bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing adult child and spousal caregiver burden and potential causes.
Authors: Fenton Anny , Keating Nancy Lynn , Ornstein Katherine A. , Kent Erin E. , Litzelman Kristin , Rowland Julia H. , Wright Alexi A.
Published: June 2021

12127

Background: Adult children caring for a parent with cancer comprise a significant segment of caregivers. Demographic trends indicate this caregiving population will grow as the baby boomer generation ages. Yet little is known about adult child caregivers’ needs and experiences and how they differ from the well-studied spousal caregiver. This knowledge gap may hinder efforts to ameliorate adult children’s caregiver burden and its impact on patients. Methods: We analyzed adult child and spousal/partner caregivers’ surveys from the Cancer Care Outcomes Research and Surveillance consortium, a multi-regional population-based study of approximately 10,000 persons with newly diagnosed colorectal and lung cancer. We used t-tests and a series of multivariate regression models to assess whether adult child and spousal caregivers’ caregiving responsibilities, social/emotional burden, and financial burden (scaled 0-10) differed and examined patient and caregiver characteristics’ mediation of variation in burden. Results: Compared to spouses/partners (N=1029), adult children (N=230) completed similar levels of caregiving tasks but spent less time (14 vs. 24 hours/week; p<0.001). However, adult children experienced higher social/emotional burden (2.9 vs. 2.4; p<0.01). In baseline models controlling for patient clinical factors, caregiving characteristics, and caregiver demographics, adult children’s average social/emotional and financial burdens were statistically higher than spouses/partners. Additional adjustment for caregivers’ childcare responsibilities and employment eliminated social/emotional and financial burden disparities. Additional adjustment to the baseline model for caregiver-patient gender concordance eliminated the social/emotional burden gap. Communication quality was a large and statistically significant predictor of both burdens (p<0.001). Conclusions: Adult children spend less time caregiving than spouses/partners but experience higher caregiving burden. Adult children’s childcare and career responsibilities help explain this increased burden. Gender concordance between caregiver and patient may also contribute to social/emotional burden, adding important context to prior research indicating female caregivers experience the greatest burden. Interventions to improve communication between caregivers and patients have the potential to reduce both adult child and spouses/partners caregiver burden. Adjusted average caregiver burden for adult child and spousal caregivers. [Table: see text]
Impact of machine learning-directed on-treatment evaluations on cost of acute care visits: Economic analysis of SHIELD-RT.
Authors: Natesan Divya , Thomas Samantha M. , Eisenstein Eric , Eclov Neville , Dalal Nicole , Stephens Sarah J. , Malicki Mary , Shields Stacey , Cobb Alyssa , Mowery Yvonne Marie , Niedzwiecki Donna , Tenenbaum Jessica , Palta Manisha , Hong Julian C.
Published: June 2021

1509

Background: SHIELD-RT was a randomized controlled quality improvement study (NCT03775265) that implemented electronic health record-based machine learning (ML) to direct supplemental visits for high risk (HR) patients undergoing radiotherapy (RT). Acute care visits (ER visits or hospitalizations) were reduced from 22% to 12%. We evaluated the costs associated with acute visits in this study. Methods: Patients who initiated RT between 1/7/19 and 6/30/19 at a single institution were evaluated by a ML algorithm to identify HR courses (>10% risk of acute visit during RT). HR patients were randomized to standard weekly (S) or intervention of twice weekly (TW) evaluation during RT. Cost data associated with acute visits were obtained and compared between patients who underwent S or TW evaluations. Missing cost data were imputed using disease related groups (DRGs). Mean costs (standard deviation) were compared between arms with non-parametric Wilcoxon Rank Sum tests. Results: 311 HR courses were identified and randomized to either S (n=157) or TW (n=154) evaluations during RT. 85 patients (S: 51; TW: 34) had 121 distinct acute care visits (S: 74; TW: 47). Patients in the TW evaluation arm had fewer hospitalizations (29 vs 41) and ER visits (18 vs 33) than those in the S arm. There were fewer acute visits per patient in the TW arm (0.34) compared to S arm (0.49). Actual cost data was available for 102 visits at our institution, and imputed for 19 outside hospital visits. Mean cost associated with acute visits was lower in the TW arm ($1939, SD $5912) compared with the S arm ($4002, SD $11568; p=0.03). Differences in mean cost between arms are presented in the table. Conclusions: ML-directed evaluations for HR patients undergoing RT resulted in decreased costs of ER visits and hospitalizations. Costs were decreased across revenue centers, with the largest difference related to inpatient room costs. Future analyses will incorporate intervention costs, which are currently bundled with RT reimbursement.[Table: see text]
Baseline and short-term financial burden (FB) in colorectal cancer (CRC) treated with curative intent: Early results of ECOG-ACRIN EAQ162CD.
Authors: Kircher Sheetal Mehta , Duan Fenghai , Gareen Ilana F. , Underwood Ellen , Sicks Jorean , Suga Jennifer Marie Marie , Kehn Heather , Mehan Paul Tracy , Acoba Jared David , Taylor Michael A , Wagner Lynne I. , Carlos Ruth
Published: June 2021

1518

Background: Cancer therapy costs continue to rise, resulting in FB. FB in CRC treated with curative intent remains unexplored, particularly in the community oncology setting. We assess baseline FB and 3-month change, including predictors. Methods: Patients with newly diagnosed CRC treated with curative intent were enrolled through NCI Community Oncology Research Program (NCORP) Community Sites in a longitudinal study and completed the validated FACIT Comprehensive Score for Financial Toxicity (COST) instrument at baseline and 3 months. Higher COST score (range 0-44) indicates greater financial well-being. Pearson correlation compared baseline and 3-month measurements. Effects of patient demographics, clinical, self-efficacy variables (Table) and practice safety net affiliation were assessed using linear regression for baseline COST and COST difference at 3 months. F-tests identified covariates significantly predicting FB. Results: 450 and 296 participants completed the baseline and 3-month survey with a mean COST score 23.5 ± 11.9 and 24.6 ± 12.4, respectively (r=0.80, p<0.001), considered grade 1 or mild. Financial resource indicators such as income, insurance type, high-deductible insurance and savings, along with self-efficacy strongly predicted baseline FB (Table). No assessed covariates predicted COST difference. Safety-net affiliation (54/450, 12%) did not predict COST outcomes. Conclusions: Among those with CRC treated with curative intent in community settings, FB at treatment initiation and 3-month follow-up are highly correlated. Financial resources predict FB at treatment initiation. Self-efficacy to manage finances predicted FB, suggesting interventions such as financial counseling and navigation delivered early in the treatment course may minimize downstream FB. Clinical trial information: NCT03516942. [Table: see text]
Comparing FOLFOX delivery in trial and real-world populations using longitudinal cumulative dose.
Authors: Webster-Clark Michael , Sanoff Hanna Kelly , Keil Alexander P. , Sturmer Til , Westreich Daniel , Lund Jennifer Leigh
Published: June 2021

1521

Background: Patterns of chemotherapy delivery are likely to differ between trial and real-world populations. Typical measures used to compare these patterns are calculated at treatment completion, potentially missing key differences in the timing and trajectory of delays and dose reductions. We used a new measure, longitudinal cumulative dose (LCD), to compare treatment delivery over time in trial and real-world populations. Methods: We compared chemotherapy delivery in patients with stage II-III colon cancer enrolled in the MOSAIC trial of 5-fluorouracil (5FU) vs oxaliplatin + 5FU (FOLFOX4) to patients treated from 2008-2019 in the US Oncology Network with FOLFOX4, FOLFOX6, or mFOLFOX6. For each patient, we computed oxaliplatin LCD as the cumulative oxaliplatin dose received at a given timepoint (t) divided by the final standard oxaliplatin dose. We then estimated the median and 25th and 75th percentiles for oxaliplatin LCD within each regimen at day 68 (before the standard timing of the 7th dose), 168 (two weeks after the standard end of treatment), and 250. Results: The table shows the number of patients receiving each treatment regimen and the median and interquartile range for oxaliplatin LCD at each time. Higher LCDs in the trial show delivery closer to standard treatment, meaning fewer delays, dose reductions, and discontinuations. Differences between the medians, 25th percentiles, and 75th percentiles of LCD in each regimen were small at day 68 but grew considerably by days 168 and 250. Conclusions: Divergence from the standard dosing schedule was larger in real-world versus trial settings and varied by oxaliplatin regimen. LCD, as a longitudinal measure, showed that differences in delivery between trial and real-world populations grew substantially over time (even after 168 days and the standard end of treatment) possibly as real-world patients experienced more side effects and barriers to treatment than trial participants. These discrepancies in LCD may cause poorer outcomes in real-world settings than expected based on randomized trials.[Table: see text]
Impact of a regimen-level prior authorization tool on provider adherence to clinical guidelines’ and cost savings in a Medicare advantage population.
Authors: Yeon Heeseon , Brito Rogelio Alberto , Kong Wenjing , Cavers William , Mcgovern-Siembab Allendre , Claussen Anne , Johnson Kjel Andrew , Avalos-Reyes Elisea
Published: June 2021

1522

Background: In 2019, a large national health system developed a comprehensive approach to improve the quality and cost of cancer care; this solution included a Web-based clinical decision support prior authorization (PA) tool, Novologix (NLX), which approves at the regimen rather than drug level to reduce administrative burden and elevate the quality of care. Evidence-based guidelines in NLX are updated in real-time via a partnership with the National Comprehensive Cancer Network (NCCN). The purpose of this study is to evaluate the NCCNconcordance of PAs submitted via NLX and total cost of care for Medicare Advantage (MA) patients with non-small cell lung cancer (NSCLC) who received NCCNconcordant versus non-concordant therapies. Methods: Eligible patients included MA patients diagnosed with NSCLC; our initial analysis included only the NSCLC subset of patients. NCCN regimen concordance was identified from pharmacy and medical claims and defined as concordant if the entire prescribed treatment regimen matched an NCCNregimen; patients not receiving an NCCN recommended regimen were deemed to be non-concordant. Total cost of care for MA patients with NSCLC were calculated. Results: From April-December 2020, 279 PAs were submitted via NLX and 83% were automatically approved in real-time. PAs not automatically approved were deemed concordant after peer-to-peer consultations that led to NCCN concordance; no PA denials were made. In the first half of 2020, 2,690 MA patients with NSCLC were identified; 2,166 (81%) patients were defined as NCCN concordant. Beginning with and including the first treatment and for 30-days thereafter, total cost of care for concordant patients averaged $19,321 while non-concordant patients averaged $26,405, a statistically significant savings of $7,084 (p < 0.001). Conclusions: Preliminary findings with a MA NSCLC population suggest engaging oncology practices through an enhanced payer-provider collaboration and implementing an automated regimen-level precertification process with real-time NCCNupdates can facilitate lower cost, and more efficient oncology care. Financial savings by encouraging providers to follow NCCN guidelines may lead to a 27% reduction in total cost of care with further proper adjustment on population bias; this may lead to similar findings in other cancer types. Future studies are needed to measure the long-term impact of this program on total cost of care for other care models and cancer types.
An automated EHR-based tool to facilitate patient identification for biomarker-driven trials.
Authors: Lakhanpal Shailendra , Hawkins Kailee , Dunder Steven G. , Donahue Karri , Richey Madeline , Liu Edward , Brachfeld Alexandra , Sutton Lauren , Shelley Addison , Green Dominic , Siu Evelyn , Hamrick James
Published: June 2021

1539

Background: Clinical trial eligibility increasingly requires information found in NGS tests; lack of structured NGS results hinders the automation of trial matching for this criterion, which may be a deterrent to open biomarker-driven trials in certain sites. We developed a machine learning tool that infers the presence of NGS results in the EHR, facilitating clinical trial matching. Methods: The Flatiron Health EHR-derived database contains patient-level pathology and genetic counseling reports from community oncology practices. An internal team of clinical experts reviewed a random sample of patients across this network to generate labels of whether each patient had been NGS tested. A supervised ML model was trained by scanning documents in the EHR and extracting n-gram features from text snippets surrounding relevant keywords (i.e. 'Lung biomarker', 'Biomarker negative'). Through k-fold cross-validation and l2-regularization, we found that a logistic regression was able to classify patients' NGS testing status. The model's offline performance on a 20% hold-out test set was measured with standard classification metrics: sensitivity, specificity, positive predictive value (PPV) and NPV. In an online setting, we integrated the tool into Flatiron's clinical trial matching software OncoTrials by including in each patient's profile an indicator of "likely NGS tested" or "unlikely NGS tested" based on the classifier's prediction. For patients inferred as tested, the model linked users to a test report view in the EHR. In this online setting, we measured sensitivity and specificity of the model after user review in two community oncology practices. Results: This NGS testing status inference model was characterized using a test sample of 15,175 patients. The model sensitivity and specificity (95%CI) were 91.3% (90.2, 92.3) and 96.2% (95.8, 96.5), respectively; PPV was 84.5% (83.2, 85.8) and NPV was 98.0% (97.7, 98.2). In the validation sample (N = 200 originated from 2 distinct care sites), users identified NGS testing status with a sensitivity of 95.2% (88.3%, 98.7%). Conclusions: This machine learning model facilitates the screening for potential patient enrollment in biomarker-driven trials by automatically surfacing patients with NGS test results at high sensitivity and specificity into a trial matching application to identify candidates. This tool could mitigate a key barrier for participation in biomarker-driven trials for community clinics.
Trends of cancer associated with modifiable behavior in the U.S.: Is there a difference based on age, gender, or race?
Authors: Liao Cheng-I , Caesar Michelle Ann P. , Chan Chloe , Richardson Michael , Kapp Daniel Stuart , Francoeur Alex Andrea , Chan John
Published: June 2021

1542

Background: To examine trends in modifiable behaviorally related cancers among men and women in the United States. Methods: Data were obtained from the United States Cancer Statistics (USCS) database for all cancers diagnosed between 2001 and 2017. Alcohol-associated cancers, HPV-associated, obesity-associated, physical inactivity-associated, and tobacco-associated were defined using ICD-O-3 site codes. SEER*Stat 8.3.8 and Joinpoint regression program 4.8.0.1 were used to calculate the trends of associated cancers expressed per 100,000. Results: In 2017 the incidence of cancers in women associated with alcohol, smoking and obesity were 168/100,000, 134/100,000 and 121/100,000 respectively. Based on analysis of trends of women from 2001 to 2017, alcohol, smoking and physical inactivity related cancers decreased with an annual percent change (APC -0.51%, -0.96%, -0.92% respectively, p < 0.001). However, there was no significant change in obesity, HPV, or post menopausal female breast cancer related cancers (APC: 0.07%, 0.09%, -0.08% respectively, p = 0.303, 0.181, 0.569). Based on age, in women less than 65, rates of obesity related cancers are increasing. Based on racial groups, all rates of cancer associated with modifiable factors are decreasing, however Hispanic women have an increasing rate of obesity related cancers (APC 0.46%, p < 0.001). When examining differences in region, all rates of cancer are decreasing or unchanged except the south has an increasing rate of obesity related cancer (APC 0.28%, p < 0.001). Using a projection model, obesity will become the highest incidence cancer in Hispanic women by 2035, surpassing alcohol and tobacco. In 2017 the incidence of cancers in men associated with tobacco, obesity, and alcohol were 209/100,000, 111/100,000 and 81/100,000 respectively. Based on analysis of trends in men alcohol, smoking, physical inactivity and obesity related cancers decreased (APC -1.42%, -1.59%, -3.15%, -0.41% respectively, p < 0.001). HPV related cancers have increased (APC 2.36%, p < 0.001). In men less than 60 years old, the rates of obesity related cancers are increasing. Using a prediction model, obesity is predicted to surpass tobacco as the most common social cause of cancer in 2020 for men 35-39, 2024 in men 40-44 and in 2030 in men 45-49. Conclusions: In women, most modifiable factors associated with cancer are decreasing except in obesity and HPV related cancers. In men, these rates of cancer are decreasing except HPV related cancers. However, rates of obesity related cancers are on the rise in Hispanic women and younger men. Obesity is set to become the major modifiable factor for many associated cancers.
Automated identification of immune related adverse events in oncology patients using machine learning.
Authors: Xu Wenxin , Gusev Alexander , Groha Stefan , Rahma Osama E. , Schrag Deborah , Choueiri Toni K. , Kehl Kenneth L.
Published: June 2021

1551

Background: Immune related adverse events (irAEs) are a major cause of morbidity among cancer patients treated with immune checkpoint inhibitors (ICIs). irAEs are difficult to identify systematically, which represents a major barrier to the conduct and reproducibility of irAE research. Automated approaches would facilitate cohort identification and understanding of risk factors for irAEs following ICI therapy. Methods: Patients treated with one or more ICIs at a single tertiary cancer center were identified. Patients who received ICIs outside the clinical trial context were used as a development cohort. For each date containing clinical documentation, proxy outcomes expected to correlate with grade 2+ irAEs including irAE related diagnosis codes, key laboratory values, prescriptions for topical and systemic steroids, and irAE keywords were extracted. Intermediate machine learning models were trained to predict the presence of each proxy outcome using structured and unstructured patient data. We used clinical trial irAEs extracted from adverse event tables found in the electronic health record as the “gold standard” outcome for a final training and evaluation cohort. A logistic regression model was used to combine predictions from each intermediate model and generate an overall probability score for each irAE type on a given encounter date. Ten-fold cross-validation was used to evaluate the final machine learning model on a held-out sample of clinical trial patients. Encounter level models were evaluated for predicting the onset of a given irAE on a given date, and patient level models for predicting irAE onset within 6 months of ICI initiation. Results: We identified 3,765 patients treated with ICIs off-trial and 1106 patients treated on ICI clinical trials. Among trial patients, overall incidence of any grade 2+ irAE was 21%. The combined irAE models were able to predict prospective gold standard irAE labels with accurate discrimination at both the encounter and patient level (Table). Conclusions: Machine learning models can identify irAEs among cancer patients in an automated manner, which may facilitate research to mitigate toxicities and optimize clinical outcomes. Validation of these methods in an external institutional cohort is underway.[Table: see text]
Trajectories of machine learning-predicted mortality risk among patients with cancer and associated end-of-life utilization.
Authors: Liu Manqing , Chen Jinbo , Li Eric , Li Runze , Parikh Ravi Bharat
Published: June 2021

1553

Background: Machine learning (ML) algorithms outperform traditional tools used for prognostication and may facilitate earlier discussions between oncologists and patients (pts) about hospice enrollment and treatment modification. Identifying longitudinal trajectories of mortality risk may help clinicians and health systems understand which populations such algorithms are likely to benefit. Methods: We identified trajectories of mortality risk and their association with existing metrics of end-of-life care quality, using electronic health and registry data from a prospective cohort of 3,280 pts with cancer who were seen in 18 tertiary or community medical oncology practices within a large academic health system between January 2018 and May 2020 and died prior to November 2020. A validated ML algorithm (c-statistic 0.89; Parikh et al, JAMA Oncol, 2020) prospectively generated mortality risk predictions prior to all encounters. Functional principal component analysis (FPCA) identified modes of variation for all patient-level mortality risk predictions associated with encounters prior to death. Adjusted logistic regression analyses tested associations between mortality risk trajectory and metrics of high-quality end-of-life care. Results: FPCA revealed 2 trajectories that represented 36% and 64% of all pts in the cohort. The first cluster (“unpredictable”) consisted of pts whose ML-predicted mortality risk rose sharply within 30 days of death. The second cluster (“predictable”) consisted of pts whose ML-predicted mortality risk was higher at baseline and rose gradually until death. Individuals with predictable mortality risk trajectories were more likely to have worse performance status, high comorbidity burden, and gastrointestinal (GI) malignancies (Table). Predictable trajectories were associated with higher hospice enrollment (adjusted odds ratio [aOR] 1.87, 95% CI 1.48-2.37), less inpatient death (aOR 0.72, 95% CI 0.56-0.92), less end-of-life intensive care unit admissions in the last 30 days of life (aOR 0.74, 95% CI 0.57-0.95), and less chemotherapy in the last 14 days of life (aOR 0.77, 95% CI 0.55-1.08). Conclusions: Over one-third of deaths among pts with cancer follow an unpredictable trajectory. For pts with unpredictable trajectories, overreliance on ML predictions could perpetuate aggressive end-of-life care.[Table: see text]
Clinical accuracy of information extracted from prostate needle biopsy pathology reports using natural language processing.
Authors: Wong Risa Liang , Sagar Medha , Hoffman Jacob , Huang Claire , Lerma Angelica , Kanabolo Diboro , Caldwell Joshua , Gore John L.
Published: June 2021

1557

Background: Patients with prostate cancer are diagnosed through a prostate needle biopsy (PNB). Information contained in PNB pathology reports is critical for informing clinical risk stratification and treatment; however, patient comprehension of PNB pathology reports is low, and formats vary widely by institution. Natural language processing (NLP) models trained to automatically extract key information from unstructured PNB pathology reports could be used to generate personalized educational materials for patients in a scalable fashion and expedite the process of collecting registry data or screening patients for clinical trials. As proof of concept, we trained and tested four NLP models for accuracy of information extraction. Methods: Using 403 positive PNB pathology reports from over 80 institutions, we converted portable document formats (PDFs) into text using the Tesseract optical character recognition (OCR) engine, removed protected health information using the Philter open-source tool, cleaned the text with rule-based methods, and annotated clinically relevant attributes as well as structural attributes relevant to information extraction using the Brat Rapid Annotation Tool. Text pre-processing for classification and extraction was done using Scispacy and rule-based methods. Using a 75:25 train:test split (N = 302, 101), we tested conditional random field (CRF), support vector machine (SVM), bidirectional long-short term memory network (Bi-LSTM), and Bi-LSTM-CRF models, reserving 46 training reports as a validation subset for the latter two models. Model-extracted variables were compared with values manually obtained from the unprocessed PDF reports for clinical accuracy. Results: Clinical accuracy of model-extracted variables is reported in the Table. CRF was the highest performing model, with accuracies of 97% for Gleason grade, 82% for percentage of positive cores ( < 50% vs. ≥50%), 90% for perineural or lymphovascular invasion, and 100% for presence of non-acinar carcinoma histology. On manual review of inaccurate results, model performance was limited by PDF image quality, errors in OCR processing of tables or columns, and practice variability in reporting number of biopsy cores. Conclusions: Our results demonstrate successful proof of concept for the use of NLP models in accurately extracting information from PNB pathology reports, though further optimization is needed before use in clinical practice.[Table: see text]
Improving tumor mutational burden calibration in non-European patients.
Authors: Adib Elio , Nassar Amin , Akl Elie , Abou Alaiwi Sarah , El Zarif Talal , El-Khoury Habib , Ananda Guruprasad , Choueiri Toni K. , Gusev Alexander
Published: June 2021

1559

Background: Tumor mutational burden (TMB), has been recently granted FDA approval as a biomarker for ICI treatment in tumors with a high mutation load (≥ 10/MB). To leverage this biomarker in the clinical setting, it is necessary to evaluate its application in diverse patient populations. In particular, tumor-only sequencing may overestimate TMB in non-EUR populations for which reference panels are small or unavailable, and thus have poorer predictive performance. Herein, we investigate the effect of TMB overestimation in non-EURs on patient diagnosis and clinical outcomes in a real world patient cohort. Methods: TMB was computed using a tumor-only NGS platform (Oncopanel) for 8349 cancer patients (pts) of 7 cancer types (Table). Genetic ancestry was inferred directly from tumor sequencing data and confirmed for a subset of pts using germline SNP arrays. TMB was compared using Wilcoxon rank-sum test between European (EUR) and non-EUR pts. TMB percentile rank by ancestral group (East Asian, African, European) and tumor histology was computed. In non-EUR pts, TMB was calibrated by reassigning its value to the corresponding percentile rank in EUR pts having the same cancer (AH-TMB). Tumors with raw TMB ≥ 10/MB were assigned as TMB-high (TMBH; with the rest referred to as TMBL for TMB-low) and those with calibrated AH-TMB ≥ 10/MB as AH-TMBH. A subset of pts treated with ICIs at DFCI and of EUR ancestry were analyzed. TMB was intentionally mis-calibrated in EURs (MC-TMB), to mimic the TMB overestimation observed in non-EURs. Associations between TMBH status and overall survival (OS) was assessed using Cox regression. Results: Uncalibrated TMB was significantly higher in tumors from non-EUR pts overall (p <0.0001, Table) as anticipated, whereas this difference was not observed in tumors with matched germline filtering. We reassigned non-EUR pts into low/high groups using calibrated AH-TMB, which changed the assignment of 67/670 non-EURs, most of which (65/67) were downgraded from TMBH to AH-TMBL. In the ICI-treated cohort, there was a strong association between TMBH status and OS (p = 2e-11, HR = 0.6) whereas MC-TMBH (mimicking the miscalibration in non-EUR samples) had a weaker association with OS (p = 0.0013, HR = 0.8). Importantly, pts assigned to TMB-H due to miscalibration (90 TMB-L/MC-TMBH pts) had shorter OS compared to the true TMBH group (p = 3e-6, HR = 1.9). Conclusions: In this analysis, we showed that tumor-only sequencing platforms can overestimate TMB in non-EUR pts, impacting treatment decisions and outcomes. Replacing raw TMB counts with an ancestry-adjusted measure could optimize TMB application in the clinical setting when germline sequencing is not available. Accurate ancestry inference can be performed using tumor-only sequencing.[Table: see text]
A machine learning tool to predict mortality risk among patients with metastatic cancer in outpatient oncology care.
Authors: Butler Brandon , Tayiab Nadaa , Phu Serra , Hoang Susan Nga , Turnwald Brian , Garey Jody S. , He Bo , Hoverman John Russell
Published: June 2021

1560

Background: End-of-life management is a well-known challenging aspect of cancer care. In particular, timely hospice enrollment is a leading quality metric in the Oncology Care Model that has substantial room for improvement. An automated algorithmic tool that can incorporate the wealth of available EHR data and rapidly identify patients with a high risk of imminent mortality could be a valuable asset to supplement important clinical decisions and improve timely hospice care. Methods: A retrospective study cohort was formed using patients with metastatic cancer from US Oncology Network (USON) practices participating in the Oncology Care Model (OCM) between January 1, 2017 and June 30, 2019. Patients were required to have at least one record for lab values and vital signs in the EHR database. Patients were excluded from the study cohort if they were not enrolled in the OCM program or did not have a diagnosis for metastatic cancer. The patients satisfying the selection criterion were used to train and optimize the model. The training dataset was also used for internal validation and hyperparameter tuning until the final model was produced. As external validation, the final model was independently tested on 3 separate holdout datasets including OCM patients between July 1, 2019 and March 31, 2020. To avoid bias, all holdout datasets used for validation were excluded from the model. Results: A multivariable model to predict 90-day mortality was developed using a retrospective dataset derived from EHR data and Medicare claims data. A logistic regression algorithm using L1 (lasso) regularization yielded the best performance compared to other model candidates. The performance on the training cohort was given by a cross-validated AUC score of 0.85 (95% CI, 0.84 to 0.86). Further, external validation conducted using 3 independent holdout datasets demonstrated impressive generalizability marked by stable performance scores across multiple time periods (AUC between 0.84 and 0.85). Conclusions: This study builds upon previous work and further establishes the utility of machine learning to predict risk of imminent mortality for advanced cancer patients using available EHR data. A data-driven tool that estimates the probability of 90-day mortality could be leveraged as a powerful supplementary aid to clinicians managing end-of-life care at oncology practices.
Oncology trial enrollment trends following the first wave of the COVID-19 pandemic.
Authors: Lamont Elizabeth W. , Blum Kevin , Katriel Ron G. , Ensign Lisa G
Published: June 2021

1565

Background: The first wave of the COVID-19 pandemic was associated with a negative effect on global oncology clinical research and development including documented decreases in new trial launches and patients enrolling on existing trials. We sought to evaluate secular trends in patient enrollment on global oncology trials of drugs and/or biological agents coincident with trends in the pandemic through 10/31/2020. Methods: This time-series study of global oncology clinical trial patient enrollment relied on data from the Medidata Enterprise Data Store, which comprises studies using the RAVE electronic data capture platform, and eCDC. We quantified the number of patients enrolling on oncology trials according to contiguous pandemic waves between 01/05/2020 and fixed right censoring at 10/31/2020. For this study, we defined pandemic waves empirically through review of peaks and nadirs of global case counts/week from eCDC data: wave 1 (01/05/2020-05/02/2020), wave 2 (05/03/2020-08/22/2020), wave 3 (08/23/2020-10/31/2020). We used negative binomial regressions to evaluate associations between time periods (i.e., waves) and counts of new trial enrollees and time periods and counts of new COVID-19 patients. Results: A total of 54,752 patients enrolled in 1,176 oncology trials world-wide during the observation period: 14,888 patients during wave 1, 19,631 patients during wave 2, and 20,233 patients during wave 3 of the pandemic. Compared to wave 1, the incidence of new trial enrollees increased by 10% in wave 2 (incidence rate ratio [IRR] 1.10, 95% confidence interval [CI] 0.95-1.26) and by 29% in wave 3 (IRR 1.29, 95% CI: 1.10-1.52). Over the same period, 47,336,995 people were diagnosed with COVID-19 infection: 3,481,165 patients during wave 1, 20,205,244 patients during wave 2, and 23,650,586 patients during wave 3. Compared to wave 1, the incidence of new COVID-19 patients increased by 617% in wave 2 (IRR 6.17, 95% CI: 2.76-13.77) and by 1,155% (IRR 11.55, 95% CI: 4.61-28.95) in wave 3. Conclusions: Despite substantial increases in the incidence of COVID-19 following the first wave of the pandemic, the number of patients enrolled in oncology clinical trials increased notably during the same period. Future research seeks to understand the mechanism through which the oncology research and development enterprise adapted to the shock of the COVID-19 pandemic. eCDC Source: European Centre for Disease Prevention and Control. Data provided subject to license available at: https://www.ecdc.europa.eu/en/copyright
AI-based imaging biomarker in mammography for prediction of tumor invasiveness.
Authors: Nam Hyeonseob , Kim Ki Hwan , Ock Chan-Young
Published: June 2021

1568

Background: The preoperative diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) can be upstaged in the final pathology, and this possibility is linked to the controversy over whether axillary staging is necessary in primary operation. In this study, we developed an artificial intelligence (AI)-powered Imaging Biomarker in Mammography (IBM) that can predict tumor invasiveness in preoperative mammography and evaluated its performance in an external validation cohort. Methods: A total of 151,764 exams of 4-view mammograms were collected from five institutions of three countries to develop the AI algorithm for breast cancer detection, where 31,776 were cancer exams. In previous studies, the performance of this breast cancer detection algorithm has already been evaluated, and in this study, we further developed the AI-powered IBM for predicting tumor invasiveness on top of the AI algorithm for breast cancer detection. To develop the AI-powered IBM for predicting invasiveness, final diagnosis information was collected for 8,251 cancer exams (472 DCIS, 388 ductal carcinoma in situ (DCIS-MI), and 7,391 invasive ductal carcinoma (IDC)), and 886 cancer exams (44 DCIS, 51 DCIS-MI, 791 IDC) were additionally collected for internal validation. The AI-powered IBM was developed via two stages of training – 1) training with diagnosis labels (cancer vs non-cancer), followed by 2) fine-tuning with invasiveness labels (DCIS, DCIS-MI, IDC). The AI-powered IBM also tested in an external validation cohort of 699 cancer exams (68 DCIS, 19 DCIS-MI, 612 IDC) and all the exams were confirmed by surgical biopsy. Results: The AI-powered IBM showed an area under the curve (AUC) values of 0.968 for breast cancer detection and it successfully distinguished IDC from DCIS and DCIS-MI with AUC values of 0.898 and 0.851, respectively (Table). In addition, the AUC value in terms of discriminating between DCIS and DCIS-MI was 0.752. When the AI-powered IBM was tested in the external cohort, it could detect breast cancer with the AUC of 0.952 and, its performance in terms of invasiveness prediction was similar that of the internal validation (IDC vs DCIS, 0.810; IDC vs DCIS-MI, 0.846), which supports the AI-powered IBM is applicable to the unseen mammography exam. Conclusions: The AI-powered IBM can distinguish IDC from DCIS and DCIS-MI in mammography. The results support that the AI-powered IBM can be used as a biomarker to help determine the surgical plan that includes whether or not to perform the axillary dissection.[Table: see text]
Superior overall survival (OS) and disease-free survival (DFS) predictions for patients with glioblastoma multiforme (GBM) using Cellworks Singula: myCare-022-03.
Authors: Wen Patrick Y. , Castro Michael , Watson Drew , Kapoor Shweta , Agrawal Ashish , Alam Aftab , Roy Kunal Ghosh , Rajagopalan Swaminathan , Basu Kabya , Lala Deepak Anil , Mundkur Nirjhar , Christie Jim , Pampana Anusha , Basu Sayani , Sahu Diwyanshu , Narvekar Yugandhara , Singh Divya , Nair Prashant , Ahluwalia Manmeet Singh
Published: June 2021

2017

Background: The Cellworks Singula Therapeutic Response Index (TRI) has been developed to assist clinicians and GBM patients in choosing between competing therapeutic options. In contrast to approaches that consider single aberrations, which often yield limited benefit, Cellworks utilizes an individual patient’s next generation sequencing results and a mechanistic multi-omics biology model, the Cellworks Omics Biology Model (CBM), to biosimulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. For any individual patient and alternative therapy, Cellworks integrates this biologically modeled multi-omics information into a continuous Singula TRI Score, scaled from 0 (low therapeutic benefit) to 100 (high therapeutic benefit). We demonstrate that Singula is strongly associated with OS and DFS beyond standard clinical factors, including patient age, patient gender, and physician prescribed treatments (PPT). Methods: In this study, Singula’s ability to predict response was evaluated in a retrospective cohort of 100 GBM patients with OS and DFS data from The Cancer Genome Atlas (TCGA) project, treated with PPT. As a primary analysis of the CBM and TRI Score, Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used to assess the hypothesis that Singula is predictive of OS and DFS above and beyond patient age, patient gender, and PPT. A p-value < 0.05 for the corresponding likelihood ratio statistic was required to be considered significant. Results: Multivariate analyses were performed to assess the performance of the Singula Therapy Response Index after adjusting for the contribution of standard clinical factors. The same Singula TRI algorithm and clinical cutoffs were used for all clinical outcome measures. These analyses, shown in the table, suggests that the proposed Singula TRI provides predictive value of OS and DFS above and beyond patient age, patient gender, and PPT. Conclusions: The Singula TRI Score provides a continuous measure scaled from 0 (low benefit) to 100 (high benefit) for alternative GBM therapeutic options. In this retrospective cohort, Singula was strongly predictive of OS and DFS and provided predictive value beyond PPT, patient age and gender. These results will be further validated in larger scale, prospectively designed clinical studies.[Table: see text]
Efficacy finding cohort of a cancer peptide vaccine, TAS0313, in treating recurrent glioblastoma.
Authors: Arakawa Yoshiki , Okita Yoshiko , Narita Yoshitaka
Published: June 2021

2038

Background: TAS0313 is a cancer vaccine cocktail comprising three long peptides with a total of 12 cytotoxic T lymphocyte (CTL) epitope peptides. These peptides were derived from eight cancer-associated antigens (EGFR, KUA, LCK, MRP3, PTHRP, SART2, SART3, and WHSC2) that are overexpressed in various cancer types including glioblastoma (GBM). We performed a single-arm, multicenter efficacy confirmatory cohort study as part of the TAS0313 Phase I/II study in patients with recurrent GBM. Methods: The enrolled patients with histologically or cytologically confirmed grade IV GBM (including gliosarcoma, giant cell glioblastoma, and epithelioid glioblastoma) had at least one of the following HLA types: HLA-A*02:01, -A*02:06, -A*02:07, -A*11:01, -A*24:02, -A*31:01, or -A*33:03. Eligible patients were those who received standard radiotherapy and temozolomide, had a confirmed first or second recurrence, or progression with measurable disease outcomes with good performance status (Karnofsky Performance Status score > 70). TAS0313 (27mg) was subcutaneously administered on days 1, 8, and 15 of cycles 1 and 2 and day 1 of cycle 3 or later in 21-day cycles until disease progression or unacceptable toxicity occurred. Tumor response was evaluated using The Response Assessment in Neuro-Oncology criteria. The antigen-specific CTL and immunoglobulin G (IgG) were analyzed by ELISPOT assay and Luminex assay, respectively, before and after treatment. The primary objective was to evaluate the efficacy of TAS0313. Secondary and exploratory methods were used to evaluate the safety and immune response of TAS0313. Results: As of 10th September 2020, 10 patients have been treated with TAS0313 (eight with GBM and two with gliosarcoma). The median age of participants was 56.5 [range, 33–69 years], and methylation status of the MGMT promoter was methylated in four patients, unmethylated in four, and unknown in two. The best overall response was partial response 1/10 (10.0%) and stable disease 4/10 (40.0%). One case showed 69.1% tumor shrinkage. The treatment of two patients was ongoing for over 7 months. The 6-month progression-free survival (PFS) rate was 25.0%, and the median PFS was 2.3 months. The most common adverse drug reactions (ADRs) were grade 1–2 injection site reactions and pyrexia. There were no grade 4 or 5 ADRs. In some patients, TAS0313 treatment was confirmed to increase CTL and IgG levels compared with pre-treatment samples. Conclusions: TAS0313 showed promising efficacy with expected immune responses and favorable safety and tolerability in patients with recurrent GBM. Further investigation of TAS0313 is warranted to validate our findings. Clinical trial information: JapicCTI-183824.
Safety and feasibility of rhenium-186 nanoliposome (186RNL) in recurrent glioma: The ReSPECT phase 1 trial.
Authors: Brenner Andrew J. , Bao Ande , Phillips William , Stein Gregory , Awasthi Vibhudutta , Patel Toral R. , Weinberg Jeffrey , Floyd John
Published: June 2021

2061

Background: While external beam radiation therapy (EBRT) remains a central component of the management of primary brain tumors, it is limited by tolerance of the surrounding normal brain tissue. Rhenium-186 NanoLiposome (186RNL) permits the delivery of beta-emitting radiation of high specific activity with excellent retention in the tumor. We report the results of the phase 1 study in recurrent glioma. Methods: A Phase 1 dose-escalation study of 186RNL in recurrent glioma utilizing a standard 3+3 design was undertaken to determine the maximum tolerated dose of 186RNL. 186RNL is administered by convection enhanced delivery (CED). Infusion is followed under whole body planar imaging and SPECT/CT. Repeat SPECT/CT imaging is performed immediately following, and at 1, 3, 5, and 8 days after 186RNL infusion to obtain dosimetry and distribution. Subjects were followed until disease progression by RANO criteria. Results: Eighteen subjects were treated across 6 cohorts. The mean tumor volume was 9.4 mL (range 1.1 – 23.4). The infused dose ranged from 1.0 mCi to 22.3 mCi and the volume of infusate ranged from 0.66 mL to 8.80 mL. From 1 – 4 CED catheters were used. The maximum catheter flow rate was 15 µl/min. The mean absorbed dose to the tumor volume was 239 Gy (CI 141 – 337; range 9 - 593), to normal brain was 0.72 Gy (CI 0.34 – 1.09; range 0.005 – 2.73), and to total body was 0.07 Gy (CI 0.04 – 0.10; range 0.001 – 0.23). The mean absorbed dose to the tumor volume when the percent tumor volume in the treatment volume was 75% or greater (n = 10) was 392 Gy (CI 306 – 478; range 143 – 593). Scalp discomfort and tenderness related to the surgical procedure did occur in 3 subjects. The therapy has been well tolerated, no dose-limiting toxicity has been observed, and no treatment-related serious adverse events have occurred despite markedly higher absorbed doses typically delivered by EBRT in patients with prior treatment. Responses have been observed supporting the clinical activity. Final results from the dose escalation will be presented. Conclusions: 186RNL administered by CED to patients with recurrent glioma results in a much higher absorbed dose of radiation to the tumor compared to EBRT without significant toxicity. The recommended Phase 2 dose is 22.3 mCi in 8.8 mL of infusate. Clinical trial information: NCT01906385. [Table: see text]
Changes in T lymphocyte subsets in different tumors before and after radiotherapy: A meta-analysis.
Authors: Zhu Xiaoxia , Wang Qin , Li Shangbiao , Qiao Simiao , Zheng Zhihao , Duan Xiaotong
Published: June 2021

2525

Background: Radiation therapy (RT) induces an immune response, but the relationship of this response with tumor type is not fully understood. Methods: We searched English-language electronic databases including PubMed, EMBASE, and the Cochrane Library to collect studies about the changes in CD3+ T lymphocytes, CD4+ T lymphocytes, and CD8+ T lymphocytes before and after radiotherapy in tumor patients from January 2015 to December 2019. The quality of the included literature was evaluated using the NOS scale provided by the Cochrane Collaboration, and statistical software RevMan 5.4 was used to analyze the included literature. p < 0.05 was considered to indicate statistical significance. Results: A total of 17 studies in 15 articles involving 1735 tumor patients were included. All data were collected within 1 month before or after radiotherapy. Meta-analysis showed that numbers of CD3+ T lymphocytes were significantly reduced after radiotherapy compared with before treatment (standard mean difference [SMD]: -0.76; 95% CI [-1.46, -0.06]; p = 0.03), as were those of CD4+ T lymphocytes (SMD: -0.50; 95% CI: [-0.88, -0.12]; p = 0.01), but there was no statistically significant difference for CD8+ T lymphocytes (SMD: 0.19; 95% CI: [-0.23, 0.62]; p = 0.38). Subgroup analysis showed significant decreases in CD3+ T lymphocytes in liver cancer,esophageal cancer, head and neck cancer, pancreatic cancer and breast cancer after radiotherapy. Numbers of CD4+T lymphocytes increased after radiotherapy in breast cancer,and a decrease was observed in liver cancer, esophageal cancer, colorectal cancer, and head and neck cancer. CD8+ T lymphocyte numbers also increased compared with before radiotherapy in esophageal cancer, lung cancer, and colorectal cancer. But a decrease in liver cancer and head and neck cancer. Conclusions: Numbers of CD3+ and CD4+ T lymphocytes decreased after radiotherapy, whereas CD8+ T lymphocytes showed no significant change. Within 1 month of radiotherapy, the tumor microenvironment showed an immunosuppressive state. The degree of immune response induced by radiotherapy differed between tumor types.