bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coordinating a multi-disciplinary team to reduce financial hardship from cancer: A clinic-based financial navigator approach.
Authors: Banegas Matthew P. , Locher Blake W. , Tuzzio Leah , Schneider Jennifer L. , Rivelli Jennifer S. , Henrikson Nora B. 
Published: June 2022

e13527 Background: As efforts to reduce financial hardship within oncology settings increase, development of multidisciplinary approaches based on principles of care coordination – effective communication, shared goals, role clarity, handoff – are essential. Cancer Financial Experiences (CAFÉ) is a randomized controlled trial [NCT05018000] of a financial navigation intervention in two regions of Kaiser Permanente. Our objective was to develop workflows for CAFÉ Financial Navigators (CNs) to provide navigation to trial participants by engaging a multidisciplinary team. Methods: Workflows are based on our conceptual framework of unique care pathways to address financial concerns among cancer patients: resolving acute financial needs; planning for out of pocket (OOP) costs; and making cost-informed care decisions. Influenced by user-centered design, we collected multi-stakeholder perspectives through interviews with approximately 39 staff from 15 departments between 2019-2021 including clinicians (e.g. physicians, nurses, social workers), health care staff (e.g. case managers, patient navigators) and operations/business staff (e.g. business operations analysts, financial counselors). Topics included the current state; existing organizational and informal relationships between operations units; and opportunities for improvement relative to current evidence on patient needs for cancer-related financial navigation. Results: We identified several opportunities to create or enhance workflows to provide financial navigation for oncology patients. We also identified organizational barriers that require further work (e.g., providing detailed oncology-specific fee estimates for treatment and OOP costs). Workflows centered the CN as a primary contact for patients to facilitate engagement with services and ensure effective, consistent connections between patients and care delivery and operations units. We developed (1) cost coordination maps outlining the healthcare team member points of contact within departments and sequence of contacts, for addressing each financial pathway and (2) resource directories that detail the unique financial needs, contact information and role for CNs. We maintained these strategic relationships throughout the trial to serve participants and support sustainability. Conclusions: Our multi-stakeholder strategy aligns clinical and healthcare operations workflows to optimize patient experience and outcomes to reduce financial hardship from cancer. Our work suggests research teams can facilitate process improvement within care delivery settings. However, barriers to certain financial navigation processes remain, reflecting future research needs. An oncology-specific financial navigation model supported by multidisciplinary workflows is key to addressing financial hardship from cancer.
Rapidly adapting the clinical research environment at an NCI-designated comprehensive cancer center to the COVID-19 pandemic.
Authors: Lassman Andrew B. , Brogan Frances L. , Shelton Ryan J. , Bentlyewski Edward , Kelsen Moshe A. , Kim Helen , Ruotolo Brenda L. , Rustgi Anil K. , Jurcic Joseph G. 
Published: June 2022

e13534 Background: In March 2020, responding to COVID-19, the Columbia University Institutional Review Board (IRB) mandated all research pause in-person activities except studies “offering the potential for direct benefit” to patients. New enrollment was also paused. Columbia University Irving Medical Center concurrently mandated all non-essential employees work remotely, and all feasible patient visits transition to telehealth. The clinical research infrastructure at the Herbert Irving Comprehensive Cancer Center (HICCC) was required to adapt its operations in response. Methods: The HICCC convened an executive committee (EC), consisting of multi-disciplinary leadership, canvassed peer institutions, and engaged the IRB Executive Director (ED), Chief of Infectious Disease, a bioethicist, and others ad hoc. Results: The IRB ED agreed with the EC that all approved interventional therapeutic clinical trials for cancer (n = 554) offered the potential for direct benefit to patients. The EC established an “Emergency Preparedness” standard operating procedure (SOP) with input from the IRB and HICCC Protocol Review & Monitoring and Data & Safety Monitoring Committees. This SOP required assessment of each protocol for risks/benefit, transition to remote processes when possible, and referenced the Code of Federal Regulations allowing changes to a research plan without prospective IRB-approval if “necessary to eliminate apparent immediate hazard”. For new subjects, the EC created a process for enrollment as exceptions to the research pause requiring written justification by the PI that included risk-mitigation strategies and approval by the EC. 116 patients were accrued under this process. All external monitoring and auditing was transitioned to remote. Finally, 25% of research nurses and 10% of coordinators were redeployed to other short-staffed areas of the medical center. After 3 months, the IRB lifted the research pause in tranches, and the EC charged its Disease Based Teams with prioritizing trials to reopen. Accrual nadired at 30% of pre-pandemic levels in June 2020, which fully recovered by March 2021. Conclusions: A centralized research infrastructure working closely with established institutional committees was critical for the unprecedented yet effective and rapid ramp down/up of clinical trials. After 1-year, accrual recovered to pre-pandemic levels, leveraging new processes such as electronic consent, telehealth, and remote work for staff.
Validation study of end-of-life risk prediction model in active chemotherapy patients.
Authors: Hill Melinda S. , Abdo-Matkiwsky May D. , Biran Noa , Chatiwala Jumana , Chen Aileen L. , Childs Julianne Wilkins , Fitzgerald Denis , Graham Deena Mary Atieh , Horkheimer Ian , Levenbach Rachel L. , McLaughlin Joseph Francis , Salwitz Kimberly A. , Smith Frederick P. , Tassan Robert F. , Ejumejowo Akunna J. 
Published: June 2022

e13543 Background: In February of 2020, Integra Connect (IC) released a machine learning (ML) risk prediction model called Integra Predict (IP) to predict End-of-Life (EOL) in active chemotherapy patients within the next six months. Pilot Study: Regional Cancer Care Associates (RCCA) physicians rated of likelihood of EOL in the next six months to compare concordance with the IP model predictions. Part 2 (April 2022): Patient date of death updated at the end of the six-month period will undergo post-hoc comparisons. Physician Prediction vs Actual Outcome. Integra Prediction vs Actual Outcome. Methods: A stratified random sample of 265 active chemotherapy patients from 33 RCCA providers was securely emailed via pdf documents or excel sheets to RCCA for each provider. Worksheets contained provider name, patient name, age, cancer type, and the risk of dying in the next six months (Low, Medium, High). The sample included patients from the top 10 cancer types: breast, carcinoma in situ of breast, chronic leukemia, gastro/esophageal, lung, lymphoma, multiple myeloma, pancreatic, prostate, and small intestine/colorectal cancers. The sample was comprised of patients with active chemotherapy in the care of RCCA between July 2021 through September 2021. RCCA emailed the study documentation to each provider and asked providers securely emailed risk assessments to IC. Provider responses for EOL in the next six months were scored categorically - low risk (1), medium risk (2), and high risk (3). Concordance between provider responses and IP risk predictions was determined. E.g., a provider rating of low compared to a risk model rating of high would result in a difference of 1-3 = -2. Results: Forty-two percent (14/33) providers responded to our request to rate patient specific EOL which corresponds to 40% (106/265) patients sampled. Current results have a large standard deviation (0.235) resulting in a large confidence interval (.175, .64) or (18% to 64% concordance). Ninety-five percent of the time concordance will fall in this range. Table: As displayed in Table, provider estimates matched the IP risk score estimates 41.3% of the time. The average concordance between provider risk estimate and IP risk estimate was -0.7, which is almost one risk level lower, range (-1.4, -0.3). Conclusions: Study hypothesized that risk estimates would not be 50% concordant at an alpha of 0.05. 41% are concordant with a large standard deviation of 0.235, which resulted in overlapping confidence intervals. There is no significant difference in the two risk estimates.
Assessment of the quality of multidisciplinary cancer conferences: A prospective observational study.
Authors: Ibekwe Opuruiche , Gaudioso Carmelo , Attwood Kristopher , Whalen Philip , Nwogu Chukwumere E. 
Published: June 2022

e13548 Background: Multidisciplinary cancer conferences (MCCs) serve as venues for input from a variety of clinicians in patient management. Research suggests that leveraging technology improves efficiency and effectiveness of MCCs. This study examined the current state of practice in three specialized MCCs before the implementation of a tumor board technology solution. Methods: A single-site prospective observational study was conducted at a comprehensive cancer center. A validated tool was adapted to evaluate the quality of information and discussion at the MCCs. Variables were scored on a 1-5 rating scale (poor to excellent). Audio recordings supplemented the observational data collection. Surveys were also administered to assess participants’ satisfaction with the process and case discussions. Endpoints of interest included time and quality of case preparation, presentation and discussion, and rate of clinical trials consideration. Descriptive statistics were used to summarize the demographic/clinical characteristics of patients and calculate overall scores for the MCC performance. Results: From September 2020 to March 2021, a total of 257 patients were presented at the three MCCs with a median age of 66 years. The distribution of clinical stages (AJCC)1-4 was 30.5%, 11.6%, 17.2% and 7.7%, respectively with 33.0% of the values missing. The overall quality of information presented, and the discussion was deemed average. However, presentation of patient views, case history and MCC chair contribution were considered poor, above average, and excellent, respectively (median scores = 1.0, 3.5 and 5.0). The median time per case presentation and discussion was 9.0 minutes. The median time per case preparation by the radiologist and pathologist was 8.6 and 12.0 minutes, respectively. Two hundred and eighty-three surveys were completed by attending physicians. 77.5% of them agreed that the case discussions were insightful and helped change their minds about the cases discussed and 91.2% were satisfied with the depth of case discussion. The rate of clinical trial consideration was 10.8%. Conclusions: These findings demonstrate various strengths and weaknesses of a conventional approach to MCCs in three disease sites – thoracic, breast and gynecology. A comparison of relevant parameters when a tumor board technology platform is utilized is ongoing. This may address some of the weaknesses identified in our study and extend the benefits of MCCs to more patients.
External validation of machine learning models for prediction of survival in undifferentiated pleomorphic sarcoma.
Authors: Lee Linus , Yi Thomas , Fice Michael , Jones Conor , Klein Evan , Buac Neil , Lopez-Hisijos Nicolas , Colman Matthew W. , Gitelis Steven , Blank Alan T. 
Published: June 2022

e13551 Background: Machine learning (ML) algorithms to predict cancer survival have recently been reported for a number of sarcoma subtypes, but none have investigated undifferentiated pleomorphic sarcoma (UPS). ML is a powerful tool that has the potential to better prognosticate UPS. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried from 2004 to 2015 for cases of histologically confirmed undifferentiated pleomorphic sarcoma (UPS) and malignant fibrous histiocytoma (MFH). Patient, tumor, and treatment characteristics were recorded, and various machine learning (ML) models were built to predict 1-, 3-, and 5-year survival. The best performing ML models were externally validated using an institutional cohort of UPS patients. Results: All ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.71 to 0.73 at the 5-year time point. Similarly, all ML models performed best at 1-year and worst at 5-year on external validation. The best performing models had c-statistics of 0.81 at the 5-year time point on external validation, demonstrating good performance in survival prediction. Conclusions: Machine learning models perform well for survival prediction in UPS, though this sarcoma subtype may be more difficult to prognosticate than other subtypes. Future studies are needed to further validate the machine learning approach for UPS prognostication. [Table: see text]
A DEIRGs-based immune score model for predicting prognosis and immunotherapy effects in breast cancer.
Authors: Qu Fei , Li Wei , Yin Yongmei 
Published: June 2022

e13558 Background: Immune-based strategies have reignited prospect for the treatment of breast cancer(BRCA).Nevertheless, due to the molecular and genetic heterogeneity of tumors, a single biomarker can no longer meet the clinical needs in the highly complex tumor microenvironment(TMB).The main purpose of this study was to explore the immunobiological markers of BRCA prognosis in help to guide patient management and establish personalized risk assessment. Methods: Univariate/multivariable Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) Cox analysis was used to establish a DEIRGs-based model in BRCA.GSEA analysis was used to explore biological signaling pathways. The immune infiltration landscape of BRCA was conducted via CIBERSORT algorithms and immunotherapy response was calculated through TIDE algorithm and TMB analysis. Results: In this study, 1178 BRCA samples from The Cancer Genome Atlas (TCGA) were used as the training set, and 140 samples from Gene Expression Omnibus (GEO) database were used as the validation set. Immune-related genes were collected from ImmPort and Innatedb database. A total of 547 differentially expressed immune related genes(DEIRGs) were identified. Enrichment analysis of the DEIRGs revealed that they were involved in the regulation of the P13K-Akt signaling pathway and cytokine–cytokine receptor interaction. To identify the genes most relevant to BRCA, we performed a WGCNA analysis to select the modules with the strongest correlation between the module traits. A signature based on six DEIRGs(CETP, DES, IL33, TXNIP, RNF125, STAT5B) was constructed. These cases were stratified into high- and low-risk groups according to the median risk score. Kaplan–Meier analysis showed that OS in the high-risk group was significantly shorter than that in the low-risk group (P<0.001).The model presented unique characteristics in terms of immune cell infiltration and immune function in TME. The results from the tumor mutation burden(TMB) and tumor immune dysfunction and exclusion(TIDE) analysis revealed that the model could efficiently predict the potential response of immunotherapy. ROC curves were primarily used to assess the predictive power of the prognostic model, with AUC values of 0.736, 0.816 and 0.836 at 10, 15 and 20 years, respectively. More importantly, we researched the relationship between this model and TMB and TIDE score, which confirmed that this signature gave the best prognostic value. Conclusions: We have developed a novel immune score model, which can effectively and efficiently predict the prognosis of BRCA patients as well as the effect of immunotherapy. The high-risk subtype was featured by higher TIDE score and lower TMB. Our findings suggest that immunotherapy may be efficacious for low-risk groups of BRCA patients. The findings can be served as reference for the further research and validation of biomarkers.
Diagnostic efficacy of deep learning-based prediction of origin for patients with cancers of unknown primary.
Authors: Lan Lan , Wang Bin , Sun Haiyan , You Dong , Zhang Yuman , Li Xing , Wang Kai 
Published: June 2022

e13560 Background: Despite of routine medical history inquiry, laboratory analysis, histopathological analyses, endoscopy and imaging methods, 3-5% of metastatic cancers are diagnosed with unknown primary (CUP). It poses a considerable challenge as modern therapeutic options for patients with CUP are mainly limited to empirical chemotherapy (paclitaxel, carboplatin, etc.), leading to an overall poor prognosis. In current clinical trials (CUPISCO trial, PACET-CUP trial) targeting CUP for precision therapy, genomic data suggest that only 31% of CUP patients could be matched for at least one targeted or immune agent. The determination of primary tumor origin and genetic mutations could probably provide more therapeutic choices and help to significantly improve the outcome in CUP patients. Methods: To identify the primary lesion of CUP, we built a weighted ensemble model by Automated machine learning, including 13 models ( XGBoost, RandomForestGini, ExtraTreesGini, etc.), using genetic and clinical datasets containing 22,018 patients of 28 tumor types, enrolled between 2016-2021. Our genetic and clinical datasets contained 10 features including age, tumor site, gene alteration, TMB, etc. Among them, 813 sites related to tumor were covered by gene alteration. The 5-fold Cross-Validation method was used to estimate the accuracy of our diagnosis models. Results: The models were evaluated using top-k differential diagnosis accuracy, which measured how frequently the top-k predictions capture any of the primary diagnoses in the reference standard. Our diagnostic model was then performed on 8 CUP patients. All of them were successfully confirmed their diagnosis of primary origin tumor. Among these patients, 2/8 CUP patients have begun relevant systemic therapy based on the confirmed diagnosis: 1 patient with diagnosis of esophageal squamous cell carcinoma receiving the immune combination TP Regimen (Albumin paclitaxel plus cisplatin) and achieved a 9-month PFS; another patient diagnosed as colorectal cancer achieved a 2-month PFS after treatment with Oxaliplatin-based chemotherapy regimen. Preliminary results of our study demonstrated the efficiency of this Artificial Intelligence model for the diagnosis in CUP patients. Conclusions: We developed a diagnostic model on CUP using deep learning based on genomic and clinical data and preliminarily applied in clinic. Genetic characterization of CUP patients contributes to the selection of targeted therapies under unclear diagnosis, whereas our diagnosis model could equally adjust more proper precision treatment for CUP patients. In CUP patients, the combined strategy of deep learning-predicted diagnosis and genetic mutations-based therapy could help to maximize the clinical benefit in a clinical setting and optimize the clinical development of innovative therapies.
Development of a new HRD algorithm which was highly associated with anthracycline-containing neoadjuvant chemotherapy.
Authors: Cao Wen-Ming , Pan Jiani , Wang Xiaojia , Lou Feng , Li Mingwei , Wang Meng , Wang Rong , Ye Weiwu 
Published: June 2022

e13575 Background: Homologous recombination deficiency (HRD) can be resulted from dysfunction of BRCA and is associated with sensitivity to platinum, PARP inhibitor and other DNA-damaging drugs. The results from a neoadjuvant trial showed that pathological complete response (pCR) was not significantly higher with cisplatin than with doxorubicin-cyclophosphamide in BRCA1/2-mutated breast cancers (BC). It suggests that BC with HRD might benefit from anthracycline-containing regiment. There are many commercial HRD detection assays, including the FoundationFocus CDx BRCA LOH and myChoice CDx, but there is still no uniform standard in China. Methods: A total of 96 in-house BC samples and 6 HRD positive standard cells (Cat No. CBP90023) were analyzed by whole-genome sequencing (WGS). Besides, 122 BCs from the TCGA database were down-sampled to ̃1X WGS. We constructed a new algorithm for HRD score based on WGS at low coverage as input data to estimate large-scale copy number alteration (LCNA) events on the genome. The sensitivity and specificity were compared between our algorithm and the ShallowHRD. A clinical cohort of 50 BCs (15 cases carrying BRCA mutation) was used to assess the association between HRD status and anthracyclines-containing neoadjuvant treatment outcomes. Results: A 100kb-window was defined as the optimal size by using 41 in-house cases and the TCGA dataset. The threshold of HRD was determined as the number of 10 LCNAs by using 55 in-house BCs with BRCA mutation, with the goal of achieving 95% sensitivity. The sensitivity and specificity of our algorithm were both 100%, while those of the ShallowHRD were 40% and 100%, respectively, by testing standard samples with positive HRD. Meanwhile, similar results were also observed that the sensitivity of our algorithm was far superior to ShallowHRD (87% vs 13%) in the clinical cohort. The association between HRD status and BRCA mutations was compared between our algorithm and the ShallowHRD by 120 BC WGS samples (20 cases carrying BRCA mutation) from the TCGA database. The results showed that BRCA status was significantly associated with HRD status by our algorithm and ShallowHRD (P = 0.00838 and P = 0.00284, respectively). However, our algorithm had a higher positive concordance rate than the ShallowHRD algorithm (70% vs 60%). In the clinical cohort of neoadjuvant treatment, HRD group was more likely to respond to anthracycline-containing chemotherapy than non-HRD group, with outcomes of pCR (OR = 9.5, 95% CI: 1.11–81.5, p = 0.04) and residual cancer burden score of 0 or 1 (RCB0/1) (OR = 10.29, 95% CI: 2.02–52.36, p = 0.005). Among 35 patients lacking BRCA mutations, HRD group tended to have RCB0/1 responses compared to non-HRD group (OR = 6.0, 95% CI: 1.00–35.91, p = 0.05). Conclusions: Here, we developed a new stable algorithm for HRD score. It’s a promising assay for clinical application to predict sensitivity of DNA-damaging drugs.