Given that the coronavirus disease 2019 (COVID-19) mainly spreads through the respiratory system and is associated with severe pulmonary complications, lung cancer patients may have worse outcomes than those with other tumors. There is no confirmed evidence about the mortality comparison between COVID-19 patients with lung cancer and other tumors. We performed a systematic review and pooled analysis to provide precise estimates of the mortality rate of COVID-19 patients with lung cancer and other tumors. Our study systemically included and reviewed 13 studies on the characteristics of COVID-19 patients with lung cancer published up to November 1, 2020. The primary endpoint was all-cause mortality. We also compared the all-cause mortality rates in China and other regions as a secondary endpoint. The mortality rate was assessed with a fixed-effects model, which was used to derive the pooled mortality and 95 % confidence interval (CI). Thirteen studies from different countries, involving 1,229 patients with both COVID-19 and cancer, were selected for the pooled analysis. A total of 343 deaths were recorded in this population: 86 for lung cancers and 257 for other tumors. The mortality rate varies from 18 % to 60 % for patients with lung cancer and COVID-19 and 10%–41% for other tumor patients with COVID-19. The overall meta-analysis did not show a significant mortality difference for the lung cancer and other tumor subgroups (OR = 1.47, 95 %CI = 0.98–2.20, p = 0.06, I2 = 23 %). Nevertheless, in regions other than China, the pooled mortality of lung cancer patients with COVID-19 was 42 %, which was significantly higher than that of other tumors (24 %) (OR = 2.73, 95 % CI = 1.54–4.86, p = 0.0006, I2 = 16 %). Appropriate and aggressive preventive measures should be implemented to reduce the risk of COVID-19 in patients with cancer and optimally manage those who contract the infection.

There is no standard-of-care for recurrent, metastatic nasopharyngeal carcinoma (rmNPC) after first-line chemotherapy. Here, we report the efficacy and safety data of apatinib in rmNPC patients. Thirty-five biopsy-proven rmNPC patients received apatinib at 500 mg/day under a compassionate access programme. Primary end-point was objective response rate (ORR; RECIST v1.1). Kaplan-meier method was used to estimate progression-free survival (PFS) and overall survival (OS). Toxicity was assessed by CTCAE v4.0. 82.9% (29 of 35) of patients had poly-metastatic rmNPC. All patients, except five, were platinum-refractory; 37.1% (13 of 35) received ≥ 2 lines. Median number of apatinib cycles was 4.0 (IQR: 2.0-8.0). ORR was 31.4% (11 of 35 [95% CI: 16.9-49.3]) and disease control rate was 74.3% (26 of 35 [95% CI: 56.7-87.5]); 11 (31.4%) and 4 (11.4%) patients demonstrated response for ≥ 6 and ≥ 12 months, respectively. Median PFS and OS was 3.9 (95% CI: 3.1-5.5) months and 5.8 (95% CI: 4.5-8.0) months, respectively. Among the ≥ 12-month responders, all patients had pre-apatinib EBV DNA titer of <700 (range: 353-622) copies/ml; this was consistent with the association of PFS with pre-apatinib EBV DNA titer (adjusted HR 3.364 [95% CI: 1.428-7.923] for ≥ 4000 copies/ml, P = 0.006). 42.9% (15 of 35) of patients required dose reduction. Nonetheless, only five (14.3%) patients suffered from G3 toxicities (two haematological, one hypertension, one hand-foot syndrome and one elevated aminotransferases). Our data suggests potential efficacy of apatinib in rmNPC patients. Although incidence of severe toxicities was low, dose modification was required in 42.9% of patients.

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 ( $$\mathrm{n}=1635$$n = 1635 ). 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 ( $$\ge 1000$$≥ 1000 vs. $$<1000$$< 1000 , HR: 1.92), LAR ( $$>6.15$$> 6.15 vs. $$\le 6.15$$≤ 6.15 , HR: 2.05), NLR ( $$>4.84$$> 4.84 vs. $$\le 4.84$$≤ 4.84 HR: 1.54), and PLR ( $$>206.33$$> 206.33 vs. $$\le 206.33$$≤ 206.33 , 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.

Background: Lactate dehydrogenase (LDH) is a known prognostic biomarker for the endemic variant of nasopharyngeal carcinoma (NPC). Here, we investigate whether serial changes in LDH level between chemotherapy (CT) cycles are associated with tumour response to CT. Methods: Patients with biopsy-proven, recurrent or treatment-naïve metastatic NPC (mNPC) were recruited. All patients had received at least two cycles of platinum-based doublet or triplet CT, with serial assessment of LDH prior to every cycle of chemotherapy (CT1–6). Patients harbouring conditions that affect LDH levels (IU/L) were excluded. Tumour response was assessed after every two cycles of CT by RECIST v1.1. Results: A total of 158 patients were analysed, including 77 with recurrent and 81 with treatment-naïve mNPC. High pre-CT LDH was associated with an inferior overall survival [hazard ratio 1.93 for ⩾240 versus <240 (1.34–2.77), p < 0.001], which is consistent with published literature. We found that both absolute LDH levels and LDH ratios (LDH_CTn: LDH_CTn–1) were associated with tumour response [partial response versus progressive disease: median value across CT1–6 = 168–190 versus 222–398 (absolute); 0.738–0.988 versus 1.039–1.406 (ratio)], albeit LDH ratio had a tighter variance between patients. Finally, we showed that an LDH ratio cut-off of 1.0 at CT1, CT3 and CT5 was predictive of progressive disease at CT2, CT4, CT6 [area under the curve of 0.73 (0.65–0.80)]. Conclusion: Herein, we characterised the longitudinal variation of LDH in response to CT in mNPC. Our findings suggest the potential utility of interval LDH ratio to predict subsequent tumour response to CT.

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.

Background: Pseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective. Methods: 34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation. Results: There was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5–99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation. Conclusions: This is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis.