bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors associated with delays in receipt of neoadjuvant chemotherapy in patients with operable breast cancer.
Authors: Wooster Margaux , DeStephano David , Beauchemin Melissa , Wang Shikun , Hur Chin , Wright Jason Dennis , Hershman Dawn L. , Accordino Melissa Kate
Published: May 2024

e13541

Background: Delays in neoadjuvant chemotherapy initiation (NACTI) after breast cancer (BC) diagnosis have been associated with worse outcomes. We evaluated incidence and predictors of delays of NACTI among high clinical risk patients treated with NACT on a national level. Methods: We conducted a retrospective cohort study using the National Cancer Database. Eligibility included age ≥18 years, diagnosis of non-metastatic BC between 1/1/2010 – 12/31/2020, who were treated with NACT within 180 days of diagnosis. Patients who received neoadjuvant endocrine therapy were excluded. Time to chemotherapy (TTC) in days was recorded. Patients were categorized into sub-cohorts by receptor status and clinical stage: 1) Hormone Receptor positive/HER-2 negative (HR+/HER2-) and were lymph node (LN) LN+; 2) HER-2 positive (HER2+) cT2+ or LN+; and 3) triple negative BC (TNBC) cT2+ or LN+. Multivariable logistic regression, adjusted for demographic, socioeconomic and tumor characteristics, was used to identify characteristics associated with delays > 60 days from diagnosis to NACTI. Multiple imputation was used for missing data. Results: We identified 145,697 eligible patients, 74% were White, 18% were Black, 3.3% were Asian, and 4.6% were Other/Unknown. Across all racial categories, 9.2% were Hispanic. Median age at diagnosis was 52 years (range 18-90), median TTC was 32 days (range 0-180), and 10% had a delay > 60 days. There were 40,345 patients in the HR+/HER2- cohort, 56,868 in the HER2+ cohort, and 48,484 in the TNBC cohort; 12%, 9.0%, and 8.9% (p <0.001) had a delay in NACTI, respectively. In multivariable analysis among the HR+/HER2- sub-cohort, characteristics associated with delays in NACTI included Black race (OR 1.71, 95%CI 1.57-1.86) and Hispanic ethnicity (OR 1.76, 95%CI 1.60-1.94) compared to White and non-Hispanic patients. Patients with Medicare, Medicaid, or who were uninsured were more likely to be delayed (OR 1.44 95%CI 1.30-1.58, 2.10, 95%CI 1.92-2.29, 2.28, 95%CI 1.99-2.61) compared to those with commercial insurance. Results were similar in the HER2+ and TNBC cohorts, as seen in Table. Conclusions: Among patients with operable BC who received NACT, patients who were Black, Hispanic, had Medicaid or no insurance had as high as a twofold increase in odds of a delay in NACTI. Targeted interventions to address these racial and socioeconomic disparities are necessary to ensure equitable care. Multivariable logistic regression adjusted for demographic, socioeconomic and tumor characteristics variables (n= 145,697). [Table: see text]
Greening Ireland’s oncology practice: Reducing climate toxicity and improving sustainability in a national chemotherapy formulary.
Authors: Lynch Emer , Bredin Philip Hugh , Weadick Catherine , Barry Simon L , Dorney Niamh , Hamid Munzir , Hennessy Maeve , Myo Oo Nay , Bryan Alex J. , Cooke Katie , O'Mahony Deirdre , van Leeuwen Roelof W.F. , Van De Poll Matthijs , Collins Dearbhaile Catherine , O'Reilly Seamus
Published: May 2024

e13557

Background: Climate change has been acknowledged by the World Health Organisation (WHO) as a “fundamental threat” to healthcare delivery. However, healthcare now accounts for more than 5% of global emissions. Over 25% of the oncology services contribution to this relates to the development, transport and administration of systemic anticancer therapies (SACT), and represents a modifiable factor. In Ireland, the National Cancer Control Programme (NCCP) determines the standard of care systemic anticancer therapy (SACT) protocols. This study proposes to review existing protocols and assess for and highlight nationally implementable climate toxicity mitigation strategies. Methods: NCCP SACT protocols (available at https://www.hse.ie/eng/services/list/5/cancer/profinfo/chemoprotocols/) were reviewed for impact reduction potential under the following headings: 1) Baseline and regular tests and investigations; 2) Pre-medications; 3) De-escalation, and 4) ESMO Magnitude of Clinical Benefit. Results: A total of N = 333 protocols were reviewed. N = 32 protocols had multiple indications across more than one tumour stream. Overall, N = 702 opportunities were identified where the protocol impact could be reduced, either by consolidation of pre-medication and pre-hydration to oral administration, regimen or investigation de-escalation, dose banding, switching non-oral to equivalent oral SACT alternative or re-considering regimen use where clinical benefit is measured as non-substantial. In addition, there were a further N = 507 opportunities identified where baseline and regular investigations required by the protocol could be incorporated into routine visits, minimising hospital travel and time toxicity to patients. Conclusions: This study considered just one aspect of oncology care in Ireland, the delivery of SACT, and identified over 1200 opportunities across standardised national protocols where associated climate toxicity could be reduced without compromising standard of care. A sustainability matrix to promote identified action points is proposed for inclusion within all protocols and could be used in other jurisdictions. Further work to consider impact reduction opportunities in other areas of cancer care, including radiology and diagnostics should be considered.
Representativeness of race, ethnicity and social determinants of health in patients with advanced non-small cell lung cancer via linkage of real-world electronic health records, administrative claims, and consumer financial data.
Authors: Calip Gregory Sampang , Choi Yookyung Christy , Cai Mona , Liede Alexander , Monegro Natalie , McNeill Annie
Published: May 2024

e13559

Background: Social determinants of health (SDOH) are both drivers of health disparities and barriers to clinical research participation, particularly among historically underserved or marginalized populations. Using real-world data (RWD), diversity plans in cancer clinical trials frequently aim to reduce disparities and improve representation. Our aim was to describe the representativeness of patients with advanced non-small cell lung cancer (aNSCLC) with respect to race, ethnicity and person-level SDOH in a curated electronic health record (EHR) database. Methods: This was a retrospective cohort study of individuals diagnosed with aNSCLC between 2016–2022 in a US-based oncology EHR database (ConcertAI). Information on person-level SDOH was derived from linkage of curated EHR data to administrative claims and consumer financial data. These data were also compared to a sample of patients diagnosed with first primary NSCLC from the Surveillance, Epidemiology, and End Results (SEER) Program and American Community Survey (ACS), where possible. The Kaplan-Meier product limit estimator was used to compare time from advanced diagnosis to treatment initiation. Results: From an overall sample of 17,772 patients with aNSCLC, a total of 10,390 patients had 1 or more SDOH characteristics of interest documented. Compared with patients without linked SDOH data, a higher proportion of patients with linked person-level SDOH data were non-Hispanic (NH) White patients (79% vs 71%) and lower proportions were NH Black (10% vs 13%), NH Asian/Pacific Islander (2% vs 4%) and Hispanic patients (2% vs 4%). Patients with linked SDOH were also more likely to have aNSCLC progressed from an earlier stage and received biomarker testing for targetable alterations. Overall, 54% of patients were married, 38% completed a bachelor’s degree or higher, 14% worked in a blue-collar occupation, and 61% had an individual income of ≤$35,000. All 4 of these SDOH characteristics differed significantly by race/ethnicity. Differences in time-to-treatment initiation were statistically significant by marital status (median of 50 [married] vs 55 [not married] days; P=0.02) and individual income (median of 50 [>$60,000] vs 55 [≤$30,000] days; P=0.04). Patients with aNSCLC in these data differed from patients in SEER and ACS estimates with respect to greater representation of patients who are NH White, have lower levels of education, and lower individual income. Conclusions: This analysis demonstrated the feasibility of measuring person-level SDOH using EHR from patients with aNSCLC treated at oncology clinics across the US. RWD with linked SDOH characteristics should be used cautiously to address the representativeness gap in evidence from oncology trials given their potential inherent selection biases.
Optimizing clinical research in a regional cancer center network: A survey-based approach.
Authors: Preeshagul Isabel Ruth , Syldor Angemael , Terzulli Stephanie Lucia , Gordon Ruth Ann , Evans Lauren A , Moran Solange , Eng Juliana , Daly Robert Michael , Rao Devika , Momtaz Parisa , Brockway Julia P , Joshi Smita Suhas , Houston Collette , Riely Gregory J. , Reidy Diane Lauren
Published: May 2024

e13560

Background: Conducting clinical research (CR) within regional care networks (RCNs) of comprehensive cancer centers presents unique challenges. This study aimed to identify and address such barriers at Memorial Sloan Kettering Cancer Center's (MSKCC) RCN, encompassing six sites across New York and New Jersey. Methods: A survey was conducted among medical oncologists at the RCN sites (February 2022-February 2023) to assess their perspectives on research-related challenges. The 23-question survey included multiple-choice, open-ended, and ranking questions regarding protocol availability, consenting processes, staffing support, and more. Results: 92/108 (85%) medical oncologists completed the survey, revealing three key challenges: 1. Limited protocol availability: Only 47% of the time was there a suitable trial available in the RCN for patients interested in clinical trials. Notably, 50% of attendings referred over 5 patients out of the RCN to MSKCC Main Campus for trial enrollment. Identified barriers to opening desired protocols (n=23 in total) included closed to accrual, pending amendments, and sponsor approval issues. 2. Inadequate investigator representation: 278 protocols lacked RCN physicians listed as consenting providers hindering potential patient participation. 3. Insufficient research staff including clinical trial nurses and administrative staff. Only 33% were satisfied with the research support, and 67% of physicians identified lack of clinical research support staff as the biggest barrier to accrual. 58% reported delays of more than one week in screening eligible patients due to insufficient support. Following these observations, interventions were implemented. Thirteen of the 23 studies were opened to accrual, all RCN physicians were added to the 278 protocols, and an analysis of staffing needs increased clinical trial nursing and administrative staff. Therapeutic and non-therapeutic accruals in the Department of Medicine rose by 10.5%, and total research visits increased by 21.4% compared to the previous year. Conclusions: This survey-based approach identified and addressed key barriers to CR within MSKCC's RCN, and in turn, increased research activity was noted. These findings offer a potential roadmap for optimizing research in similar networks by enhancing protocol accessibility, addressing staffing needs, and fostering collaboration. Further investigations are ongoing to address challenges in other clinical departments.
Effect of alternative dosing strategies on sustainable healthcare: A carbon footprint analysis of nivolumab and pembrolizumab treatment in the Netherlands.
Authors: Malmberg Ruben , Loosveld Jurrien , Schilte Hans-Peter , van Leeuwen Roelof W.F. , Burdorf Alex
Published: May 2024

e13564

Background: Hospitals contribute significantly to greenhouse gas (GHG) emissions and face a moral obligation to prioritize reduction of GHG emissions. Drugs constitute an important component of the GHG emissions of hospitals. Alternative dosing strategies (ADS) have been implemented to improve the cost-effectiveness of pembrolizumab and nivolumab (Table). However, the impact of these ADS on GHG emissions remains unknown. Therefore, this study aimed to analyse the effect of ADS implementation on the carbon footprint of treatment with pembrolizumab and nivolumab. Methods: Life Cycle Assessment (LCA) methodology was used to quantify the environmental impact. The impact category climate change was used to quantify the carbon emissions (CO2e). The GHG Protocol was used to categorize the emissions into scopes 1, 2 and 3. Life Cycle Inventory and Impact data from an academic hospital in Rotterdam were used in the LCA to calculate the CO2e for pembrolizumab and nivolumab, their different dosing intervals, and the impact of ADS. Results: In 2022, the carbon emissions related to nivolumab and pembrolizumab treatment were 445 tons CO2e, with an average of 94 kg CO2e per dosage. Pharmaceutical production was the main driver of treatment-related GHG emissions (93% of total emissions on average). Applying ADS, resulted in carbon emission reductions of 21.4-26.2% and 9.3-11.2% for pembrolizumab and nivolumab respectively (table 1). Conclusions: This study shows the substantial environmental impact of cancer treatments with pembrolizumab and nivolumab and calls for further implementation of ADS for pembrolizumab and nivolumab and other anti-PD-(L)1 monoclonal antibodies and more sustainable pharmaceutical production processes. These findings help to create environmental awareness and contribute to the promotion and understanding of sustainable low-carbon healthcare practices. Overview of registered doses of pembrolizumab and nivolumab, alternative dosing strategies and potential reductions in GHG emissions, based on real world data in The Erasmus University Medical Center in Rotterdam, the Netherlands. [Table: see text]
Assessing the landscape of integrative oncology at US comprehensive cancer centers (CCC).
Authors: Kim Elyssa , Cheng Jessica , Fertal Jamie , Munoz Frank , Saritoprak Seyma , Truong Amy , DiCristofano Christy , Ku Sarah , Rodriguez Art , Soni Preeti , Reb Anne Marie , Rose Judy , Lee Richard T.
Published: May 2024

e13565

Background: Interest in integrative oncology (IO) has grown among patients and its availability has expanded within CCCs. This study assesses the availability of IO programs in U.S. CCCs. Methods: In 2023, a cross-sectional survey was sent to CCCs including NCI-designated CCCs by email regarding IO programs. Participants were asked to describe the importance of IO within their cancer center, the programs and services offered, and how the programs are provided. Results: Of ninety-four survey invitations sent, fifty-eight completed surveys were returned (response rate 62%). Three-fourths of completed surveys (74%) came from NCI-designated CCCs. Over half of respondents (57%) rated IO services to be important (or very important) at their center with 53% stating that their center has a formal IO clinical program. Univariate analysis showed that non-NCI centers were more likely to perceive IO services as being important compared to NCI-designated centers (100% vs. 77%, p=0.04). Respondents stated that their IO program has been in existence for ≥10 years (40%), 5-9 years (26%), and 0-4 years (34%). Among the surveyed cancer centers, availability of IO program was 33% (adult and pediatric) vs. 49% (adult only). The IO programs are available to patients with active cancer (91%), patients in survivorship off-treatment (81%), and healthy patients without a history of cancer (22%). The IO services were offered in an outpatient clinic on the main campus (76%), community sites (29%), or inpatient settings (40%). The most common oncology specific services reported were nutrition (51%), exercise counseling (42%), mind-body medicine (38%), and acupuncture (33%). The clinicians who provided integrative medicine consultations were primarily in internal medicine (31%), family medicine (26%), and medical oncology (19%). In inpatient and outpatient settings, insurance was billed for physical therapy (50% vs. 67%) and diet/nutrition (36% vs. 50%) services, whereas acupuncture (47%) and massage (43%) services were charged by cash/card in an outpatient setting only. Music therapy (33% vs. 47%) and pet therapy (34% vs. 43%) services were offered for free most commonly in both inpatient and outpatient settings, respectively. The highest average out of pocket fee service was acupuncture ($42±27) followed by integrative medicine consultations ($34±29). Conclusions: Despite increasing patient interest and established benefits of integrative therapies as noted by recently published ASCO clinical guidelines, only half of CCCs reported having a formal IO program. More research is needed to understand the gap between patient interest, the growing evidence-base, and how to expand integrative oncology services within existing cancer centers.
Relationship between immune phenotype and treatment selection of Chemo-IO vs. IO-only in TPS-high NSCLC using hypothetical test-and-control group generation based on survival data extracted from phase III trials.
Authors: Oum Chiyoon , Moon Jimin , Seo Melody SeungHui , Lim Yoojoo , Ahn Chang Ho , Nesmith Ken , Ock Chan-Young
Published: May 2024

e13569

Background: Immunotherapy has become the treatment of choice for the first-line treatment of unresectable NSCLC with high PD-L1 tumor proportion score (TPS). However, the optimal treatment regimen, particularly when to add chemotherapy, remains unclear. To evaluate whether an immune microenvironment biomarker could help guide the treatment regimen, we have explored computational hypothetical analysis applying historical proportion of inflamed immune phenotype (IIP) to published outcome data of clinical trials. Methods: R package “IPDfromKM” was used to reconstruct raw time-to-event outcomes from Kaplan-Meier (KM) curves. Data from KN-189 and KN-407 for anti-PD1 with chemotherapy (Chemo-IO), and KN-024 and KN-042 for anti-PD1 (IO-only) in the first-line treatment of NSCLC were included for analysis. Overall survival (OS) data were computed from KM of pts with PD-L1 TPS of ≧ 50% from these studies (KN-189 n=132, KN-407 n=73, KN-024 n=154, KN-042 n=299). It was assumed that Lunit SCOPE IO classified pts into IIP or non-IIP at a ratio of 57% vs. 43%, based on previous analysis. One hundred hypothetical sets of Chemo-IO and IO-only groups satisfying hazard ratio (HR) of IIP to non-IIP as 0.81 and 0.61 (± 0.025) while maintaining the proportion of IIP were randomly generated. A total of 10,000 (100 x 100) combinations of cohorts were analyzed to test our hypothesis of superiority of Chemo-IO over IO-only in non-IIP as well as non-superiority of Chemo-IO over IO-only in IIP. Results: Patient-level OS data for a total of 205 pts with Chemo-IO and 453 pts with IO-only were reconstructed. The median OS was favorable in Chemo-IO vs IO-only (HR 0.73, 95% confidence interval [CI] 0.54-0.99, p = 0.045). From 10,000 hypothetical combinations of cohorts, the median HR of Chemo-IO compared to IO-only was 0.61 (interquartile range [IQR] 0.60-0.63) in non-IIP group, whereas that was 0.86 (IQR 0.84-0.89) in IIP group (Table). The upper 95% bound of HR was < 1 for 8,611 combinations of non-IIP groups, but > 1 for all possible combinations of IIP groups. Given a non-superiority margin of 1.37(=1/0.73), 8,306 combinations were positive for the upper 95% bound of HR < 1.37. Overall, 6,917 matched combinations supported our hypothesis. Conclusions: From the computational analysis, 69.17% cases of hypothetical combinations support that Chemo-IO would be superior to IO-only in non-IIP group, whereas Chemo-IO would not be superior to IO-only in IIP group, implying a potential value of confirmatory biomarker analysis in providing evidence for regimen selection. [Table: see text]
Implementation of a virtual tumor board through VA National TeleOncology service.
Authors: Chiba Akiko , McWhirter Gina , Moss Haley , Colonna Sarah Violet , Rodgers Thomas David , Jonna Sushma , Johnson Margaret O , Van Echo David C. , Kelley Michael J. , Passero Vida Almario
Published: May 2024

e13571

Background: Multidisciplinary tumor boards are critical to optimize treatment of patients with malignancies. The Veterans Affairs (VA) National TeleOncology (NTO) service was created to provide equitable, state of the art oncologic care virtually to centers across the largest integrated health care system in the US. According to American Society of Clinical Oncology report, 66% of rural counties have no oncologist. Fifty-six percent of Veterans who received cancer care through NTO in 2022 lived in rural areas. We describe a virtual multi-institutional, multidisciplinary tumor board across several malignancies. Through NTO, virtual tumor board (VTB) demonstrates a novel approach to leveraging nationwide oncologic expertise to support patients receiving cancer care at VA Medical Centers (VAMC) across the US. Methods: A VTB was conducted with participation of multiple VAMCs throughout the US. There were five separate VTB oncologic sub-specialties: hematologic, breast/gynecologic, CNS, thoracic and cutaneous malignancies. Each case was presented by the treating provider or designated representative. Pathology and pertinent imaging were reviewed and presented by disease-specific expert specialists. Core attendees included medical oncologists, surgical oncologists, radiation oncologists, pathologists, radiologists, genetic counselors, clinical pharmacists, advanced practice providers and nurses. VTB records were analyzed from VTB inception on March 1, 2022 to February 1, 2024. Data collected included attendance and case presentation information. Results: A total of 113 VTB sessions were conducted every 2 weeks to monthly depending on specialty (35 hematologic, 32 beast/gyn, 24 thoracic, 13 CNS, 9 cutaneous malignancies) with a total of 233 patient cases presented from 51 VA facilities in 33 states and Puerto Rico. The total unique attendees were 468 in 41 states, DC and Puerto Rico. The highest case volume per VA location were from Durham, Houston, Pittsburgh, Sioux Falls and Salem. The highest attendee locations included Durham, Boston, Palo Alto, Pittsburgh, and Richmond. Conclusions: This study demonstrates the feasibility of a national multi-institutional, multi-disciplinary VTB. Tumor board sessions provide access to specialized oncologic expertise with the goal of improving cancer care across the entire VA healthcare system. Further growth of national VTB to increase participation at additional sites and incorporated discussion of clinical trials is ongoing. Future research evaluating quality and outcomes for those cases discussed at VTB will be considered.
Financial impact of fixed-dose versus weight-based dosing approaches for pembrolizumab and nivolumab in the Oncology Care Model for The US Oncology Network (The Network).
Authors: Fernandez Jennifer , Indurlal Puneeth , Garey Jody S. , Wilfong Lalan S.
Published: May 2024

e13573

Background: PD-1/PD-L1 antagonists, pembrolizumab and nivolumab, are being used in an increasing number of cancer types. The FDA initially approved both agents based on a weight-based dosing (WBD) approach, but subsequently included a therapeutically equivalent fixed-dose (FD) approach for most indications. The Oncology Care Model (OCM) was a value-based care (VBC) program from Medicare that required total cost of care (TCOC) accountability from participating practices. We evaluated the impact of using the FD vs WBD on TCOC in the OCM for both pembrolizumab and nivolumab. Methods: Using Medicare claims data for 14 OCM-participating practices in The Network from July 2016 to June 2022, and corresponding weight data from the Electronic Health Record (EHR), we evaluated the distribution of patient weights, use of FD vs WBD, and the financial impact of the use of FD vs WBD vs weight-based dosing capped at the fixed dose (WBDc). Results: Summary statistics are shown (Table). In most cases, the FD was higher than the WBD for both nivolumab and pembrolizumab. FD led to an increase in drug cost of 8.4% for nivolumab and 27.2% for pembrolizumab, corresponding to a 11.3% increase in TCOC for the episodes where FD nivolumab and pembrolizumab were used. Conclusions: Fixed-dosing approaches for commonly used high-cost anti-neoplastic drugs can drive increases in TCOC. The use of WBD could lead to a decrease in drug cost and 11.3% savings of TCOC within OCM-like models, likely without impacting clinical outcomes. These findings encourage practices in risk-bearing models to reflect on the widespread adoption of FD. The withdrawal of varying sizes of vials can limit the use of WBD. Pharmaceutical manufacturers should assist with WBD by providing multi-use vials and varying sizes. The feasibility of using the WBD for pembrolizumab with an every 6-week regimen remains to be explored. [Table: see text]
Workforce evaluation in the field of radiation oncology: National data from a limited-resource setting.
Authors: Karapetyan Aren , Karapetyan Nune , Mkhitaryan Sergo , Hovhannisyan Julieta , Manukyan Narek , Harutyunyan Lilit , Tamamyan Gevorg , Sargsyan Lilit , Saghatelyan Tatul
Published: May 2024

e13576

Background: The workforce shortage in the field of radiation oncology is a critical challenge in developing countries. This study's main objective was to evaluate the workload of radiation oncologists (RO) in Armenia. Methods: In this cross-sectional study, the adapted questionnaire from Gunasekera et al. (2021) survey was used. The study setting was the only two RO centers operating in Armenia (public and private). For data collection purposes, census sampling was done and self-administered questionnaires were distributed. The data collection time frame was 15-25 July 2023. Descriptive statistics was done to analyze and present data through portions. Results: In total, data on 16 ROs and residents was collected. The ratio of ROs per million population was 6 in 2023, in Armenia. Primary consultations in median lasted 25 minutes (range: 10-40 mins). Similarly, consults of the patients on active treatment lasted in median of 12.5 minutes (min-60 mins). The median duration of follow-up oncology care consults was 7.5 minutes per day (range: 10- 40 mins). In terms of clinic hours, physicians in public settings reported spending a median of 50 hours per week. According to the respondents, on average radiation therapy specialists allocated 50% of their working time to providing direct patient care, 35% to fulfilling administrative responsibilities, 5% to educational and teaching endeavors, and 10% to conducting scientific research. The median radiation therapy delay was 1 week. Namely, treatment delay in the non-metastatic setting was 1․19, and in the metastatic setting 1 week. By the ROs, the most common barriers to delivering cancer care were treatment high cost (68%, n=11), lack of access to imaging (18.7%, n=3) and pathological (18.7%, n=3) diagnostic procedures, and lack of physicians' time to dedicate to patients (31%, n=5). Conclusions: Our study offers some insight into the radiation oncology workload in Armenia. From this research, we conclude that Armenian healthcare is understaffed in terms of radiation oncologists and medical physicists compared to other upper-middle-income and high-income income countries. Moreover, the median daily workload of the ROs exceeds the upper limit of the amount of consults suggested by the international societies. Hence, further research is required for a detailed evaluation of the RO workforce and specific intervention development that will be able to address this issue in Armenia and other resource-limited settings.
Using an AI trial screening tool to assess language as a barrier for enrollment of underrepresented minorities in cancer clinical trials.
Authors: Corvese Laura , Weiss Christina , Huie Sharon , Zhou Bin , Deng Yanhong , Gong Guannan , Kunz Pamela L. , Fischbach Neal A.
Published: May 2024

e13586

Background: A major limitation of clinical trials is limited generalizability due to low enrollment of underrepresented minorities (URM). The percentage of patients who are non-English speaking (NES) is higher in URM and language barriers may discourage providers to offer participation in clinical trials. We have developed an artificial intelligence-based system to identify patients eligible for clinical trials, Clinical Trials Patient Matching (CTPM). This platform offers an ideal opportunity to explore bias in enrolling patients on clinical trials (CT). Among patients identified by CTPM as eligible for CT we assessed the impact of NES on trial enrollment. Methods: Individual algorithms were constructed within CTPM to identify patients potentially eligible for two trials open at Yale Cancer Center, DARE (NCT04567420) and EMBER-4 (NCT05514054). Eligibility was confirmed by Clinical Trials Office staff and notification of eligibility was communicated to the treating provider. Retrospectively, we evaluated the impact of NES status on clinical trial enrollment. This analysis occurred between 9/1/2022 and 11/30/2023 and all study-related activities were approved by the Yale IRB. Results: Out of the 13,466 unique patients presenting to the Yale Cancer Center and Smilow Cancer Network breast program within the period of study, 5.1% were NES and 94.9% were English speaking (ES). Of the patients identified by CTPM as eligible for the 2 clinical trials 7.9% were NES and 92.14% were ES. However, out of patients enrolled, only 1.59% were NES and 98.41% were ES. For the DARE trial, enrollment rate was 38.3% for ES vs 0% for NES (p=0.0081). For the EMBER4 trial, enrollment was 13.4% for ES vs 14.3% for NES (p=1.0). The aggregate data for both trials revealed an enrollment rate of 29.4% for ES vs 5.6% for NES (p=0.0285). Conclusions: We found that patients who identify as NES were less likely to be enrolled in cancer clinical trials. CTPM appears to be an effective tool to prospectively identify underrepresented populations in clinical trials and serve as a platform for developing interventions to address disparities in trial participation, including language. [Table: see text]
Natural language processing for automated breast cancer recurrence detection and classification in computed tomography reports.
Authors: Lee Jaimie , Zepeda Andres , Arbour Gregory , Isaac Kathryn V. , Ng Raymond T. , Nichol Alan
Published: May 2024

e13591

Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]
An unsupervised H&E-based machine-learning approach for precise prediction of tumor microenvironment subtypes.
Authors: Sarachakov Alexander , Tyshevich Andrey , Belozerova Anna , Postovalova Ekaterina , Bagaev Alexander , Kushnarev Vladimir , Fowler Nathan Hale
Published: May 2024

e13593

Background: Precision oncology increasingly relies on accurate classification of the tumor microenvironment (TME), which influences therapeutic responses and prognostication in cancer. While histological observation via H&E staining remains the gold standard due to the detailed spatial context and morphological information provided, its lack of reproducibility limits its use in clinics. Next-generation sequencing (NGS) reliably provides valuable insights into the molecular landscape of tumors. Here we debut an unsupervised machine-learning approach that leverages both the rich detail in H&E slides and the reproducibility of NGS to enhance TME classification. Methods: An ML-based image analysis algorithm was developed using a convolutional neural network (CNN) deep learning model. Using over 600 images from the TCGA COAD and READ datasets and from 139 colorectal cancer (CRC) samples in an internal cohort, we trained the CNN model to curate features from small patches of whole slide images (WSIs). Then, clusters were created from these H&E WSIs without prior labeling. The clusters were validated by experienced pathologists to confirm cluster homogeneity and filter artifacts. We trained a classifier to categorize molecular features from the WSIs into TME subtypes. Performance was measured using the area under the receiver operating characteristic curve (AUC-ROC), based on the model's ability to correctly classify the Fibrotic (F) and Immune Desert (D) TME subtypes (1), as well as MSI status identified by whole exome sequencing. The F subtype corresponds with high stromal content, low immune infiltration, and the worst prognosis. The D subtype corresponds with a cold immune TME and an intermediate prognosis. Progression-free survival (PFS) data from TCGA were used to examine prognostic significance. Results: We obtained 0.86, 0.79, 0.72, and 0.71 AUC scores for predicting the ID and F TMEs, MSI status, and KRAS mutation status, respectively. PFS data analysis showed our H&E-based approach to yield similar or better prognostic significance compared to NGS-based TME classification (ID vs others, p=0.05 (H&E) and 0.2 (NGS); F vs others, p=0.07 (H&E) and 0.09 (NGS)). Our findings showed a notable correlation with RNA- and DNA-based predictions of ID and F TMEs and MSI, with enhanced precision and more robust subtype identification while achieving high embedding consistency. Conclusions: By harnessing the inherently rich detail in histological images, our H&E-based approach complements NGS-based TME analysis. Our findings advocate for the integration of advanced image analysis into the standard TME characterization workflow to refine prognostic and therapeutic strategies for CRC. We will expand this algorithm for use with other cancer types to realize its potential as a promising adjunct to NGS for precise TME classification. 1. Bagaev et al., 2021.
Impact of Exigent Research Network’s partnership with the Tempus AI TIME Program on the screening and matching of patients for clinical trials.
Authors: Gajra Ajeet , Mallahan Samantha , Blau Sibel , Peguero Julio Antonio , Shoukier Mahran , Kanaan Mohamad , Huynh Chanh Trung , Efiom-Ekaha Daniel N. , Vankayala Hema M. , Kitchen Kyle , Kendall Stephan Disean , Orsini James Michael , Babu Sunil , Hellyer Jessica , Weart Thomas , Meyer Janelle Marie , Clifford Christian , Skelly Danielle , Patnaude Emily , Cooney Matthew M.
Published: May 2024

e13599

Background: Exigent Research is comprised of independent community oncology practices with a central patient data repository to support clinical research. The Tempus AI TIME program provides clinical trials solutions including a diverse trial portfolio, TApp an AI powered patient matching platform, patient tracking LINK portal, and a rapid trial activation process. This partnership facilitated Tempus AI to continuously search the data repository and enabled Exigent to increase trial enrollment. Methods: TApp is a clinical trial matching platform that utilizes patient data, trial eligibility criteria, and NLP models to execute AI powered matching of subjects to trials. TApp searches the entire patient population for TIME trial matches. New patient clinical information or changes to trial eligibility criteria triggers a new TApp search and matches are reviewed by Tempus AI nurses. Trials were opened in an accelerated “just in time” manner if a subject was ready immediately or prospectively prior to a first patient being identified. Data collected included trial activations, number of patients, available studies, patient and trial updates, TApp searches, nursing effort, matches, and consents. Interventional Phase I to III trials and observational studies were included. Results: From 2022 - 2023, there were 16 Exigent sites in the TIME network. The patient population screened was 244,986 matched to 189 clinical trials. There were 3,179,079 changes to individual patient records and 2,152 trial updates. This triggered 216,712,093 unique TApp searches and 33,083 matches reviewed by nurses. In total, 1,312 patients matched to observational studies and 6,040 patients for interventional trials. Cumulatively, nurses spent 6,617 hours on patient screening resulting in a $330,830 cost. There were 71 trial activations resulting in 312 patient consents (169 observational, 143 interventional). The average duration for study activation was 12.3 days for a just in time activation and 33.8 days for a prospective activation. Conclusions: Exigent Research leveraged a central data repository with the Tempus AI TIME program to continuously screen a large patient population for 185+ trials. During a two-year period, 215M+ unique TApp searches occurred which resulted in over 310 patient consents with rapid activation times. Future clinical trial strategies should partner AI screening technologies with robust trial infrastructure solutions to maximize enrollment success. [Table: see text]
Treatment patterns among HCPs and concordance with expert recommendations in HER2-negative early breast cancer: Analysis of an interactive online treatment decision support tool.
Authors: Brandt Kelly G , Becker Marie N. , Gupta Tanya , Kalinsky Kevin , Spring Laura , Traina Tiffany A. , Yardley Denise A. , Rosenthal Kristen M.
Published: May 2024

e13601

Background: Management of HER2- EBC continues to evolve with expansion of targeted therapies, including abemaciclib, olaparib, and pembrolizumab, in the adjuvant setting for eligible pts. We sought to identify contemporary practice patterns among HCPs for HER2- EBC and compare them with those of experts using an online interactive decision support tool. Methods: An online decision support tool was developed with input from 5 BC experts to provide tx recommendations for 19 unique pt case scenarios based on considerations including subtype, tx history, and recurrence risk. HCPs entered specific patient criteria along with their intended management for that case, and then were shown 5 expert recommendations. HCPs were asked if the recommendations impacted their intended approach. An analysis of expert recommendations and HCP-selected tx was performed. Results: Between Oct 2023 and Jan 2024, 83 HCPs entered 105 cases. Of the 97 cases where HCPs noted a definitive tx plan, recommendations were discordant from experts in 29%. The most commonly queried cases, expert recommendations, and discordance between HCPs and experts are outlined in the table. Discord with experts was similar among HCP tx plans for cases of TNBC (33%) and HR+/HER2- EBC (27%). HCPs predominantly sought guidance for pts with HR+/HER2- EBC, no g BRCAm, and high-risk disease per monarchE trial criteria (79%), with 22% discordance from expert recommendations. Regardless of BC subtype, the highest level of discord between HCPs and experts (67%) was observed in cases where pts had received neoadjuvant tx and had no residual disease following surgery, although this represents a small subset (n = 12). When asked if the expert recommendations changed their tx choice, 7 of 27 (26%) HCPs who responded indicated that expert recommendations changed their intended tx, whereas 15% noted that there were barriers to implementing those recommendations. Conclusions: These data suggest ongoing challenges with incorporating the newest targeted therapies for high-risk EBC management. Continued education and development of resources for HCPs, including online decision support tools, may be increasingly important as the treatment of EBC continues to evolve. [Table: see text]
Prediction of therapeutic response and cancer outcomes in solid tumours via in silico clinical trials.
Authors: Asghar Uzma Saddia , Griffiths Matthew , Kubeyev Amanzhol , Laurie Jordan , Giorni Andrea , Zillmann da Silva Luiz A , Sivasubramaniam Prabu , Foster Matthew , Biankin Andrew V.
Published: May 2024

e13603

Background: Oncology therapeutic development has high failure rates with substantial costs. Advances in technology, including molecular characterisations of cancer and computational power, provide the opportunity to better model therapeutic response for existing and emerging therapies. Methods: Concr adapted Bayesian statistical principles used by astrophysicists to effectively integrate multi-dimensional data (genomic & transcriptomic) for accurate modelling of cancer biology. Our proprietary computational framework was applied to predict clinical response and survival through construction of digital twins and in silico simulations of clinical trials. Predicted log odds ratios (LOR) were generated for drug response and compared to published data from 8 suitable historical clinical trials. Three distinct models underpin this framework: Drug Efficacy, Treatment Response & Overall Survival (OS). Three publicly available datasets were used for training: Cancer Therapeutic Response Portal ( in vitro dose-response data), Cancer Cell Line Encyclopaedia and TCGA. Results: For all 8 clinical studies, the digital twin model accurately simulated both trial arms, compared drug efficacy across arms and predicted which treatment was most active. Unblinded evaluation: Our model accurately predicted gemcitabine would have greater clinical benefit than 5-FU (LOR -0.10, P <0.0001) in metastatic pancreatic cancer. In advanced breast cancer, the model predicted docetaxel had a higher response rate than doxorubicin, and carboplatin response rates were similar to docetaxel in BRCA wild-type patients. In platinum-sensitive recurrent ovarian cancer, the model predicted cisplatin-based combination had a higher response rate than paclitaxel. Blinded evaluation: Using data for paclitaxel, the model correctly predicted that nab-paclitaxel+gemcitabine response rates were higher than gemcitabine in metastatic pancreatic cancer (predicted LOR -0.090, p = <0.001). In all 3 early breast cancer studies, predictions mirrored clinical trial results, adjuvant ECF > CMF, adjuvant TC > AC and adjuvant tamoxifen was superior to capecitabine. The underlying pan-tumour Overall Survival model used a Random Survival Forest approach, trained on 23 cancers with predictive accuracy of 0.78 AUC ROC and C-index 0.71. To enable cohort enrichment for drug response, the model segmented patient cohorts into treatment responder and non-responder. Using this approach, the log odds increased for drug response rate for 16 of the 19 different cancers and 14 of the different 17 drugs. Conclusions: Our digital twin model represents transformative technology for clinical trial simulation with strong potential for accelerating and de-risking clinical drug development, and utility for synthetic control arms.
OncoGPT: Enhancing oncological decision-making with ASCO guideline–trained AI.
Authors: El-Assal Ramy , Nazha Aziz
Published: May 2024

e13607

Background: General-purpose language models like ChatGPT, while proficient in a wide array of topics, often lack the specialized knowledge and precision required for specific fields such as oncology. The American Society of Clinical Oncology (ASCO) guidelines offer a comprehensive resource for oncology professionals, yet sifting through extensive documents for specific information can be daunting. To bridge this gap, we introduce OncoGPT, a specialized GPT model, tailored to provide precise and guideline-adherent oncological advice. Methods: We leveraged GPT-4's advanced language understanding capabilities as the foundation for OncoGPT. This customization involved an extensive training regimen using the complete text of the ASCO clinical guidelines. The training process was meticulously designed to ensure that OncoGPT not only assimilates the detailed information within these guidelines but also understands the context and intricacies of oncological care. Additionally, we created disease-specific versions of OncoGPT, each fine-tuned with guideline subsets pertinent to particular oncological conditions. Further, the training was augmented with specific prompts to foster kind, sincere, and respectful interactions, especially for patient-oriented responses. The model was designed to discern whether the user is a healthcare professional or a patient and adjust the complexity of its responses accordingly. For patients, additional emphasis was placed on delivering information in a kind, responsible, and empathetic manner. Results: OncoGPT's performance was rigorously evaluated for accuracy and reasoning against real-world clinical scenarios. The responses generated by the model were meticulously reviewed by an experienced oncologist to confirm adherence to ASCO guidelines and clinical relevance. This assessment revealed a high level of precision and contextual understanding in OncoGPT's outputs, demonstrating its potential as a valuable tool for oncology practitioners. Importantly, it showed a remarkable ability to tailor its communication style based on the user's background, providing simplified explanations for patients and more detailed responses for professionals. An example of a specific OncoGPT: Breast Cancer OncoGPT: https://chat.openai.com/g/g-VcuECT3jg-oncogpt-breast-cancer Conclusions: OncoGPT represents a significant advancement in the application of AI for medical decision-making. By integrating the depth of ASCO guidelines into a responsive and accurate AI model, OncoGPT offers oncology professionals a powerful tool for accessible, guideline-compliant advice. This development marks a crucial step towards integrating AI more profoundly into specialized medical fields, enhancing both the efficiency and accuracy of clinical decision-making.