bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship between tumor size and metastatic site in patients with stage IV breast cancer: A large SEER-based study.
Authors: Dong Qian , Zhang Mi , Jiang Da 
Published: June 2021

e13065 Background: To analyze the correlation between tumor size and metastatic site in first-diagnosed stage IV breast cancer patients. Methods: Stage IV breast cancer patients diagnosed from 2010 to 2015 were screened by the Surveillance, Epidemiology, and End Results (SEER) database. The characteristics of clinical variables were represented by a frequency table, and the Chi-square test was used for comparison. At the same time, the Chi-square test was used to analyze the relationship between tumor size and organ metastasis. Correlation between tumor size and the prognosis of patients was contributed by KM curve and Log-rank test. Results: Regardless of tumor size, the proportion of bone metastasis was higher and brain metastasis was lower in breast cancer patients. There were significant differences in the site of metastases based on different subtype. Luminal A and Luminal B breast cancer had the highest proportion of bone metastases; brain metastasis accounted for the highest proportion in triple-negative breast cancer (TNBC); while the incidence of liver metastasis was the highest in Her-2(+) breast cancer. At the same time, the results indicated that Luminal A breast cancer with a tumor size > 5 cm was more likely to develop multi-site metastasis and lung metastasis, while Luminal B breast cancer with a tumor size ≤ 5 cm was more likely to develop liver metastasis. The results also revealed that TNBC patients with a tumor size of 0 - 2cm were more likely to develop bone metastasis than those with a tumor size > 5 cm, and the incidence of lung metastasis in triple-negative patients showed an increasing trend with the increase of tumor size. Conclusions: Based on subtype, we found that there was a significant difference between tumor size and metastatic site in patients with stage IV breast cancer, and the difference was statistically significant. This study provided evidence-based basis for decision-making of stage IV breast cancer treatment.
Feasibility of using data collected through the St. Jude Pediatric Oncology Facility integrated local evaluation (PrOFILE) tool to determine previously defined levels of pediatric hematology-oncology care.
Authors: Udoko Aniekan , Mazotti-Roso Pedro , Suriano Enrico , Silva-Perez Fernando , Forrest Heather , Bass Madison , Gonzalez-Guzman Miriam , Friedrich Paola 
Published: June 2021

e13530 Background: Pediatric hematology and oncology (PHO) facilities are not all equally equipped to diagnose and treat the full range of childhood cancers. Proposed facility tier models have been published to facilitate the context-based adaptation of PHO diagnostic and treatment guidelines. However, a tool to assist facilities in identifying their level of PHO care does not exist. We assessed the feasibility of using the St. Jude Pediatric Oncology Facility Integrated Local Evaluation (PrOFILE) tool to identify (1) facility levels as defined by the International Society of Pediatric Oncology (SIOP) and the Lancet Oncology Commission (LOC) for sustainable care for children with cancer, and (2) treatment strata in the Adapted Resource and Implementation Application (ARIA) Guide. Methods: This study employed a multi-step mapping approach. We mapped the 1,279 PrOFILE tool variables to the nine SIOP service lines, ten LOC domains, and ten ARIA resource groups. Mapping was performed by two teams and carried out in two different rounds. The first round aimed to define if an indicator was fully or partially covered by PrOFILE. Multiple PrOFILE variables could be assigned to each concept to meet its content specificity. Various cut-offs were applied when necessary to assign mutually exclusive responses to each tier. The second round consisted of expert validation for each indicator previously identified. Results: Most (97%) of SIOP’s indicators were partially or fully covered in the PrOFILE tool (Table). Radiation therapy tools and appointment scheduling and call back system could not be mapped. Of the LOC indicators, 89% were covered (Table). Pathology technical staff, chemotherapy complexity (low, moderate, and high), lymph node biopsy, organ preservation, liver transplantation, intensity-modulated radiotherapy, and research infrastructure could not be mapped. Finally, 77% of the indicators used by the ARIA Guide were covered (Table). The ophthalmology resource group could not be fully mapped. Conclusions: This exercise demonstrates the feasibility of using PrOFILE tool variables to identify facility PHO care levels as defined by SIOP and LOC. The tool also contains most of the variables necessary to apply the ARIA Guide to diagnose, treat, and manage childhood cancer. Future PrOFILE versions will address concepts that are not covered or are partially covered in its current version.[Table: see text]
Evaluation of in silico tools for variant classification in clinically actionable NSCLC variants.
Authors: Kim Eugene , Duarte Samantha , Fridland Stas , Nam Myungwoo , Hwang Jin Young , Lee Alice Daeun , Lee Grace , Yu Emma , Chae Young Kwang 
Published: June 2021

e13545 Background: Genetic variants beyond FDA-approved drug targets are often identified in NSCLC patients. To address this challenge, in silico variant classification tools are available to determine whether specific variants contribute to disease pathogenicity or remain benign. Although the performance of in silico tools has been analyzed in previous studies, it has not been analyzed for actionable targets of FDA-approved therapies for NSCLC. The aim of this study is to compare the performance of commonly used in silico tools in classifying the pathogenicity of actionable variants in NSCLC. Methods: We evaluated the performance of several in silico tools: PolyPhen-2, Align-GVGD, and MutationTaster2. A curated set of targetable NSCLC missense variants (n = 179) was used. The dataset consisted of variants in the BRAF, EGFR, ERBB2, KRAS, MET, ALK, and ROS1 genes based on their indications as molecular targets in the NCCN Guidelines for NSCLC. Pathogenic variants (n = 80) were curated based on available literature and annotations according to the NCCN Guidelines, OncoKB, My Cancer Genome, and AACR Project GENIE. Benign variants (n = 99) were curated from the dbSNP database with the inclusion criteria of a benign or likely benign ClinVar assertion. The overall accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) of each in silico tool were determined. The performance of each in silico tool in predicting pathogenicity for subsets of sensitizing (n = 18) and resistant (n = 57) variants was also evaluated. Results: PolyPhen-2 HumVar demonstrated the highest overall accuracy (0.80), specificity (0.69), and MCC (0.63) of the in silico tools analyzed. PolyPhen-2 HumDiv (0.75) and MutationTaster2 (0.69) had similar overall accuracies while Align-GVGD (0.50) had the lowest overall accuracy. Align-GVGD also had the lowest MCC (0.08), with the other in silico tools ranging from 0.50 to 0.63. All the in silico tools achieved high sensitivities, with MutationTaster2 performing the highest (1.00) and Align-GVGD performing the lowest (0.86). The specificities were remarkably low (0.20-0.69) for all the in silico tools, with the lowest specificity demonstrated by Align-GVGD. The overall accuracies when classifying the subsets of sensitizing and resistant variants were generally high, ranging from 0.84 to 1.00. Conclusions: These results suggest that the performance of the evaluated in silico tools to predict the pathogenicity of clinically actionable NSCLC missense variants is not fully reliable. The tools analyzed in this study could be acceptable to rule out pathogenicity in variants given their higher sensitivities, but are limited when it comes to identifying pathogenicity in variants due to low specificities.
A systematic evaluation of models predicting short-term mortality for cancer patients to highlight uncertainty about model performance and high risk of bias.
Authors: Lu Sheng-Chieh , Xu Cai , Nguyen Chandler , Meyer Larissa , Sidey-Gibbons Chris 
Published: June 2021

e13559 Background: Short-term cancer mortality prediction has many implications concerning care planning. An accurate prognosis allows healthcare providers to adjust care plans and take appropriate actions, such as initiating end-of-life conversations. Machine learning (ML) techniques demonstrated promising capability to support clinical decision-making via providing reliable predictions for a variety of clinical outcomes, including cancer mortality. However, the evidence has not yet been systematically synthesized and evaluated. The objective of this review was to examine the performance and risk-of-bias for ML models trained to predict short-term (≤ 12 months) cancer mortality. Methods: We identified relevant literature from five electronic databases: Ovid Medline, Ovid EMBASE, Scopus, Web of Science, and IEEE Xplore. We searched each database with predefined MeSH terms and keywords of oncology, machine learning, and mortality using AND/OR statements. Inclusion criteria included: 1) developed/validated ML models for predicting oncology patient mortality within one year using electronic health record data; 2) reported model performance within a dataset that was not used to train the models; 3) original research; 4) peer-reviewed full paper in English; 5) published before 1/10/2020. We conducted risk of bias assessment using prediction model risk of bias assessment tool (PROBAST). Results: Ten articles were included in this review. Most studies focused on predicting 1-year mortality (n = 6) for multiple types of cancer (n = 5). Most studies (n = 7) used a single metric, the area under the receiver operating characteristic curve (AUROC), to examine their models. The AUROC ranged from .69 to .91, with a median of .85. Information on samples (n = 10), resampling methods (n = 6), model tuning approaches (n = 9), censoring (n = 10), and sample size determinations (n = 10) were incomplete or absent. Six studies have a high risk of bias for the analysis domain in the PROBAST. Conclusions: The performance of ML models for short-term cancer mortality appears promising. However, most studies report only a single performance metric that obfuscates evaluation of a model’s true performance. This is especially problematic when predicting rare events such as short-term mortality. We found little-to-no information on a given model’s ability to correctly identify patients at high risk of mortality. The incomplete reporting of model development poses challenges to risk of bias assessment and reduces the confidence in the results. Our findings suggest that future studies should report comprehensive performance metrics using a standard reporting guideline, such as transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD), to ensure sufficient information for replication, justification, and adoption.
Electronic patient reported outcome in Brazil: Insurance provider impact on adherence.
Authors: Jorge Thiago William Carnier , Filho César , Campagnaro Melina , Cartolano Lorenzo , Singh Jean , Sena Kamila , dos Prazeres Luiz Felipe , Guerrero Juliana , Ansarah Simone 
Published: June 2021

e13590 Background: Routine monitoring of cancer patients' symptoms during their journey can improve symptom management, quality of life, and survival. Wecancer App is an electronic patient-reported outcome tool, which also provides on-demand multidisciplinary digital support by nurses, psychologists and nutritionists. It also encompasses real time remote patient monitoring, symptoms diary to patient and medical staff, symptom monitoring, medication control and a content hub with reliable information. It is free to any patient who download the app. In this study, we aim to understand if insurance provider type (private, public or out of pocket) impact on app adherence. Methods: This study examined daily symptom reporting (with electronic patient-reported outcomes [e-PROs]) patterns in breast cancer patients, according to insurance provider. The following were observed: app adherence, numbers of reports, chat interactions, specific symptoms, symptoms alerts and wellness reports. Patients could report sixteen common symptoms through a mobile application, on their smartphones and tablets, for monitoring, symptom management and follow-up with a specialized nurse, determining care based on digital support clinical protocols through online chat. The nursing team action started at the moment of patient inclusion with the tutorial to download the App and training it with the patient/caregiver. The alerts made through the chat system guided the clinical actions of the nursing team in relation to online clinical management that can stratify the risk by crossing the intensity of symptoms by the CTCAE grading scale and PRO-CTCAE Measurement System and promote preventive educational conduct of vigilance and risk mitigation. Results: From January 2020 to January 2021, 543 female eligible patients downloaded the app. The e-PRO completion rates were superior to 90% at baseline, and 55.47% completed all on-treatment assessments. The app adherence for private insurance patients (PIP), 37.06% for public insurance patients (PIP) and 7.45% for out of pocket patients (OPP). 2,626 reports were made (46.41% IP; 28.58% OPP; 25% PHP); 1008 chat interactions (52.18% IP; 40.28% PHP; 7.54% OPP); 1144 number of symptoms reported (46,41% IP; 28.58% OPP; 25% PHP); specific symptoms most reported in all kind of service was tiredness, pain, nausea, tingling; 547 symptoms alerts (54.84% IP; 35.46% PHP; 9.7% OPP) and 451 wellness reports (55.43% IP; 35.03% PHP; 9.53% OPP). Conclusions: Private insurance patients are more adherent to ePROS than public insured or out of pocket patients. Lack of free internet access and digital literacy could be impacting on adherence and, ultimately, quality of life and survival. Further studies are warranted to understand how to improve equality for digital health tools.
Very low seroprevalence of sars-cov-2 among health care personnel (HCP) in a French northern comprehensive cancer center at the end of first national containment.
Authors: Doutrelant Philippe , Penel Nicolas , Renaudat Charlotte , Von Platen Cassandre , Lebitasy Marie Paule , Smis-Papillon Pauline , Hoen Bruno , Lartigau Eric , Robin Yves-Marie 
Published: June 2021

e13604 Background: To limit SARS-Co-2 transmission in general population, French government had set up a first National containment from March 16, 2020 to June 2, 2020. Furthermore, General Direction of our hospital have implemented and organized physical distancing (telework, teleconsultation, virtual multi-disciplinary board), mask wearing, use of alcoholic ... to limit interpersonal contacts. To assess the impact of this policy, we have carried out a seroprevalence study and identified risk factors for SARS-Co-2 prevalence among HCP in May/June 2020. Methods: This is part of CORSER Study (“Etude séro-épidémiologique du virus SARS-CoV-2 en France d’une Cohorte CORSER-2d de personnels d’établissements de santé »), registered with ClinicalTrials.gov (NCT04325646). Two serological tests were applied in this cohort: S-Flow assay and Luciferase-linked immune-sorbent assay (LuLisa-N test). Results: Between 04 May to 26 June 2020, 392 Osar Lambret Cancer Center employees were included in the present study (about 40% of all staff). The most common jobs were: 92 nurses (23.4%), 80 radiology/radiotherapy manipulators (20.4%), 51 physicians (13.0%), 33 medical interns (8.4%) and 47 administrative staff members (11.9%). There were 98 men (25.0%) and 294 women (75.0%). The mean age was 38.7 (+/- 11.4). There were 7 seropositive cases; the seroprevalence was 1.8% (95%-CI: 0.7-3.6). Among the 7 positive cases, 5 were symptomatic (71.4%). In univariate analysis, factors associated with SARS-Co-2 seroprevalence: symptoms suggesting viral infection within 2 months (OR=5.33), dysgueusia (OR=37.00), anosmia (OR=66.29) and HCP exposed to COVID-19 patient outside work (OR=6.69). Gender, tobacco consumption, O blood group, HCP versus administrative staff, HCP working in different services, HCP providing care to suspected COVID-19 patients, HCP providing high-risk cares were not found to be associated with seropositivity. Of note, we have noticed that BMI≥ 24 kg/m² was associated with seroprevalence (OR=15.45), without biological rational. Conclusions: Our study suggests that strict implementation of protective measures was associated with low SARS-Cov-2 prevalence at the end of first National Containment, including among HCP treating COVID-19+ patients. HCPs seroprevalence seemed lower than seroprevalence of the general population at the same period (4-5%). Clinical trial information: NCT04325646.
A prospective data analysis of targeted therapy combined with concurrent radiation therapy for brain metastasis from NSCLC with driver gene mutation.
Authors: Duan Xiaotong , Zhu Xiaoxia , Wang Lijuan 
Published: June 2021

e14006 Background: Previous studies have shown that brain metastases of non-small cell lung cancer (NSCLC) with positive driver genes have poor prognosis. There is still lack of prospective studies on the efficacy and safety of targeted therapy combined with concurrent radiotherapy for brain metastases(BM). Methods: NSCLC patients, with ECOG score 0-2, having MRI confirmed brain or meningeal metastases were eligible. Patients must have driver gene mutation and received corresponding targeted therapy. The intracranial radiotherapy regimen was SRS or whole brain radiotherapy. The primary objective was iPFS (intracranial progression-free survival); Secondary objectives were: iORR (intracranial objective response rate), PFS (progression-free survival), OS (overall survival). MMSE (Mini Mental State Examination) and FACT-Br was carried out before/after weekly radiotherapy and during systematic treatment. Treatment-related toxicities were assessed according RTOG/EORTC criteria. Tumor responses were evaluated using RECIST V1.1 criteria. Survival analysis was performed using the Graphprism version 6.0 by Kaplan-Meier method and log-rank test. Results: 23 NSCLC with BM was included. Among them, 10 patients were newly diagnosed with NSCLC BM. 2 patients’ BM progressed after targeted therapy. 11 NSCLC patients were newly diagnosed with BM after targeted therapy. 91.3% of patients presented an EGFR mutation, including primarily EGFR 19-exon deletion, EGFR 21-L8585R. 11.5% presented with c-MET mutation. Median age was 58.34 yrs(44-71yrs). Patients were mostly treated with Erolotinib and Gefitinib. All patients were adenocarcinoma. At last follow-up, for patients newly diagnosed with NSCLC BM, 8 patients had achieved intracranial progression, and 7 patients had reached OS, of which 1 died before completing WBRT. The median iPFS was 9.3m(95%CI:0.571-4.055) and the median OS was 11.9m (95%CI:0.2752 -2.732). As for patients who progressed after targeted therapy, one patient’s OS was 4.4m, iPFS of the other patient was 3.9m. Among NSCLC patients who were newly diagnosed with BM after targeted therapy, 8 patients had achieved intracranial progression and 5 patients had reached OS. The median iPFS was 6.13m (95%CI:0.247-1.751) and the mOS was 13.8m (95%CI:0.3660-3.634). Common adverse effects include dry skin, fatigue, dizziness, headache, anorexia, and grade I myelosuppression and no serious adverse events (SAEs); MMSE and FACT-Br scores were no significant differences at baseline and follow-up. Conclusions: In stage IV brain metastatic NSCLC with driver gene mutation, targeted therapy combined with concurrent radiotherapy for BM is tolerable, and there is no significant impact on the quality of life and cognitive function after radiotherapy. The evaluation of efficacy requires further follow-up. Support:LC2019ZD009,81972853 and 81572279.
Stem cell properties of GT101, a tumor infiltrating lymphocyte (TIL) product with multiple indications.
Authors: Liu Yarong , Zhao Deping , Shi Zixiao , Zhou Rong , Sun Jingwei , Han Yong , Zhao Peipei 
Published: June 2021

e14506 Background: Tumor infiltrating lymphocyte (TIL) is a kind of adoptive T cell therapy that has demonstrated clinical benefits in several types of solid tumors. GT101 is an autologous TIL therapy for the treatment of multiple solid tumors. Previous studies showed that in vitro expansion resulted in over 80% terminally differentiated effector T cells in the final TIL products, which may show good cytotoxicity but weak capability of persistence and proliferation in vivo. Herein we present phenotype and function characterization of our TIL product GT101 with a focus on its stemness attributes. Methods: We have produced more than 30 batches of GT101 TIL samples from 10 different cancer indications and analyzed their proliferation, cytokine production and phenotype at preREP and/or REP stage. Results: GT101 products keep a consistent T cell phenotype and good proliferation capacity, and are capable of secreting large amounts of cytokines after stimulation by anti-CD3/CD28 antibody, reflecting its good functionality. The residues in the final products, such as feeder cells and tumor cells, are below the detection limit. In contrast to equivalent TIL products previously reported across the world, we find that GT101 features a manufacturing process that preserves the stem cell characteristics of T cells. Analysis of all the 30 GT101 TIL samples demonstrates that the average percentage of central memory T cells (Tcm) and naïve T cells is 40-50% and 10-20% respectively in both CD8+ and CD4+ populations at the end of preREP stage. Meanwhile, CD8+TCF1+ cells are close to 50%, mainly in the form of PD-1-. In the phase of REP, the stemness of T cell is still well maintained—the average proportion of both Tcm and naïve T cells only decreases by about 5%, and that of CD8+TCF1+ cells down by about 10%. Conclusions: In general, GT101 demonstrates good stemness with a higher percentage of Tcm and TCF1+ than equivalent products previously reported across the world, suggesting better performance in persistence and proliferation in clinic.