bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors associated with endowed chair allocation in medical oncology divisions and departments in the United States.
Authors: Jia Lena , Rooney Michael Kevin , Jacobson Clare , Griffith Kent A. , Jagsi Reshma , Markham Merry Jennifer 
Published: June 2021

11044 Background: Despite an increasing number of female physicians in the workforce, a gender disparity remains in many leadership roles in medicine. Endowed chairs can provide a multitude of research and career opportunities; thus, they are coveted positions in academic medicine. We examined factors associated with holding endowed chairs in oncology across the US, with a focus on whether a gender difference existed, as has been demonstrated in top internal medicine departments more generally. Methods: In 2019, we identified 95 academic oncology divisions/departments in the US, using the Oncology Division Chiefs & Department Chairs listing in the American Society of Clinical Oncology (ASCO) myConnection forum to define the institutions included. We requested a list of full professors and endowed chairs in those divisions/departments, relying on public data on websites when an institution did not respond. Using public data (e.g., institutional websites, NIH reporter, Scopus, state licensing boards), we collected information on gender, degree, total NIH funding as PIs, H-indices, publication and citation numbers, and graduation year for these individuals. We then created a multivariable logistic regression model to examine if, after controlling for other variables, gender was independently associated with an increased likelihood of holding an endowed chair. Results: One thousand thirty-three oncology full professors were identified, 290 (25.6%) of whom held endowed chairs. Overall, and in an adjusted model, greater research productivity (as measured through publications, citations, and h-index) and greater levels of NIH funding were significantly associated with having an endowed chair. Gender was not significantly associated with endowed chair status (21.9% [95% CI:17.2-27.3] of females and 26.8% [23.8-29.9] for males held endowed chairs; p = 0.11) on bivariable analysis, nor was it significant in the adjusted multivariable model (p = 0.50). Power to detect the observed difference given the total number of professors and their gender distribution was found to be ̃53%, suggesting a 47% chance of failing to reject the null hypothesis of equal gender distributions in endowed chairs when the observed difference is as large as estimated. Conclusions: Among oncology full professors, gender was not significantly associated with endowed chair status, although the number of professors in this field is too small to definitively rule out a modest gender difference. This finding contrasts with prior work that revealed a more substantial difference by gender that remained significant after controlling for other factors in a study, including all divisions in the Departments of Internal Medicine at top medical institutions. Further work is necessary to understand what specialty-specific and institutional cultural factors may help mitigate challenges in the pursuit of equity, diversity, and inclusion.
Long-term quality of life of cancer survivors: A case-control study.
Authors: Ponce-Zepeda Joaquin , Hoang Tuyen 
Published: June 2021

12077 Background: There were extensive reports in literature about the debilitating health experienced by cancer patients during treatment. This study examined how the quality of life of cancer survivors changed over time. Methods: The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the U.S. This study involved participants in NHANES from 2000-2020. Participants who reported having had a cancer diagnosis were matched one to one with participants who reported no cancer diagnosis by age, gender, race, year of recruitment into NHANES, and comorbidities. Quality of life measures including self-reported general/physical/mental health, examinations, and laboratory tests were compared between cancer cases and matched controls using paired t tests. Results: This study included 5,166 pairs of cancer cases and matched controls. Mean age was 66 (±15 years). Male 47% and female 53%. White 69%, Black 14%, Hispanic 12%, others 5%. Most common comorbidities were hypertension (56%), arthritis (50%), diabetes (19%), and thyroid (18%). About 38% of cancer cases had survived in 1-5 years; 22% in 6-10 years; 39% in 10+ years. Most prevalent cancers were skin (28%), breast (15%), and prostate (15%). Compared to controls, cancer cases who had survived in 1-5 years reported higher rates of poor general health (38% vs. 27%, p <.0001), hospitalizations (31% vs. 17%, p <.0001), mental health visits (9% vs. 7%, p =.0204). There were no significant differences in general health and healthcare utilization between cancer cases who survived > 5 years and controls. There were no clinically meaningful differences in laboratory tests and examinations between cancer cases and controls regardless of survival time. Conclusions: During the first 5 years after cancer diagnosis, survivors reported worse health than controls. As survival time extended, there was no difference between cancer cases and controls. The debilitating health reported during the first 5 years could not be explained by examinations and laboratory tests alone. Future research should explore neurochemical and hormonal markers to investigate adverse effects of cancer treatment on long-term quality of life.
The association of the affordable care's Medicaid expansion on survival in gynecologic cancer: A National Cancer Database study.
Authors: Smith Anna Jo , Applebaum Jeremy , Nickles Fader Amanda 
Published: June 2021

1501 Background: Under the Affordable Care Act’s 2014 Medicaid expansion, more than 12 million Americans gained health insurance. Whether such gains in insurance improve survival in gynecologic cancer is unknown. This study aims to determine whether Medicaid expansion is associated with improved survival among women with gynecologic cancers. Methods: We conducted a retrospective cohort study using a difference-in-differences study design comparing insurance status, stage at diagnosis, delays in treatment, and one-year survival before and after the ACA’s Medicaid expansion in Medicaid expansion states (intervention group) compared to women in non-expansion states (control group). Using hospital-reported data from the 2010-2016 National Cancer Database, we compared outcomes overall for women ages 40-64 years old with endometrial, cervical, ovarian, or vulva/vaginal cancer and then stratified by cancer type, stage, race, and rural/urban status. We adjusted for patient (area-level income, area-level education, distance traveled for care, comorbidities), clinical (co-morbidities, grade) and hospital (academic facility) characteristics. Results: Our sample included 241,713 women with gynecologic cancer, 119,392 in expansion states and 122,321 in non-expansion states. Post-Medicaid expansion, there was a statistically significant 0.8 % increase in 1-year survival among patients in expansion states compared to non-expansion states (95% CI 0.1-1.5). There was also a significant reduction in uninsurance (-1.1%, 95%CI, --1.5, -0.7) and delays of 30+ days from diagnosis to treatment (-2.4%, 95%CI -3.4, -1.2). There was no significant change in early-stage diagnosis (0%; 95%CI -0.7-0.7). Improvements in one-year survival after Medicaid expansion were driven by ovarian cancer (difference-in-differences 2.2%, 95%CI 0.6-3.8) and in white women (difference-in-differences 0.8%, 95%CI 0.1-1.5), while there was no significant difference in one-year survival for non-white or rural women. Conclusions: The Affordable Care Act’s Medicaid expansion was significantly associated with 1-year survival and insurance access among patients with gynecologic cancer. Insurance expansion efforts in non-Medicaid expansion states may improve survival for women with gynecologic cancer.
Germline testing in community oncology patients with somatic BRCA1/2 mutations.
Authors: Principe Kate , Nichols Trisha , Patel Gayle , Bunnell Ann , Fisher Camille , Lord Katharine , Carlisle Steven , Torres Vanessa , Wilfong Lalan S. , Kodali Dhatri 
Published: June 2021

1523 Background: The primary purpose of tumor mutation profiling (TMP) is to molecularly characterize tumors to identify targeted treatments and improve outcomes, but it may also uncover germline mutations with implications for patients and families. In September 2016, the National Comprehensive Cancer Network (NCCN) added “BRCA1/2 mutation detected by TMP” as a criterion for germline BRCA1/2 testing. This study aims to assess rates of germline testing, with and without genetic consultations, for individuals with a somatic BRCA1/2 finding in community oncology centers. Methods: Retrospective data was abstracted from an internal database of results from four TMP laboratories. Individuals with a somatic BRCA1/2 pathogenic/likely pathogenic (P/LP) variant reported from January 1, 2017 to December 31, 2019 were included. Clinical data was obtained from electronic medical records. The project was approved by the Texas Oncology Privacy Board. Results: 221 patients had a P/LP somatic BRCA1/2 result on TMP, 138 of which were BRCA1/2 spectrum tumors (breast, ovary, pancreas, prostate). 144/221 patients (65.2%) had BRCA1/2 germline testing. 133/221 (60.2%) met NCCN guidelines for germline BRCA1/2 testing independent of their somatic results; they were statistically more likely to undergo germline testing than patients who did not otherwise meet germline BRCA1/2 testing criteria (p=2.3e-16) (Table). 70/144 (48.6%) had a germline P/LP BRCA1/2 mutation identified. At locations with genetic providers versus those without, there was a significant difference in rates of genetic consultation (p=0.02) but not in the rate of germline testing (p=0.3). This indicates patients were more likely to receive pre- and post-test counseling at clinics offering in-house genetic consultations. Conclusions: Most individuals who had a P/LP somatic BRCA1/2 mutation identified on TMP underwent germline genetic testing. As 60.2% of our cohort qualified independently for germline testing, somatic BRCA1/2results may not have been the driving force behind germline testing. However, individuals who had a non- BRCA1/2-spectrum tumor were significantly less likely to have the recommended confirmatory germline testing. Quality improvement initiatives can focus on improving rates of counseling and germline testing for patients with somatic BRCA1/2 mutations, regardless of tumor type.[Table: see text]
Use of deep learning frameworks to detect super-responder and super-survivor stage IV squamous non-small-cell lung cancer (NSCLC) patients treated with a gemcitabine and cisplatin combination.
Authors: Galmarini Carlos Maria , Lucius Maximiliano 
Published: June 2021

1548 Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF). Methods: Through www.projectdatasphere.org, we accessed the control arm clinical data (N = 548) of the randomised phase III SQUIRE trial (NCT00981058). This trial included patients with stage IV squamous NSCLC who had not received previous chemotherapy. These patients were treated with gemcitabine 1,250 mg/m2 (IV, 30-min infusion, d1/d8) and cisplatin 75 mg/m2 (IV, 120 min infusion, d1) on a 3-week cycle for a maximum of six cycles. Synthetic fingerprints resulted of the integration of 180 features collected during the first 3 cycles including demographics, medical history, physical exam, concomitant medication, histopathology, PK parameters, adverse events and common labs. These fingerprints were used as input for the UDLF. The resultant clusters were correlated with overall-response rate (ORR) and overall survival (OS). Results: After missing data removal and feature standardization, 192 patients were eligible for the study. The UDLF was able to generate two different clusters: P0 (n = 107) and P1 (n = 84). ORR was higher in the P1 than in the P0 cluster (mean 41.6% [95% CI 31.7-52.3] vs. 28.0% [95% CI 20.4-37.2]; p = 0.04). OS was significantly longer in the P1 than in the P0 cluster (median 13.2 months vs. 9.7 months; hazard ratio 1.56 [95% CI 1.12-2.17; p = 0.008]). Feature contribution analysis showed that P1 had more patients and more events of grade III/IV neutropenia. In contrast, P0 had more patients and more events of grade III/IV nausea and vomiting. Other major differences were observed on vital signs (SBP, DBP, HR, RR, Temp), concomitant medication (osmotically-active laxatives, dexamethasone, furosemide, granisetron and ondansetron) and in hematological (RBC, HGB, HCT, MCV, WBC, neutrophils, monocytes, lymphocytes) and biochemistry (albumin, globulins, ALP, LDH, creatinine, BUN, urea, sodium, magnesium and phosphate) tests. Conclusions: Our findings show that synthetic fingerprints and subsequent deep learning analysis can be of use to identify patients with clinical characteristics associated with high-response rate and long-term survival.
Automated imaging-based stratification of early-stage lung cancer patients prior to receiving surgical resection using deep learning applied to CTs.
Authors: Torres Felipe Soares , Akbar Shazia , Raman Srinivas , Yasufuku Kazuhiro , Baldauf-Lenschen Felix , Leighl Natasha B. 
Published: June 2021

1552 Background: Computed tomography (CT) imaging is an important tool to guide further investigation and treatment in patients with lung cancer. For patients with early stage lung cancer, surgery remains an optimal treatment option. Artificial intelligence applied to pretreatment CTs may have the ability to quantify mortality risk and stratify patients for more individualized diagnostic, treatment and monitoring decisions. Methods: A fully automated, end-to-end model was designed to localize the 36cm x 36cm x 36cm space centered on the lungs and learn deep prognostic features using a 3-dimensional convolutional neural network (3DCNN) to predict 5-year mortality risk. The 3DCNN was trained and validated in a 5-fold cross-validation using 2,924 CTs of 1,689 lung cancer patients from 6 public datasets made available in The Cancer Imaging Archive. We evaluated 3DCNN’s ability to stratify stage I & II patients who received surgery into mortality risk quintiles using the Cox proportional hazards model. Results: 260 of the 1,689 lung cancer patients in the withheld validation dataset were diagnosed as stage I or II, received a surgical resection within 6 months of their pretreatment CT and had known 5-year disease and survival outcomes. Based on the 3DCNN’s predicted mortality risk, patients in the highest risk quintile had a 14.2-fold (95% CI 4.3-46.8, p < 0.001) increase in 5-year mortality hazard compared to patients in the lowest risk quintile. Conclusions: Deep learning applied to pretreatment CTs provides personalised prognostic insights for early stage lung cancer patients who received surgery and has the potential to inform treatment and monitoring decisions.[Table: see text]
Nodule net: A prospective safety net program to reduce loss to follow-up and increase early detection of lung cancer.
Authors: Singh Harpreet , Jani Chinmay , Rupal Arashdeep , Tewari Arti , Walker Alexander , Casasola Marcel , Khoory Joseph , Ranker Lynsie , Koster Megan , Thomson Carey 
Published: June 2021

1564 Background: Inadequate follow-up of suspicious lung nodules can result in a delay in diagnosis and potential progression to advanced staged lung cancer. A multidisciplinary lung nodule program entitled "Nodule Net" was implemented in 2017 to provide a safety net, increase the rate of follow-up, streamline management. The program consisted of a multidisciplinary team with EMR notification by the radiologist to a centralized nurse navigator for inclusion in a follow-up database, outreach with reminders to the primary care provider if follow-up was not completed, and referral for management where appropriate. In this study, we sought to evaluate program effectiveness in tracking and rate of follow-up imaging of suspicious pulmonary nodules. Methods: 2,398 chest CT scans were reviewed between January and May 2018 for the presence of a lung nodule that required follow-up. Nodules known to be inflammatory or associated with a metastatic malignancy were excluded. Baseline demographics, medical history, primary care affiliation, type of imaging scan, nodule characteristics, and presence and specifics of follow-up recommendations were collected. For reports that did not include a follow-up recommendation, Fleischner’s recommendations were applied or an independent pulmonologist’s review was completed. The rate of follow-up imaging was recorded and compared with historical rates prior to Nodule Net implementation. Prevalence ratios were generated for each comparison. Results: 1,367 (57%) reported lung nodules. Recommendations for follow-up imaging were recorded in 632 (46.2%), and 523 (82.8%) of these were reported to the program navigator. The rate of follow-up completion of those referred to the program was significantly higher [408 (78%)] than standard of care prior to program implementation [442/1202 (36.8%), (2.90, 95% CI: 2.65-3.18)]. Out of 408 patients who completed follow-up, nodule net outreach was required in 116 (28.4%). Of these 116, malignancy was identified in 4/116 (3.4%). Increased nodule size requiring referral was identified in 17 (14.7%). Out of 109 who were not transmitted to the program navigator and not present in the database, 57 (52.3%) had completed the recommended follow-up compared with 78% among those referred (1.49, 95% CI:1.23-1.79). Conclusions: Management of lung nodules is a complex process with poor follow-up completion reported in prior studies (29%-33%). Implementation of a multidisciplinary lung nodule care program for tracking lung nodules led to a significant increase in completion of recommended follow-up imaging. Developing a comprehensive lung nodule management program using software and navigation may further enhance detection, reduce human errors, augment the necessary follow-up for suspicious lung nodules, and ultimately the prevalence of advanced stage lung cancer.
Serial plasma and CSF cell-free tumor DNA (cf-tDNA) tracking in diffuse midline glioma patients undergoing treatment with ONC201.
Authors: Cantor Evan , Wierzbicki Kyle , Tarapore Rohinton , Thomas Chase , Cartaxo Rodrigo , Yadav Viveka Nand , Ravindran Ramya , Bruzek Amy , Franson Andrea , Mody Rajen , Garton Hugh , Odia Yazmin , Kline Cassie , Vitanza Nicholas A , Khatua Soumen , Mueller Sabine , Allen Joshua E. , Gardner Sharon L. , Koschmann Carl Johannes 
Published: June 2021

2012 Background: Diffuse midline glioma (DMG) with the H3K27M mutation is a lethal childhood brain cancer, with patients rarely surviving 2 years from diagnosis. There are few available means of monitoring the disease beyond serial MRI scans, making clinical decision making slow, difficult, and often reactive. Methods: We conducted a multi-site phase 1 trial of the imipridone ONC201 for children with H3K27M-mutant glioma (NCT03416530). Patients enrolled on Arm D of the trial (n=24) underwent serial lumbar puncture (baseline, 2 and 6 months) for cell-free tumor DNA (cf-tDNA) analysis at time of MRI. Additionally, patients on all arms of the trial at the University of Michigan underwent serial plasma collection. CSF collection was feasible in this cohort, with no procedural complications. We collected a total of 96 plasma samples and 53 CSF samples from 29 patients, including those with H3F3A (H3.3) (n=13), HIST13HB (H3.1) (n= 4), and unknown H3 status/not biopsied (n=12) [range of 0-8 CSF samples and 0-10 plasma samples]. We performed digital droplet polymerase chain reaction (ddPCR) analysis and/or amplicon-based electronic sequencing (Oxford Nanopore) of cf-tDNA samples and compared variant allele fraction (VAF) to radiographic change (maximal 2D tumor area on MRI). Results: Preliminary analysis of samples (n=58) demonstrates a correlation between changes in tumor size and H3K27M cf-tDNA VAF, when removing samples with concurrent bevacizumab. Analysis of remaining CSF and plasma samples is ongoing, including analysis of novel biomarkers of response. In multiple cases, early reduction in CSF cf-tDNA predicts long-term clinical response (>1 year) to ONC201 and does not increase in cases of later-defined pseudo-progression (radiation necrosis). For example, a now 9-year old patient with thalamic H3K27M-mutant DMG underwent treatment with ONC201 after initial radiation and developed an increase in tumor size at 4 months post-radiation (124% baseline) of unclear etiology at the time. Meanwhile, her ddPCR declined from baseline 6.76% VAF to <1%, which has persisted, with now near complete response (85% tumor reduction) at 30 months on treatment from diagnosis. Conclusions: In summary, we present the feasibility and utility of serial CSF/plasma monitoring of a promising experimental therapy for DMG.
SER-ONCOVID: Seroconversion in solid-tumor cancer patients after COVID-19 diagnosis.
Authors: Pous Anna , Felip Eudald , Etxaniz Olatz , Hernandez Gonzalez Ainhoa , España Sofia , Romeo Margarita , Pinato David James , Esteve Anna , Mesia Ricard 
Published: June 2021

2544 Background: Cancer patients (pts) represent a high-risk population for severe COVID-19. Cancer-associated immunosuppression may hinder in the development of anti-SARS-CoV-2 antibodies. Methods: Data regarding baseline characteristics, COVID-19 and anti-SARS-Cov2 IgG were collected from cancer pts (solid tumors) who tested positive for COVID-19 (PCR+) between March and April 2020 at Catalan Institute of Oncology. We prospectively assessed anti-SARS-Cov2 IgG seroprevalence at 3 and 9 months post infection and explored clinico-pathologic factors associated with IgG positivity. We explored the impact of potential factors influencing antibody production at >9 months. Results: Of 49 pts registered between 10th March-26th April 2020, 21 died <3 months after the infection and 5 pts refused to participate, leaving 23 eligible pts for IgG testing. With respect to those not tested, IgG tested cohort was younger (median age: 64.0 vs 72.9 years, p = 0.001) and presented oncologic remission in 68.2% of cases (vs 34.6%, p = 0.043) at COVID-19 diagnosis. Median time from PCR+ to first and second IgG determination was 3.2 months (Interquartile range [IQR]: 2.9-4.1) and 9.5 months (IQR: 8.8-9.8), respectively. Out of 23 pts, 15 had both determinations and 8 had only one (3 in the first time point, 5 in the second one). We identified 16/18 pts IgG+ (88.9%) at 3 months and 17/20 pts IgG+ (85%) at 9 months. One IgG+ pt became IgG- at the second determination, one was IgG- at both timepoints, and one had an inconclusive result at the first but negative at the second timepoint. Key characteristics of patients by IgG result 9 months after COVID-19 diagnosis are shown in the table. Conclusions: We describe a high seroprevalence of anti-SARS-CoV-2 IgG at 3 and 9 months after COVID-19 diagnosis in solid tumour patients, irrespective of anti-cancer therapy exposure. Pts who were IgG+ at 9 months were older, and more likely to have required oxygen during prior COVID-19 in comparison to IgG- pts suggesting that infection severity may promote durable immunity. Frequency of early stage cancers was higher among IgG+ pts, suggesting less cancer-related immunosupression. Older (>70 years) and advanced cancer pts were under-represented in this series, warranting confirmation of these preliminary results in a larger cohort.[Table: see text]