A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China.

OBJECTIVE: To create a nomogram for predicting the likelihood of venous thromboembolism (VTE) in colon cancer patients from China. METHODS: The data of colon cancer patients from Chongqing University Cancer Hospital between 2019 and 2022 were analyzed. Patients were divided into training set and internal validation set by random split-sample method in a split ratio of 7:3. The univariable and multivariable logistic analysis gradually identified the independent risk factors for VTE. A nomogram was created using all the variables that had a significance level of p < 0.05 in the multivariable logistic analysis and those with clinical significance. Calibration curves and clinical decision curve analysis (DCA) were used to assess model's fitting performance and clinical value. Harrell's C-index (concordance statistic) and the area under the receiver operating characteristic curves (AUC) were used to evaluate the predictive effectiveness of models. RESULTS: A total of 1996 patients were ultimately included. There were 1398 patients in the training set and 598 patients in the internal validation set. The nomogram included age, chemotherapy, targeted therapy, hypertension, activated partial thromboplastin time, prothrombin time, platelet, absolute lymphocyte count, and D-dimer. The C-index of nomogram and Khorana score were 0.754 (95% CI 0.711-0.798), 0.520 (95% CI 0.477-0.563) in the training cohort and 0.713 (95% CI 0.643-0.784), 0.542 (95% CI 0.473-0.612) in the internal validation cohort. CONCLUSIONS: We have established and validated a nomogram to predict the VTE risk of colon cancer patients in China, which encompasses a diverse age range, a significant population size, and various clinical factors. It facilitates the identification of high-risk groups and may enable the implementation of targeted preventive measures.

Comparison of survival outcomes between clinical trial participants and non-participants of patients with advanced non-small cell lung cancer: A retrospective cohort study.

Background: Clinical trials for advanced non-small cell lung cancer (NSCLC) have been conducted extensively. However, the effect of participation in clinical trials on survival outcomes remains unclear. This study aimed to assess whether participation in clinical trials was an independent prognostic factor for survival in patients with advanced NSCLC. Methods: We analyzed the medical records of patients aged ≥18 years who were newly diagnosed with stage IIIB or IV NSCLC and received chemotherapy or immunotherapy from September 2016 to June 2020 in this retrospective cohort study. To reduce the impact of confounding factors, propensity score matching (PSM) was performed. The Kaplan-Meier method and log-rank test were used to calculate and compare the overall survival (OS) and progression-free survival (PFS) of the patients. Finally, Cox proportional hazards regression was employed to examine the correlation between clinical trial participation and survival outcomes. Results: The study enrolled 155 patients in total, of which 62 (40.0 %) patients participated in NSCLC clinical trials. PSM identified 50 pairs of patients in total. The median PFS and OS of clinical trial participants and non-participants were 17.2 vs. 13.9 months (p = 0.554) and 32.4 vs. 36.5 months (p = 0.968), respectively. According to the results of multivariate Cox proportional hazards regression analysis, clinical trial participation was not an independent prognostic factor for advanced NSCLC patients (HR: 0.89, 95 % CI: 0.50-1.61; p = 0.701). Conclusions: The clinical trial participants with advanced NSCLC displayed similar survival outcomes compared with the non-participating patients in this cohort.

Antibody responses to SARS-CoV-2 Omicron infection in patients with hematological malignancies: A multicenter, prospective cohort study.

Little is known about antibody responses to natural Omicron infection and the risk factors for poor responders in patients with hematological malignancies (HM). We conducted a multicenter, prospective cohort study during the latest Omicron wave in Chongqing, China, aiming to compare the antibody responses, as assessed by IgG levels of anti-receptor binding domain of spike protein (anti-S-RBD), to Omicron infection in the HM cohort (HMC) with healthy control cohort (HCC), and solid cancer cohort (SCC). In addition, we intend to explore the risk factors for poor responders in the HMC. Among the 466 HM patients in this cohort, the seroconversion rate was 92.7%, no statistically difference compared with HCC (98.2%, p = 0.0513) or SCC (100%, p = 0.1363). The median anti-S-RBD IgG titer was 29.9 ng/mL, significantly lower than that of HCC (46.9 ng/mL, p < 0.0001) or SCC (46.2 ng/mL, p < 0.0001). Risk factors associated with nonseroconversion included no COVID-19 vaccination history (odds ratio [OR] = 4.58, 95% confidence interval [CI]: 1.75-12.00, p = 0.002), clinical course of COVID-19 ≤ 7 days (OR = 2.86, 95% CI: 1.31-6.25, p = 0.008) and severe B-cell reduction (0-10/µL) (OR = 3.22, 95% CI: 1.32-7.88, p = 0.010). Risk factors associated with low anti-S-RBD IgG titer were clinical course of COVID-19 ≤ 7 days (OR = 2.58, 95% CI: 1.59-4.18, p < 0.001) and severe B-cell reduction (0-10/µL) (OR = 2.87, 95% CI: 1.57-5.24, p < 0.001). This study reveals a poor antibody responses to Omicron (BA.5.2.48) infection in HM patients and identified risk factors for poor responders. Highlights that HM patients, especially those with these risk factors, may be susceptible to SARS-CoV-2 reinfection, and the postinfection vaccination strategies for these patients should be tailored. Clinical trial: ChiCTR2300071830.

Development and validation of a nomogram model for predicting the risk of venous thromboembolism in lymphoma patients undergoing chemotherapy: a prospective cohort study conducted in China.

BACKGROUND: The mechanism of Venous thromboembolism (VTE) is complicated and difficult to prevent due to factors such as bone marrow invasion, therapy, and immune-mediated effects. This study aims to establish a nomogram model for predicting the risk of thrombosis in lymphoma patients undergoing chemotherapy, which has been increasing over the past 30 years. METHODS: The data of lymphoma patients from the Affiliated Cancer Hospital of Chongqing University in China between 2018 and 2020 were analyzed. This included age, sex, body mass index, ECOG score, histological type, Ann Arbour Stage, white blood cells count, haemoglobin level, platelet count, D-dimer level, and chemotherapy cycle. Univariate and multivariate cox analysis was used to determine the risk factors for VTE. Characteristic variables were selected to construct a nomogram model which was then evaluated using ROC curve and calibration. RESULTS: Age, sex, PLT, D-dimer and chemotherapy cycle were considered as independent influencing factors of VTE. The mean (standard deviation) of the C index, AUC and Royston D statistics of 1000 cross-validations of the Nomogram model were 0.78 (0.01), 0.81 (0.01) and 1.61(0.07), respectively. It indicates a good calibration degree and applicability value as shown by the calibration curve. The DCA curve showed a rough threshold range of 0.05-0.60 with a good model. CONCLUSIONS: We have established and validated a nomogram model for predicting the risk of thrombosis in lymphoma patients. This model can assess the risk of thrombosis in each individual patient, enabling the identification of high-risk groups and targeted preventive treatment.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China.

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

Association of immune checkpoint inhibitors therapy with arterial thromboembolic events in cancer patients: A retrospective cohort study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as a standard treatment for various malignancies. However, research indicates blocking the immune checkpoint pathway may exacerbate atherosclerotic lesions. OBJECTIVES: We aimed to investigate whether ICI therapy increases the risk of arterial thromboembolic events (ATEs). METHODS: A retrospective cohort study was conducted on patients with histologically confirmed cancer at our institution between 2018 and 2021, using the propensity score matching method. The primary endpoint was ATEs occurrence, comprising acute coronary syndrome, stroke/transient ischemic attack, and peripheral arterial thromboembolism. Subgroup analyses assessed whether the ICI treatment effect on ATEs varied over time by limiting the maximum follow-up duration. Logistic regression analysis identified ATE risk factors in ICI-treated patients. RESULTS: Overall, the ICI group (n = 2877) demonstrated an ATEs risk 2.01 times higher than the non-ICI group (RR, 2.01 [95% CI (1.61-2.51)]; p < 0.001). Subgroup analysis revealed no significant increase in ATEs risk for ICI-treated patients within 1 year (Limited to a max 9-month follow-up, p = 0.075). However, ATEs risk in the ICI group rose by 41% at 1 year (p = 0.010) and 97% at 4 years (p ≤ 0.001). Age, diabetes, hypertension, peripheral atherosclerosis, atrial fibrillation, chronic ischemic heart disease, distant cancer metastasis, and ICI treatment cycles contributed to ATEs risk elevation in ICI-treated patients. CONCLUSION: ICI-treated patients may exhibit a higher risk of ATEs, especially after 1 year of treatment.

Development and validation of prediction model for overall survival in patients with lymphoma: a prospective cohort study in China.

OBJECTIVE: The survival of patients with lymphoma varies greatly among individuals and were affected by various factors. The aim of this study was to develop and validate a prognostic model for predicting overall survival (OS) in patients with lymphoma. METHODS: We conducted a prospective longitudinal cohort study in China between January 2014 and December 2018 (n = 1,594). After obtaining the follow-up data, we randomly split the cohort into the training cohort (n = 1,116) and the validation cohort (n = 478). The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the predictors of the model. Cox stepwise regression analysis was used to identify independent prognostic factors, which were finally displayed as static nomogram and web-based dynamic nomogram. We calculated the concordance index(C-index) to describe how the predicted survival of objectively confirmed prognosis. The calibration plot is used to evaluate the prediction accuracy and discrimination ability of the model. Net reclassification index (NRI) and decision curve analysis (DCA) curves were also used to evaluate the prediction ability and net benefit of the model. RESULTS: Nine variables in the training cohort were considered to be independent risk factors for patients with lymphoma in the final model: age, Ann Arbor Stage, pathologic type, B symptoms, chemotherapy, targeted therapy, lactate dehydrogenase (LDH), ß2-microglobulin and C-reactive protein (CRP). The C-indices of OS were 0.749 (95% CI, 0.729-0.769) in the training cohort and 0.731 (95% CI, 0.762-0.700) in the validation cohort. A good agreement between prediction by nomogram and actual observation was shown in the calibration curve for the probability of survival in both the training cohort and validation cohorts. The areas under curve (AUC) of the area under the receiver operating characteristic (ROC) curves for 1-year, 3-year, and 5-year OS were 0.813, 0.800, and 0.762, respectively, in the training cohort, and 0.802, 0.768, and 0.721, respectively, in the validation cohort. Compared with the Ann Arbor Stage system, NRI and DCA showed that the model had a higher predictive capacity and net benefit. CONCLUSION: The prediction models reliably estimate the outcome of patients with lymphoma. The model had high discrimination and calibration, which provided a simple and reliable tool for the survival prediction of the patients, and it might help patients benefit from personalized intervention.

Association of public health insurance with cancer-specific mortality risk among patients with nasopharyngeal carcinoma: a prospective cohort study in China.

Objective: Health insurance programs are effective in preventing financial hardship in patients with cancer. However, not much is known about how health insurance policies, especially in Southwest China with a high incidence of nasopharyngeal carcinoma (NPC), influence patients' prognosis. Here, we investigated the association of NPC-specific mortality with health insurance types and self-paying rate, and the joint effect of insurance types and self-paying rate. Materials and methods: This prospective cohort study was conducted at a regional medical center for cancer in Southwest China and included 1,635 patients with pathologically confirmed NPC from 2017 to 2019. All patients were followed up until May 31, 2022. We determine the cumulative hazard ratio of all-cause and NPC-specific mortality in the groups of various insurance kinds and the self-paying rate using Cox proportional hazard. Results: During a median follow-up period of 3.7 years, 249 deaths were recorded, of which 195 deaths were due to NPC. Higher self-paying rate were associated with a 46.6% reduced risk of NPC-specific mortality compared to patients with insufficient self-paying rate (HR: 0.534, 95% CI: 0.339-0.839, p = 0.007). For patients covered by Urban and Rural Residents Basic Medical Insurance (URRMBI), and for patients covered by Urban Employee Basic Medical Insurance, each 10% increase in the self-paying rate reduced the probability of NPC-specific death by 28.3 and 25%, respectively (UEBMI). Conclusion: Results of this study showed that, despite China's medical security administration improved health insurance coverage, NPC patients need to afford the high out-of-pocket medical costs in order to prolong their survival time.

Public health insurance and the risk of cancer-specific mortality in patients with cervical cancer: A Chinese prospective cohort study.

Objective: Cervical cancer has one of the highest incidence and mortality rates of any malignant tumor of the female reproductive tract, and its longer treatment period will place significant financial strain on patients and their families. Little is known about how health insurance policies influence cervical cancer prognosis, particularly in developing countries. The relationship between cervical cancer specific death and cervical cancer all-cause mortality with public health insurance, self-payment rate, and the combined effect of public health insurance and self-payment rate was investigated in this study. Materials and methods: From 2015 to 2019, a prospective longitudinal cohort study on cervical cancer was carried out in Chongqing, China. We chose 4,465 Chongqing University Cancer Hospital patients who had been diagnosed with cervical cancer between 2015 and 2019. The self-payment rate and public health insurance are taken into account in our subgroup analysis. After applying the inclusion and exclusion criteria, we describe the demographic and clinical traits of patients with various insurance plans and self-payment rates using the chi-square test model. The relationship between cervical cancer patients with various types of insurance, the self-payment rate, and treatment modalities is examined using the multivariate logistic regression model. After applying the inclusion and exclusion criteria, we summarize the demographic and clinical traits of patients with various insurance plans and self-payment rates using the chi-square test model. The association between cervical cancer patients with various types of insurance, the self-payment rate, and treatment modalities is examined using the multivariate logistic regression model. The cumulative hazard ratio of all-cause death and cervical cancer-specific mortality for various insurance types and self-payment rates was then calculated using the Cox proportional hazard model and the competitive risk model. Results: This study included a total of 3,982 cervical cancer patients. During the follow-up period (median 37.3 months, 95% CI: 36.40-38.20), 774 deaths were recorded, with cervical cancer accounting for 327 of them. Patients who obtained urban employee-based basic medical insurance (UEBMI) had a 37.1% lower risk of all-cause death compared to patients who received urban resident-based basic medical insurance (URBMI) (HRs = 0.629, 95% CI: 0.508-0.779, p = 0.001). Patients with a self-payment rate of more than 60% had a 26.9% lower risk of cervical cancer-specific mortality (HRs = 0.731, 95% CI: 0.561-0.952, p <0.02). Conclusions: The National Medical Security Administration should attempt to include the more effective self-paid anti-tumor medications into national medical insurance coverage within the restrictions of restricted medical insurance budget. This has the potential to reduce not only the mortality rate of cervical cancer patients, but also their financial burden. High-risk groups, on the other hand, should promote cervical cancer screening awareness, participate actively in the state-led national cancer screening project and enhance public awareness of HPV vaccine. This has the potential to reduce both cervical cancer patient mortality and the financial burden and impact.

Neutrophil-to-lymphocyte ratio is a negative prognostic biomarker for luminal A breast cancer.

Background: Inflammation plays an important role in the occurrence, development, and metastasis of tumors. However, the prognostic role of the neutrophil-to-lymphocyte ratio (NLR) in patients with luminal A breast cancer has rarely been reported in the literature. The purpose of this study was to investigate the relationship between preoperative peripheral blood NLR and the survival rate of patients with luminal A breast cancer. Methods: Data from 226 eligible patients with luminal A breast cancer at the Chongqing University Cancer Hospital between 2011 and 2016 were obtained. The cut-off value for NLR for predicting overall survival (OS) rate was obtained from the receiver operating characteristic (ROC) curve. The baseline characteristics of 2 groups were compared using the Chi-square test or Fisher's exact test, and OS was estimated using the Kaplan-Meier method. Cox analysis was performed to determine the correlation between clinicopathological parameters and prognosis. Results: ROC curve analysis showed that the cutoff value for NLR to predict OS was 2.0. Kaplan-Meier analysis revealed that the OS of patients with a NLR <2.0 was significantly longer than that of patients with a higher NLR >2 (P<0.0001). The area under the curve (AUC) for NLR to predict OS was 0.781 [95% confidence interval (CI): 0.712-0.851], sensitivity was 54.17%, and specificity was 97.06%. In univariate Cox regression analysis, NLR, tumor (T) stage (T3-T4 vs. T1-T2), and histological grade (II-III vs. I) were all significantly associated with OS. In multivariate Cox regression analysis, NLR and histology grade (II-III vs. I) were independent prognostic factors for OS. Conclusions: The results suggested that higher preoperative NLR was associated with worse prognosis in luminal A breast cancer.

Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer.

OBJECTIVE: The aim of this study was to establish a nomogram graph model to accurately predict the venous thromboembolism (VTE) risk probability in the general population with lung cancer. METHODS: Based on data from patients with lung cancer in Chongqing University Cancer Hospital of China, the independent risk factors of VTE were identified by the logistic univariable and multivariable analysis and were integrated to construct a nomogram, which was validated internally. The predictive effectiveness of the nomogram was evaluated by the receiver operating characteristic curve (ROC) and calibration curve. RESULTS: A total of 3398 lung cancer patients were included for analysis. The nomogram incorporated eleven independent VTE risk factors including karnofsky performance scale (KPS), stage of cancer, varicosity, chronic obstructive pulmonary disease (COPD), central venous catheter (CVC), albumin, prothrombin time (PT), leukocyte counts, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), dexamethasone, and bevacizumab. The C-index of the nomogram model was 0.843 and 0.791 in the training and validation cohort, respectively, demonstrating good discriminative power. The calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities. CONCLUSIONS: We established and validated a novel nomogram for predicting the risk of VTE in patients with lung cancer. The nomogram model could precisely estimate the VTE risk of individual lung cancer patients and identify high-risk patients who are in need of a specific anticoagulation treatment strategy.

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients.

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.

Development and validation of a risk prediction model for overall survival in patients with nasopharyngeal carcinoma: a prospective cohort study in China.

OBJECTIVE: Nasopharyngeal carcinoma (NPC) is prevailing in Southern China, characterized by distinct geographical distribution. Aimed to predict the overall survival (OS) of patients with nasopharyngeal carcinoma, this study developed and validated nomograms considering demographic variables, hematological biomarkers, and oncogenic pathogens in China. METHODS: The clinicopathological and follow-up data of the nasopharyngeal carcinoma patients obtained from a prospective longitudinal cohort study in the Chongqing University Cancer Hospital between Jan 1, 2017 and Dec 31, 2019 ([Formula: see text]). Cox regression model was used to tested the significance of all available variables as prognostic factors of OS. And independent prognostic factors were identified based on multivariable analysis to model nomogram. Concordance index (C-index), area under the receiver operating characteristic (AUC), calibration curve, and decision curve analysis (DCA) were measured to assess the model performance of nomogram. RESULTS: Data was randomly divided into a training cohort (1227 observers, about 70% of data) and a validation group (408 observers, about 30% of data). At multivariable analysis, the following were independent predictors of OS in NPC patients and entered into the nomogram: age (hazard ratio [HR]: 1.03), stage (stage IV vs. stage I-II, HR: 4.54), radiotherapy (Yes vs. No, HR: 0.43), EBV ([Formula: see text] vs.[Formula: see text], HR: 1.92), LAR ([Formula: see text] vs.[Formula: see text], HR: 2.05), NLR ([Formula: see text] vs. [Formula: see text] HR: 1.54), and PLR ([Formula: see text] vs.[Formula: see text], HR: 1.79). The C-indexes for training cohort at 1-, 3- and 5-year were 0.73, 0.83, 0.80, respectively, in the validation cohort, the C-indexes were 0.74 (95% CI 0.63-0.86), 0.80 (95% CI 0.73-0.87), and 0.77 (95% CI 0.67-0.86), respectively. The calibration curve demonstrated that favorable agreement between the predictions of the nomograms and the actual observations in the training and validation cohorts. In addition, the decision curve analysis proved that the nomogram model had the highest overall net benefit. CONCLUSION: A new prognostic model to predict OS of patients with NPC was developed. This can offer clinicians treatment making and patient counseling. Furthermore, the nomogram was deployed into a website server for use.

Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters.

Objective: To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. Methods: A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Results: Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets (P <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased (P < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a P value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. Conclusion: The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules.

Clinical characteristics and outcomes of newly diagnosed patients with HIV-associated aggressive B-cell NHL in China.

Little is known about the incidence, clinical characteristics and prognostic factors in HIV associated lymphoma as these are less common than HIV-negative lymphoma in China. Currently, there are no standard guidelines for treatment of these patients. Therefore, we performed a study to analyse the clinical characteristics and outcomes of newly diagnosed HIV-associated aggressive B-cell non-Hodgkin's lymphoma (NHL) patients in Chongqing University Cancer Hospital (CUCH). Totally 86 newly diagnosed HIV-associated aggressive B-cell NHL patients in CUCH, southwest China, from July 2008 to August 2021, were analysed. In the entire cohort, median age was 48 years (range, 23-87 years), and more patients were male (87.2%). Most patients had elevated lactate dehydrogenase (LDH) (82.6%), advanced ann arbor stage (80.2%) and high IPI score (IPI score, 3-5) (62.7%) at diagnosis. Median CD4+ T-cell count at diagnosis was 191/µl (range, 4-1022), 84 patients (97.7%) were on combination antiretroviral therapy (cART) at lymphoma diagnosis. In DLBCL patients, cox multivariate analysis showed that age ≥ 60 (HR = 2.251, 95%CI 1.122-4.516; p = 0.012), elevated LDH (HR = 4.452, 95%CI 1.027-19.297; p = 0.041) and received less than two cycles of chemotherapy (HR = 0.629, 95%CI 0.589-1.071; p = 0.012) were independent risk factors for adverse prognosis based on PFS. Age ≥ 60 (HR = 3.162, 95%CI 1.500-6.665; p = 0.002) and received less than two cycles of chemotherapy (HR = 0.524, 95%CI 0.347-0.791; p = 0.002) were also independent risk factor for adverse prognosis based on OS. In BL patients, cox multivariate analysis showed that elevated LDH and received less than two cycles of chemotherapy were independent risk factors for adverse prognosis. In the DLBCL group, median PFS times in the received rituximab and no received rituximab groups were not reached and 12 months, respectively (p = 0.006). Median OS times were not reached and 36 months, respectively (p = 0.021). In the BL group, median PFS times in the received rituximab and no received rituximab groups were not reached and 4.8 months, respectively (p = 0.046). Median OS times were not reached and 10.1 months, respectively (p = 0.035). Overall, these data indicated that standardized anti-lymphoma therapy and rituximab administration were significantly associated with improved outcomes in patients with HIV-associated DLBCL and BL.

Characteristics and Predictors of Venous Thromboembolism Among Lymphoma Patients Undergoing Chemotherapy: A Cohort Study in China.

Background: Venous thromboembolism (VTE) is a potential complication among lymphoma patients. We evaluated the incidence rate and predictors of VTE in lymphoma patients undergoing chemotherapy. Methods: The present study retrospectively studied 1,069 patients with lymphoma who were treated with chemotherapy from 2018 to 2020. We investigated clinical predictors of VTE among all patients. The follow-up results were obtained via telephone communication and from inpatient and outpatient records. Results: A total of 1,069 patients underwent chemotherapy for lymphoma. During a mean follow-up of 23.1 months, 52 (4.9%) patients developed VTE. According to a multivariate analysis, the five variables found to be independently associated with VTE were male sex (HR 2.273, 95% CI 1.197-4.316, p = 0.012), age >64-years-old (HR 2.256, 95% CI 1.017-5.005, p = 0.045), the number of cycles of chemotherapy (HR 4.579, 95% CI 1.173-17.883, p = 0.029), platelet count ≥350 × 109/L (HR 2.533, 95% CI 1.187-5.406, p = 0.016), and D-dimer >0.5 mg/L (HR 4.367, 95% CI 2.124-8.981, p < 0.001). Conclusion: This population-based study confirms the risk factors for VTE among patients with lymphoma who underwent chemotherapy and confirms that targeted thromboprophylaxis may reduce the burden of VTE in this population.

Lung Cancer-Specific Mortality Risk and Public Health Insurance: A Prospective Cohort Study in Chongqing, Southwest China.

Objective: The incidence and mortality of lung cancer rank first among malignant tumors, and its long treatment cycle will bring serious economic burdens to lung cancer patients and their families. There are few studies on the prognosis of lung cancer and insurance policies. This article explores the relationship between the lung cancer-specific death and public health insurance, self-paying rate, and the joint effect of public health insurance and self-paying rate. Materials and Methods: A prospective longitudinal cohort study was conducted in Chongqing, China from 2013 to 2019. The selected subjects were patients with C33-C34 coded according to the tenth edition of the International Classification of Diseases (ICD-10), aged 20 years or older. We conduct a subgroup analysis based on public health insurance types and self-paying rates. After following the inclusion and exclusion criteria, the chi-square test was used to describe the demographic and clinical characteristics of patients with different insurance types and different self-paying rates. Multivariate logistic regression was used to analyze the relationship between patients with different insurance types, self-paying rates, and lung cancer treatment methods. Finally, the Cox proportional hazard model and the competitive risk model are used to calculate the cumulative hazard ratio of all-cause death and lung cancer-specific death for different insurance types and different self-paying rate groups. Results: A total of 12,464 patients with lung cancer were included in this study. During the follow-up period (median 13 months, interquartile range 5.6-25.2 months), 5,803 deaths were observed, of which 3,781 died of lung cancer. Compared with patients who received urban resident-based basic medical insurance (URBMI), patients who received urban employee-based basic medical insurance (UEBMI) had a 38.1% higher risk of lung cancer-specific death (Hazard Ratios (HRs) = 1.381, 95% confidence interval (CI): 1.293-1.476, P < 0.005), Compared with patients with insufficient self-paying rate, patients with a higher self-paying rate had a 40.2% lower risk of lung cancer-specific death (HRs = 0.598, 95% CI: 0.557-0.643, P < 0.005). Every 10% increase in self-paying rate of URBMI reduces the risk of lung cancer-specific death by 17.6%, while every 10% increase in self-paying rate of UEBMI reduces the risk of lung cancer-specific death by 18.0%. Conclusions: The National Medical Security Administration should, under the condition of limited medical insurance funds, try to include the original self-paid anti-tumor drugs into the national medical insurance coverage. This can not only reduce the mortality rate of lung cancer patients, but also reduce the family burden of lung cancer patients. On the other hand, high-risk groups should increase their awareness of lung cancer screening and actively participate in the national cancer screening project led by the state.

A prognostic model using the neutrophil-albumin ratio and PG-SGA to predict overall survival in advanced palliative lung cancer.

OBJECTIVE: Inflammation and malnutrition are common in patients with advanced lung cancer undergoing palliative care, and their survival time is limited. In this study, we created a prognostic model using the Inflam-Nutri score to predict the survival of these patients. METHODS: A retrospective cohort study was conducted on 223 patients with advanced, histologically confirmed unresectable lung cancer treated between January 2017 and December 2018. The cutoff values of the neutrophil-albumin ratio (NAR) and Patient-Generated Subjective Global Assessment (PG-SGA) score were determined by the X-tile program. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis were performed to identify prognostic factors of overall survival (OS). We then established a nomogram model. The model was assessed by a validation cohort of 72 patients treated between January 2019 and December 2019. The predictive accuracy and discriminative ability were assessed by the concordance index (C-index), a plot of the calibration curve and risk group stratification. The clinical usefulness of the nomogram was measured by decision curve analysis (DCA). RESULTS: The nomogram incorporated stage, supportive care treatment, the NAR and the PG-SGA score. The calibration curve presented good performance in the validation cohorts. The model showed discriminability with a C-index of 0.76 in the training cohort and 0.77 in the validation cohort. DCA demonstrated that the nomogram provided a higher net benefit across a wide, reasonable range of threshold probabilities for predicting OS. The survival curves of different risk groups were clearly separated. CONCLUSIONS: The NAR and PG-SGA scores were independently related to survival. Our prognostic model based on the Inflam-Nutri score could provide prognostic information for advanced palliative lung cancer patients and physicians.

HBV Infection Status Does Not Influence the Initial Metastatic Pattern and the Prognosis of Breast Cancer Patients with de novo and Relapsed Metastatic Disease.

Purpose: This study aimed to evaluate the influence of hepatitis B virus (HBV) infection status on the initial metastatic pattern and prognosis in metastatic breast cancer (MBC). Methods: MBC patients admitted to Chongqing University Cancer Hospital between January 2011 and December 2019 were enrolled. The association of HBV infection status with clinicopathological features was analyzed. The impact of HBV infection status on initial metastatic pattern and survival was evaluated. Results: A total of 1124 patients with MBC, including 310 with de novo (cohort A) and 814 with relapsed metastatic disease (cohort B), were eligible for this study. Seropositive HBsAg was identified in 28 (9.0%) and 68 (8.4%) patients in cohort A and B, respectively. The clinicopathological features are similar between HBsAg-positive and HBsAg-negative patients. There was no significant association of HBV infection status with the rate of metastasis at each site in de novo and relapsed MBC. HBsAg-positive patients tended to have longer metastasis-free survival (MFS) and/or overall survival (OS) time, but it was not the independent prognostic factor. Conclusion: In conclusion, HBV infection status does not influence the initial metastatic pattern and the prognosis of MBC patients.

Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning.

Background: There is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients. Methods: Data of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model. Results: In total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision-recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% CI: 0.893-0.926) and an AUPRC of 0.43 (95% CI: 0.363-0.500). For the simplified model, five most relevant features were selected: Karnofsky Performance Status (KPS), a history of VTE, recombinant human endostatin, EGFR-TKI, and platelet count. We re-trained a random forest classifier with results of the AUROC of 0.87 (95% CI: 0.802-0.917) and AUPRC of 0.30 (95% CI: 0.265-0.358), respectively. Conclusion: According to the study results, there was no conspicuous decrease in the model's performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer.