Serial therapies oriented by surgery for large primary liver carcinoma

Objective: To discuss the methods and effects of serial therapies oriented by surgery in the treatment of pri- mary large liver cancers. Methods: From January 1993 to June 1999, 191 pa- tients with large liver carcinoma were treated surgi- cally. The size of tumors varied from 5.2 to 19.7 cm (mean 9.4 cm). Several types of liver resections were made in 121 patients and as a supplement, cry- osurgery was carried out for the remaining 70 pa- tients. Importable drug delivery system was institu- ted intraoperatively. Transcatheter arterial chemo- embolization (THP 30-60 mg, E-ADM 20-40 mg, CDDP 40-80 mg, MMC 10-20 mg, iodin oil 5-30 ml), percutaneous ethanol injection, bioimmunother- apy and traditional Chinese medicine were used pre- and post-operatively. CT angiography and CT dur- ing arterial portography were used to find satellite nodules. Early stage recurrences were predicted by AFPmRNA in peripheral blood. Child-Pugh's classi- fication plus branch chain amino acid/aromatic ami- no acid ratio (BCAA/AAA) was adopted in evalua- ting pre-operative liver functions. Results: Marked results were observed after serial treatments oriented by surgery. The 1-, 3- and 5- year survival rates in resection group were 75.8 %, 45.6% and 30.4%. respectively. The 1- and 3-year survival rates in cryosurgery group were 63.2 % and 37.0 %. The operative mortality was 1.57 %. Recur- rence rates were 69.2 % in AFPmRNA positive group and 33.3% in AFPmRNA negative group (P< 0.05). The BCAA/AAA ratio was lower than 1.5 in two patients who died of hepatic failure after resec- tion. Conclusions: Serial treatments with surgery as the chief modality gives satisfactory results in patients with large primary liver carcinoma. This regimen should be regarded as a main strategy to deal with large liver carcinoma. AFPmRNA in the peripheral blood, signifying a recurrence, may become a new clinical parameter. The BCAA/AAA ratio plus Child-Pugh's classification is able to evaluate more accurately liver function reserve before surgery.

Clinical value of assessing serum levels of inflammatory cytokines in the early diagnosis of patients with primary liver carcinoma:a retrospective observational study

Objective:To identify potential early diagnostic markers for hepatitis B progression to primary liver carcinoma using routine immunological tests based on 6 cytokine combinations.Methods:Eight hundred and ninety-nine patients with hepatitis B progressing to early primary liver carcinoma admitted to and treated at Changhai Hospital,Naval Military Medical University,Shanghai,China between March 2015 and June 2017 were included in this observational study,including 666 patients with HBsAg^(+),HBeAb^(+),HBcAb^(+)liver carcinoma and 233 patients with HBsAg+,HBeAg+,HBcAb+liver carcinoma.Receiver operating characteristic(ROC)curves were used to evaluate the efficiency of the different cytokine in the diagnosis of hepatocellular carcinoma in patients with hepatitis B.This study was approved by the Institutional Review Board of Changhai Hospital,Naval Military Medical University,China(approval No.CHEC2020-080)on June 6,2020.Results:Changed levels of interleukin(IL)-1β,IL-2R,IL-8,and tumor necrosis factor(TNF)-a were statistically significant(P<0.05).The area under the ROC curve,sensitivity,specificity,positive predictive value,negative predictive value,and Youden index for the diagnosis of primary liver carcinoma using the combination of IL-1β,IL-2R,IL-8,and TNF-α were 0.938,79.2%,96.7%,96%,82.0%,0.759,respectively.The serum alpha-fetoprotein level in patients with primary liver carcinoma was positively correlated with IL-2R(r=0.3502,P<0.001),IL-8(r=0.1558,P=0.0273),and TNF-α(r=0.2544,P<0.001)levels.The equation fitted to the results was logit(P)=0.086+0.01
IL-2R–0.001
IL-8–0.033
TNF-α–0.041
IL-1β.Conclusion:Our study establishes a novel,potentially valuable diagnostic model based on four cytokines related to the early stages of liver carcinoma.

Expression of MUC1 and its significance in hepatocellular and cholangiocarcinoma tissue

AIM： To investigate the relation between MUC1 expression, distribution, and prognosis in hepatocellular and cholangiocarcinoma （HCC and CC） and cirrhotic liver tissues, and their significance in HCC and CC diagnosis. METHODS： Expression and distribution of MUC1 were examined by immunohistochemical assay with anti-MUC1 mAb in 59 samples of HCC and 37 samples of CC, 20 samples of cirrhotic liver tissues, and 10 samples of normal liver tissues, seeking possible associations between MUC1 positive expression, distribution in HCC and CC （primary liver cancer, PLC） cases and the studied clinical data. RESULTS： Immunohistochemical analysis of MUC1 expression showed that in the 96 PLC samples, 68 （70.8%） were strong positive, and 6 （6.2%） were weak positive. Only 4 in the 20 cirrhotic liver tissues were found to be weak positive, while no expression of MUC1 was detected in normal liver tissues. Apparently, the high expression rate of MUC1 in PLC tissues was statistically significant in comparison to that in cirrhotic and normal liver tissues. The expressed MUC1 protein, stained in dark brownish or brownish-yellow particles, chiefly localized on the cancer cell membranes or in cytoplasm. In the 68 strong positive samples, 40 were detected on cell membrane and the other 28 were in cytoplasm. In addition, follow-up studies of those PLC cases demonstrated that MUC1 expression on cell membrane or in cytoplasm was closely associated with PLC prognosis. The expression of HUC1 in PLC had little statistical significance in respect of the pathological types and sizes of the tumors, but a strong relationship regarding histological differentiation, metastasis of lymph nodes, portal canal emboli, and post-operational recurrence of the carcinomas. After 3 years of tumor excision, the metastasis rate in MUC1 positive expression group （67.6%） was much higher than that in MUC1 weak expression group （33.3%） and negative expression group （31.8%）, and thus the survival rate in MUC1-positive expression group was significantly different from that in weak and negative expression groups. CONCLUSION： Expression and localization of MUC1 proteins in primary liver carcinomas （PLCs） may act as prognostic markers, and MUC1 molecules might be helpful in differential diagnosis.

Diagnosis of primary clear cell carcinoma of the liver based on Faster region-based convolutional neural network

Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to establish a Faster region-based convolutional neural network(RCNN)model for the accurate differential diagnosis of PCCCL and CHCC.Methods:In this study,we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020.A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients’data in the training validation set,and established a convolutional neural network model to distinguish PCCCL and CHCC.The accuracy,average precision,and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.Results:A total of 4392 images of 121 patients(1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC)were uesd in test set for deep learning and establishing the model,and 1072 images of 30 patients(320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC)were used to test the model.The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962(95%confidence interval[CI]:0.931-0.992).The average precision of the model for diagnosing PCCCL was 0.908(95%CI:0.823-0.993)and that for diagnosing CHCC was 0.907(95%CI:0.823-0.993).The recall of the model for diagnosing PCCCL was 0.951(95%CI:0.916-0.985)and that for diagnosing CHCC was 0.960(95%CI:0.854-0.962).The time to make a diagnosis using the model took an average of 4 s for each patient.Conclusion:The Faster RCNN model can accurately distinguish PCCCL and CHCC.This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.