Extramedullary hematopoiesis (EMH) usually occurs in hematological disease, but more rarely develops in cases of malignant solid tumors. Due to its features on computed tomography (CT) and magnetic resonance imaging (MRI) that are atypical, EMH in tumor patients might easily be misdiagnosed as metastasis leading to the improper TNM staging and inappropriate therapy. Here, we reported the first case of pleural EMH occurring in a patient with esophageal carcinoma whose pleural lesion was first diagnosed as metastasis and confirmed EMH after the needle biopsy. In addition, a retrospective review was conducted by analyzing patients presented with EMH with malignant solid tumors from PubMed and Medline databases. A total of 42 solid tumor patients with EMH were enrolled, and breast cancer was the most common (n=13, 31.0%), followed by renal carcinoma (n=7, 16.7%) and lung cancer (n=6, 14.3%). A wide variety of body sites may be affected by EMH in malignant solid tumor patients, of which the lymph nodes (n=8, 19.0%) and liver (n=7, 16.7%) were the most common, followed by the kidney (n=6, 14.3%). All patients were diagnosed with EMH by excision, biopsy, or autopsy. Treatment strategies for EMH included surgery (n=25, 59.5%), hydroxyurea (n=1, 2.4%), and blood transfusions (n=2, 4.8%); a further 14 patients (33.3%) were subjected to clinical observation without intervention. Of the patients for whom outcome was reported, 10 patients maintained a good performance status (23.8%) and a further six patients died from the malignant tumor. This was the first study to summarize the presentations of EMH in malignant solid tumors, and our findings might provide some useful guidance for clinical practice, especially for treating patients harboring nonresponse lesions during the antitumor treatment.

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The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on 18 F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4-13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7-12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods : This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results : The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively ( P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively ( P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUV max value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions : Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

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Background: Bevacizumab combined with chemotherapy is still one of the standard options for treatment of advanced non-small cell lung cancer (NSCLC) patients without driver mutations. Serum inflammatory factors, representing the systemic immune status, are shown to have complicated relationships with tumor angiogenesis, and proved to be associated with survival of advanced NSCLC patients. However, the information from the baseline factors is relatively limited, which cannot reflect the dynamic changes of systemic immune status during bevacizumab treatment. We, thus, attempted to evaluate longitudinal kinetics of systemic inflammatory factors during treatment of bevacizumab and to explore their predictive role in treatment response and patient outcomes in advanced NSCLC.Method: Systemic inflammatory factors (neutrophil/lymphocyte (NLR), platelet/lymphocyte (PLR), neutrophil×platelet/lymphocyte (SII) and lymphocyte/monocyte (LMR)) and clinical variables were collected and analyzed from 161 advanced NSCLC patients treated with bevacizumab. Mixed effect regression models were first performed for longitudinal analysis of the changes of serum inflammatory factors during bevacizumab treatment. Then, univariate and multivariate Cox models were used for overall survival (OS) and progression free survival (PFS) analyses to determine the independent prognostic factors.Results: In the first 6 cycles of bevacizumab treatment, patients with complete response/partial response (CR/PR) had a -0.11, -0.066, -0.15, and 0.073 change every 2 cycles in transformed NLR (95%CI: -0.19--0.03, p=0.008), PLR (95%CI: -0.12--0.013, p=0.015), SII (95%CI: -0.23--0.05, p<0.001) and LMR (95%CI: 0.049-0.14, p=0.036), respectively, compared to patients with progressive disease (PD). With respect to analysis of the longitudinal changes before progression, patients experienced a significant increase in transformed NLR (Coef=0.09, 95%CI: 0.019-0.17, p=0.014), PLR (Coef=0.05, 95%CI: 0.002-0.10, p=0.04), and SII (Coef=0.091, 95%CI: 0.015-0.17, p=0.019), but a decrease in transformed LMR (Coef=-0.08, 95%CI: -0.14-0.018, p=0.012). On multivariate Cox model analyses, decrease of LMR (HR=0.62, 95% CI: 0.4-0.96, p=0.033) was shown to be the independent risk factor for PFS; and low level of baseline LMR (HR=0.4, 95% CI: 0.17-0.94, p=0.036), increase of NLR (HR=2.36, 95%CI: 1.25-4.44, p=0.008), and decrease of LMR (HR=0.42, 95%CI: 0.18-0.97, p=0.041) were the independent risk factors for death.Conclusion: The activation of systemic immune status evaluated by the kinetic changes of serum inflammatory factors was associated with good response to bevacizumab; however, the suppressive status may indicate the resistance to bevacizumab. Dynamic changes of systemic inflammatory factors also had prognostic value in predicting outcomes of advanced NSCLC patients treated with bevacizumab.

Previous studies have revealed the influence of cultural values on volunteering; however, few have focused on the Confucian value of benevolence. This study examined the relationship between the Confucian value of benevolence and volunteering, as well as the mediating role of volunteer motives. A total of 473 Chinese college students completed questionnaires to assess the Confucian value of benevolence, including familism, unity, and harmony (UH), six functional motives to volunteer and volunteering. The results revealed a positive relationship between UH and volunteering and the mediating role of functional motives; however, there was no significant association between familism and volunteering. Furthermore, a multigroup analysis suggested that the mediation model was similar across genders among college students. Practical implications and limitations are also discussed.

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