bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lost in translation? Information exchange during breast cancer care and implications for technology design.
Authors: Salwei Megan , Reale Carrie 
Published: June 2023

1558 Background: Almost 4 million women in the US have had breast cancer, with 300,000 women diagnosed each year. Treatment decision-making following breast cancer diagnosis can be challenging as there are often several clinically appropriate treatment options, each with differing impacts on patient short- and long-term quality of life. Further, patients must manage this information during a time of high emotional burden, which can hinder decision-making. Not surprisingly, studies have found that less than 50% of patients believe their treatment plan aligns with their preferences. Numerous decision aids have been developed to support patient decision-making. However, sustained use of decision aids is limited due to poor integration in clinical workflow. We need to better understand the complex workflows and needs of decision aid users - patients and breast cancer clinicians. In this study, we investigate the information exchange between patients and clinicians when making breast cancer treatment decisions and identify design requirements for patient-centered decision support tools. Methods: This study is part of a larger research project to design a COMputerized PAtient-centered Collaborative Technology (COMPACT) to support personalized breast cancer decision-making. We observed clinicians at one academic medical center’s breast center from February-August 2022. Participating clinicians were followed throughout their clinical shift. Using a tablet computer and smart pencil, we recorded all aspects of clinician workflow. Written notes were transcribed and uploaded into Dedoose. Two human factors researchers inductively coded each observation in a consensus-based process. Results: We observed 79 hours of clinical care and 119 patient encounters across 20 observations of various clinical roles. Overall, we found that patients with breast cancer must manage an enormous amount of highly complex information that includes technical medical terms, mathematical concepts (e.g., risk), and uncertainty. We identified over 20 distinct decisions that patients face during their cancer journey, which were interdependent and evolved over time. We identified numerous patient-specific factors that influenced treatment decision-making, such as the patient’s work and family circumstances. For instance, one patient was organizing a conference and needed to plan her surgery around this event. Patients were frequently asked to remember and relay information across members of the care team, indicating inadequate system support of information transfer within the large cancer team. Based on these findings, we identified design requirements for interventions to support care coordination and patient-centered decision-making. Conclusions: These findings can inform the design of interventions to support breast cancer decision-making and improve patient-centered cancer care.
Accuracy of a clinical decision support system in identifying patients at risk of cancer in primary care.
Authors: Bakshi Bea , Payling Miles 
Published: June 2023

1562 Background: 85% of patients diagnosed with cancer are symptomatic at presentation. Yet a delay in diagnosis (e.g. missing a presentation) can result in late stage detection and poorer outcomes. Clinical decision support systems (CDSS) have been used in other fields to calculate risk and support standardization of care. However, little has been reported about successful implementation of CDSS with determining cancer risk. C the Signs is a CDSS which uses artificial intelligence, mapped with the latest evidence and guidance, to assist healthcare professionals in identifying patients at risk of cancer and determining the most appropriate pathway for diagnosis. The aim of this study is to assess the accuracy of C the Signs in a real-world setting. Methods: A retrospective observational study was conducted in the National Health Service in England utilizing all patients risk assessed for cancer through the Electronic Healthcare Record (EHR) integrated C the Signs platform between 1st January 2021 and 31st August 2022. A total of 118,677 patients were risk assessed. All patients were followed up for 6 months post risk assessment to determine outcome. A sub-analysis was performed on the patients who were flagged at risk of cancer through C the Signs and subsequently diagnosed with cancer to establish the CDSS’s cancer origin accuracy (the ability for the system to accurately predict which cancer the patient is at risk of). Results: 7,295 patients were diagnosed with cancer within 6 months of a C the Signs risk assessment. 7,056 patients were successfully identified at risk of cancer by the CDSS with a sensitivity of 96.7%. There were 103,168 false positives with a specificity of 7.37%. Of the 8,453 patients who were identified as not at risk of cancer using the CDSS, 239 had a confirmed cancer diagnosis within 6 months of risk assessment (with a negative predictive value 97.2%). The cancer origin accuracy of C the Signs was 85.6% across all cancers. Conclusions: A cancer CDSS like C the Signs can be used to accurately identify patients at risk of cancer. More research is needed to understand the specificity and false positive rate, and the impact of identifying these patients with non-cancer disease.
Deep learning to estimate RECIST in patients with cancer treated in real-world settings.
Authors: Riaz Irbaz Bin , Ashraf Noman , Harris Gordon J , Choueiri Toni K. , Kehl Kenneth L. 
Published: June 2023

1564 Background: Creating large oncology clinical-genomic datasets is laborious and time-consuming. Deep learning approaches that extract RECIST outcomes at scale from observational electronic health records (EHR) data could tremendously facilitate precision oncology research. Methods: This retrospective study included patients with solid tumors treated on therapeutic clinical trials from 2004-2022 at Dana-Farber Cancer Institute with radiology reports in EHR and RECIST labels available from the tumor imaging metrics core. Each RECIST label was generated using corresponding radiology reports at a given time point. Patients were sampled into training, validation, and held-out test sets. A deep learning model (RECIST model) was trained to predict two outcomes: overall response and progressive disease at each time point, using reports from that time point and prior time points for each patient. This trained RECIST deep learning model was deployed on real-world radiology reports, and results were compared with true labels abstracted by trained human curators using the PRISSMM framework. Results: This study included 5153 patients with a total of 99,318 radiological reports who had RECIST annotations (median age at protocol enrollment [IQR] 60 years [52-57]; females 61% [3133]; and white 90% [4653]). The most common cancer types included breast (n=1006; 20%) lung (n=573; 11%), and ovarian cancer (n=539; 10%). The training subset included 4121 (79.9%) patients, the validation subset 518 (10.1%), and the held-out test set 514 (9.9%) patients. In the test set, the results showed an AUC of 0.86, and 0.87 with the best F1 scores of 0.72 and 0.63 for predicting overall response, and progressive disease, respectively. The real-world data set included 4482 patients. RECIST model evaluation on real-world radiology reports showed good performance for ascertaining PRISSMM annotations of progression from those reports (AUC 0.83, best F1 0.68), but poor performance for ascertaining PRISSMM annotations of response (AUC 0.63, best F1 0.28). Evaluation metrics are outlined in the Table. Conclusions: This study demonstrated the feasibility of using deep learning approaches to predict RECIST outcomes from radiology reports for patients with solid cancer tumors. The results show that the deep learning model accurately predicted RECIST labels on a held-out test set and real-world radiology reports for the outcome of progressive disease. These findings could accelerate precision oncology research providing a scalable way of ascertaining cancer outcomes from observational EHR data.[Table: see text]
Trends in U.S and global patient enrollment from 2014 to 2022 in lung cancer clinical trials supporting marketing applications: An FDA analysis.
Authors: Akinboro Oladimeji , Kumar Vaibhav , Drezner Nicole Lauren , Larkins Erin A. , Pazdur Richard , Kluetz Paul , Singh Harpreet 
Published: June 2023

1576 Background: Oncology drug development is an increasingly global endeavor, however it is unclear how enrollment in registrational clinical trials for lung cancer have been impacted. Clinical trial data must be reflective of the intended population in order to be considered applicable to U.S patients and medical practice, and for FDA review. Methods: Registrational clinical trials supporting lung cancer indications submitted to FDA from 2014 to 2022 were included. Patient enrollment per country was extracted for each trial, and countries were grouped into 9 global regions. The proportion of patients enrolled in the U.S and global regions were calculated for each trial. For the purpose of this analysis, MRCTs were defined as trials that enrolled at least 5% of patients from each of ≥ 3 global regions with no region accounting for ≥ 70% of patient enrollment. Trial-level patient enrollment was aggregated into 3 triennia: 2014-2016, 2017-2019, and 2020-2022. Results: A total of 66 pivotal trials supporting 59 applications submitted to FDA were evaluated during the study period. Of these, 91% ( n=60) were for non-small cell lung cancer while 9% ( n=6) were for small cell lung cancer; 59% ( n=39) were randomized controlled trials (RCTs) while 41% ( n=27) were single-arm trials; 53% ( n=35) were for biologics while 47% ( n=31) were for non-biologics; and 86% ( n=30) of the biologics-based regimens were anti-programmed death (ligand)-1 antibody-containing regimens. The proportion of trials with no U.S patient enrollment increased from 4.8% in 2014-2016 to 19.0% in 2020-2022 while those with ≥ 10% U.S patient enrollment declined from 71.4% in 2014-2016 to 52.3% in 2020-2022. There was a decline in the proportion of trials conducted as MRCTs since 2014 from 85.7% in 2014-2016 to 66.7% in 2020-2022. Conclusions: This exploratory analysis suggests that there may be declining U.S patient enrollment in lung cancer pivotal trials, and the proportion of these trials conducted as MRCTs may also be declining. These patterns of decline appear to be driven by the countries selected for patient enrollment in RCTs rather than in single-arm trials. Pivotal trials that do not enroll U.S patients and are not conducted as true MRCTs may have limited applicability to U.S patients.[Table: see text]
Completion rates and trends of the patient-reported outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) across 14 oncology clinical trials.
Authors: Emerson Jessica A , Hughes Lindsay , Dumais Kelly 
Published: June 2023

1579 Background: With growing emphasis on patient-centered regulatory decision making, there is a greater need to incorporate the patient’s voice in oncology clinical trials. Regulatory guidelines outline core patient-reported outcomes (PROs) to include which can inform risk/benefit evaluation. Self-reported symptomatic adverse events (AE) form part of these core PROs, with the new 2023 draft FDA guidance acknowledging the benefits of including them to enhance the assessment of tolerability in early phase dose optimization trials. Given the clinical symptoms and disease burden in oncology, patient burden needs to be thoughtfully considered when incorporating PROs into trials. We assessed trends and feasibility of electronic data collection for symptomatic AEs by analyzing completion compliance and time to complete the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) across 14 oncology trials. Methods: All PRO-CTCAE were collected electronically on Clario Tablets. Operational data for completion status (completed or missed) and duration of time to complete the PRO-CTCAE in seconds was extracted. Completion rates over time were examined overall and for every 3-month interval (quarters Q1 to Q9+). The median was selected for analyses of time to complete to account for skewness. Results: The sample included 14 studies across Phase I-III trials across a range of cancers including multiple myeloma, mesothelioma and cancers of the lung, prostate, head/neck, gastric, breast, and ovary. Study durations were 24 weeks and up. The PRO-CTCAE form was completed 13,122 times and missed in 1,182 instances, giving an overall completion rate of 91.7%. On average, 25 questions (range 5-47) covered an average of 13 symptoms (range: 4-25). Completion rate was not associated with the number of questions asked or time since enrollment. The overall median time to complete the PRO-CTCAE was 82s (study-specific median range: 24s to 276s). Examining time to completion as a function of the number of questions revealed a significant positive association, such that each additional question added 5.2 seconds to the time it took to complete the assessment. Conclusions: Completion compliance was high for PRO-CTCAE and remained high regardless of study length and the number of questions included in the PRO-CTCAE form. This suggests that electronic PRO-CTCAE is a feasible solution for collecting self-reported symptomatic AEs. Although time to complete the form increased with the number of questions, results suggest that including a higher number of PRO-CTCAE questions does not negatively impact compliance rate. Our data showing high completion rates may also reflect the benefits of electronic PROs in reducing patient burden and enhancing data quality, as acknowledged by regulatory agencies.
Patient-reported usefulness of an intervention to promote job retention among women undergoing (neo)adjuvant breast cancer chemotherapy.
Authors: Blinder Victoria Susana , Finik Jackie , Patil Sujata , Narang Bharat , Claros Maria , Suarez Jennifer , Martinez Samantha , Muppidi Monica , Makower Della F. , Gillis Theresa , Kuo Ting-Ting , Gany Francesca 
Published: June 2023

1586 Background: Women who undergo (neo)adjuvant breast cancer chemotx are at risk of job loss, especially if they lack work accommodations. We developed TEAMWork (Talking to Employers And Medical staff about Work), an English/Spanish mobile application (app) to help women identify work accommodations relevant to their needs, negotiate with their employers to receive these accommodations, and communicate with the clinic team to manage symptoms. TEAMWork will be compared to a booklet as a job-retention tool in an ongoing randomized clinical trial (RCT). Here we present acceptability data based on RCT participants’ perceptions of the app’s usefulness. Methods: Eligible participants were women aged 18-64, who spoke English/Spanish, were able to use and had access to a smartphone/tablet, were undergoing or planned to undergo (neo)adjuvant chemotx for stage I-III breast cancer, and were working full or part time for pay. Women who had stopped working due to the diagnosis were eligible. Those who had stopped working for reasons unrelated to breast cancer (e.g., prior illness disability) were ineligible. Online surveys were self-administered or administered interview-style (per participant preference) at baseline and midway through the planned chemotx. In the second survey, participants were asked if they had used TEAMWork at all since enrollment; those who said they had used it were asked about the app’s usefulness. Results: 58 participants received the app; 56 received the booklet. 64% were white, 18% were Black, and 12% were Asian; 7% were Hispanic. 4% were Spanish-speaking. 79% had at least a college education. When surveyed half-way through their chemotx regimen, 62% of those randomized to the app said they had used the app; only 25% of those randomized to the booklet said they had used the booklet (p<0.0001). Among those who reported having used the app (n=36), 86% said they were very (44%) or somewhat (42%) satisfied with TEAMWork, and 94% said they thought TEAMWork would be very (61%) or somewhat (33%) helpful to other patients. Additional details about the perceived usefulness of the app are shown. Conclusions: Compared to participants randomized to the booklet, more than twice as many who received the app reported that they had used their assigned intervention. Among those who used it, the app was well-received and was deemed especially helpful for communicating with medical providers. Recruitment to the RCT is ongoing, and job retention results will be presented when data are mature. Efforts are under way to increase the diversity of the study sample based on ethnicity, language, and education. Clinical trial information: NCT03572374 .[Table: see text]
Association of accelerated approval (AA) status with National Comprehensive Cancer Network (NCCN) guideline recommendation for cancer drug indications: A cross-sectional study.
Authors: Cliff Edward Robert Scheffer , Rome Rachel S , Kesselheim Aaron S , Rome Benjamin N 
Published: June 2023

1599 Background: Many oncology drugs are approved via FDA’s accelerated approval pathway based on trials showing changes to surrogate measures only ‘reasonably likely’ to predict clinical benefit. Despite this preliminary evidence, accelerated approval drugs are incorporated into clinical practice and included in guidelines. We assessed NCCN evidence and preference ratings for drugs with accelerated vs traditional approval. Methods: Accelerated approval oncology drugs were identified using an FDA list as of June 30 2022. Drug labelling revealed other oncology indications of these drugs. Accelerated approvals were stratified if they had been converted to traditional approval after confirmatory studies or withdrawn. Indications with no NCCN guidelines or tumor-agnostic approvals were excluded. Relevant NCCN guidelines were reviewed as of Oct 19 2022. Each indication was assessed for ratings of level of evidence and treatment preference. Levels of evidence included category 1 (consensus, high-level evidence), 2A (consensus, lower-level evidence), 2B (lower-level evidence without consensus), or 3 (major disagreement). Treatment preference rankings included preferred, alternative preferred, other recommended, or useful in certain circumstances. Results: Among 315 indications for 100 drugs, 156 (50%) had traditional approval and 159 (50%) had current or previous accelerated approval. Among accelerated approval indications, 78 (49%) had been converted to traditional approval and 21 (13%) withdrawn. Among all indications, 105 (33%) were rated as category 1 evidence, 185 (59%) as 2A, 6 (2%) as 2B, and 2 (0.6%) as 3. The treatment preference for 153 (49%) was preferred, for 55 (17%) was recommended, for 5 (2%) was alternative preferred, and for 38 (12%) was useful in certain circumstances. Compared to traditional approval, accelerated approval indications less often had category 1 evidence (3% v 47%), and were less often listed as preferred (40% v 58%). Converted accelerated approval indications had mid-range ratings for evidence (38% category 1) and treatment preference (47% preferred). 11 withdrawn accelerated approval indications remained in NCCN guidelines. Conclusions: In most cases, NCCN guidelines identified accelerated approval indications as having lower evidence than traditionally approved cancer drugs. Many traditional approvals and conversions from accelerated approvals had only category 2A evidence, suggesting better evidence is needed for traditional approval of oncology drugs. But in about half of accelerated approval indications withdrawn by the drug’s manufacturer, NCCN continued recommending the drug. Professional organizations like NCCN should ensure their guidelines reflect that accelerated approval drugs are being marketed based on preliminary evidence.
Impact of state policies on telehealth use among the privately insured with newly diagnosed cancer.
Authors: Shih Ya-Chen Tina , Pan I-Wen , Yen Tina W.F. 
Published: June 2023

1607 Background: The COVID-19 public health emergency (PHE), declared on January 31, 2020, relaxed many telemedicine restrictions. Yet there remains wide variation across states on the coverage and reimbursement of telemedicine services and rules to permit practicing across state lines. Research has shown expanded access to telemedicine is beneficial to cancer patients, making it important to understand the relationship between state policies and the use of telemedicine. Methods: We identified privately insured non-elderly patients newly diagnosed with any of five common cancers (female breast, prostate, lung, colorectal, and lymphoma) between March 2019 and March 2021 from Optum Clinformatics Data Mart. We characterized state telehealth policies by parity status (coverage & payment parity, coverage parity only, and none) and rules for cross-state practice (allowed with vs. without limitations). We applied interrupted time series analysis to examine the trend of telemedicine use before and after the declaration of COVID-19 PHE and conducted multivariable logistic regression to examine the association between state policies and telemedicine use while controlling for other confounders. Results: Of the 10,813 privately insured non-elderly patients, the adjusted rate of telemedicine use was 23.6%. The average age was 53.2 (SD = 9.24). Race/ethnicity distribution was 71.20% non-Hispanic White, 10.07% non-Hispanic Black, 8.56% Hispanics, 3.77% Asians, and 6.39% others. Lymphoma, female breast, colorectal, lung, and prostate cancer accounted for 10.55%, 46.55%, 13.27%, 7.56%, and 22.07% of patients. A sharp increase was observed early on (from 32.4% in March 2020 to 42.5% in April 2020), followed by a steady decline, down to < 15% by March 2021. Compared to patients residing in states with coverage and payment parity, those in states with only coverage parity and no parity were significantly less likely to use telemedicine (OR = 0.79, 95% CI: [0.71 – 0.88]; OR = 0.77, [0.68 – 0.87]). Patients residing in states with more restrictions/regulations on cross-state practice were significantly less likely to use telemedicine (OR = 0.84, [0.76 – 0.93]) than those in states that were less restrictive. Other factors predicting lower likelihood of telemedicine use included older age (OR = 0.69 [0.62-0.77] for age 50-64 vs. 20-49), non-Hispanic Black (OR = 0.84 [0.72 – 0.98] vs. non-Hispanic White), colorectal cancer (OR = 0.82 [0.70 – 0.96] vs. female breast), and more recent diagnosis. Conclusions: With the approaching end of COVID-19 PHE, states are deciding whether to permanently relax certain telemedicine policies or return to existing more restrictive policies. Our study suggests patients residing in states that move toward less generous parity and more restrictive cross-state practice rules may be less likely to benefit from telemedicine.