Preoperative PDW levels predict pulmonary metastasis in patients with hepatocellular carcinoma.

BACKGROUND: In hepatocellular carcinoma (HCC), pulmonary metastasis (PM) after hepatectomy is associated with poor clinical outcomes. The crucial phases of tumour cell proliferation, angiogenesis, and metastasis all entail platelet activation. In HCC, platelet distribution width (PDW) suggests platelet size changes and predicts a worse prognosis. The aim of this study was to assess the association between PDW and PMs in HCC patients receiving hepatectomy. MATERIAL/METHODS: From January 2013 to December 2015, a cohort of patients who underwent hepatectomy for HCC at the Harbin Medical University Cancer Hospital in China were retrospectively evaluated. The relationship between PDW levels and clinical and demographic parameters was examined. To investigate the relationships between predicted factors and PM, a competing risk model was used. From January 2016 to December 2018, a validation cohort of 109 patients from the First Affiliated Hospital of Harbin Medical University was studied independently. RESULTS: In the primary cohort, 19 out of 214 patients had postoperative PMs. In HCC patients with PM, PDW levels were lower than in those without PM. There was a significant difference in the cumulative incidence of 2-year PM between the high-PDW and low-PDW groups after controlling for competing risk events (death prior to the development of PM) (p < 0.001). In addition, PDW was also found to be an independent predictor for PM in a multivariable competing risk analysis. The results were externally validated in another cohort. CONCLUSIONS: In HCC, preoperative PDW is significantly associated with PM. PDW could be a biomarker for post-operative PM in HCC patients.

Utility of mean platelet volume in differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are the most prevalent histologic types of primary liver cancer. HCC and ICC differ in treatment and prognosis, warranting an effective differential diagnosis between them. This study aimed to explore the clinical value of mean platelet volume (MPV) to discriminate between HCC and ICC. MATERIAL/METHODS: We performed a retrospective analysis of ICC and HCC patients who were from the Harbin Medical University Cancer Hospital, China. Logistic regression analysis was used to identify the independent factors for the differentiation of HCC and ICC. A receiver operating characteristic curve was built to evaluate the diagnostic performance of the potential model. An independent validation study was performed to validate the diagnostic ability. RESULTS: ICC patients were detected in 146 out of 348 patients in the primary cohort. MPV levels were decreased in ICC patients compared with those in HCC patients. Logistic regression analysis revealed that MPV was an independent factor in distinguishing HCC from ICC. A combination of sex, hepatitis B surface antigen, MPV, alpha-fetoprotein, and carbohydrate antigen 19-9 demonstrated a good capability to differentiate HCC from ICC. Similar results were achieved in the validation cohort. CONCLUSIONS: MPV may be a new marker to help distinguish ICC from HCC. Further validation studies are required.

Incorporating adipose tissue into a CT-based deep learning nomogram to differentiate granulomas from lung adenocarcinomas.

We aimed to build and validate a computed tomography (CT)-based deep learning nomogram for discriminating granulomas from lung adenocarcinomas. A retrospective study of 1,159 patients with solitary lung nodules from three institutions in China who underwent pre-operative lung CT scans was performed. The patients were divided into one training, one validation, one test, and two external validation cohorts. Deep learning features were extracted from CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for dimension reduction and feature selection. Logistic regression analysis showed that age, gender, intranodular and perinodular (IPN) features, and adipose features were the significant predictors of malignancy presence (all p < 0.05). The nomogram was built by incorporating these four factors and achieved better diagnostic accuracy than the single-factor model. The nomogram demonstrates satisfactory discrimination and calibration. In addition, decision curve analysis revealed the considerable clinical usefulness of the nomogram.

Lean body mass predicts postoperative liver failure in patients with hepatocellular carcinoma.

BACKGROUND: Post-hepatectomy liver failure (PHLF) is a severe complication of liver surgery in hepatocellular carcinoma (HCC) patients. Reduced lean body mass (LBM) decreases the immune activity and increases adverse clinical outcomes among cancer patients. OBJECTIVE: We aimed to assess the association between LBM and PHLF in HCC patients. METHODS: PHLF was defined and graded based on the International Study Group of Liver Surgery (ISGLS) criteria. Patients with Grade B or Grade C were included in PHLF ⩾ Grade B group, while others in PHLF < Grade B group. LBM was measured via preoperative computed tomography images. Binary logistic regression was applied for investigating the association between LBM and PHLF. The receiver operating characteristic curve was used to identify potential cut-off values and assess the predictive ability of the measured variables. RESULTS: The PHLF ⩾ Grade B group had significantly lower LBM levels (means ± standard deviation: 57.0 ± 14.1) than PHLF < Grade B group (67.2 ± 15.7) (p< 0.001). After controlling other variables, LBM was an independent protective factor for PHLF ⩾ Grade B (Odds Ratio: 0.406, 95% confidence interval: 0.172-0.957, p= 0.039). The prevalence of PHLF ⩾ Grade B in each quartile of LBM was 29.4% (15/51), 25.5% (13/51), 19.2% (10/52) and 4.0% (2/50), respectively (ptrend< 0.001). CONCLUSIONS: LBM might be a protective factor for PHLF in HCC patients. Our findings might help to develop a novel strategy to reduce the occurrence of hepatic dysfunction following major liver resection. Multicentric prospective studies and further molecular biologic investigation are needed.

The Association between Platelet Glycocalicin and High Microsatellite Instability in Colorectal Cancer.

Background: Elevated platelet volume is the risk factor for the development and poor overall survival of colorectal cancer (CRC) patients. Both microsatellite status and platelet glycoprotein Ibα (GPIbα) are related to platelet volume in CRC patients. This study aimed to investigate platelet GPIbα ectodomain (termed glycocalicin) levels among CRC patients and the association between the glycocalicin levels and microsatellite status in CRC. Methods: The clinical and laboratory data of 430 CRC patients between January 2018 and December 2018 in Harbin Medical University Cancer Hospital were collected. The microsatellite status was determined with a polymerase chain reaction. The participants were separated into high microsatellite instability (MSI-H) and microsatellite stable (MSS) groups according to microsatellite status. The glycocalicin levels were measured with an enzyme-linked immunosorbent assay, and the cut-off point was determined with the receiver-operating characteristics curve. The clinical and pathological characteristics were collected via electronic medical records. Logistic regression was used to explore the association between glycocalicin and microsatellite status. Results: Among the 430 CRC patients enrolled, 64 patients (14.9%) were identified as MSI-H and others as MSS CRC. Glycocalicin levels were significantly reduced in patients with MSI-H than those with MSS. After controlling for potential confounders, logistic regression analysis revealed that glycocalicin levels were independently associated with MSI-H CRC. Conclusions: Reduced glycocalicin levels are associated with the MSI-H subtype of CRC. Further research is needed to elucidate the mechanisms of the association between glycocalicin and MSI-H in CRC patients.