tbuckley/cabot-search huggingface dataset:
Implementing a mutidisciplinary approach for older adults with cancer: Challenges and achievements in a geriatric oncology program in Sao Paulo, Brazil.
Authors: Luciola De Barros Pontes, Jennifer Kamila da Silva, Juliana duarte Santos, Regiane Proença, Nadia Haidar Buesa, Adriana Fatima Dutra, Fabiola Alves de Santana Borges, Maria Livia Leite de Abreu Gomes, Raquel de Sousa Neto, Bianca Vasconcellos da Conceição, Carolina Ferraioli Porro, Jadna Helena dos Santos França, Nivalda Gonçalves Ferreira, Carla Jussaria de Oliveira, Thassio Pastre Criminazzo, Leonardo Daniel Mendes, Jakline Maria Freire, Amanda Costa Rosa da Silva, Evelyn Aparecida Nascimento
Journal: Journal of Clinical Oncology (2024)

e13769 Background: Treating an older adult with cancer is complex and requires multidisciplinary effort. Multiple studies demonstrate the benefits of timely integration of Geriatric Oncology (GO) with routine oncologic care. Despite this growing body of evidence, there are very few cancer centers around the world that have a dedicated GO program, notably in low- and middle- income countries (LMIC). The objective of this abstract is to describe the first year of the GO program in a private oncology center in São Paulo, Brazil. Methods: As an effort of oncologists, geriatricians and nurses, the team built a consultative model with a nurse performing the G8 triage score to every cancer patient aged ≥ 70 years. For those with a G8 less than 14 points, a comprehensive geriatric assessment (CGA) was performed. The results of the CGA were than provided as a report to the treating physician and all the members of the multidisciplinary team, so that a geriatric assessment (GA) driven intervention could be planned. The patients were then followed for 3, 6, and 12 months to detect falls, emergency rooms visits, hospitalizations, and death. Results: From January 2023 to January 2024, 110 patients with a median age of 79 were evaluated, with 57% having a G8 less than 14. 70 patients we submitted to a CGA. The majority of the tumors were gastrointestinal (30%), lung (20%) and genito-urinary (17%). Regarding the GA domains: 70% of the patients had a possible or probable cognitive impairment by the 10-point cognitive screener; 53% mild psychological distress by PHQ-4 Screening Tool; 47% showed important reduction in hand grip strength scale calling the attention to sarcopenia; 48% were found to be at nutritional risk and 42% were classified as malnourished at the mini-nutritional assessment; polypharmacy was detected in 67% of the patients. Despite these findings, 78% of the patients we classified as ECOG 0 or 1. After GA driven interventions, at 3 and 6 months follow up, 88% of the patients remained with no falls, with a mild rate of hospitalizations (40%) and a low rate of treatment discontinuation (15%). By the end of 6 month follow up, 24% of the patients have died. Conclusions: Geriatric conditions such as functional impairment are common and frequently unrecognized or inadequately addressed in older adults with cancer. Identifying geriatric conditions by performing a GA can help the health team to manage these conditions and provide better oncology care.
Real-world implementation of an EHR-integrated, SMS text remote patient monitoring platform at an academic cancer center.
Authors: Sumit Shah, Gayani Kadurugamuwa, Alison Holmes Tisch, Monika Lu, Shiva Modarresi, Caleb Johnston, James Colbert
Journal: Journal of Clinical Oncology (2024)

e13669 Background: Remote monitoring studies in oncology have shown improved quality of life, decreased healthcare utilization, and even improved overall survival in an oncologic population, however applications in a real-world setting have been limited. Barriers to successful deployment include low patient engagement rates with smartphone apps, lack of integration with the EHR and burdensome clinical workflows for staff. Methods: We sought to assess the feasibility of an EHR-integrated, SMS text conversational AI agent to automate the collection of ePRO data as well as the assessment and triage of common patient symptoms for patients with GI and GU tumors receiving anti-cancer therapy. This program was developed collaboratively by a health system digital health team, nursing informatics, IT support resources, oncology nurses, medical oncologists, and representatives from Memora Health. Core components included 1) weekly ePRO collection based on the NIH’s CTCAE, 2) NLP activated symptom triage for 16 common symptoms and toxicities 3) weekly symptom check-in 4) weekly medication adherence assessment for patients on oral therapy. Patients received all messages through SMS on their phones, and all data collected were routed into the EHR. In-EHR visualizations allowed nurse coordinators to track ePRO symptoms over time and symptoms that met pre-specified criteria triggered EHR-based alerts. Results: Of 99 patients ordered into this SMS program, 83 enrolled (84% enrollment rate), with 55 Uro Onc patients; 44 GI Onc patients. A total of 2,583 surveys were sent to patients. 98% of patients completed at least 1 survey. Overall patient survey completion rate was 62%. Medication adherence surveys had the highest completion rate at 81%. ePRO completion rates were higher for GU (65%) vs GI (39%) patients (p&lt;0.01). 71% of ePRO surveys generated a symptom alert to the nursing staff. Patient satisfaction was high with 93% rating this SMS program as “excellent” or “good” (n=78). Care team surveys found that 3 of the 6 Oncologists believe ePRO collection is valuable and 3 of 6 used ePRO data to adjust a care plan. Nurse coordinators had a mixed view of the program with a majority reporting that there were too many symptom alerts. Conclusions: We successfully deployed an EHR-integrated SMS program to collect ePRO data, track patient symptoms, and monitor adherence to oral therapy. We believe having a physician champion influenced patient engagement in the GU patient population. Patients had a favorable impression of this program and survey completion rates demonstrated ease of use for patients. Clinical staff, particularly nurses, felt that there were too many alerts. Additional workflow changes may reduce alert burden and better incorporate ePRO learnings into clinical practice.
Effect of a novel artificial intelligence (AI) –enabled multi-trial matching system on patient matching using real-world data.
Authors: Selin Kurnaz, Arturo Loaiza-Bonilla, Darla Carvallo Castañeda, Oz Huner, Dersu Giritlioglu
Journal: Journal of Clinical Oncology (2024)

e13501 Background: Currently clinical research teams at sites are manually matching patients to clinical trials, where each trial in our experience takes at least 25 minutes using highly trained dedicated resources. This limits cancer patients’ clinical trial options and enrollment rates. Moreover, the lack of NGS testing results may also negatively impact matching to clinical trials in targeted therapies. PCC, a consortium of 7 of the Top 20 Pharmaceutical companies, collaborated with Massive Bio to demonstrate how cancer patients’ matching rates can be increased if patients are prescreened to multiple trials simultaneously (i.e. multi-trial matching) instead of a single trial and NGS testing results are integrated into the matching process. Methods: Massive Bio has developed a first-in-class AI-enabled matching system to extract 180 structured clinical data from medical records using computer vision and natural language processing and a recommendation system for matching patients to parameterized inclusion/exclusion (I/E) criteria from over 14,000 actively recruiting interventional clinical trials.Our analysis included a sample of 5,707 cancer patients from the Massive Bio Synergy AI real-world database, corresponding to tumor types in 23 selected trials. We developed a decision-tree algorithm for retrospective matching analysis using modeled I/E criteria and compared these results to the theoretical matching rate (TMR) for each trial based on tumor type and biomarker prevalence, extent of disease at diagnosis and prior treatment history. Results: 690 patients matched to at least one trial were matched to 1.82 times more trials in multi-trial matching; 2.10 times more potential matches were obtained with hypothetical 100% NGS testing; and 99.9% matching time was reduced with AI enabled system; key findings are summarized (Table). Conclusions: This study confirms the underutilization of NGS technology and substantial benefits of AI and NGS in cancer trial matching. Their use resulted in a nearly two-fold increase in potential patient eligibility for trials, and an unprecedented reduction in manual effort, underscoring a significant advancement in the efficiency of the clinical trial matching process. A prospective country-wide analysis is under consideration.[Table: see text]
Value of a multidisciplinary geriatric oncology committee on patient care in a community-based, academic cancer center.
Authors: Gurbani Singh, Lena Morant, McKenzie Bedra, Jennifer Emel, Kelly Harris, Yudhishtra Markan, Christopher deBorja, Monica Tong, Patrice Downs, Cherif Boutros
Journal: Journal of Clinical Oncology (2024)

e13771 Background: The heterogeneity in health and functional ability among older patients makes the management of cancer a unique challenge. The Geriatric Oncology Program at the University of Maryland Baltimore Washington Medical Center (BWMC) was created to optimize cancer management and recommendations for older patients. This study aimed to assess the benefits of the implementation of such a program at a community- based academic cancer center. Materials and Methods: We retrospectively analyzed patients aged ≥80 years presenting to the Geriatric Oncology Program between January 2017 and July 2022. A multidisciplinary team of specialists collectively reviewed each patient using geriatric-specific domains and stratified each patient into one of three management groups- Group 1: those deemed fit to receive standard oncologic care (SOC); Group 2: those recommended to receive optimization services prior to reassessment for SOC; and Group 3: those deemed to be best suited for supportive care and/ or hospice care. ANOVA, chi-square, and Kaplan-Meier analyses were used to assess patient outcomes. Results: Among 233 patients included, 76 (32.6%) received SOC (Group 1), 43 (18.5%) were optimized (Group 2), and 114 (49.0%) received supportive care or hospice referral (Group 3). There was no significant difference in sex, race, or age among all three groups. The Canadian Study of Health and Aging-Clinical Frailty Scale (CSHA-CFS) score was implemented in 2019 (n=90). Patients receiving supportive/ hospice care only had an average score of 5.8, while the averages for those in the optimization and SOC groups were 4.6 and 4.1, respectively (p=&lt; .001). SOC patients had the longest average survival of 2.71 years compared to the optimization (2.30 years) and supportive care groups (0.93 years) (p= &lt;0.001). 69.8% of optimized patients were deemed fit for SOC upon re-evaluation following optimization services. For all patients that underwent surgical interventions, post- operatively, 23 patients (85%) were discharged home and 4 (15%) were discharged to a rehab facility. The average survival after surgery for all patients was 3.16 years, while patients who were optimized prior to surgery had an average survival after surgery of 3.21 years. Conclusions: The present study demonstrates the need for specialized consideration of the heterogeneity that cancer diagnoses present in older individuals. The Geriatric Oncology Program at BWMC is able to maximize treatment outcomes for geriatric patients through the provision of SOC therapies and optimization services, while also minimizing unnecessary interventions on an individual patient-centric level.
Access to germline testing and prevalence of somatic mutations in patients with breast cancer from disadvantaged areas: A single-center study.
Authors: Malak Alharbi, Arya Mariam Roy, Jayasree Krishnan, Archit Patel, Riya Jayesh Patel, Ankita Kapoor, Kristopher Attwood, Kayla Catalfamo, Angela Omilian, Elizabeth R. Gage-Bouchard, Song Yao, Tracey L. O'Connor, Ellis Glenn Levine, Shipra Gandhi
Journal: Journal of Clinical Oncology (2024)

e13750 Background: Socioeconomic disparities affect clinical outcomes in several malignancies including breast cancer (BC). The Area Deprivation Index (ADI) is a tool used to classify regions based on socioeconomic factors, including employment, education, housing, and poverty. Here, we investigate ADI among patients (pts) with BC seen at Roswell Park Comprehensive Cancer Center and its association with recurrence free survival (RFS), time to next treatment (TNT), overall survival (OS), and access to somatic mutation via next-generation sequencing (NGS) and germline testing of BC pts. Methods: We conducted a retrospective analysis of 409 pts diagnosed with stages 1-3 and de-novo stage 4 BC from 2014 to 2018. ADI scores were derived from patients' home addresses using www.neighborhoodatlas.medicine.wisc.edu and categorized into four quartiles: Q1 (80-100%), Q2 (60-79%), Q3 (40-59%), and Q4 (0-39%) with Q1 representing areas with highest deprivation. Demographic and clinicopathological characteristics were summarized by ADI. Kruskal-Wallis and Chi-square was used for comparing continuous and categorical variables respectively. Results: Among 409 pts, 92% were female, 77% White, and 10% Black. 30% (n=123/409) lived in the most disadvantaged area Q1, 26% (106/409) Q2, 26% (104/409) Q3, and 12% (51/409) Q4, and 6% (25/409) unknown. 72% pts were hormone receptor positive, 14% HER2+, and 14% triple negative BC. No significant differences were noted in the racial distribution, employment, comorbidities, smoking, screening mammogram, type of insurance, BC stage and subtype, and adherence to therapy by ADI. Access to germline testing was significantly lower among pts from Q1 and Q2: (68/232 or 28%) vs. (65/156 or 42%) from Q3 and Q4, p= 0.03. There was no difference in RFS and OS by ADI. There was a trend towards decreased TNT (9 vs. 11 months, p=0.4) among BC pts living in areas with higher vs. lower ADI (&gt;60% vs &lt;60%). Notably, among stage 4 pts, in disadvantaged areas with ADI &gt;60%, there was higher prevalence of Estrogen Receptor1(ESR1) 72% (8/11) vs. 27% (3/11) and TP53 75% (9/12) vs. 25% (3/12) somatic mutations compared to less disadvantaged areas with ADI &lt;60%, p= 0.08 and 0.04, respectively. Conclusions: Our study shows disparities in access to germline testing, with lower rates among pts from more deprived areas. Moreover, somatic testing results show distinct mutational profiles across different ADI groups, suggesting potential impact of ADI on somatic characteristics. Larger studies are needed to investigate the association of ADI with somatic mutation burden of BC.
Challenges and solutions to recruiting diverse populations to oncology clinical trials: A mixed-methods study of clinical research coordinators.
Authors: Ana I. Velazquez Manana, Evelin Trejo, Sorbarikor Piawah, Celia Kaplan
Journal: Journal of Clinical Oncology (2024)

1619 Background: Multiple barriers limit the enrollment of diverse populations in oncology clinical trials (CTs). A review of our institutional CTs recruitment dashboard identified underrepresentation of Black and Latinx adults among thoracic and gastrointestinal (GI) oncology CTs participants compared to demographics of our catchment area and patient population. We conducted a mixed-methods study to identify barriers and potential solutions to the enrollment of diverse populations in oncology CTs in our institution. Methods: We conducted a mixed methods study enrolling clinical investigators and clinical research coordinators’ (CRCs) at an NCI-designated CCC in Northern California. Data from CRCs is presented. All CRCs supporting CTs in the thoracic and GI medical oncology were invited to participate in qualitative, one-on-one, semi-structured interviews. Participants completed 60-90 minutes interview and a short survey. Data were analyzed using a thematic inductive approach. Results: Of 14 CRCs, the majority were female (78.6%), with primary focus on thoracic (50.0%) and GI (35.7%) oncology CTs. 64.3% of CRCs reported &gt;80% of their work was dedicated to coordinating and managing therapeutic cancer CTs. In survey responses CRCs identified inability to take time away from work/family (64.3%), inadequate insurance coverage (42.9%), lack of transportation (35.7%), and language barriers (35.7%) as major barriers to enrollment of diverse patients in CTs. In qualitative interviews CRCs expressed the need of culturally and language appropriate materials for CT recruitment, establishing a sense of connection between clinical research staff/PI and patients, accommodating socioeconomic concerns, and resources to respond to potential participants’ mistrust of medical research. Main obstacles faced by CRCs during patient enrollment in CTs included lack of time, low patient awareness, resistance from colleagues, lack of multilingual staff, and inadequate logistical support. CRCs identified in-person interpretation as beneficial and identified a strong rapport between the clinical provider and the potential participant as the most important component to boost recruitment of minorities. Lastly, CRCs highlighted that institutional efforts are needed to increase CT participation among all populations. Conclusions: Lack of representation of diverse populations in CTs remains a complex challenge. Our study of CRCs’ perceptions highlights access to in-person translators, culturally and language concordant materials, education resources, and increased rapport as key areas for intervention to facilitate patient enrollment of diverse populations. Analysis into practice-specific patterns within our cancer centers can help identify areas for interventions to support the recruitment and enrollment of diverse populations in oncology CTs.
Improving lung cancer health equity by applying deep learning to low dose CT screening of minority and disadvantaged patients.
Authors: Abdul Zakkar, Alexander Krule, Mehak Miglani, VK Gadi, Kevin Kovitz, Mary Pasquinelli, Frank Weinberg, Yamile Molina, Sage J Kim, Aly Khan, Ryan Huu-Tuan Nguyen, Ameen Abdulla Salahudeen
Journal: Journal of Clinical Oncology (2024)

1584 Background: In the US, disparities in lung cancer mortality exist for African American, Hispanic, and other minorities. Standard of care low dose CT screening (LDCT) detects early-stage disease and improves mortality, yet disparities are perpetuated in screening by eligibility criteria derived from cohorts underrepresenting these minorities. One such cohort is the National Lung Screening Trial (NLST) cohort which is 92% White. Consequently, guidelines for lung cancer screening may be insufficient to address the unique needs of diverse populations. We hypothesize that Artificial Intelligence prediction of future lung cancer risk from an individual’s LDCT can partially mitigate racial and ethnic disparities and improve health system practice guidelines by individualizing screening risk as compared to current general guidelines. Here, we benchmark a Resnet18 3D neural network trained on NLST LDCT images, Sybil, on the diverse patient population of the University of Illinois Health system (UIH) which is 20% White and 60% African American. Methods: A real-world cohort from UIH consisting of 1,450 CT studies was evaluated alongside 60,378 CT studies from the NLST cohort. All CT studies evaluated by the model were not used in model training. Using Youden’s J index as a probability cutoff, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were evaluated. Receiver operating characteristic (ROC) and precision-recall (PR) curves were generated to assess model performance between cohorts. NLST data were truncated to achieve equivalent incidence of lung cancer with UIH when generated PR curves. Results: Multi-year prediction performance (ROC-AUC and PR-AUC) between cohorts are summarized (Table). For prediction of lung cancer within 1-year of LDCT in the UIH cohort, the model respectively demonstrated sensitivity, specificity, positive predictive value, and negative predictive value among White (0.80, 0.77, 0.54, 0.92), African American (0.84, 0.78, 0.39, 0.97) races and Hispanic (0.75, 0.73, 0.60, 0.84) and non-Hispanic (0.87, 0.77, 0.42, 0.97) ethnicities. Conclusions: Model performance was similar between the NLST (92% White) and a diverse, real-world cohort at UIH (20% White) though decreases in ROC-AUC performance in year 1 predictions and may be due to insufficient representation of minority populations during model training. Prospective studies involving larger and more representative patient populations should be conducted to further optimize the model and evaluate its clinical utility to improve lung cancer health equity in minority populations.[Table: see text]
Access to care for blood cancer: Allogeneic hematopoietic cell transplant caregiver requirements.
Authors: Jaime Preussler, Anna DeSalvo, Ben Tweeten, Brenna O'Brien, Katie Schoeppner, Jennifer A Sees Coles
Journal: Journal of Clinical Oncology (2024)

e13515 Background: Caregiver requirements can be a major barrier to treatment access for many patients with cancer, especially those patients with blood cancer when alloHCT is a potential cure. This can be illustrated with allogeneic hematopoietic cell transplant (alloHCT). Patients are generally required to have a caregiver to proceed with alloHCT, often for at least 100 days. However, as different family and social structures evolve, the availability of a caregiver may lead to more access barriers. The aim of this study was to learn more about caregiver requirements and perspectives on those requirements from social workers, patients, and caregivers. Methods: We conducted interviews and focus groups from May 2022-February 2023 with 50 stakeholders, including social workers (n = 22), patients (n = 12), and caregivers (n = 16) in the United States (U.S.). Focus groups and interviews were recorded and transcribed for analysis by two independent reviewers. Results: All 22 social workers said their transplant center (TC) required a patient to have a caregiver to proceed to HCT; though requirements varied from 60 to 100 days post-HCT. One participant noted, “ I do have worries that patients aren't being referred who don't have caregivers and we just don't know about them and they're falling through the cracks.” Most patients and caregivers said their TC required a caregiver to proceed to alloHCT, but the length of time varied (from no requirement up to 180 days post-HCT). All patients who participated had a caregiver. Patients and caregivers had differing perspectives on the requirements, with some saying it was too short and they needed more time, and others saying it was too long and they could have used less time. Many said the amount of time a caregiver was needed depended on the patient’s recovery. All participants identified several solutions to help patients who did not have a caregiver; including a volunteer caregiver registry, having the patient stay in the hospital, using technology solutions (e.g., telehealth visits), changing/removing the requirements, having a shared caregiver in a community setting, having funding available for patients and caregivers, and/or home visits and checks. Conclusions: Patients with cancer face many barriers to treatment; for alloHCT and many other treatments, the need to have a caregiver is one of those barriers. This study found variation across TCs, suggesting a lack of evidence informing the requirements. These requirements can also lead to equity issues. This study was limited to patients who received an alloHCT; further research is needed to learn about the perspectives of patients who are unable to receive an alloHCT due to not having a caregiver available.