PurposeTumor associated macrophages (TAMs) are considered with the capacity to have both negative and positive effects on tumor growth. The prognostic value of TAM for survival in patients with solid tumor remains controversial.Experimental DesignWe conducted a meta-analysis of 55 studies (n = 8,692 patients) that evaluated the correlation between TAM (detected by immunohistochemistry) and clinical staging, overall survival (OS) and disease free survival (DFS). The impact of M1 and M2 type TAM (n = 5) on survival was also examined.ResultsHigh density of TAM was significantly associated with late clinical staging in patients with breast cancer [risk ratio (RR)  = 1.20 (95% confidence interval (CI), 1.14–1.28)] and bladder cancer [RR = 3.30 (95%CI, 1.56–6.96)] and with early clinical staging in patients with ovarian cancer [RR = 0.52 (95%CI, 0.35–0.77)]. Negative effects of TAM on OS was shown in patients with gastric cancer [RR = 1.64 (95%CI, 1.24–2.16)], breast cancer [RR = 8.62 (95%CI, 3.10–23.95)], bladder cancer [RR = 5.00 (95%CI, 1.98–12.63)], ovarian cancer [RR = 2.55 (95%CI, 1.60–4.06)], oral cancer [RR = 2.03 (95%CI, 1.47–2.80)] and thyroid cancer [RR = 2.72 (95%CI, 1.26–5.86)],and positive effects was displayed in patients with colorectal cancer [RR = 0.64 (95%CI, 0.43–0.96)]. No significant effect was showed between TAM and DFS. There was also no significant effect of two phenotypes of TAM on survival.ConclusionsAlthough some modest bias cannot be excluded, high density of TAM seems to be associated with worse OS in patients with gastric cancer, urogenital cancer and head and neck cancer, with better OS in patients with colorectal cancer.

This study examines the relationships between government interventions, risk perception, and the public's adoption of protective action recommendations (PARs) during the COVID-19 coronavirus disease emergency in mainland China. We conducted quota sampling based on the proportion of the population in each province and gender ratios in the Sixth Census and obtained a sample size of 3837. Government intervention was divided into government communication, government prevention and control, and government rescue. We used multiple regression and a bootstrap mediation effect test to study the mechanism of these three forms of government intervention on the public's adoption of PARs. The results show that government prevention and control and government rescue significantly increased the likelihood of the public adopting PARs. Risk perception was significantly associated with the public's adoption of PARs. The effects of government interventions and risk perception on the public's adoption of PARs was not found to vary by region. Risk perception is identified as an important mediating factor between government intervention and the public's adoption of PARs. These results indicate that increasing the public's risk perception is an effective strategy for governments seeking to encourage the public to adopt PARs during the COVID-19 pandemic.

Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential to optimizing treatment. Positron emission tomography (PET) and computed tomography (CT) imaging are routinely used to identify LNM. Although large or highly active lymph nodes (LNs) have a high probability of being positive, identifying small or less reactive LNs is challenging. The accuracy of LNM identification strongly depends on the physician's experience, so an automatic prediction model for LNM based on CT and PET images is warranted to assist LMN identification across care providers and facilities. Radiomics and deep learning are the two promising imaging-based strategies for node malignancy prediction. Radiomics models are built based on handcrafted features, while deep learning learns the features automatically. To build a more reliable model, we proposed a hybrid predictive model that takes advantages of both radiomics and deep learning based strategies. We designed a new many-objective radiomics (MO-radiomics) model and a 3-dimensional convolutional neural network (3D-CNN) that fully utilizes spatial contextual information, and we fused their outputs through an evidential reasoning (ER) approach. We evaluated the performance of the hybrid method for classifying normal, suspicious and involved LNs. The hybrid method achieves an accuracy (ACC) of 0.92 while XmasNet and Radiomics methods achieve 0.79 and 0.79, respectively. The hybrid method provides a more accurate way for predicting LNM using PET and CT.

Background and PurposeIntensity modulated radiation therapy (IMRT) can deliver higher doses with less damage of healthy tissues compared with three-dimensional radiation therapy (3DCRT). However, for the scenarios with better clinical outcomes for IMRT than 3DCRT in prostate cancer, the results remain ambiguous. We performed a meta-analysis to assess whether IMRT can provide better clinical outcomes in comparison with 3DCRT in patients diagnosed with prostate cancer.Materials and MethodsWe conducted a meta-analysis of 23 studies (n = 9556) comparing the clinical outcomes, including gastrointestinal (GI) toxicity, genitourinary (GU) toxicity, biochemical controland overall survival (OS).ResultsIMRT was significantly associated with decreased 2–4 grade acute GI toxicity [risk ratio (RR) = 0.59 (95% confidence interval (CI), 0.44, 0.78)], late GI toxicity [RR = 0.54, 95%CI (0.38, 0.78)], late rectal bleeding [RR = 0.48, 95%CI (0.27, 0.85)], and achieved better biochemical control[RR = 1.17, 95%CI (1.08, 1.27)] in comparison with 3DCRT. IMRT and 3DCRT remain the same in regard of grade 2–4 acute rectal toxicity [RR = 1.03, 95%CI (0.45, 2.36)], late GU toxicity [RR = 1.03, 95%CI (0.82, 1.30)] and overall survival [RR = 1.07, 95%CI (0.96, 1.19)], while IMRT slightly increased the morbidity of grade 2–4 acute GU toxicity [RR = 1.08, 95%CI (1.00, 1.17)].ConclusionsAlthough some bias cannot be ignored, IMRT appears to be a better choice for the treatment of prostate cancer when compared with 3DCRT.

Purpose: Locoregional recurrence (LRR) is the predominant pattern of relapse after nonsurgical treatment of head and neck squamous cell cancer (HNSCC). Therefore, accurately identifying patients with HNSCC who are at high risk for LRR is important for optimizing personalized treatment plans. In this work, we developed a multi-classifier, multi-objective, and multi-modality (mCOM) radiomics-based outcome prediction model for HNSCC LRR. Methods: In mCOM, we considered sensitivity and specificity simultaneously as the objectives to guide the model optimization. We used multiple classifiers, comprising support vector machine (SVM), discriminant analysis (DA), and logistic regression (LR), to build the model. We used features from multiple modalities as model inputs, comprising clinical parameters and radiomics feature extracted from X-ray computed tomography (CT) images and positron emission tomography (PET) images. We proposed a multi-task multi-objective immune algorithm (mTO) to train the mCOM model and used an evidential reasoning (ER)-based method to fuse the output probabilities from different classifiers and modalities in mCOM. We evaluated the effectiveness of the developed method using a retrospective public pretreatment HNSCC dataset downloaded from The Cancer Imaging Archive (TCIA). The input for our model included radiomics features extracted from pretreatment PET and CT using an open source radiomics software and clinical characteristics such as sex, age, stage, primary disease site, human papillomavirus (HPV) status, and treatment paradigm. In our experiment, 190 patients from two institutions were used for model training while the remaining 87 patients from the other two institutions were used for testing. Results: When we built the predictive model using features from single modality, the multi-classifier (MC) models achieved better performance over the models built with the three base-classifiers individually. When we built the model using features from multiple modalities, the proposed method achieved area under the receiver operating characteristic curve (AUC) values of 0.76 for the radiomics-only model, and 0.77 for the model built with radiomics and clinical features, which is significantly higher than the AUCs of models built with single-modality features. The statistical analysis was performed using MATLAB software. Conclusions: Comparisons with other methods demonstrated the efficiency of the mTO algorithm and the superior performance of the proposed mCOM model for predicting HNSCC LRR.

ObjectiveTo evaluate the predicting value of MUC1 expression in lymph node and distant metastasis of colorectal cancer (CRC).MethodsPubmed/ MEDLINE and EMBASE were searched to identify eligible studies that evaluated the correlation between MUC1 and CRC. A meta-analysis was conducted to evaluate the impact of MUC1 expression on CRC metastasis.ResultsA total of 18 studies (n = 3271) met inclusion criteria and the mean Newcastle-Ottawa Scale (NOS) score was 6.3 with a range from 4 to 8. The pooled OR in the meta-analysis of 15 studies indicated that positive MUC1 expression correlated with more CRC node metastasis (OR = 2.32, 95% CI = 1.63–3.29). The data synthesis of 6 studies suggested that MUC1 expression predicted more possibility of CRC distant metastasis (OR = 2.22, 95% CI = 1.23–4.00). In addition, the combined OR of 7 studies showed that MUC1 expression indicated higher Duke’s stage (OR = 3.02, 95% CI = 2.11–4.33). No publication bias was found in the mate-analysis by Begg’s test or Egger’s test with the exception of the meta-analysis of MUC1 with CRC node metastasis (Begg’s test p = 0.729, Egger’s test p = 0.000).ConclusionsDespite of some modest bias, the pooled evidence suggested that MUC1 expression was significantly correlated with CRC metastasis.

The majority of patients surveyed expressed willingness to contribute to patient safety, but their knowledge about patient safety practices was generally very limited.

Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential for treatment optimization. PET and CT imaging are routinely used for LNM identification. However, uncertainties of LNM always exist especially for small size or reactive nodes. Radiomics and deep learning are the two preferred imaging-based strategies for node malignancy prediction. Radiomics models are built based on handcrafted features, and deep learning can learn the features automatically. We proposed a hybrid predictive model that combines many-objective radiomics (MO-radiomics) and 3-dimensional convolutional neural network (3D-CNN) through evidential reasoning (ER) approach. To build a more reliable model, we proposed a new many-objective radiomics model. Meanwhile, we designed a 3D-CNN that fully utilizes spatial contextual information. Finally, the outputs were fused through the ER approach. To study the predictability of the two modalities, three models were built for PET, CT, and PET&CT. The results showed that the model performed best when the two modalities were combined. Moreover, we showed that the quantitative results obtained from the hybrid model were better than those obtained from MO-radiomics and 3D-CNN.