tbuckley/cabot-search huggingface dataset:
The physician oncology referral process and its effect on tumor board outcomes.
Authors: Barbara Oureilidis-DeVivo
Journal: Journal of Clinical Oncology (2024)

e13752 Background: Oncology settings are known for their hierarchical structure, with distinct status demarcations among professional occupational specialties. The physician oncology referral process, a critical aspect of cancer management, reflects this hierarchical nature. This process, initiated upon suspicion of cancer, entails sequential clinical evaluation and treatment by physicians in the following order: surgeons, medical oncologists, specialty physicians, and referral physicians like interventional radiologists and radiation oncologists. Those at the top of the hierarchy wield considerable influence over the oncology referral process, directing subsequent patient care. While several studies have criticized the effectiveness of cancer management teams, little attention has been given to the potential role of the referral process in reinforcing hierarchical structures within tumor boards, possibly undermining their intended goals. Methods: This qualitative, ethnographic study investigated the interplay between the physician oncology referral process and tumor board outcomes in oncology settings. Data spanning three years were collected from seven research hospitals in the United States and the United Kingdom, encompassing observations from 44 tumor boards and eighteen semi- and unstructured interviews. The principal investigator conducted an analysis and synthesis of the gathered data. Results: The ethnographic study revealed that the hierarchies observed in tumor boards align with and are influenced by the traditional oncology referral process. Group interactions and treatment deliberation outcomes were significantly affected by the social dynamics of hierarchy mirrored in the referral process, ultimately compromising the intended objectives of tumor boards. Surgeons and medical oncologists, who traditionally lead the referral process, tended to dominate tumor board meetings. In contrast, sub-specialties like interventional radiologists and radiation oncologists, positioned at the end of the referral process, were less engaged in deliberations and often overlooked for treatment referrals. The higher the position in the hierarchy and referral process, the more pronounced the tacit authority and ability to direct patient treatment. Conclusions: This study highlights the alignment of traditional oncology referral processes with tumor board dynamics, showcasing similar social and clinical impacts. Despite potential resentment cultivated by reinforced hierarchy in the referral process, tumor board members tend to acquiesce, accepting decisions even when they may not align with optimal patient care.
UK Cancer and Transition Service (UCATS): Providing tailored cancer care to the gender-diverse community.
Authors: Alison May Berner, Stewart O'Callaghan, Frances Lander, Mark Bower
Journal: Journal of Clinical Oncology (2024)

e13676 Background: Gender-diverse individuals identify their gender as different to the sex they were assigned at birth. This population experiences health inequity throughout the cancer care pathway partly stemming from limited access to specialist knowledge of gender-affirming care and its intersections with cancer. Notable issues include safety of gender affirming hormones, mutual implications of gender affirming surgeries with cancer surgeries and radiotherapy, and psychological support when dysphoria is exacerbated by a cancer diagnosis. Methods: In response, we established the UK Cancer and Transition Service (UCATS), a national virtual monthly multidisciplinary meeting (MDM) and clinic. Patients are eligible for referral if they identify as gender-diverse and have active or historical malignancy. The service was coproduced with patient and public involvement, including input from a specialist cancer charity for gender and sexual minority people. The service launched in June 2022 and began advertising and requesting patient feedback from January 2023. Results: Between June 2023 and January 2024 UCATS received 20 referrals (10 from gender services, 5 from oncology, and 5 self-referrals). Patients were aged 18 - 68 years. The most common tumour types were breast (n = 6), prostate (n = 5), and gastrointestinal (n = 3). Others were haematological, testicular, gynaecological, and central nervous system. One patient had a familial cancer syndrome, not active cancer. Patients were offered a virtual clinic appointment and MDM discussion. Reasons for referral included safety of gender affirming hormones and surgeries, and expedited gender consultations in view of prognosis, or need for cancer treatments that may affect gender-affirming care. Two patients did not attend, with one sending a nominated family member. Six patients had at least one external clinician dial in for the appointment. Seven patients received follow-up appointments. There were nine completed feedback forms, 4 after first appointment and 5 after follow-up. All patients rated both the communication at the appointment and the information given as 5/5. All patients rated their likelihood to recommend the service as 4 or 5/5. Other points of positive feedback were the detailed outcome letter, speed of appointment, and a sense of advocacy. Next steps include routine follow-up appointments (in response to feedback), wider advertising, increased frequency, and recruitment of nursing support. Conclusions: UCATS successfully caters to an unmet need, coordinating cancer and gender affirming care for gender diverse patients, and providing specialist knowledge and support for patients and care teams.
AI-generated synthetic clinical-genomic data for precision oncology research: Validation using a case study on lung adenocarcinoma.
Authors: Brandon Theodorou, Amar Das, Craig Eagle, Jimeng Sun, Aaron Hardin, Nicole Zhang, Elifnur Yay Donderici
Journal: Journal of Clinical Oncology (2024)

e13627 Background: The analysis of genomic variants is crucial in precision oncology research, offering insights into cancer risks and progression, especially in diverse types such as lung adenocarcinoma (LUAD). However, such research often grapples with balancing patient privacy with the need for comprehensive, high-quality genomic datasets. Our project addresses this by creating synthetic clinical-genomic data, which maintains patient confidentiality and provides a rich resource for genomic cancer research. Methods: Leveraging the GuardantINFORM database, which includes anonymized genomic data and structured payer claims, we focused on generating synthetic data for LUAD patient cohorts. This approach involves processing real patient data into a format compatible with Medisyn’s generative AI models, ensuring the synthetic data retains the original's statistical properties, and processing the output back into the original database structure and format. This method plays a crucial role in maintaining patient privacy and serves as a valuable tool for research by enabling the generation of realistic patients with desired properties on demand. Results: Our synthetic data closely mirrors real-world genomic and claims variable distributions, evidenced by a 0.994 R2 correlation between real and synthetic data along with comparable Oncoprints. Importantly, privacy tests show that patient confidentiality is effectively maintained despite this effective performance. The synthetic data's utility was then demonstrated in a study replicating real-world findings: LUAD patients with KRAS G12C in combination with STK11 mutations showed a significantly higher risk of early mortality. This underscores the potential of synthetic data in advancing cancer research. Conclusions: This research offers a promising avenue for the cancer research community. By providing a method to share privatized, synthetic genomic data, which can be combined and generated on demand, we enable broader, more responsible data sharing. This approach protects patient privacy and offers a rich dataset for groundbreaking research, potentially accelerating advances in cancer diagnosis and treatment. [Table: see text]
Individual and neighborhood-level factors associated with disruptions in patients with gynecologic cancers undergoing radiation treatment.
Authors: Emily Claire MacArthur, Allison Froehlich, Stephanie L. Wethington, Anne Rositch, Sydney Santos, Dana Lewis, James Stuart Ferriss, Anna Beavis
Journal: Journal of Clinical Oncology (2024)

e13713 Background: Disparities in cancer health outcomes are driven in part by social determinants of health (SDOH). Health-related social needs (HSRN) are downstream modifiable manifestations of SDOH. It is critical to identify patients with HRSN who may benefit from early intervention to improve health outcomes. Methods: We identified all patients who received radiation treatment (RT) for gynecologic cancer at an urban, academic center from January 2021-August 2023 and completed HRSN screening. HRSN screening queries patients on food, housing, transportation, and financial insecurity as well as help reading hospital materials. We extracted demographic and clinical data, self-reported HRSNs, and separately screened each chart for additional evidence of transportation insecurity. We extracted area deprivation index (ADI), a measure of neighborhood level socioeconomic disadvantage, based on home address. Any RT disruption was the primary outcome. We compared patients with and without any RT disruption using Fisher’s exact tests. Univariate Poisson regression was used to examine the magnitudes of association between individual and neighborhood level factors and RT disruption. Results: A total of 127 patients met inclusion criteria. Median age was 62 (interquartile range 25%-75%: 53-71). Most (54%, n=69) patients were White, 32% were Black (n=41), and 14% were another race (n=17). Sixteen percent (n=20) reported at least one HRSN. Six percent of patients (n=8) resided in neighborhoods in the 81st-100th percentile ADI. A total of 26% (n=33) of patients had any RT disruption, and these patients differed on individual and neighborhood factors (Table). Patients with public insurance were 2.4 times as likely to have RT disruption compared to those with private insurance (95% confidence interval [CI]: 1.4-4.2). Self-reporting at least one HRSN was associated with a 2.3 times higher risk of RT disruption (95% CI: 1.31-4.11). Chart-derived evidence of transportation insecurity was associated with a 2.6 times higher risk (95%CI: 1.5-5.6) of RT disruption. Residing in a neighborhood with an ADI above the 80%ile was associated with a 3.3 times higher risk (95% CI: 2.0-5.5) of disruption. Conclusions: A variety of measures of SDOH both at the individual and neighborhood-level were associated with RT disruptions. While we did not have the power to perform multivariate analyses, our results suggest the ADI – which can be readily obtained from a patient’s home address – could help identify those patients most at risk of disruption to care. [Table: see text]
Promptable foundation model for automatic whole body RECIST measurement.
Authors: Paul Hérent, Hélène Philippe, Elodie Ferreres, Leo Machado, Corentin Dancette, Julien Khlaut, Pierre Manceron, Daniel Tordjman
Journal: Journal of Clinical Oncology (2024)

e13643 Background: RECIST 1.1 is the gold standard for tumor evaluation in clinical trials and patient care. However, it faces challenges due to the subjective selection and measurement of lesions, notably because of inter-observer variability in the 20-30% range (1), which can potentially result in inaccuracies in therapeutic response classification (2). The main objective of this study is to assess the added value of a radiological foundation model to assist the radiologist to measure RECIST across different lesion topographies, using visual prompting. Methods: We use a promptable segmentation algorithm, based on a visual foundation model, pre-trained on various CT-scans and then trained on a subset of the Medical Segmentation Decathlon (MSD) dataset, covering 538 patients with pancreas, liver, colon and lung lesions. It outputs a 3D segmentation mask of the lesion, using a visual prompt, i.e. a 3D bounding box (BBox) as input. We then measure the capacity of the model to measure RECIST on the MSD validation set and on the KiTS validation set (99 samples), which features a new lesion type (kidney). To simulate inter-reader variability of up to 23%, we provide as inputs BBoxes centered on the lesions but with a varying error (15% variability: +10 pixels, 23% variability: +15 pixels). Results: The model is able to correct variability in BBox input: with all BBox error ranges, it beats the input variability (15% and 23% thresholds in each column) across many organ types, except in the pancreas when using large error BBoxes. Moreover, the model is able to generalize to new lesion types not seen during training, on an external dataset (KiTS - Kidney). Conclusions: Unlike existing supervised machine learning models dedicated to lesion detections in specific organs, our approach is organ and lesion agnostic and offers a more reliable, precise tumor assessment. This is a first step before allowing the longitudinal evaluation of tumors while safeguarding the clinical intuition necessary for selecting the right target lesions. Moreover, we believe that these methods will be crucial in advancing beyond RECIST 1.1, facilitating the identification of new prognostic biomarkers and proxies for tumor burden derived from previously underexplored radiomics features, ultimately refining the efficacy of clinical trial assessments and elevating the standard of patient care. 1. Yoon, S et al. 2015. 2. Kuhl et al. 2018. [Table: see text]
Integrating social drivers of health screening into ambulatory cancer care.
Authors: Tessa Jones, Alison Snow, Brittany Aryeh, Mark Liu, Cardinale B. Smith, Melissa Mazor
Journal: Journal of Clinical Oncology (2024)

e13692 Background: Despite the known impact of social drivers of health (SDOH) on cancer related outcomes, the integration of SDOH screens into routine oncology clinical practice is scarce and suboptimal. Recent findings indicate feasibility of integrating these tools, yet data is limited in how to develop and implement the screens to address unmet SDOH related needs. This study aims to provide an overview of the development and integration of a SDOH screener and referral system into clinical workflow and describe the patients who completed and didn’t complete the survey. Methods: The 17-item Quality of Life and Support (QOLS) survey was based on evidence-based SDOH questionnaires, co-created with our community board, and built into the Mount Sinai Tisch Cancer Center EHR, Epic. The survey is sent out via the patient portal 7 days prior to patients’ second oncology appointment, with repeat measures every 90 days. Surveys are available in English and Spanish and evaluate SDOH needs such as transportation, food insecurity, employment, social support, financial strain, stress, and depression. If survey answers indicate need for additional support the case is ‘escalated’ to social work, chaplaincy, or family care coordinator depending on the area of need. We used descriptive statistics to describe patients who completed versus didn’t complete the survey. Of those completed, we also describe the SDOH trigger and frequency of ‘escalation’. Results: Between June 2022 - December 2023, 10,655 patients were assigned 25,353 surveys across 5 sites. 37% of the assigned surveys were completed.A total of 6,179 individuals completed at least one survey. The average number of surveys completed was 2. Of the completed surveys, 43% were completed before the appointment date. Of completed surveys, 21% were escalated to social work, 8.5% to chaplaincy, and 1.3% to family care with the most frequent reasons for escalation being distress, financial strain, and transportation. A significant relationship was found between Race (Pearson chi 2 = 107.9 p &lt; .000) and Ethnicity (Pearson chi 2 = 58.8, p&lt;.000) and survey completion with differences due primarily to White and non-Hispanic White completion vs non-completion. Notably, Black and Hispanic individuals are completing the survey in the same proportion as those receiving it. Conclusions: This study shows successful integration of a SDOH screen into ambulatory cancer care which allows for systematic referrals to SDOH resources. [Table: see text]
Improving ethnic and racial diversity in biomarker-driven clinical trials: A proof of concept with the BASECAMP-1 master prescreening study of patients with high-risk solid tumors with human leukocyte antigen-A*02 (HLA-A*02) loss of heterozygosity (LOH).
Authors: Caleb Joshua Smith, Diane M. Simeone, Patrick Grierson, Kedar Kirtane, Maria Pia Morelli, Sandip Pravin Patel, Matthew L. Ulrickson, Saurabh Dahiya, Jong Chul Park, Jennifer M. Specht, Marwan Fakih, Kirstin B. Liechty, Jessica Tebbets, John Sutton Welch, William Y. Go, David G. Maloney, Marcela Valderrama Maus, Eric Wai-Choi Ng, J. Randolph Hecht
Journal: Journal of Clinical Oncology (2024)

1604 Background: Diversity in clinical studies is essential to address health disparities, but racial and ethnic minorities are often underrepresented in clinical studies, especially in precision medicine studies using genetic data (1). BASECAMP-1 (NCT04981119) is an ongoing prescreening study to identify patients with unresectable advanced or metastatic solid tumors and tumor-associated HLA-A*02 LOH for the EVEREST-1 and -2 Tmod chimeric antigen receptor T-cell therapy trials. When enrollment began in 2021, eligibility for Part 1 was restricted to patients with germline HLA-A*02:01 based on preclinical data. Additional analysis revealed the blocker in the Tmod construct had activity across HLA-A*02 subtypes (Mock, et al. Mol Ther Oncolytics.2022). Eligibility for Part 1 was then expanded to all HLA-A*02 subtypes, allowing enrollment of more diverse patient populations. HLA-A*02 allele subtypes vary by ethnicity and race; the frequency of HLA-A*02:01 is 96% in non-Hispanic Whites, but ranges from 53% to 73% in other ethnicities and races. The frequency of all other HLA-A*02 allele subtypes (HLA-A*02:XX) is &lt;5% in non-Hispanic Whites but up to 66% in other ethnicities and races (2,3). Methods: Patients eligible for BASECAMP-1 were identified at United States study sites. All patients screened before January 16, 2024, for Part 1 were included for demographic and central lab HLA-A typing. The % increase in eligibility and % of heterozygous HLA-A*02 patients with HLA-A*02:XX were calculated by ethnicity and race. Results: A total of 1124 patients were screened for germline HLA-A*02 typing; 431 patients were HLA-A*02:01 heterozygous, of whom 37 (9%) were Hispanic, 14 (3%) African American (AA), 2 (&lt;1%) American Indian or Alaska Native, 17 (4%) Asian or Pacific Islander (API), and 324 (75%) non-Hispanic White (Table). The eligibility expansion identified 62 patients with HLA-A*02:XX heterozygosity, of whom 6 (10%) were Hispanic, 6 (10%) AA, 19 (31%) API, and 25 (40%) non-Hispanic White. Conclusions: By expanding enrollment to include HLA-A*02:XX, 16% more Hispanic, 43% more AA, and 112% more API patients were identified, improving the diversity in this trial compared with that of initial enrollment. 1. Aldrighetti, et al. JAMA Netw Open 2021. 2. Ellis, et al. Human Immunol. 2000. 3. Gragert, et al. Human Immunol.2013. Clinical trial information: NCT04981119 . [Table: see text]
Assessing survival effects of persistent poverty and payer status in non-elderly adults with lung or breast cancer.
Authors: Kirsten Yuna Eom, Amy J. Davidoff
Journal: Journal of Clinical Oncology (2024)

1608 Background: Persistent poverty (PP), defined as area poverty ≥20% over 30 years has been associated with increased cancer incidence and worse survival. We examined 1) whether residing in a PP area has larger survival effects than areas with similar contemporary poverty rates; 2) whether adjusting for sociodemographics and payer status reduces PP effects; and 3) whether effects differ for relatively poor (lung and bronchus, LCa) vs good prognosis (female breast; BrCa) cancers. Methods: We used Surveillance, Epidemiology, and End Results (SEER) data linked to Medicaid and Medicare enrollment records (2007-2013), with census-tract level indicators for PP and current poverty (CP) as of 2010. We selected adults aged 19-64, diagnosed with LCa or BrCa. We created a hierarchical poverty status measure (PP, CP&gt;20%, CP 10-20%, CP &lt;10%). Primary payer at diagnosis (SEER) was edited using Medicaid and Medicare enrollment records. Covariates include patient sociodemographics (including age, race, sex (LCa only), marital status), rurality, and region. We used Cox proportional hazard models to estimate effects of poverty status and payer on survival, starting with a limited Model I (poverty status, rurality, and cancer stage), adding sociodemographics, region, and diagnosis year (Model II), plus primary payer (Model III). Results: Among LCa (BrCa) 8.8% (5.3%) of 70,964 (246,405) patients lived in PP areas, with 19.1% (13.7%) in areas with CP &gt;20%; primary payers included 51.9% (75.0%) private, 28.7% (16.9%) Medicaid, and 3.1% (1.1%) uninsured. For both cancers, adjusted hazard of death increased with increasing poverty levels; PP areas showed the highest risk (table). The magnitude of the poverty gradient was smaller for LCa vs BrCa. Adjusting for sociodemographic measures and primary payer reduced the magnitude of poverty status effects, particularly for BrCa. Conclusions: We observed significant but distinct associations of poverty status, including PP, with survival for non-elderly adults with LCa and BrCa. With better prognosis, BrCa patients have longer exposure to PP, which may increase the impact on outcomes. Person-level characteristics and health insurance are also key factors affecting care access and survival. These results highlight the need to consider both area context and personal socioeconomic characteristics when targeting and evaluating interventions to enhance survival outcomes. [Table: see text]