fidelity CT image synthesis of liver tumor.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the development of computer-aided diagnosis and treatment in the treatment of HCC. However, there are significant challenges in the actual establishment of HCC auxiliary diagnosis model due to data imbalance, especially for rare subtypes of HCC and underrepresented demographic groups. This study proposes a GAN model aimed at overcoming these obstacles and improving the accuracy of HCC auxiliary diagnosis. METHODS: In order to generate liver and tumor images close to the real distribution. Firstly, we construct a new gradient transfer sampling module to improve the lack of texture details and excessive gradient transfer parameters of the deep model; Secondly, we construct an attention module with spatial and cross-channel feature extraction ability to improve the discriminator's ability to distinguish images; Finally, we design a new loss function for liver tumor imaging features to constrain the model to approach the real tumor features in iterations. RESULTS: In qualitative analysis, the images synthetic by our method closely resemble the real images in liver parenchyma, blood vessels, tumors, and other parts. In quantitative analysis, the optimal results of FID, PSNR, and SSIM are 75.73, 22.77, and 0.74, respectively. Furthermore, our experiments establish classification models for imbalanced data and enhanced data, resulting in an increase in accuracy rate by 21%-34%, an increase in AUC by 0.29 - 0.33, and an increase in specificity to 0.89. CONCLUSION: Our solution provides a variety of training data sources with low cost and high efficiency for the establishment of classification or prognostic models for imbalanced data.

Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

A Dynamic Context Encoder Network for Liver Tumor Segmentation.

BACKGROUND: Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks are powerful in such image processing while largely reducing human labor. However, the variable shape, fuzzy boundary, and discontinuous tumor region of liver tumors in medical images bring great challenges to accurate segmentation. The feature extraction capability of a neural network can be improved by expanding its architecture, but it inevitably demands more computing resources in training and hyperparameter tuning. METHODS: This study presents a Dynamic Context Encoder Network (DCE-Net), which incorporates multiple new modules, such as the Involution Layer, Dynamic Residual Module, Context Extraction Module, and Channel Attention Gates, for feature extraction and enhancement. RESULTS: In the experiment, we used a liver tumor CT dataset of LiTS2017 to train and test the DCE-Net for liver tumor segmentation. The experimental results showed that the four evaluation indexes of the method, precision, recall, dice, and AUC, were 0.8961, 0.9711, 0.9270, and 0.9875, respectively. Furthermore, our ablation study reported that the accuracy and training efficiency of our network were markedly superior to the networks without involution or dynamic residual modules. CONCLUSION: Therefore, the DCE-Net proposed in this study has great potential for automatic segmentation of liver lesion tumors in the clinical diagnostic environment.

Salvage radiofrequency ablation for microwave ablation treated hepatocellular carcinoma complicated by gas gangrene: case report.

AIM: Gas gangrene (GG) is a rare severe infection with a very high mortality rate mainly caused by Clostridium species. It develops suddenly, often as a complication of abdominal surgery or liver transplantation. We report a case of GG of the liver occurred after percutaneous microwave (MW) ablation of an hepatocellular carcinoma (HCC) successfully treated with percutaneous Radiofrequency ablation (RFA). CASE PRESENTATION: A 76-year-old female patient was treated with MW ablation for a large HCC in the VIII segment; 2 days later she developed fever, weakness, abdominal swelling and was hospitalized with diagnosis of anaerobic liver abscess. Despite antibiotic therapy, the patient conditions worsened, and she was moved to the intensive care unit (ICU). Percutaneous drainage was attempted, but was unsuccessful. The surgeon and the anesthesiologist excluded any indication of surgical resection. We performed RFA of the GG by 3 cool-tip needles into the infected area. The procedure was well tolerated by the patient, who left the hospital for follow-up. CONCLUSION: Percutaneous RFA could be a valuable therapy of focal GG of the liver in patients refractory to antibiotics and when surgery and OLT are not feasible. A fast and early indication is needed in case of rapid worsening of the patient's conditions.

An Organophosphorescence Probe with Ultralong Lifetime and Intrinsic Tissue Selectivity for Specific Tumor Imaging and Guided Tumor Surgery.

Organic phosphorescent materials are excellent candidates for use in tumor imaging. However, a systematic comparison of the effects of the intensity, lifetime, and wavelength of phosphorescent emissions on bioimaging performance has not yet been undertaken. In addition, there have been few reports on organic phosphorescent materials that specifically distinguish tumors from normal tissues. This study addresses these gaps and reveals that longer lifetimes effectively increase the signal intensity, whereas longer wavelengths enhance the penetration depth. Conversely, a strong emission intensity with a short lifetime does not necessarily yield robust imaging signals. Building upon these findings, an organo-phosphorescent material with a lifetime of 0.94 s was designed for tumor imaging. Remarkably, the phosphorescent signals of various organic nanoparticles are nearly extinguished in blood-rich organs because of the quenching effect of iron ions. Moreover, for the first time, we demonstrated that iron ions universally quench the phosphorescence of organic room-temperature phosphorescent materials, which is an inherent property of such substances. Leveraging this property, both the normal liver and hepatitis tissues exhibit negligible phosphorescent signals, whereas liver tumors display intense phosphorescence. Therefore, phosphorescent materials, unlike chemiluminescent or fluorescent materials, can exploit this unique inherent property to selectively distinguish liver tumor tissues from normal tissues without additional modifications or treatments.

Evaluation of surgical strategies and long-term outcomes in pediatric hepatocellular carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC), the second most common pediatric malignant liver tumor after hepatoblastoma, represents 1% of all pediatric tumors. METHODS: A retrospective study was conducted on children with HCC treated at our center from March 2002 to October 2022, excluding those with inadequate follow-up or records. Demographic data, initial complaints, alpha-fetoprotein (AFP) values, underlying disease, size and histopathological features of the masses, chemotherapy, and long-term outcomes were analyzed. RESULTS: Fifteen patients (8 boys, 7 girls) with a mean age of 11.4 ± 4.1 years (0.8-16.4 years) were analyzed. The majority presented with abdominal pain, with a median AFP of 3.9 ng/mL. Hepatitis B cirrhosis in one patient (6.6%) and metabolic disease (tyrosinemia type 1) in two patients (13.3%) were the underlying diseases. Histopathological diagnoses were fibrolamellar HCC (n:8; 53.3%), HCC (n:6; 40%). Four of the 15 patients underwent liver transplantation, and 9 underwent surgical resection. Due to late diagnosis, two patients were considered inoperable (13.3%). The survival rate for the four patients who underwent liver transplantation was found to be 75%. CONCLUSION: Surgical treatment of various variants of HCC can be safely performed in experienced centers with a multidisciplinary approach, and outcomes are better than in adults.

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

PURPOSE: To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI). MATERIALS AND METHODS: This retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test. RESULTS: BH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05). CONCLUSION: The use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

A Case of Esophageal Cancer With Markedly Elevated Soluble Interleukin-2 Receptor: A Potential of Soluble Interleukin-2 Receptor as a Biomarker.

We report an autopsy case of advanced esophageal cancer with multiple metastases that presented with a markedly high level of sIL-2R. An 83-year-old man was admitted to our hospital with a 1-week history of epigastric distress, appetite loss, and fatigue. Imaging examinations revealed a large liver tumor. Although the tumor markers for gastrointestinal and liver cancers were within normal limits, the sIL-2R level was extremely high (10,384 U/mL). The patient died immediately after admission due to the rapid course of the disease. An autopsy showed advanced esophageal cancer with multiple metastases, including the liver, lungs, and multiple lymph nodes. In histological examinations, esophageal cancer was a mixture of well- and poorly differentiated squamous cell carcinoma, in which poorly differentiated cancer cells expressed sIL-2R on immunohistochemical staining. However, we failed to detect positive staining for sIL-2R in the lymphocytes. Our findings revealed that solid tumors could express sIL-2R. Although sIL-2R is a tumor marker used for hematological malignancies, such as malignant lymphoma, this case report highlights the value of the measurement of sIL-2R levels in advanced solid tumors, including esophageal cancer. We concluded that sIL-2R has potential as a biomarker in advanced solid tumors for cancer staging and treatment response.