bluuebunny/crossref_metadata_2025_split huggingface dataset:
Oncologic Nomogram for Stage I Rectal Cancer to Assist Patient Selection for Adjuvant (Chemo)Radiotherapy Following Local Excision
Authors: Zhao Shutao , Chen Xin , Wen Dacheng , Zhang Chao , Wang Xudong 
Published: March 2021

Background: Because of the low rate of lymph node metastasis in stage I rectal cancer (RC), local resection (LR) can achieve high survival benefits and quality of life. However, the indications for postoperative adjuvant therapy (AT) remain controversial.Methods: A retrospective analysis was performed in 6,486 patients with RC (pT1/T2) using the Surveillance, Epidemiology, and End Results (SEER) database. Patients were initially diagnosed from 2004 to 2016; following LR, 967 received AT and 5,519 did not. Propensity score matching (PSM) was used to balance the confounding factors of the two groups; the Kaplan–Meier method and the log-rank test were used for survival analysis. Cox proportional hazards regression analysis was used to screen independent prognostic factors and build a nomogram on this basis. X-tile software was used to divide the patients into low-, moderate-, and high-risk groups based on the nomogram risk score.Results: Multivariate analysis found that age, sex, race, marital status, tumor size, T stage, and carcinoembryonic antigen (CEA) in the non-AT group were independent prognostic factors for stage I RC and were included in the nomogram prediction model. The C-index of the model was 0.726 (95% CI, 0.689–0.763). We divided the patients into three risk groups according to the nomogram prediction score and found that patients with low and moderate risks did not show an improved prognosis after AT. However, high-risk patients did benefit from AT.Conclusion: The nomogram of this study can effectively predict the prognosis of patients with stage I RC undergoing LR. Our results indicate that high-risk patients should receive AT after LR; AT is not recommended for low-risk patients.
POLE and Mismatch Repair Status, Checkpoint Proteins and Tumor-Infiltrating Lymphocytes in Combination, and Tumor Differentiation: Identify Endometrial Cancers for Immunotherapy
Authors: Dong Dandan , Lei Huajiang , Liu Duanya , Bai Hansong , Yang Yue , Tang Baijie , Li Ke , Liu Juan , Xu Gang , Xiao Xue 
Published: March 2021

ObjectiveAlthough Polymerase-epsilon (POLE)-mutated and mismatch repair (MMR)-deficient endometrial cancers (ECs) are considered as promising candidates for anti-PD-1/PD-L1 therapy, selecting only these patients may exclude other patients who could potentially respond to this treatment strategy, highlighting the need of additional biomarkers for better patient selection. This study aims to evaluate potential predictive biomarkers for anti-PD-1/PD-L1 therapy in addition to POLE mutation (POLEm) and MMR deficiency (MMRd).MethodsWe performed next generation sequencing for POLE from 202 ECs, and immunohistochemistry for MLH1, MSH2, MSH6, PMS2, CD3, CD8, PD-1 and PD-L1 on full-section slides from these ECs. We assessed the association of POLEm and MMRd with clinicopathologic features, expression of check point proteins, and density of tumor-infiltrating lymphocytes (TILs). Prognostic impact of these immune markers was also evaluated.ResultsPOLEm, MMRd and high-grade tumors exhibited elevated level of TILs. Increased expression of PD-1 and PD-L1 was observed in MMRd and high-grade ECs. A subgroup of MMR proficient ECs also harbored increased density of TILs, and positive expression of PD-1 and PD-L1. In addition, negative expression of checkpoint proteins and high density of TILs in combination was associated with good prognosis.ConclusionsCandidates for PD-1 blockade may extend beyond POLEm and MMRd ECs, additional factors such as tumor grade, and combination of TILs levels and expression of checkpoint proteins may need to be considered for better patient selection.
Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis
Authors: Kang Yao , Zhu Xiaojun , Wang Xijun , Liao Shiyao , Jin Mengran , Zhang Li , Wu Xiangyang , Zhao Tingxiao , Zhang Jun , Lv Jun , Zhu Danjie 
Published: March 2021

BackgroundBladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.MethodIn order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).ResultsA total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P &lt; 0.001) and AJCC clinical stage (P &lt; 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.ConclusionOur study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.
CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer
Authors: Fan Shuxuan , Cui Xiaonan , Liu Chunli , Li Xubin , Zheng Lei , Song Qian , Qi Jin , Ma Wenjuan , Ye Zhaoxiang 
Published: March 2021

Objective: To evaluate whether a radiomics signature could improve stratification of postoperative risk and prediction of chemotherapy benefit in stage II colorectal cancer (CRC) patients.Material and Methods: This retrospective study enrolled 299 stage II CRC patients from January 2010 to December 2015. Based on preoperative portal venous-phase CT scans, radiomics features were generated and selected to build a radiomics score (Rad-score) using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The minority group was balanced by the synthetic minority over-sampling technique (SMOTE). Predictive models were built with the Rad-score and clinicopathological factors, and the area under the curve (AUC) was used to evaluate their performance. A nomogram was also constructed for predicting 3-year disease-free survival (DFS). The performance of the nomogram was assessed with a concordance index (C-index) and calibration plots.Results: Overall, 114 features were selected to construct the Rad-score, which was significantly associated with the 3-year DFS. Multivariate analysis demonstrated that the Rad-score, CA724 level, mismatch repair status, and perineural invasion were independent predictors of recurrence. Results showed that the Rad-score can classify patients into high-risk and low-risk groups in the training cohort (AUC 0.886) and the validation cohort (AUC 0.874). On this basis, a nomogram that integrated the Rad-score and clinical variables demonstrated superior performance (AUC 0.954, 0.906) than the clinical model alone (AUC 0.765, 0.705) in the training and validation cohorts, respectively. The C-index of the nomogram was 0.872, and the performance was acceptable.Conclusion: Our radiomics-based model can reliably predict recurrence risk in stage II CRC patients and potentially provide complementary prognostic value to the traditional clinicopathological risk factors for better identification of patients who are most likely to benefit from adjuvant therapy. The proposed nomogram promises to be an effective tool for personalized postoperative surveillance for stage II CRC patients.
Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer
Authors: Liu Ying , Wu Minghao , Zhang Yuwei , Luo Yahong , He Shuai , Wang Yina , Chen Feng , Liu Yulin , Yang Qian , Li Yanying , Wei Hong , Zhang Hong , Jin Chenwang , Lu Nian , Li Wanhu , Wang Sicong , Guo Yan , Ye Zhaoxiang 
Published: March 2021

ObjectiveWe aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy.Methods197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction.ResultsRadiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P &lt;0.001).ConclusionEarly response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.
Development and Validation of a Nomogram for Predicting Bronchopulmonary Dysplasia in Very-Low-Birth-Weight Infants
Authors: Zhang Jingdi , Luo Chenghan , Lei Mengyuan , Shi Zanyang , Cheng Xinru , Wang Lili , Shen Min , Zhang Yixia , Zhao Min , Wang Li , Zhang Shanshan , Mao Fengxia , Zhang Ju , Xu Qianya , Han Suge , Zhang Qian 
Published: March 2021

Background: Bronchopulmonary dysplasia is a common pulmonary disease in newborns and is one of the main causes of death. The aim of this study was to build a new simple-to-use nomogram to screen high-risk populations.Methods: In this single-center retrospective study performed from January 2017 to December 2020, we reviewed data on very-low-birth-weight infants whose gestational ages were below 32 weeks. LASSO regression was used to select variables for the risk model. Then, we used multivariable logistic regression to build the prediction model incorporating these selected features. Discrimination was assessed by the C-index, and and calibration of the model was assessed by and calibration curve and the Hosmer-Lemeshow test.Results: The LASSO regression identified gestational age, duration of ventilation and serum NT-proBNP in the 1st week as significant predictors of BPD. The nomogram-illustrated model showed good discrimination and calibration. The C-index was 0.853 (95% CI: 0.851–0.854) in the training set and 0.855 (95% CI: 0.77–0.94) in the validation set. The calibration curve and Hosmer-Lemeshow test results showed good calibration between the predictions of the nomogram and the actual observations.Conclusion: We demonstrated a simple-to-use nomogram for predicting BPD in the early stage. It may help clinicians recognize high-risk populations.
Efficacy of Conversion Surgery Following Apatinib Plus Paclitaxel/S1 for Advanced Gastric Cancer With Unresectable Factors: A Multicenter, Single-Arm, Phase II Trial
Authors: Xu Zhiyuan , Hu Can , Yu Jianfa , Du Yian , Hu Ping , Yu Guofa , Hu Conggang , Zhang Yu , Mao Wei , Chen Shanqi , Cheng Xiangdong 
Published: March 2021

Objective: Conversion therapy (surgical resection after chemotherapy) is a promising option for unresectable gastric cancer (GC) patients. Addition of anti-angiogenesis drug improves response to chemotherapy. Hence, this study explored the feasibility and efficacy of preoperative paclitaxel (PTX)/S1 chemotherapy combined with apatinib for unresectable GC.Methods: Thirty-one eligible patients with a single unresectable factor were enrolled in this multi-center, single-arm trial. Apatinib (500 mg qd) was administered continuously, while PTX (130 mg/m2) on day 1 and S1 (80 mg/m2) on day 1–14 were given every 3 weeks. The treatment was given for three cycles preoperatively, but the last cycle did not include apatinib. The primary objective measurements included R0 resection rate, objective response rate (ORR) and morbidity of preoperative treatment.Results: Among the 31 patients, 30 patients were evaluable for tumor response, the ORR to preoperative treatment was 73.3%. Eighteen of 30 patients underwent surgery, and R0 resection was achieved in 17 patients. The patients who underwent the conversion surgery had a superior OS compared with those who did not (3 years OS: 52.9 vs 8.3%, p = 0.001). The surgery was operated after apatinib had stopped for a median duration of 4 weeks. Neither anastomotic leakage nor wound healing complications was observed. No increased bleeding event was observed compared with historical data. During preoperative treatment, grade 3 or 4 toxicities were experienced by 58.1% of the patients.Conclusion: Chemotherapy in combination with apatinib demonstrated higher rates of conversion and R0 resection and a superior survival benefit in initial unresectable GC. It is safe and reasonable to suspend apatinib for 4 weeks before the gastrectomy.
Mood Disorder in Cancer Patients Undergoing Radiotherapy During the COVID-19 Outbreak
Authors: Nardone Valerio , Reginelli Alfonso , Vinciguerra Claudia , Correale Pierpaolo , Calvanese Maria Grazia , Falivene Sara , Sangiovanni Angelo , Grassi Roberta , Di Biase Angela , Polifrone Maria Angela , Caraglia Michele , Cappabianca Salvatore , Guida Cesare 
Published: March 2021

Introduction: Novel coronavirus (COVID-19) is having a devastating psychological impact on patients, especially patients with cancer. This work aims to evaluate mood disorders of cancer patients undergoing radiation therapy during COVID-19 in comparison with cancer patients who underwent radiation therapy in 2019.Materials and Methods: We included all the patients undergoing radiation therapy at our department in two-time points (once a week for a month in May 2019) and during the COVID-19 outbreak (in April 2020). All the patients were asked to fulfill a validated questionnaire (STAI-Y1, State trait anxiety inventory scale), the Symptom Distress thermometer (SDT) (from 0 to 10 score), and the Beck Depression Inventory v.2 (BDI-2). We took into account the COVID-19 outbreak and also sex, age, week of radiation treatment, and disease.Results: We included 458 patients (220 males and 238 females), with a median age of 64 years. STAI-Y1 median score was 40 (mean 41,3, range 19–79), whereas the median score of SDT was five and BDI-2 median score was 11. STAI-Y1, SDT, and BDI-2 were significantly correlated with the COVID-19 outbreak (p &lt; 0,001 for all the tests), sex (p: 0,016 for STAI-Y1, p &lt; 0.001 for SDT, p:0.013 for BDI-2), week of treatment (p: 0.012 for STAI-Y1 and p: 0.031 for SDT), and disease (p:0.015 for STAI-Y1, p &lt; 0.001 for SDT and p:0.020 for BDI-2).Conclusions: The prevalence of mood disorders in patients undergoing radiation therapy is higher than expected and even higher during the COVID-19 outbreak. These measurements could be useful as a baseline to start medical humanities programs to decrease these scores.
Clinical Efficacy and Cost-Effectiveness of Imagery Rescripting Only Compared to Imagery Rescripting and Schema Therapy in Adult Patients With PTSD and Comorbid Cluster C Personality Disorder: Study Design of a Randomized Controlled Trial
Authors: van den End Arne , Dekker Jack , Beekman Aartjan T. F. , Aarts Inga , Snoek Aishah , Blankers Matthijs , Vriend Chris , van den Heuvel Odile A. , Thomaes Kathleen 
Published: March 2021

Background: Posttraumatic stress disorder (PTSD) is a serious and relatively common mental disorder causing a high burden of suffering. Whereas evidence-based treatments are available, dropout and non-response rates remain high. PTSD and Cluster C personality disorders (avoidant, dependent or obsessive-compulsive personality disorder; CPD) are highly comorbid and there is evidence for suboptimal treatment effects in this subgroup of patients. An integrated PTSD and CPD treatment may be needed to increase treatment efficacy. However, no studies directly comparing the efficacy of regular PTSD treatment and treatment tailored to PTSD and comorbid CPD are available. Whether integrated treatment is more effective than treatment focused on PTSD alone is important, since (1) no evidence-based guideline for PTSD and comorbid CPD treatment exists, and (2) treatment approaches to CPD are costly and time consuming. Present study design describes a randomized controlled trial (RCT) directly comparing trauma focused treatment with integrated trauma focused and personality focused treatment.Methods: An RCT with two parallel groups design will be used to compare the clinical efficacy and cost-effectiveness of “standalone” imagery rescripting (n = 63) with integrated imagery rescripting and schema therapy (n = 63). This trial is part of a larger research project on PTSD and personality disorders. Predictors, mediators and outcome variables are measured at regular intervals over the course of 18 months. The main outcome is PTSD severity at 12 months. Additionally, machine-learning techniques will be used to predict treatment outcome using biopsychosocial variables.Discussion: This study protocol outlines the first RCT aimed at directly comparing the clinical efficacy and cost-effectiveness of imagery rescripting and integrated imagery rescripting and schema therapy for treatment seeking adult patients with PTSD and comorbid cluster C personality pathology. Additionally, biopsychosocial variables will be used to predict treatment outcome. As such, the trial adds to the development of an empirically informed and individualized treatment indication process.Clinical Trial registration:ClinicalTrials.gov, NCT03833531.
Prospective Long-Term Cohort Study of Subjects With First-Episode Psychosis Examining Eight Major Outcome Domains and Their Predictors: Study Protocol
Authors: Peralta Victor , Moreno-Izco Lucía , García de Jalón Elena , Sánchez-Torres Ana M. , Janda Lucía , Peralta David , Fañanás Lourdes , Cuesta Manuel J. ,   SEGPEPs Group
Published: March 2021

Background:Our current ability to predict the long-term course and outcome of subjects with a first-episode of psychosis (FEP) is limited. To improve our understanding of the long-term outcomes of psychotic disorders and their determinants, we designed a follow-up study using a well-characterized sample of FEP and a multidimensional approach to the outcomes. The main goals were to characterize the long-term outcomes of psychotic disorders from a multidimensional perspective, to address the commonalities and differential characteristics of the outcomes, and to examine the common and specific predictors of each outcome domain. This article describes the rationale, methods, and design of a longitudinal and naturalistic study of subjects with epidemiologically defined first-admission psychosis.Methods:Eligible subjects were recruited from consecutive admissions between January 1990 and December 2009. Between January 2018 and June 2021, we sought to trace, re-contact, and re-interview the subjects to assess the clinical course, trajectories of symptoms and functioning, and the different outcomes of psychotic disorders. Since this is a naturalistic study, the research team will not interfere with the subjects' care and treatment. Predictors include antecedent variables, first-episode characteristics, and illness-related variables over the illness course. We assess eight outcome domains at follow-up: psychopathology, psychosocial functioning, self-rated personal recovery, self-rated quality of life, cognitive performance, neuromotor dysfunction, medical and psychiatric comorbidities, and mortality rate. The range of the follow-up period will be 10–31 years with an estimated mean of 20 years. We estimate that more than 50% of the baseline sample will be assessed at follow-up.Discussion:The study design was driven by the increasing need to refine the ability to predict the different clinical outcomes in FEP, and it aims to close current gaps in knowledge, with a broad approach to both the definition of outcomes and their determinants. To the best of our knowledge, this study is one of the few attempting to characterize the very long-term outcome of FEP and the only study addressing eight major outcome domains. We hope that this study helps to better characterize the long-term outcomes and their determinants, enabling better risk stratification and individually tailored, person-based interventions.