The prognostic roles of hepatitis B virus antibody in diffuse large B-cell lymphoma patients

Serum hepatitis B core antibody as the prognostic factor for diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL), strongly associated with viral infections. Although the link between hepatitis B virus (HBV) infection and DLBCL is well-documented, effective clinical markers reflecting HBV-associated DLBCL remain scarce. This study aims to identify prognostic indicators for HBV-associated DLBCL through retrospective analysis of the relationship among tissue marker molecules, HBV serum markers, and clinical prognosis in DLBCL patients. Here, we found the results that DLBCL patients who tested positive for hepatitis B core antibody (HBcAb) had significantly reduced overall survival (OS) rates compared with those who tested negative. Additionally, a strong correlation was observed between an elevated HBcAb-positive rate and reduced expression of the CD23 molecule in DLBCL tissue samples. Stratifying DLBCL patients based on combined HBcAb-CD23 status revealed significant disparities in OS rates. Therefore, integrating CD23 with HBcAb could be applied to prognostic assessments for individuals with HBV-associated DLBCL. This study identifies novel indicators and diagnostic strategies for HBV-associated DLBCL.IMPORTANCEThis study identifies hepatitis B core antibody (HBcAb) as a significant prognostic indicator for hepatitis B virus (HBV)-associated diffuse large B-cell lymphoma (DLBCL). The findings reveal that patients with DLBCL with positive HBcAb have significantly reduced overall survival rates. Additionally, a strong negative correlation is observed between serum HBcAb and the expression of the CD23 molecule in DLBCL tissues. These results highlight the potential of integrating HBcAb and CD23 as prognostic markers in clinical assessments of HBV-associated DLBCL, offering new insights for risk stratification and treatment planning in this patient population.

Psychometric performance of EQ-5D-5L and SF-6Dv2 in patients with lymphoma in China

AIM

To assess and compare the measurement properties of EQ-5D-5L and SF-6Dv2 among lymphoma patients in China.

METHODS

A face-to-face survey of Chinese lymphoma patients was conducted at baseline (all types) and follow-up (diffuse large B-cell). EQ-5D-5L and SF-6Dv2 health utility scores (HUSs) were calculated using the respective Chinese value sets. Ceiling effect was assessed by calculating the percentage of respondents reporting the optimal health state. Convergent validity of EQ-5D-5L and SF-6Dv2 was assessed using the Spearman rank correlation coefficient (r) with QLQ-C30 as a calibration standard. Known-groups validity of the two HUSs was evaluated by comparing their scores of patients with different conditions; and their sensitivity was further assessed in the known-groups using relative efficiency (RE). Test-retest reliability and responsiveness was tested using ICC and standardized response mean (SRM), respectively.

RESULTS

Altogether 200 patients were enrolled at baseline and 78 were followed up. No ceiling effect was found for SF-6Dv2 compared to 24.5% for EQ-5D-5L. Correlation between the two HUSs and with QLQ-C30 score was strong (r > 0.5). Each dimension of EQ-5D-5L and SF-6Dv2 had moderate or greater correlations with similar dimensions of QLQ-C30 (r > 0.35). Both EQ-5D-5L and SF-6Dv2 could only a minority known-groups, and the latter may have better sensitivity. EQ-5D-5L had better test-retest reliability (ICC = 0.939); while both of them were responsive to patients with worsened and improved clinical status.

CONCLUSIONS

EQ-5D-5L and SF-6Dv2 were found to have good convergent validity and responsiveness, while EQ-5D-5L had better test-retest reliability and higher ceiling effect. Not enough evidence indicates which of the two measures has better known-group validity and sensitivity.

Hepatitis Virus-associated Non-hodgkin Lymphoma: Pathogenesis and Treatment Strategies

Over the last decade, epidemiological studies have discovered a link between hepatitis C virus (HCV) and hepatitis B virus (HBV) infection and non-Hodgkin lymphoma (NHL). The regression of HCV-associated NHL after HCV eradication is the most compelling proof supporting HCV infection's role in lymphoproliferative diseases. HBV infection was found to significantly enhance the incidence of NHL, according to the epidemiological data. The exact mechanism of HCV leading to NHL has not been fully clarified, and there are mainly the following possible mechanisms: (1) Indirect mechanisms: stimulation of B lymphocytes by extracellular HCV and cytokines; (2) Direct mechanisms: oncogenic effects mediated by intracellular HCV proteins; (3) hit-and-run mechanism: permanent genetic B lymphocytes damage by the transitional entry of HCV. The specific role of HBV in the occurrence of NHL is still unclear, and the research on its mechanism is less extensively explored than HCV, and there are mainly the following possible mechanisms: (1) Indirect mechanisms: stimulation of B lymphocytes by extracellular HBV; (2) Direct mechanisms: oncogenic effects mediated by intracellular HBV DNA. In fact, it is reasonable to consider direct-acting antivirals (DAAs) as first-line therapy for indolent HCV-associated B-NHL patients who do not require immediate chemotherapy. Chemotherapy for NHL is affected by HBV infection and replication. At the same time, chemotherapy can also activate HBV replication. Following recent guidelines, all patients with HBsAg positive/HBV DNA≥2,000 IU/mL should be treated for HBV. The data on epidemiology, interventional studies, and molecular mechanisms of HCV and HBV-associated B-NHL are systematically summarized in this review.

Prognosis and complications of patients with primary gastrointestinal diffuse large B-cell lymphoma: Development and validation of the systemic inflammation response index-covered score

BACKGROUND

This study aimed to evaluate the predictive value of systemic inflammation response index (SIRI) in primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL) patients and establish a highly discriminating risk prediction model.

METHODS

This retrospective analysis included 153 PGI-DCBCL patients diagnosed between 2011 and 2021. These patients were divided into a training set (n = 102) and a validation set (n = 51). Univariate and multivariate Cox regression analyses were conducted to examine the significance of variables on overall survival (OS) and progression-free survival (PFS). An inflammation-covered score system was established according to the multivariate results.

RESULTS

The presence of high pretreatment SIRI (≥1.34, p < 0.001) was significantly associated with poorer survival and identified as an independent prognostic factor. Compared with NCCN-IPI, the prognostic and discriminatory capability of the novel model SIRI-PI showed a more precise high-risk assessment with a higher area under the curve (AUC) (0.916 vs 0.835) and C-index (0.912 vs 0.836) for OS in the training cohort, and similar results were obtained in the validation cohort. Moreover, SIRI-PI also showed good discriminative power for efficacy assessment. This new model identified patients at risk of developing severe gastrointestinal complications following chemotherapy.

CONCLUSIONS

The results of this analysis suggested that the pretreatment SIRI may be a potential candidate for identifying patients with a poor prognosis. And we established and validated a better-performing clinical model, which facilitated the prognostic stratification of PGI-DLBCL patients and can serve as a reference for clinical decision-making.

A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer

Background

Thyroid Cancer (TC) is the most common malignant disease of endocrine system, and its incidence rate is increasing year by year. Early diagnosis, management of malignant nodules and scientific treatment are crucial for TC prognosis. The first aim is the construction of a classification model for TC based on risk factors. The second aim is the construction of a prediction model for metastasis based on risk factors.

Methods

We retrospectively collected approximately 70 preoperative demographic and laboratory test indices from 1735 TC patients. Machine learning pipelines including linear regression model ridge, Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost) were used to select the best model for predicting deterioration and metastasis of TC. A comprehensive comparative analysis with the prediction model using only thyroid imaging reporting and data system (TI-RADS).

Results

The XGBoost model achieved the best performance in the final thyroid nodule diagnosis (AUC: 0.84) and metastasis (AUC: 0.72-0.77) predictions. Its AUCs for predicting Grade 4 TC deterioration and metastasis reached 0.84 and 0.97, respectively, while none of the AUCs for Only TI-RADS reached 0.70. Based on multivariate analysis and feature selection, age, obesity, prothrombin time, fibrinogen, and HBeAb were common significant risk factors for tumor progression and metastasis. Monocyte, D-dimer, T3, FT3, and albumin were common protective factors. Tumor size (11.14 ± 7.14 mm) is the most important indicator of metastasis formation. In addition, GGT, glucose, platelet volume distribution width, and neutrophil percentage also contributed to the development of metastases. The abnormal levels of blood lipid and uric acid were closely related to the deterioration of tumor. The dual role of mean erythrocytic hemoglobin concentration in TC needs to be verified in a larger patient cohort. We have established a free online tool (http://www.cancer-thyroid.com/) that is available to all clinicians for the prognosis of patients at high risk of TC.

Conclusion

It is feasible to use XGBoost algorithm, combined with preoperative laboratory test indexes and demographic characteristics to predict tumor progression and metastasis in patients with TC, and its performance is better than that of Only using TI-RADS. The web tools we developed can help physicians with less clinical experience to choose the appropriate clinical decision or secondary confirmation of diagnosis results.