bluuebunny/crossref_metadata_2025 huggingface dataset:
Will we have a match? Observations from decision making conversations between hematologists and White and Black patients considering allogeneic stem cell transplant.
Authors: Thordardottir Thorunn Halldora , Rodenbach Rachel , Campbell Toby Christopher
Published: June 2023

12133

Background: Allogeneic stem cell transplant (alloSCT) is the only treatment associated with cure for high-risk myelodysplastic syndrome (MDS), but carries substantial risks. One routinely mentioned concern is donor source. While white patients have an 80% chance of finding an unrelated fully matched donor, Black patients have among the lowest chance at 30%. The aim of this secondary analysis, from a telemedicine study assessing how hematologists engage in shared decision making with patients considering alloSCT, is to analyze how hematologists navigate discussions about donor concerns related to the apparent race of the patient. Methods: This is a qualitative analysis of transcribed video-recorded virtual encounters. We recruited US hematologists who perform alloSCT and randomized them to meet with a patient actor of Black or white race. The hematologists conducted a new patient encounter with the actors who were trained to portray the same 67 year old man with newly diagnosed high-risk MDS referred for specialty consultation to discuss treatment options. The patient character had multiple healthy siblings and children. Results: Transcripts from a total of 33 hematologists were available at the time of analysis (61% male, 30% non-white, mean age 46 +/- 11 years). Hematologists spent varying amounts of time discussing the concept of transplant donation. While some used complex language to describe matching and HLA tissue markers, others used simplified terms of finding a donor with “an immune system that’s similar to your own,” to eradicate foreign MDS cells and avoid attack on healthy cells. The majority emphasized searching for a donor among siblings, given the highest likelihood of matching. Conversations with the white actor included reassurance of finding a fully matched donor, whether through family or the international registry. Hematologists frequently disclosed the highest chance of success for people of European ancestry. While hematologists mentioned the registry in conversations with the Black actor, they rarely disclosed the lower chance of finding a fully matched unrelated donor. Instead they reassured that with better technology haploidentical match could be an option through donation from their child, “with as favorable outcomes.” Some gave the Black actor an overestimation of finding a fully matched donor through the registry. Non-white hematologists more frequently acknowledged the concept of race in stem cell donation. Conclusions: Hematologists use multiple strategies to help patients understand the donor process. They reveal to white patients the high likelihood of finding a fully matched donor while appearing to avoid discussing the lower odds for a Black patient.
Analysis of delivery of cancer survivorship education to community healthcare professionals.
Authors: Pariser Ashley , Johns Kevin Nathaniel , Champion Dena , Roberts Andrea , Fugett Susan , Holley Erin , Schreiber Candice , Hock Karen , Todd Jalyn , Honeychuck Andrew , Hunt Katherine , Lu Yurong , Ramaswamy Bhuvaneswari , Brill Seuli Bose
Published: June 2023

12136

Background: There are currently 18.1 million cancer survivors in the United States; anticipated to reach 26.0 million survivors by 2040. Educational opportunities for healthcare providers in survivorship care is needed to insure capacity for this rapidly growing population particularly in the community setting. This pilot study implemented a cancer survivorship Project ECHO model in two community hospitals within a larger community health system. Methods: Providers affiliated with the Mercy Health System were enrolled in one of two cohorts. Eligible participants included physicians, advanced practice providers, nurses, social workers, and dietitians. Six one-hour sessions were developed from a needs assessment and delivered over a 12-week period. Participants were invited to complete pre and post session surveys to evaluate the following 3 domains: professional improvement (PI), self-efficacy (SE), and knowledge (KN). A Likert scale was used with each survey item rated 1-5, where 1 represents lowest and 5 highest (agreement/confidence/knowledge). The average score for survey items within each domain was utilized to compare pre- and post-survey results. Results: Fourteen multidisciplinary providers were enrolled in both Cohort 1 and Cohort 2. Twenty-six completed the baseline survey and 22 completed the post-study survey. The mean change of each domain (PI, SE, KN) and mean overall change are summarized in the table. For cohort 1, the overall score significantly increased 0.94 (0.45,1.42) (P=0.0023). While the SE and KN domain scores increased significantly (1.1 (0.5,1.7), 1.03 (0.45,1.62) and p = 0.003, 0.0036, respectively), the PI did not change -0.09(p=0.726). For cohort 2, the overall score significantly increased 0.617 (0.042,1.193) (P=0.0378). While the SE and KN domain increased significantly (0.728(0.048,1.407), 0.665 (0.041,1.289) and p = 0.0379, 0.0387, respectively), the PI did not change (p=> 0.999). Conclusions: The Cancer Survivorship ECHO model may serve as a scalable strategy for building cancer survivorship care capacity in community-based oncology practices through equipping multidisciplinary teams to meet the needs of cancer survivors within their community. Further research is needed to assess implementation of this model into novel settings and evaluate its impact on patient outcomes. [Table: see text]
Childhood leukemia survival in the US-Mexico border: Building sustainable leukemia care using health systems strengthening models.
Authors: Aristizabal Paula , Rivera Gomez Rebeca , Ribeiro Raul Correa , Roberts William
Published: June 2023

1502

Background: Pediatric leukemia outcomes are poor in most low-and middle-income countries (LMIC) and exacerbated by healthcare systems ill-equipped to manage cancer. Effective leukemia management in LMIC involves curating epidemiologic data; providing healthcare workforce specialty training; developing evidence-based treatments and supportive care; safeguarding access to medications and equipment; providing patient and family psychosocial and financial support; partnering with non-governmental organizations, and ensuring treatment adherence. Methods: We describe the implementation of a collaborative program to achieve sustainable high-quality care for children with leukemia in a unique border health setting. In 2013, in a partnership between North-American and Mexican institutions, we used the health systems strengthening model developed by the World Health Organization (WHO) Framework for Action with domains in Services delivery, Workforce, Information systems, Access to essential medicines, Financing, and Leadership and Governance to implement a sustainable program aimed at improving acute lymphoblastic leukemia (ALL) outcomes at a public, referral hospital in the US-Mexico border region. We evaluated program sustainability indicators, and prospectively assessed clinical features, risk classification, and survival outcomes in children with ALL at Hospital General in Tijuana, Mexico from 2008-2012 (pre-implementation) and from 2013-2017 (post-implementation). Results: Our approach led to a fully-staffed leukemia service, sustainable training programs, evidence-based, data-driven projects to improve clinical outcomes, and funding for medications, supplies and personnel through local partnerships. Pre-implementation and post-implementation 5-year overall survival for children with standard-risk and high-risk ALL improved from 52% to 82% (P = 0 ×023) and from 46% to 76% ( P = 0 ×031), respectively. All sustainability indicators significantly improved between 2013 and 2017. Conclusions: By using a health systems strengthening approach, we improved leukemia care and survival in a public Mexican hospital in the US-Mexico border region. The demonstrated increase in overall survival across a decade after the implementation of our program validates the use of the health systems strengthening models, as they are not only efficacious in improving clinical outcomes, but also serve as financially and organizationally means of building sustainable capacity. Our model serves as an example for future international partnerships aimed at sustainably improving cancer outcomes in LMIC. Future research should evaluate best practices in establishing global health collaborations with particular attention paid towards the individual circumstances that impact healthcare delivery locally in each unique community.
Analysis of patient-physician portal secure message content and the associated cost savings in oncology practice.
Authors: Anderson Brandon , Lyon Liisa , Lee Michael Jisoo , Kumar Deepika , Neeman Elad , Duffens Ali , Kotak Dinesh , Sun Hongxin , Reed Mary , Liu Raymond
Published: June 2023

1503

Background: The consistent utilization of secure messaging (“SM”) since the onset of the COVID-19 pandemic has provided cancer patients with unprecedented access to their medical providers, resulting in reduced out-of-pocket costs for patients at the expense of increased workload for oncologists. Herein we analyze oncology SM content and report estimated cost savings from reduced traditional clinic visits. Methods: This population-based retrospective cohort study examined the content of patient-initiated SM threads exchanged through the patient portal website or app over a one-year period (6/1/21 to 5/31/22) at 22 Kaiser Permanente Northern California Oncology practices. A trained physician researcher reviewed a random sample of 500 SM threads and categorized them by message content type, acuity, and appropriate level of service. Standard costs were applied using the average of 2021 and 2022 Center for Medicare Services physician fee schedule and US patient average specialty copayment. Results: During the study period, 49,230 patients initiated 398,835 unique SM threads to 238 oncology providers. The average number of replies to patients’ initial messages was 2.6, with a mode of 1 (maximum 36). Of the SM threads reviewed, only 28.0% required oncologist expertise. Based on thread content, the remaining 72.0% may have been better managed by a nurse (35.4%), medical assistant (29.2%), primary care doctor (6.2%), or a different subspecialty provider (1.2%). Emergency care was required in 2.8% of the total threads reviewed. Significant medical care was provided to patients in 26.0% of the total SM threads which would traditionally require a billable visit (i.e., in-person, telephone, or video). We estimate that these clinical actions may represent 4,299,281.77 of savings in medical care (based on traditional 10-19-minute visits), and 4,562,672.40 in potentially avoided out-of-pocket co-pay costs for patients. Conclusions: Increased utilization of remote medical care and specifically SM has recently generated additional workload for oncologists that may have been more appropriately managed by alternative providers. The magnitude of unreimbursed medical care provided via SM and the use of SM by cancer patients for emergent medical situations creates an urgent need for new models to assist with practice management. An alternative architecture for triaging, managing, and billing SM that utilizes artificial intelligence, machine learning algorithms, or medical assistants, nurses, and other providers could help manage the burden experienced by oncologists.
Observational prospective study of physical activity in patients with cancer to define an objective performance status (OPS).
Authors: Ruiz-Hispán Eva , Hernández Tatiana , Doger de Spéville Bernard , Morillo Daniel , Mahillo Ignacio , Garcia-Foncillas Jesus , Moreno Victor
Published: June 2023

1506

Background: Clinical trials in patients with advanced cancer rely on a good selection of candidates based on their functional status. The Eastern Cooperative Oncology Group (ECOG) Performance Status is the most frequently used in adult patients and is based on the subjective impression of the clinician during the interview with the patient. The current use of wearable devices will allow clinicians to assess the functional status of candidates for a clinical trial in a more objective way. We aim to define an “Objective Performance Status” (OPS) by measuring the daily physical activity of patients with metastatic cancer and correlating it with the classical prognostic variables. Methods: We prospectively measured daily physical activity with a wearable device in 111 patients with metastatic cancer during the screening period for a phase 1 clinical trial to avoid treatment interference with the functional status. Daily steps/distance and sleeping hours were measured and correlated with overall survival (OS) to define the OPS. Results: Full data was available for 70 patients. Median survival was gradually shorter as physical activity decreases (HR 5.76 (95% CI 2.98-11.1) P < 0001). The cut off selected to define the OPS was determined by univariate survival analysis and showed that patients who walk less than 1200 meters per day have 5 times the risk of mortality (poor OPS). Median OS was 3.2 months (95% CI 2.5-5.8) P < 0001) for patients with poor OPS and 11.2 months for patients with good OPS (95% CI 7.2-31.3 P < 0001). Conclusions: The objective assessment of performance status (OPS) using a wearable device to monitor the physical activity in patients with cancer is an independent prognostic factor for survival and is an objective predictor of mortality.[Table: see text]
Engagement of Patients with Advanced Cancer (EPAC) randomized clinical trial: Long-term effects on survival and healthcare use.
Authors: Patel Manali I. , Agrawal Madhuri , Kapphahn Kris , Blayney Douglas W. , Asch Steve , Bundorf M. Kate , Milstein Arnold
Published: June 2023

1513

Background: The Engagement of Patients with Advanced Cancer (EPAC) randomized Veterans with newly diagnosed stage 3 or 4 cancer or recurrent disease to usual care (control) or usual care with 6-month community health worker (CHW) support (intervention) to engage Veterans in end-of-life (EOL) goals and care preference discussions. At 6- and 15-months follow-up, the intervention tripled goals of care documentation (primary outcome), doubled hospice (secondary outcome), and reduced end-of-life (EOL) acute care use (secondary outcome). This post-hoc analysis after 10 years from study initiation evaluates effects on overall survival (OS) and EOL healthcare use. Methods: We abstracted dates of death, emergency department (ED), hospitalization, palliative care (PC), and hospice use within and outside Veterans Affairs (VA) using electronic health record and claims before data cut-off on February 1, 2023 for all EPAC participants. We compared OS using Kaplan Meier and log-rank, risk of death using Cox-proportional hazard models, ED, hospitalization, PC, and hospice use in the last 30 days of life using Logistic Regression, and incidence rate ratios of ED and hospitalizations using Poisson regression. Results: Among 213 participants, there were no imbalances in demographic or clinical characteristics. 165 (77.4%) were White, 211 (99%) males, 80 (37.6%) had lung cancer, and 118 (55.3%) had stage 4 disease. Median follow-up was 298 days (range 12-3438). Intervention group participants had greater OS (log rank p=0.04), 25% reduction in risk of death, lower acute care use, and greater PC and hospice use than control group participants. Conclusions: The CHW-led intervention significantly improved OS and maintained robust effects on EOL healthcare use. The intervention may have had unintended, unmeasured effects on other aspects of care, such as treatment and follow-up adherence, and/or hospice acceptance, which require investigation. Nonetheless, results support integration of community health workers into cancer care to improve clinical outcomes. Clinical trial information: NCT02966509 .[Table: see text]
Improving cancer care in underserved regions through technology-enabled patient-reported outcomes.
Authors: Ndoh Kingsley , Ntizimira Christian Ruchaho , Dankwa-Mullan Irene , Maniragaba Theoneste
Published: June 2023

1520

Background: The growing cancer burden in Africa is a major public health challenge. There were 8,944 new cases in Rwanda, with over 6,044 deaths reported in 2020. Unfortunately, there are only 13 oncologists in Rwanda for a population of 13 million people. The use of artificial intelligence (AI) tools and digital health in cancer treatment has the potential to mitigate the oncology workforce shortages, support clinical decision-making, and increase access to care. Hurone AI is a Seattle MedTech startup building culturally sensitive AI applications to address the oncologist-patient gaps and improve drug safety in underserved populations. Hurone’s premier application, Gukiza, launched at the Rwanda Cancer Center in 2022 to conduct beta testing. Gukiza is a remote patient monitoring system that ensures patients can report side effects or symptoms from their phones and oncology care teams can get real-time treatment insights and provide timely and effective care. Methods: Hurone received ethical approval from the Rwanda National Ethical Committee to launch a pilot in September 2022. An initial 45 breast cancer patients aged 20 – 65 who were either newly diagnosed or on active treatment were recruited. Each patient periodically received prompts that asked them about side effects and to score the degree of severity. The questions were adapted from the NCI’s adverse events repository. Gukiza analyzed each response and presented a visual analysis of each patient, enabling the cancer care team to send text-based interventions to the patient’s phone. Potential emergencies are flagged and sent emergency numbers to call. Through Amazon’s cloud analytic tools, Gukiza provides treatment insights to support oncologists' clinical decisions. Results: Results are summarized. Conclusions: Resource-appropriate digital technologies can be a useful tool in mitigating adverse events during cancer treatment and increasing access to timely care for patients. The data built in such systems can be a useful resource to individualize and improve cancer care and treatment outcomes.[Table: see text] [Table: see text]
The impact of the 4R Oncology model of team-based care delivery on timing and sequence of guideline recommended interdependent care in breast and lung cancers.
Authors: Liu Raymond , Trosman Julia R. , Linehan Elizabeth Shurell , Gordon Nancy P. , Hennings Marti , James Henie , Abbe Thea , Katzel Jed Abraham , Ng Chun Fai , Tomita Megumi , Velotta Jeffrey B. , Sakoda Lori C. , Beringer Kimberly , Ravelo Arliene , Zhu Zheng , Rapkin Bruce , Weldon Christine B.
Published: June 2023

1523

Background: Team-based care improves quality, reduces fragmentation and lowers clinician burnout related to ad hoc care coordination. A key principle of teamwork is managing timing and sequence of interdependent care (Vogel JOP 2016). The 4R Oncology model featured by the NCI ASCO Teams project fosters effective teamwork by facilitating systematic timing / sequence of interdependent care (Trosman JOP 2016). 4R is Right Info / Care / Patient / Time. Using 4R, a large health system created a high-functioning care team and conducted optimizations of interdependent care in breast and lung cancers Oct 2020 - Dec 2021 (Liu JCOOP 2022). Optimizations focused on guideline-based care with suboptimal timing / sequencing in respective cancers. We present results of these optimizations. Methods: We compared data from electronic health record on timing and sequence of care between historical control cohorts of patients who received care pre-4R (breast n=270, lung n=173) to the post-optimization 4R cohorts (breast n=208, lung n=139). Patient and tumor characteristics between control and 4R cohorts in both breast and lung cancers were similar. Results: Timing and sequence of care for 5 of the 6 breast cancer metrics and all 6 lung cancer metrics was significantly improved between the control and 4R cohorts. Despite significant increase, the rate of timely care for some metrics remained low, such as referrals based on distress screening in breast cancer and palliative care in lung cancer. Conclusions: The 4R Oncology model is effective in applying teamwork principles and improving timing and sequence of guideline-based care. Opportunities for further improvement exist to expand the benefit of 4R to a higher number of patients. A learning system being established in our institution will inform iterative care optimizations.[Table: see text]
Assessing mail-in cryopreservation for fertility preservation.
Authors: Belarmino Andre R , Morris Jerrine , Kenfield Stacey A. , Colin Daniel , Griscom Bailey , Honig Stanton , Dupree James M. , Samplaski Mary K. , Halpern Joshua , Behr Barry , Lamb Dolores , Matthews William , Smith James F.
Published: June 2023

1525

Background: Over 90% of oncologists agree that sperm banking should be offered to all men at risk of subfertility associated with cancer treatment, but fewer than 50% bring up the topic. Barriers cited include lack of time for the discussion, perceived high cost, and lack of convenient facilities. The advent of mail-in semen analysis (SA) testing and cryopreservation may improve access to this service via lower patient cost, decreased required oncologist logistical support, and home rather than local lab collection. That said, average sperm recovery rate after sample thaw is ~50% with immediate banking, and sperm recovery rate after delayed cryopreservation with a mail-in approach remains unknown. Methods: This is a prospective preliminary study assessing freeze-thaw total motile count at 3 time points of cryopreservation: upon receipt of sample, at 24 hours, and at 48 hours post collection. We collected 39 fresh semen samples from 13 healthy adult volunteers. Samples were diluted with 5mL of Fellow Health preservation solution and analyzed (World Health Organization guidelines V5). Samples were divided into 3 aliquots: Aliquot #1, T0 was immediately frozen with equal part cryoprotectant. Aliquot #2, T24 and aliquot #3, T48 were left at room temperature for 24 and 48 hrs respectively, analyzed, and then frozen. Aliquots were then thawed on a heat block at 37C for 10 minutes and analyzed. Results: Baseline SA values were: volume 3.3ml +/- 1.3, concentration 138.3 +/-57.4 x 106, motility 49% +/-6%; Total Motile Count (TMC) 213 +/- 110 million. TMC was the primary endpoint as measured by baseline semen volume, concentration, and motility at 3 time points (T0, T24, T48). The average loss from freeze/thaw in TMC at T0 was 45%, at T24 was 55% relative to T24 pre-freeze, and at T48 was 59% relative to T48 pre-freeze (p = 0.013). On average, samples with starting TMC between 50-100 million had enough motile sperm for six intrauterine insemination (IUI) cycles when frozen and thawed at T0, two at T24, and two at T48. Samples with starting TMC between 100-200 million had enough motile sperm for 16 IUI cycles when frozen and thawed at T0, eight at T24, and four at T48. Samples with starting TMC > 200 million had enough motile sperm for 42 IUI cycles when frozen and thawed at T0, 21 at T24, and nine at T48. All samples had ample viable cells ( > 50,000) at all three time points for in vitro fertilization (IVF). Conclusions: Mail-in cryopreservation using our improved methodology may increase access to this service for oncological patients while still facilitating reasonable assisted reproductive technology care relative to in-person cryopreservation.
Team-based versus technology-based supportive cancer care (SCC): Assessment of implementation across 24 different clinical settings.
Authors: Moufarrej Sacha , Mathew Marika S. , Wood Emily H. , Medrano Hector S. , Perez Cynthia C. , Fuller Keely E. , Agrawal Madhuri , Patel Manali I.
Published: June 2023

1528

Background: Advanced care planning and symptom management, otherwise known as Supportive Cancer Care (SCC), are fundamental services that patients should receive after a cancer diagnosis. We launched a 24-clinic site cluster randomized clinical trial in July 2022 comparing two SCC delivery approaches: a team-based intervention in which patients engage in advanced care planning and symptom assessments with a lay health worker versus a technology-based intervention in which patients receive internet-based SCC education. The preliminary aim of this study is to evaluate clinic characteristics that may impact implementation and effectiveness of these SCC delivery approaches. Methods: We invited study staff at all 24 sites, including community oncology clinics, Kaiser Permanente clinics, Veterans Affairs facilities, safety-net hospitals, and academic centers, to participate in a survey and semi-structured interview. We used Consolidated Framework for Implementation Research (CFIR) and Reach, Effectiveness, Adoption, and Implementation (RE-AIM) frameworks to assess organizational culture, readiness, facilitators, and barriers to SCC delivery. Surveys were analyzed using descriptive statistics; interviews were analyzed using thematic analysis. Results: Among 30 interview participants across 24 clinics, of which 23 completed surveys, 9 (30%) were clinic principal investigators, 11 (36.7%) were research staff, and 10 (33.3%) were implementation staff. Survey respondents implementing team-based SCC were more confident that their approach: 1) accounted for patient care preferences (93.3%) and 2) had strong evidence (86.7%) than respondents implementing technology-based SCC (62.5% and 25.0% respectively). More respondents implementing team-based SCC expressed confidence in organization leadership (100%) and adequate resources for SCC (86.7%) than respondents implementing technology-based SCC (75.0%). Among interview participants, team-based approaches for SCC delivery were considered more effective, as they could: 1) reduce time burden for clinicians 2) tailor delivery to patient preferences; and 3) increase patient support. Participants believed that team-based approaches would also be more difficult and costly to implement. Barriers to both SCC delivery approaches include: a) limited technology access for patients, b) competing organization priorities which limit staffing and funding for SCC, and c) information overload for patients and caregivers. Conclusions: Implementation of SCC delivery approaches is needed but requires organizational leadership and implementation resources. Team-based approaches are considered more effective, but barriers such as staffing and funding may inhibit the scalability. Implementation evaluations are crucial to understand real-world applicability and scalability of SCC delivery.
Impact of geography on receipt of medical oncology consultation and neoadjuvant chemotherapy for triple negative and HER2 positive breast cancer.
Authors: Yee Elliott Kenneth , Hallet Julie , Look Hong Nicole Jannelle , Nguyen Lena , Coburn Natalie G. , Wright Frances Catriona , Gandhi Sonal , Jerzak Katarzyna Joanna , Eisen Andrea , Roberts Amanda
Published: June 2023

1530

Background: Consensus guidelines recommend consideration of neoadjuvant chemotherapy (NAC) for most patients with early-stage triple negative (TN) and HER2 positive (HER2+) breast cancer. Based on our previous work, most patients are not seen by a medical oncologist prior to surgery and do not receive NAC. Although potential barriers are not well understood, distance to cancer centres has been shown to mediate medical oncology referral and use of systemic therapy for other malignancies. We therefore aimed to characterize the impact of region of residence and cancer centre proximity on receipt of pre-treatment medical oncology consultation and NAC for patients with TN and HER2+ breast cancer. Methods: Using linked administrative healthcare datasets in Ontario, Canada, we performed a retrospective population-based geographic analysis of women diagnosed with stage I-III TN or HER2+ breast cancer from 2012-2019. Outcomes were pre-treatment medical oncology consultation and initiation of NAC. We created choropleth maps to assess the spatial distribution of outcomes across census divisions, displaying cancer centres and outcomes by colour gradient using geographic information system analysis. To assess the relationship between distance to the nearest cancer centre and outcomes, we performed a multivariable regression analysis adjusted for sociodemographic and clinical factors, including tumour extent and nodal status. Results: Within the cohort of 12,881 patients, there was no statistically significant relationship between incremental distance to the nearest cancer centre (≤5 vs. 5-10, 10-25, and ≥25 km) and rate of medical oncology consultation or receipt of NAC. Mapping analysis demonstrated high interregional outcome variability, ranging across census divisions from 12.2% to 64.3% for medical oncology consultation rate, and 9.3% to 64.3% for NAC rate. 77.7% of patients referred to medical oncology received NAC. Conclusions: Among patients with TN and HER2+ early breast cancer, rates of medical oncology consultation and NAC were not significantly impacted by distance to cancer centres but varied highly by region. This variability suggests that regional and/or provider practice patterns, rather than distance, may underlie discrepancies in referral for NAC. These findings can inform further studies and interventions aiming to improve equitable access to NAC for patients with TN and HER2+ breast cancer.
Best practices study to enhance the quality of multi-disciplinary teams in lung cancer care.
Authors: Gallego Llorente Marcos , Verbaas Lidewey , Cui Yingjing Poka , Gomes Marcio , Moucquot Adrien , Moses Heather , Dellamonica David , Amesz Bas
Published: June 2023

1532

Background: Regular meetings at multi-disciplinary teams (MDTs) constitute a key moment in the care pathway in lung cancer, where physicians collectively discuss patient cases and decide on treatment plans. MDTs arguably increase the volume of patients treated, improve diagnosis, and positively impact on survival. However, the way MDTs are organized and function is variable across hospitals, countries and continents. Variations in MDT processes may lead to sub-optimal outcomes, such as inconsistencies on the numbers of patients treated with curative intent as well as variable 5-years survival rates for lung cancer. The importance of MDTs is ever increasing, as the therapeutical landscape is getting more complex every year, and treatments are evolving from a one-size-fits-all to a personalized approach. Methods: In this study, we gather the best practices in MDT processes, capabilities and governance in 24 leading hospitals throughout Europe and Canada. We map the MDT practices of each hospital into a coherent framework, and identify KPIs and best practices by conducting semi-structured interviews with health care professionals in each hospital and performing workshops with the entire MDT team. The primary research focuses on five key categories: access to the MDT, processes, technology (such as EMR, data sharing tools, and artificial intelligence), culture and capabilities. Results: We see a high variation in the way MDTs are organized, in time for discussion per patient, in the model of care and current MDT practices across the 24 hospitals in this project, along our five identified categories. We developed an MDT maturity model where we score individual elements within these five categories to identify the hospitals with the best MDT practices, and a total of 23 key success factors along the MDT process. The maturity model and the key success factors can be used as a self-assessment tool for MDT-teams for continuous quality improvement. Conclusions: MDTs have the potential to be a standard part of the patient care pathway, where excellence is consistently achieved when discussing patient cases. This work has identified the practices that are done best in leading hospitals and that have the potential to be progressively adopted. Furthermore, we see that the potential of decision support tools and other artificial intelligence is rarely leveraged by MDTs.
Decreasing the burden of febrile neutropenia through dynamic remote patient monitoring: The DEFeNDER program.
Authors: Pritchett Joshua , Paludo Jonas , Shi Qian , Khera Nandita , Yetmar Zachary , Wilson John W. , McCoy Rozalina , Leuenberger Angela , Phillips Sarah , Pearson Kristina , McColley Samantha , Haemmerle Robert , Sangaralingham Lindsey R. , Ryu Alexander J. , Phelan David , Gangat Naseema , Molina Julian R. , Tilburt Jon Charles , Borah Bijan J. , Haddad Tufia C.
Published: June 2023

1535

Background: Ambulatory management of select patients with febrile neutropenia (FN) may substantially decrease patients’ burden of illness and improve their quality of life, and is recommended by American Society of Clinical Oncology (ASCO) guidelines. However, in the United States, inpatient management remains the universal standard. We hereby report the health care utilization associated with the DEFeNDR remote patient monitoring (RPM) program, which leverages technology and virtual centralized nurse monitoring to facilitate ambulatory management of FN as an alternative to hospital-based care. Methods: Adult patients with cancer hospitalized with FN were assessed daily for eligibility to participate in the DEFeNDR program. Eligibility criteria included MASCC score >21 (per ASCO guidelines), and clinical judgment of the attending provider. Patients receiving cellular therapy (CAR-T, BMT) in the prior 100 days were excluded. Eligible patients agreeing to participate in the 30-day program were provided a kit containing cellular-enabled, pre-connected devices to facilitate monitoring, including twice-daily vital signs and electronic symptom questionnaires. Data were EHR-integrated in real-time and embedded decision trees facilitated alerts for adverse trends. RPM nurses responded to alerts and escalated care as indicated by pre-specified care pathways. Program assessment was performed at 20 weeks after full implementation. The primary outcome was the mean proportion of evaluable days spent inpatient (mPEDI) within 30 days of eligibility compared between those who participated in the program vs. those who declined. Results: 35 patients were offered participation in the DEFeNDR program. Of these, 17 participated and 18 declined. Baseline characteristics are provided. Participating patients experienced mPEDI of 6.9% (Standard Error [SE], 3.7%), compared with 17.7% (SE, 3.6%) for patients who declined participation. Due to small sample size, this difference (10.8%) did not reach statistical significance (Wilcoxon Rank Sums p-value, 0.15). 30-day mortality did not differ between groups. Mean inpatient days were also lower for patients participating in the program. Additionally, when readmitted, patients participating in RPM experienced a trend toward shorter length of stay and lower rates of intensive care unit utilization. Conclusions: A trend towards decreased burden of inpatient care is evident among patients participating in the DEFeNDR program. Further studies are warranted to assess comparative effectiveness of this model vs usual care.[Table: see text]
An AI tool for chest X-ray interpretation and lung cancer pre-screening in developing countries.
Authors: González Espinoza Iván Romarico , Alatorre-Alexander Jorge , Juárez-Salazar Diego A. , Castro-Ponce Abraham , Juárez-Salazar Gabriela , Martínez-Hernández Diego A. , Cardona-Ferrer Edgar E. , González-Lozada Laura E. , Miguel Cruz Enrique , Sánchez-Sosa Sergio , Acosta Ponce de León Eric , Chiquillo-Domínguez Mariana
Published: June 2023

1548

Background: The current lung cancer screening strategy has not had a major impact on public health, facing many challenges during its implementation in developing countries. Although chest radiography (CXR) is not the study of choice for screening; it is one of the most accessible studies used at all levels of care, so implementing an AI tool capable of CXR interpretation as well as lung cancer risk assessment on a large scale could improve the selection of patients for lung cancer screening. The aim of our study was to develop and evaluate this tool. Methods: The dataset used for this tool development was obtained from the NIH "ChestX-ray14” public database. The set was divided into 3 subgroups for training, testing, and validation in a ratio of 80/10/10% respectively. We performed a structuration of metadata and an exploratory analysis as well as preprocessing of the images to standardize their size and resolution before the training. A model based on the adaptation of the momentum for learning was used as an optimizer of the CNN algorithm used. Additionally, a questionnaire based on the PLCO clinical criteria was included for the identification of patients at high risk of developing lung cancer. Results: Standard performance metrics were evaluated as shown. Conclusions: Our model is comparable to certified radiologists in accuracy at identifying patterns of a CXR. The use of clinical patient information improves the pre-selection of patients with a high risk of developing lung cancer. In developing countries, the implementation of this tool on a large scale will allow more patients to be screened and early diagnosed.[Table: see text]
Deep learning-based approach to predict multiple genetic mutations in colorectal and lung cancer tissues using hematoxylin and eosin-stained whole-slide images.
Authors: Konishi Teppei , Grynkiewicz Mateusz , Saito Keita , Kobayashi Takuma , Goto Akiteru , Umakoshi Michinobu , Iwata Takashi , Nishio Hiroshi , Katoh Yuki , Fujita Tomonobu , Matsui Tomoya , Sugawara Masaki , Sano Hiroyuki
Published: June 2023

1549

Background: The presence of genetic mutations is a vital prognostic in many types of cancer. However, genomic testing is expensive and challenging to perform. In contrast, hematoxylin and eosin (H&E) staining is relatively inexpensive and straightforward. Thus, in this study, we propose a method of predicting the presence of genetic mutations using H&E-stained whole-slide images (WSIs). Methods: We divided each H&E–stained WSI into small pieces or “patches.” We used a deep learning model to classify each patch based on the presence of tumor-containing regions. We then extracted image features from each tumor-containing patch using a deep learning-based feature extractor. We created image features for the entire WSI by concatenating the features of the patches. We then trained genetic mutation classification models using the WSI features as the input and the presence or absence of genetic mutations as the output. Finally, we evaluated the performance of these models using the area under the receiver operating characteristic curve (AUC). Results: First, we evaluated our methods using The Cancer Genome Atlas (TCGA) colorectal cancer dataset. We used H&E–stained WSIs and data associated with Microsatellite Instability ( MSI) and BRAF gene mutations, which are directly relevant to therapeutic strategies, obtained from an independent clinical cohort of 566 patients with TCGA colon and rectum adenocarcinoma. We divided the data into training, validation, and test splits, comprising 367, 90, and 109 patients, respectively. We used the training and validation splits for model training and selection, and the test split for model evaluation. The AUC values of the classification models and associated 95% confidence intervals (CIs) were 0.721 (CI = 0.572–0.870) for MSI and 0.712 (CI = 0.547–0.877) for BRAF gene mutations. We also applied our approach to MUC16, KRAS, and ALK mutations using the TCGA lung cancer dataset. We divided 909 TCGA lung adenocarcinoma and lung squamous cell carcinoma patients into training, validation, and test splits, comprising 582, 146, and 181 patients, respectively. In contrast with those of the colorectal dataset, WSI image features were generated using all patches. The AUC values on the test splits were 0.897 (CI = 0.85–0.95) for MUC16, 0.845 (CI = 0.75–0.94) for KRAS, and 0.756 (CI = 0.57–0.94) for ALK mutations. Conclusions: We proposed an approach to predict the presence of genetic mutations using only H&E–stained WSIs and evaluated its performance using colorectal and lung cancer datasets. Our model has the potential to predict the presence of certain genetic mutations with superior performance. These predictions can be used to improve the accuracy of prognostic prediction using WSIs alone.
Gene expression-based machine learning approach to predict immunotherapy response and survival in patients with advanced cancer.
Authors: Zhang Zheng , Wang Xianyun , Tang Qi , Yang Kaiwei , Guo Xuanjun , Wang Aixiang , Zhang Yiqun , Hao Yantong , Pei Zhihua , Wang Dongliang , Zhou Qiming , He Zhisong
Published: June 2023

1550

Background: Immune checkpoint blockades (ICBs) have drastically improved the clinical outcomes of cancer patients, while only ~30% of them get significant benefits from immunotherapy. TMB, PD-L1, TNB, GEP (Cristescu et al. 2018) and other conventional biomarkers can explain only ~60% of the ICB response, suggesting that novel factors are yet to be discovered. Methods: Gene expression profiles and clinical information of 672 patients were collected from IMvigor210 (n=348, bladder cancer), Pender et al. (n=98, 20 tumor types), Kim et al. (n=45, gastric cancer) and CheckMate (n=181, kidney cancer) cohorts, in which all patients were treated with ICBs. In each sample, gene expression intensities normalized by RPKM were ranked in descender order (range of 1-20000) and scaled into the 0-1 range lastly. To identify immune signatures that correlated with both response and survival, 1000+ immune genes were downloaded from InnatDB database, and their expression values were compared between the two groups of IMvigor210 data which were divided according to response (CR+PR vs. SD+PD, Wilcoxon test) or overall survival (≥mOS vs. ＜mOS, Cox survival analysis). Bayesian-regularization neural networks (BRNN) model was used to develop the risk model to predict ICB response and prognosis. 75% of IMvigor210 data were randomly selected as the training set, and the remaining 25% as the test set. The performance of predictor was independently validated in Pender et al., Kim et al., and CheckMate cohorts subsequently. ROC curves were used to evaluate the predictive accuracy to response. Survival plots were created using the Kaplan-Meier estimator, and data were analyzed by log-rank test. Results: 40 DEGs (including top20 DEGs positively correlated with ORR/OS and top20 DEGs negatively correlated with ORR/OS) were identified as immune signatures, in which each gene signature could significantly distinguish ICB response and survival. Our model outperformed TMB or GEP in the prediction of response in IMvigor210 (Model = 0.986 vs. TMB = 0.728 vs. GEP = 0.551), Pender et al. (Model = 0.757 vs. TMB = 0.593 vs. GEP = 0.621), Kim et al. (Model = 0.881 vs. GEP = 0.828), and CheckMate (Model = 0.927 vs. TMB = 0.543) cohorts when AUC was used as performance metric. Meanwhile, our model showed promising results for differentiating longer survivors from shorter survivors with p vale ＜0.0001, 0.064, 0.021, and 0.0006 in IMvigor210, Pender et al., Kim et al., and CheckMate cohorts respectively. Conclusions: We proposed a deep learning model to predict ICB response and survival, in which feature selection based on immune gene expression order, allowing all kinds of platforms and expression data types. Our model showed a robust prediction not only in bladder cancer, but also in other pan-cancer types.
Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.
Authors: Aharonov Ranit , Dinstag Gal , Shulman Eldad , Elis Efrat , Ben-Zvi Doreen , Tirosh Omer , Maimon Eden , SInha Sanju , Nair Nishanth Ulhas , Apolo Andrea B. , Lipkowitz Stanley , Berger Raanan , Kurzrock Razelle , Papanicolau-Sengos Antonios , Karzai Fatima , Rajagopal Padma Sheila , Gilbert Mark R. , Aldape Kenneth D. , Beker Tuvik , Ruppin Eytan
Published: June 2023

1553

Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types, importantly, without training on previous treatment response data. Consequently, in addition to its ability to predict patients' response to approved and well-studied therapies, ENLIGHT can predict the response to new treatments in early development, even before clinical data has accumulated. Accordingly, we study ENLIGHT in two translationally relevant scenarios: Personalized Oncology (PO), aimed at prioritizing approved treatments to a given patient, and Clinical Trial Design (CTD), selecting the subset of most likely responders in a patient cohort. Results: Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets spanning 11 indications and 15 different drugs in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types, with an overall odds ratio of 2.59 ( p=3.41e-8), and a 36% increase in response rate over the baseline ( p=3.30e-13). Its prediction accuracy is better than other state-of-the-art transcriptomics-based signatures. Unlike most signatures that are prognostic or provide insights for only very few, specific treatments, ENLIGHT provides matching scores to a broad range of treatments. Quite strikingly, its performance is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, our results show that by excluding non-responders ENLIGHT can enhance clinical trial success for immunotherapies and other monoclonal antibodies, achieving > 90% of the response rate attainable under an optimal exclusion strategy. Conclusions: ENLIGHT is a powerful transcriptomics-based precision oncology pipeline developed by Pangea Biomed that broadly predicts response to both extant and novel targeted and immune therapies, going beyond context-specific biomarkers.