bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring AI-generated content and professional guidelines in cancer symptom management: A comparative analysis between ChatGPT and NCCN guidelines.
Authors: Lazris David , Schenker Yael , Thomas Teresa
Published: May 2024

e13610

Background: Artificial intelligence (AI)-driven tools, like ChatGPT, have become widely-available sources for online health information. Limited research has explored the congruity between AI-generated content and professional treatment guidelines. This study seeks to compare recommendations for cancer-related symptoms generated from ChatGPT with guidelines from National Comprehensive Cancer Network (NCCN), a provider focused source requiring users to log in or register to access its recommendations. Utilizing a provider-focused source like NCCN serves as a benchmark to assess whether ChatGPT recommendations align with the standards typically endorsed by clinicians. Methods: We extracted treatment recommendations from four NCCN Supportive Care webpages (Cancer Pain, Antiemesis, Cancer-Related Fatigue, and Distress Management) and five subsections of the NCCN Palliative Care webpage (dyspnea, constipation, diarrhea, sleep disturbances, and anorexia/cachexia). We then entered "How can I reduce my cancer-related [symptom]" into ChatGPT 3.5 and extracted its recommendations. We calculated and compared word count and Flesch-Kincaid Grade Level readability for each NCCN and ChatGPT section. We completed a comparative content analysis focusing on recommendations for medications, consultations, and non-pharmacological strategies. Results: Across the nine NCCN Supportive Care and Palliative Care webpages, the mean word count was 2393.8 (SD=2601.4) and the mean Flesch-Kincaid Grade Level was 17.3 (SD=1.4) vs 382.4 (SD=29.6) and 11.6 (SD=0.8) for ChatGPT. The mean percent agreement between NCCN and ChatGPT recommendations was 44.6% (range 14.3%-81.8%). ChatGPT and NCCN guidelines shared fewer than half of their symptom-related recommendations in all but one section, fatigue. NCCN offered specific medication recommendations across all sections. ChatGPT's recommendations lacked the specificity observed in NCCN's guidelines including often not suggesting any medications. ChatGPT recommended specific medications in the shortness of breath and diarrhea sections that were not recommended by NCCN. NCCN's guidelines often did not include recommendations related to spirituality or palliative care consults, areas that ChatGPT addressed. Conclusions: While ChatGPT provides concise, accessible supportive care advice including many non-medical support recommendations, discrepancies with guidelines raise concerns for patient-facing symptom management recommendations. Overall, AI-generated content like ChatGPT can assist in providing preliminary information but should be researched in conjunction with comprehensive, evidence-based guidance provided by healthcare professionals. Healthcare providers should work with AI developers to ensure data sources are high-quality and accurate.
A meta-analysis of models for predicting the incidence of brain metastasis in patients with lung cancer.
Authors: Chhabra Sufal , Shin Julia , Zhang Yanjia , Zhang Zhenwei , Ahmed Md Ashfaq , Khosla Atulya Aman , Batra Nitya , Roy Mukesh , Ramamoorthy Venkataraghavan , Saxena Anshul , Rubens Muni , Chisti Mohammad Muhsin
Published: May 2024

e13613

Background: Brain metastasis (BM) represents a significant clinical challenge in the management of lung cancer, impacting patient prognosis and treatment decisions. While various predictive models have been developed to assess the risk of BM development, their performance and applicability remain under scrutiny. Understanding the accuracy and reliability of these models is crucial for informing clinical practice and improving patient care. We sought to evaluate the predictive ability of existing models predicting BM incidence in lung cancer patients, providing valuable insights into their clinical utility and areas for further refinement. Methods: Two reviewers conducted a systematic search of numerous databases, including PubMed, EMBASE, Cochrane Library, and CINAHL, and then extracted the relevant information from the eligible studies. For this study, we conducted a meta-analysis using a random-effects model. Pooled estimates were calculated for any combination of outcomes and population when at least two studies had relevant data. Study heterogeneity was assessed using I² statistics. The area under the curve (AUC) of the best model and 95% confidence intervals were set as effect measures. Additionally, funnel plots and results from meta-regression analysis were reported. Meta-analysis was performed with R software ver. 4.3.1, and quality evaluation of evidence was conducted according to the GRADE standard. Results: A total of seven studies with 535,418 participants were eligible. A random-effects model showed that models that could predict the risk of developing BM among lung cancer cases had a pooled AUC of 81% (95% CI: 74% to 87%). However, the studies had significantly higher heterogeneity, with an I2 = 99% (p<0.01). Results from meta-regression analysis, accounting for the year of publication (?=-0.013), sample size (?< 0.00), number of variables (?=-0.015), and latitude (?=0.0024) did not show any statistically significant associations with a p>0.05. Egger’s regression test for publication bias did not indicate small study effects (Bias estimate: 4.91, SE = 6.01, p< 0.44). Conclusions: Our meta-analysis underscores the utility of existing models in predicting the incidence of BM in LC patients, with a pooled AUC of 81%. Further research aimed at refining predictive models and addressing sources of heterogeneity is imperative to enhance clinical decision-making and improve patient outcomes in this population. [Table: see text]
A web-based, LLM-powered AI symptom summarization tool (ASST) for monitoring of breast cancer treatment toxicity.
Authors: Bibault Jean-Emmanuel , Wu David JH
Published: May 2024

e13622

Background: Traditional methods for capturing patient-reported outcomes (PROs) are often time-consuming and may lead to underreporting of symptoms by physicians. With advancements in artificial intelligence (AI), particularly large language models (LLMs) like GPT-4, there is potential to revolutionize this aspect of patient care. Methods: This study developed and tested a GPT-4-based web application for monitoring cancer treatment toxicity in breast cancer patients. The application utilized 35 items from the PRO-CTCAE scale to create an interactive form for patients to report treatment-related symptoms. Upon form completion, a natural language summary of the patient’s responses was generated using the GPT-4 API for physician review. Nine radiation oncologists (5 attendings, 4 residents) evaluated the summaries of virtual patients using the adapted Physician Documentation Quality Index (PDQI-9). PDQI-9 consists of 9 items scored using a 5-point Likert scale (1 -not at all- to 5. -extremely-). IRB approval was not required because this study did not use real patient data, in accordance with the 2018 Revised Common Rule Requirements of the NIH. All data used were researcher-generated and non-identifiable. Results: The mean time for textual summary generation was 7 (5.7-9.2) seconds. The AI-Symptom Summarization Tool (ASST) mean scores were 4.25 for accuracy and 4 for thoroughness. Usefulness, organization, comprehensibility, concision, synthesis quality and consistency were all rated between 3 and 4. Overall score was 42/50. No hallucinations (false information made up by the LLM) were found. Conclusions: The GPT-4-based application for cancer treatment toxicity monitoring demonstrates significant promise in enhancing the quality and efficiency of patient care. By automating the documentation of patient-reported symptoms, this tool could allow physicians to focus more time on patient interaction and individualized care. As AI technologies continue to evolve, their integration into clinical practice must be approached with caution, emphasizing augmentation over replacement and the importance of verification to maintain trust in these new tools.
Generative AI enhanced with NCCN clinical practice guidelines for clinical decision support: A case study on bone cancer.
Authors: Wang Yanshan , Wu Xizhi , Carlson Luke , Oniani David
Published: May 2024

e13623

Background: Bone cancer is a complex and challenging disease to diagnose and treat in clinical practice. Recently, generative AI, especially large language models (LLMs), has demonstrated potential as a decision support tool for cancer. However, most implementations have overlooked the integration of available cancer guidelines, such as the NCCN Bone Cancer Guidelines, in fine-tuning the outputs of generative AI models. Incorporating these guidelines into LLMs presents an opportunity to harness the extensive clinical knowledge they contain and improve the decision-support capabilities of the model. Methods: In this study, the aim is to enhance the LLM with cancer clinical guidelines to enable accurate medical decisions and personalized treatment recommendations. Therefore, we introduce a novel method for incorporating the NCCN Bone Cancer Guidelines into LLMs using a Binary Decision Tree (BDT) approach. The approach involves constructing a BDT based on NCCN Bone Cancer Guidelines, where internal nodes represent decision points from the Guidelines, and leaf node signify final treatment suggestions. Then the LLM makes decision at each internal node, considering a given patient's characteristics, and guides toward a treatment recommendation in the leaf node. To assess the efficacy of Guideline-enhanced LLMs, an oncologist from our team created 11 hypothetical osteosarcoma patients’ medical progress notes. Each note contains their demographics, medical history, current illness, physical exams, diagnostic tests. We tested three LLMs in the implementation (GPT-4, GPT-3.5, and PaLM 2) and compared the LLM-generated treatment recommendations with the gold standard treatment across four runs with different random seeds (random seeds is a setting to control the LLM outputs). The results are reported as the average of four runs. The original LLMs are used as baseline methods for comparison. Results: The table below provides a comparison between the performance of original LLMs and those augmented with cancer guidelines for osteosarcoma treatment recommendations. We can observe that the PaLM 2 model demonstrated superior performance compared to its counterparts, underscoring the effectiveness of integrating cancer guidelines into LLMs for decision support. Conclusions: The clinical decision support capabilities of the LLMs are promising when enhanced by NCCN Bone Cancer Guidelines using our approach. To fully exhibit the potential of our proposed method as a clinical decision support tool, further investigation into other subtypes of bone cancer should be conducted in the future study. [Table: see text]
Reliability of YouTube content targeting Arabic-speaking patients with breast cancer regarding post-mastectomy reconstruction.
Authors: Alotaibi Haifa , Alsuhaibani Marya , Alsulaimani Abeer , Belzile Eric , Morena Nina , Meguerditchian Ari N.
Published: May 2024

e13626

Background: The dynamic and easily accessible nature of social media platforms, particularly YouTube (YT), has revolutionized the way breast cancer (BC) patients seek and process information. This study aims to evaluate the quality of YT videos addressing post-mastectomy reconstruction (PMR) that target Arabic-speaking women (ASW), a heavily digitally connected segment of society that may experience challenges in accessing mainstream medical information in Arabic Language (AL). Methods: YT was searched incognito, using the terms: "post mastectomy reconstruction" in (AL) in July 2023. Upload date, video length, number of views, poster identity and other relevant information were compiled. Video understandability and actionability were assessed by physician-reviewers (PR) with the Patient Education Materials Assessment Tool for audiovisual materials (PEMAT A/V), while the DISCERN tool appraised quality. Sponsorship presence, patient vs health care professional narratives, and intended audience were documented. Thematic analysis of most discussed topics was performed. Additionally, cluster analysis was employed to categorize the videos based on their quality. Results: Of 109 videos (mean length: 7 mins.), 67% had sponsorship (of which 45% corporate); 92% were information-based (vs experience-based); 82% targeted general population over BC patients; 93.6% were in AL, and 5.5% had translation. Mean PEMAT scores were 61.3% (understandability) and 20% (actionability). Mean DISCERN score was 2.6/5. Only 21% of videos featured women. Predominant themes were awareness (82.6%), appearance, and body image (68.8%). Less prevalent: sexuality and fertility (4.6%). The likelihood that the PR would recommend the video to a patient was low at 43.1%. Cluster analysis showed that high-quality videos had a higher understandability (74%), actionability (40.5%) and greater likelihood of being recommended by a PR (90.5%). Conclusions: YT content on PMR for ASW is easily understandable but lacks actionability and has moderate overall quality. Videos are highly sponsored and questionably representative of patients’ and women’s perspective. Therefore, Improving YT content, especially in AL, is crucial for supporting BC patients exploring PMR. The identified themes and insights into gender representation provide valuable guidance for content creators aiming to improve digital resources for this digitally connected demographic.
Comprehensive genomic profiling in solid tumors: Enhancing treatment selection and prognosis.
Authors: Wang Minghui , Shang Yanhong , Yuan Zhen-gang , Hao Qing , Wang Zhizheng , You Dong , Liu Yang , Pang Fei , Rao Wei , Wu Yinying , Lu Leilei
Published: May 2024

e13631

Background: The emergence of novel therapeutic targets, targeted drugs, and immune checkpoint inhibitors has catalyzed a paradigm shift from single-target testing to comprehensive genomic profiling in the treatment of solid tumors. However, the tangible benefits of this shift in terms of treatment optimization and patient prognosis improvement have yet to be fully substantiated. Methods: In this retrospective study, we analyzed genomic data from 1032 patients with solid tumors to identify actionable genetic alterations, calculate tumor mutation burden (TMB), measure PD-L1 expression via immunohistochemistry, and assess microsatellite instability (MSI) status. These assessments were utilized to evaluate potential therapeutic targets and immunotherapy-related markers for each patient. Results: Between January 2018 and July 2020, 1032 patients underwent genomic profiling, received treatment, and were followed up. Of 1156 treatment episodes, 423 involved targeted monotherapy or combination therapy, 114 involved immune checkpoint inhibitor therapy, and 619 involved chemotherapy or anti-angiogenic therapy. Patients receiving matched therapy (n=491) demonstrated significantly higher partial response rates (31.7% vs 10.4%, P<0.001) and longer median progression-free survival (PFS) (159 days vs 96 days, P<0.001) compared to those receiving unmatched therapy (n=665). Based on the highest level of mutation carried, there were 475, 8, 153, 405, and 115 patients with grade I, II, III, IV, and V alterations, respectively. Among patients with grade I-II alterations, 65.63% received targeted therapy, resulting in significantly longer PFS compared to those undergoing immunotherapy or chemotherapy (200 days vs 109 days vs 94 days). Conversely, only 16.8% of patients with grade III-IV alterations received targeted treatment, with marginal PFS improvement (112 days vs 114 days vs 92 days). Notably, an increase in the number of actionable variants was associated with a significant decrease in PFS among patients receiving targeted therapy. However, the number of actionable variants did not significantly affect PFS in patients receiving chemotherapy or immunotherapy. Within our cohort, 44 patients with cancer of unknown primary (CUP) underwent molecular diagnostic evaluation, leading to the reclassification of 32 patients based on their molecular profiles. Notably, 24 patients harbored actionable genetic variants, 9 patients had a high TMB (≥10 Muts/Mb), and 3 patients exhibited high PD-L1 expression (TPS ≥50%). Conclusions: Comprehensive genomic profiling demonstrates clinical utility in guiding treatment decisions and improving prognosis for patients with advanced solid tumors. These findings advocate for the integration of next-generation sequencing into the therapeutic regimens for these malignancies.
Investigating large language model (LLM) performance using in-context learning (ICL) for interpretation of ESMO and NCCN guidelines for lung cancer.
Authors: Iivanainen Sanna , Lagus Jarkko , Viertolahti Henri , Sippola Lauri , Koivunen Jussi
Published: May 2024

e13637

Background: The recent development of advanced LLMs has been suggested to improve patient care across several areas such as clinical-decision support or helping to answer patients’ questions. Hallucinations have been identified as a blocker for the use of LLMs in routine clinical practice. ICL and Retrieval Augmented Generation (RAG) could improve the LLM performance and reduce hallucinations, consecutively making the use of LLMs possible in clinical practice. Methods: A method using ICL and RAG was developed on top of health AI platform (Gosta MedKit) to interpret the most recent ESMO (Dec 2022 for NSCLC, Mar 2021 for SCLC) and NCCN (Nov 2023 for NSCLC and SCLC) clinical guidelines for lung cancer. Guidelines (including tables and diagrams) were curated into a text format, and split and stored into a vector database. OpenAI’s GPT4 Turbo model version gpt-4-1106-preview (GPT4-T), having the knowledge cutoff in April 2023, was used in all implementations. 11 questions about SCLC and 13 questions about NSCLC treatment recommendations and definitions were developed to evaluate the performance of different settings: GPT4-T (existing knowledge of the model), ICL with maximum context (ICL-MC) length (128k tokens) and ICL with RAG (ICL-RAG) heuristically including only the most relevant parts from vector database. Question prompts were generated for different settings and guidelines (ESMO, NCCN and both combined) and two oncologists evaluated 216 different responses and their alignment with ESMO and NCCN guidelines. Results: For responses using ESMO guidelines having oncologists’ consensus, ICL-MC and ICL-RAG respectively provided accurate responses for 83.3% and 79.2% of questions vs. 62.5% for GPT4-T. For responses using NCCN guidelines having oncologist consensus, ICL-RAG provided accurate responses for 83.3%, GPT4-T for 75.0% and ICL-MC for 33.3% of questions. When more flexibility was allowed in results interpretation (alignment either with ESMO or NCCN), GPT4-T provided accurate response for 87.5% vs. 70.8% with ICL-RAG and 58.3% with ICL-MC. No consensus around hallucinations was reported for ICL approaches, whereas GPT4-T hallucinated the response for 4.2% of questions with ESMO guidelines. Conclusions: ICL seems to improve the LLM performance for stricter tasks such as providing responses according to specific guidelines and reducing hallucinations. ICL outperformed GPT4-T in case of ESMO guidelines. This highlights the importance of taking local and latest guidelines into account when LLMs are used across different health systems and regulatory environments. In line with earlier studies, longer context for ICL makes models forget crucial information, which can be mitigated with the use of RAG to improve ICL performance and reduce costs when using the models.
PENTAGON (Predicting Clinical Trials in Gynecologic Oncology): A retrospective study assessing study design factors that affect enrollment in gynecologic cancer trials.
Authors: Pirzadah Humza , Klein Margaret , Magharehabed Yasmeen , Chapple Andrew G , Castellano Tara , Jernigan Amelia
Published: May 2024

e13647

Background: Obstacles to attaining a diverse clinical trial population contribute to poor representation in medicine, increasing health disparities by limiting generalizability and applicability of findings. Our objective is to evaluate trial enrollment trends according to patient and trial demographics for those who have sought care at our multi-center, mixed-setting Gynecologic Oncology practice. Additionally, we aim to determine specific trial criteria that promote or impede enrollment for patients and physicians. Methods: An IRB-approved, retrospective cohort study evaluating all patients who screened positive for a clinical trial through the Gyn Onc practice’s manual screening process was performed. Screening events were new neoplasms, recurrences or progressions, and changes in treatment. Data was collected from July 2022 to December 2023. Demographic information, trials for which each patient was considered, trials patients enrolled on, and trial characteristics were recorded. Results: After adjustment, there were no significant differences between patient factors (age, marital status, race, ethnicity, and number of screening events) and enrollment status. Of trials available, 9% require biopsy and 94.7% are in person. Most studies are interventional (78.9%) and pharmaceutical trials (73.7%). There was a drastic unadjusted estimated increase in enrollment for Medicaid patients compared to other insurances (55.2% vs 32%, p=.031). There was also an increase in enrollment for cervical cancer patients (18.4%, p=.106) compared to other cancer sites (56.5% vs 32.8%, p=.036). There was a positive relationship between distance traveled to the treatment site and clinical trial enrollment. Patients who lived within 20 miles of the clinical trial site had an enrollment percentage of 30.1% (n=93), compared to 46.55% enrollment for those who lived further (n=58). There was a significant increase in enrollment likelihood without an exclusion criterion of previous history of other cancer (50% vs 33.3%, p=.046). Trials that required no history of chemotherapy had an enrollment of 27.3% (compared to 44.1%). Conclusions: Unlike prior reports, we did not demonstrate differences in trial enrollment by race, socioeconomic or insurance status. This suggests that our objective manual screening process enhances equitable clinical trial offerings. It is possible that this objective manual screening process attenuates provider bias and offers clinical trials to historically marginalized and underrepresented groups.
Effects of preoperative breast MRI on surgical management in women aged 40 years and younger with breast cancer.
Authors: Erguven Tugce , Arafat Ayah , Brem Rachel
Published: May 2024

e13656

Background: Evaluating the use of preoperative breast MRI on the management of breast cancer can help guide surgical decision making for young female patients newly diagnosed with breast cancer. Methods: A retrospective chart review of the George Washington University database between January 2015 and December 2020 identified 125 women age 40 or younger diagnosed with breast cancer. Of the 125 women, 23 were excluded from the study as they were lost to follow up due to care transfers. The management impact of pre-operative MRI was examined and variables collected include age, race, family history, genetic predisposition, breast density, method of detection, initial imaging findings, initial biopsy results, preoperative MRI findings, additional biopsies, additional biopsy results, and surgery type. Results: Patient ages ranged from 22 to 40 (mean 34.5), 69 (67.6%) had a family history of breast cancer, 81 (79.4%) had dense breasts (30 extremely dense and 51 heterogeneously dense), and 82 (80.4%) had the cancer detected due to clinical symptoms with the remaining during surveillance due to high risk. Of all women included, 55 (53.9%) had additional findings on MRI warranting biopsy of which 25 (45.5%) were biopsy proven cancer. 39 (39.4%) patients had lumpectomy and 59 (59.6%) had mastectomy. 1 (0.01%) patient did not undergo surgery due to widespread metastasis. The use of preoperative MRI identified additional carcinoma in 25 (24.5%) out of 102 patients. 24 (96%) in the ipsilateral breast, and 1 (4%) in the contralateral breast. Conclusions: Overall, this data indicates that the use of preoperative MRI for the treatment of breast cancer in young patients increases the detection of occult breast cancer prior to definitive therapy in 24.5% of patients, critically important information for optimal management. We recommend that all young women with newly diagnosed breast cancer undergo pre-operative MRI to further optimize patient care.
Finding errors in the blink of AI: Use of an artificial intelligence tool to identify clinically important corrections to references cited by NCCN Guidelines for gynecologic malignancies.
Authors: Matthews Benjamin J , Ferriss James Stuart
Published: May 2024

e13658

Background: Scientific articles are being retracted at higher rates for many reasons. Retractions or corrections may be unknown to authors citing these materials as primary sources, unwittingly perpetuating incorrect information. Our goal was to assess the utility of a scholarly artificial intelligence (AI) tool to identify errata to sources cited in NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for gynecologic cancers and to evaluate the clinical importance of identified errata. Methods: scite (scite Inc., Brooklyn, NY) is an AI tool that analyzes citations to generate a list of linked publications, display the context for each citation, and flag any published errata or retractions for each source. Using scite with references from NCCN Guidelines as of June 2023, we identified corrections to primary sources. Each author categorized errata as having major, minor, or no clinical relevance, with resolution of conflicts through discussion until consensus. To check whether scite missed errata, we assessed a random sample (20%) of unflagged references. Results: The guidelines cited 2,364 primary sources, and scite successfully linked to 1,984 (84%) of them . scite flagged 50 references (2.5%) as having corrections; all 50 did have errata (100% accuracy). scite identified 56 total errata, which were not always apparent on journal websites or PubMed. After review, 31 errors (55%) were classified as clinically relevant; 71% of those errors had major clinical importance and 29% were considered minor. Twenty errors (65%) had relevance to the guideline statements in which the source was cited, but only 1 (3%) error had potential to change current guideline text. Among the sample of sources scite did not flag, we identified none with published errata. Conclusions: We demonstrated use of an AI tool to accurately identify sources in clinical guidelines with published corrections. Sources with errata were uncommon, and our analysis did not reveal perpetuation of major errors in NCCN Guidelines. However, the proportion of corrections with major clinical relevance underscores the importance of awareness of errata when writing clinical guidelines or reviews. Compared to manual review of each source, AI tools can improve speed and accuracy in finding important errors. [Table: see text]
Evaluation of a web-based, interactive guide to enhance patient-clinician electronic communication.
Authors: Alpert Jordan M , Amin Tithi B. , Zhang Zhongyue , Markham Merry Jennifer , Murphy Martina Cathryn , Bylund Carma L
Published: May 2024

e13659

Background: Secure messaging (SM) enables patients to ask clinicians questions asynchronously. Benefits include enhancing patient engagement, contributing to oral chemotherapy adherence, and lowering symptom distress. However, patients are unsure how to address concerns effectively, and clinicians find it challenging to decipher patients’ needs within messages. Coupled with clinicians not having adequate time to respond to messages, breakdowns in communication are common. We developed an interactive guide to help patients with cancer craft effective secure messages. It featured an oncologist providing strategies to write messages with specificity, expressing concerns, stating needs, being direct, and included a downloadable tip sheet. Methods: Patients 18+ with a current cancer diagnosis who were enrolled in the portal at a large health center were recruited. Between October 2023 and January 2024, participants were sent a link to interact with the guide, followed by a survey. The survey included several measures: 1) Communication and Attitudinal Self-Efficacy scale for cancer (CASE-cancer); 2) Digital Behavior Change Intervention (DBCI) Engagement Scale; 3) Website User Satisfaction questionnaire (WUS); 4) Acceptability of Intervention Measure (AIM); 5) Intervention Appropriateness Measure (IAM). We used R version 4.3.2 for the descriptive analysis. The Wilcoxon rank-sum test compared groups by demographic categories. The local IRB approved this study. Results: One hundred patients participated. Median age was 64 years (range=21-86), 54% male, 83% white, 35% diagnosed with prostate cancer, 35% lung cancer, and 23% hematologic cancer. Respondents reported high levels of self-efficacy based on CASE-cancer (mean 43.3 of 48). Respondents averaged 15 minutes using the guide. 96% thought the guide’s content was presented clearly, 95% found it easy to understand, 90% enjoyed using it, 89% thought the guide was suitable, and 88% could rely on its information. Results by demographic categories found that non-white respondents (M = 43.0) were significantly more engaged than white respondents (M = 28.4), P = 0.038 , and those with a bachelor’s degree+ (M = 96.5) had higher satisfaction than those without (M = 91.6), P = 0.025. Conclusions: Patients with cancer embraced an interactive guide that provided strategies to craft secure messages. They found it engaging, satisfying, acceptable, and appropriate, with racially minoritized patients being most engaged. As SM is a widely used tool for patient-clinician communication, it is crucial to concentrate on the way messages are constructed. We found potential to improve patients’ message-writing techniques, which could result in clinicians writing more complete answers to questions. Additionally, there is the possibility to enhance underserved patients’ communication skills and make them more engaged in their care.
MSK Engage: A comprehensive program for collection of patient reported outcomes at scale.
Authors: Polubriaginof Fernanda C. G. , Lipitz-Snyderman Allison , Chimonas Susan , Moy Morgan , Kuperman Gilad , Stetson Peter D.
Published: May 2024

e13666

Background: Collecting patient-reported outcomes (PROs) yields high value data for clinical care and research and is associated with improved survival in oncology. However, collection of PROs at scale requires a multifaceted program that in most institutions is not available. This study describes a comprehensive PRO program to support research and standard clinical care at scale. Methods: Memorial Sloan Kettering Cancer Center is a high-volume cancer center in New York. Beginning in 2016, we developed a comprehensive PRO program, MSK Engage, with the following capabilities: (i) A governance process to oversee the creation of questionnaires for both research and clinical care. This process includes alignment with the IRB and standardization of questions presented to patients. (ii) Technology to determine which patients should get an assessment (i.e., instance of a questionnaire) at what point in time. (iii) An interactive module in our patient portal that would present the assessment to the patient. (iv) Various methods to present the PRO data to providers to support clinical decision making. These methods included (a) alerts based on threshold logic that would send secure messages to the clinical team, (b) integration of the PRO data into the EHR to be viewed with other clinical data, and (c) the creation of specialized workflows. The results of assessments are also stored in an analytics database to support operations, research, and the creation of smart reports that compare a patient’s responses with other similar patients. Results: We analyzed the use of the program from 2016 to 2023 (Table). 189 questionnaires have been implemented, generating 3,098,574 completed assessments from 314,685 distinct patients. Completion rates varied. The median completion time for all questionnaires was 2 minutes. Examples of programs enabled by patient-reported outcomes include post-operative symptom monitoring, family history questionnaires to support clinical genetics decision making and operations, intake and follow up questionnaires that support clinical documentation and quality of life questionnaires to support goal concordant care. Conclusions: Adoption of PROs at scale for both research and standard of care in oncology is feasible and innovative programs of care can be supported while maintaining a centralized and acceptable patient experience. This model can be applied across institutions seeking to develop or scale PRO programs to assure patient data are meaningfully incorporated into decision-making. [Table: see text]
Engagement of patients with cancer with digital health platforms: An age-based analysis.
Authors: Kosmidis Paris A. , Baxevanos Panagiotis , Kosmidis Thanos
Published: May 2024

e13671

Background: Use of digital health platforms has been increasing, including those used by patients directly. In cancer, the nature of the condition, and patient age (higher vs average online users), are among the factors expected to reduce engagement. However, there is limited research to identify and quantify such correlations. We retrospectively analyze them for 4 cancer types. Methods: Patients engage directly with CareAcross platforms to receive information and support based on the data they anonymously provide. These include diagnosis, treatment, optionally year of birth; and side effects (repeatedly, with other inputs in between). On platforms used by breast, lung, prostate and colorectal cancer patients without clinician participation, the number of side effect reports submitted within 6 months of registration was analyzed as a patient engagement metric. This was correlated with patients' ages at registration, also yielding metrics for 10-year age groups around median age. Pearson correlation coefficient (r) was calculated. Results: 1010 breast, lung, prostate and colorectal cancer patients mainly from UK, Germany, France, Spain and Italy have reported birth years. 747 breast cancer patients (28-86yo; median: 53) submitted 2.34 reports on average. Patients above median age tended to submit more reports in general (2.56 vs 2.15 for younger than median); r = 0.055. 10-year age groups analysis: 34-43yo: 2.01 reports (N=109); 44-53yo: 2.19 (287); 54-63yo: 2.61 (236); 64-73yo: 2.45 (94); 74-83yo: 2.92 (12). 135 lung cancer patients (35-84yo; median: 62) submitted 3.27 reports on average. Older patients tended to submit more reports (r = 0.031) except the oldest 10-year age group analyzed (15 patients aged 73-82 reported 1.47 times). 99 prostate cancer patients (48-90yo; median: 68) submitted 2.87 reports on average. Overall correlation was positive: r = 0.039; patients above vs below median age reported 3.47 vs 2.45 times. Beyond these, age groups were not consistent: highest average number of reports was from 69-78yo (average 3.83; N=40) followed by 49-58yo (average 3.77; N=13). 29 colorectal cancer patients (38-81yo; median: 62) submitted 2.34 reports on average, showing negative correlation with age (r = -0.097). Age groups were small and their analysis inconclusive. Conclusions: Although evidence is not clear or uniform, the retrospective analysis of these self-directed digital health platforms shows that older patients appear to engage more than their younger counterparts. More, prospective, research can identify confounders and offer opportunities to improve engagement across all age groups. [Table: see text]
Disparities in cancer care: Is there a benefit for increased use of inpatient chemotherapy (IPC) in safety-net hospitals?
Authors: SUBEDI PAWINA , Gralla Richard J. , Bapatla Anusha , Richy Jessica
Published: May 2024

e13690

Background: Safety net hospitals provide care to a large number of vulnerable patients (pts) regardless of their ability to pay (1). These pts often have barriers to acceptance at many hospitals, including lack of or poor insurance, social and economic challenges. Additionally, pts frequently present with more advanced disease (2). Systemic treatment is preferentially administered on an ambulatory basis given several factors: clear financial incentives; effective supportive care; the association of good performance status (PS) with better outcomes; and patient preference (3). Only a few malignancies require inpatient systemic treatment. Strong social support is required for safe and effective ambulatory care, thus especially challenging pts at safety net hospitals in which social constraints are common. Methods: We reviewed patterns of IPC to determine efficacy and outcomes over a 3-year period at a safety net hospital, examining: malignancy type, reason for IPC treatment, treatment regimen (typically outpatient or inpatient), length of stay, and survival. Results: 82 pts met criteria. 56% had hematologic or HIV-associated malignancies. Solid tumor types included GI, breast, GYN, GU and small cell malignancies. Reasons for IPC were progression, worsening symptoms, declining PS in 55% and social constraints in 11%. Patient death within 3 months of IPC, or direct discharge to hospice/nursing home occurred in 42%. Further outpatient chemotherapy was never given in 50%. Death occurred in 15% of pts on the same hospitalization as the IPC; several additional pts were readmitted and died within one month. Individuals receiving IPC due to poor social support frequently required rapid rehospitalization, had poor outpatient follow-up, or died shortly thereafter. Three month survival was more favorable in pts with multiple myeloma or those admitted for drug-desensitization. Conclusions: Outcomes, including survival and rapid rehospitalization, were poor for pts receiving typical outpatient regimens on an inpatient basis whether due to social constraints, declining PS or worsening symptoms. A large percentage of pts never received further chemotherapy. IPC is costly, undesired by pts, and is often not a good use of resources. Pts with highly responsive cancers with fair to good PS had acceptable outcomes. IPC should be reserved for such pts if alternative outpatient regimens are not available. Earlier discussion of hospice and goals should not be replaced by chemotherapy. Institutional unavailability of key oral agents and limited outpatient resources often contribute to IPC. Better access to modern agents, enhanced social support, enlarged and better supported outpatient resources, as well as improved decision making improve cancer care. Our results do not support IPC as a frequent approach to cancer care in safety net hospitals. 1. Popescu, JAMA 2019. 2. Farkas, JCO 2012. 3. Mor, J Clin Epid 1988.
The heavy implications of racial and socioeconomic factors affecting cachexia incidence at gastrointestinal malignancy diagnosis: A single-institution study.
Authors: Choi April , Song Fei , Choi WonSeok William , Kaufman Alexander , Boyce Danielle , Buss Mary K. , Elkon Jacob Matt
Published: May 2024

e13694

Background: Cachexia, a complicated metabolic syndrome often related to cancer, is associated with anorexia, decreased quality of life, and increased mortality in advanced disease. The overall prevalence of cachexia ranges from 40% at cancer diagnosis to 70% in advanced disease. Our team aimed to determine whether racial and socioeconomic factors are associated with incidence of cachexia or being underweight at diagnosis (UAD) in patients with gastrointestinal (GI) tract cancer in a diverse, multiethnic urban medical center with high Asian-American representation. Methods: Using ICD-10 codes, adult (age ≥18) patients with diagnosis of primary GI carcinoma between 1/1/2013 to 12/31/2022 were identified. Those with secondary GI involvement from non-GI malignancies were excluded. Patient height, weight at time of diagnosis (weight measured within 4-week window from pathologic diagnosis date), and highest weight within 6 months preceding diagnosis date were recorded. Demographic data included: race, primary language, tumor origin, cancer stage and social deprivation index (SDI). Cachexia was defined as weight loss within a 6-month period of >5% of initial body weight, or weight loss >2% if body mass index (BMI) was <20kg/m2. UAD was defined as BMI <18.5kg/m2 at the time of diagnosis. Results: Of 718 patients with newly diagnosed GI cancer, 366 were analyzable for weight change. 61.2% were male. Race distributions were: 62.0% White, 23.8% Asian, 11.5% Black, and 2.7% Other. The primary languages spoken were: 76.5% English, 19.4% Asian-language, and 4.1% Other. The tumor originated from the following areas: 63.7% lower GI tract, 27.0% upper GI tract, and 8.5% hepato-pancreaticobiliary. 8.5% were UAD and 26.4% met criteria for cachexia at the time of diagnosis. Race and UAD had a significant association; Asians (p=.002) and those speaking Asian languages (p<.001) were more likely to present UAD. However, no significant relationship existed between race (p=.780) or language group (p=.656) on cachexia incidence. Tumor origin groups trended towards differences in cachexia incidence (p=.061). Mean SDI was significantly higher in those who were UAD (p=.026). The SDI was slightly higher among those who had cachexia, but this was not statistically significant (p=.244). Conclusions: GI cancer patients of Asian race, who primarily speak an Asian language, and those with higher SDI are more likely to present UAD, but these same factors are not associated with incidence of cachexia at diagnosis. Additional research is needed to determine if being UAD is associated with development of cachexia later in the illness trajectory or affects clinical outcomes for patient with GI cancer.
Bridging the gap: Leveraging AI to enhance access to clinical trials for underrepresented and underserved populations with cancer.
Authors: Gaziel Avital , Lapidot Yelena , Bader Tzvia
Published: May 2024

e13701

Background: National guidelines emphasize the crucial role of clinical trials (CTs) participation for all cancer patients. Despite this, significant barriers hinder access, particularly for underrepresented populations. Patients encounter obstacles such as geographic disparities and limited knowledge of tumor genetics, which impact their access to innovative CTs in metropolitan areas and academic centers. Furthermore, the increasing requirement for specific genetic profiles poses an additional challenge. This study aims to assess the impact of digital health platforms on improving access to CTs, with a specific focus on barriers related to ethnicity, geographic location, and disease knowledge. Methods: Leal (formerly, Trialjectory) is an AI-based platform that matches cancer patients to CTs based on self-reported medical profiles. The study included a diverse cohort of 39,031 users who completed the questionnaire. The patients’ geographic distribution across the US was analyzed, considering proximity to medical/academic centers, willingness to travel, and disease knowledge. Additionally, we examined genetic testing and biomarker knowledge across various clinical and sociodemographic groups. Results: The digital platform significantly improved geographic and ethnic diversity among CT seekers. Compared to industry standards of as low as 3% CT diversity, nearly a quarter of platform users were non-whites. Users outside major metropolitan areas, where CT enrollment is traditionally lower, were also better represented. Markedly, non-whites reported a lower willingness to travel outside their residence for CT participation compared to their white counterparts (p < 0.0001). A gap in disease knowledge was more evident in minority groups (p < 0.0001), with African Americans having a lower probability of knowing their mutation status compared to other ethnic groups. Conclusions: Access to CTs is hindered by barriers such as ethnicity, location, and knowledge. AI-based platforms swiftly identify and resolve these issues, empowering healthcare and support organizations to address individual patient concerns and boost diversity in CTs. AI analysis pinpoints underrepresented populations and regions with limited trial access, paving the way for tailored education and support initiatives to encourage informed decision-making. Strategically establishing trial sites in relevant areas addresses limited travel willingness due to cultural or socioeconomic constraints. AI technologies offer a promising approach to mitigate disparities, improve disease knowledge, ensuring equitable CT access, and enhancing healthcare outcomes for underrepresented populations.
Evaluation of a summer program for minority high school students in the clinical cancer center: The Future Gen Cancer Scholars program.
Authors: Ollila Thomas , Mega Anthony E. , Martinez Boris , Salisbry Paula , Neville Annabelle , Johnson Janell , Dickens Brett , Tarantino John , Omoaregba Luckson , Oakes Alissa , Safran Howard
Published: May 2024

e13703

Background: Individuals from minoritized communities receive less cancer screening, have higher stage at cancer diagnosis, and experience higher death rates. Helping minority high school students find a pathway to enter college and continue with advanced education and training and become leaders in cancer treatment, research and prevention may help reduce these inequities. Many public high schools in urban centers struggle in STEM education. The Rhode Island Hospital / Legorreta Cancer Center at Brown University partnered with the Papitto Opportunity Connection in Rhode Island to create the Future Gen Cancer Scholars program. Methods: High school students were invited to apply from Providence and surrounding communities. Accepted applicants became paid hospital employees and received training in professional conduct, protected health information and other skills necessary to shadow physicians in inpatient and outpatient settings. The summer curriculum included physician shadowing, radiology, pathology, student run tumor boards, seminars and community outreach. Pre/post-surveys were collected where students rated their experiences and beliefs regarding cancer careers on a 5-point Likert scale. Open-ended questions were added to evaluate interest in pursuing a career in oncology. Results: 20 high school students participated in the program’s first cohort. 70% self-identified as Hispanic/Latinx and 30% as African American, 85% were female, 90% attended a public school. Pre- and post-surveys were completed by 17 students. The 5-point Likert scale showed improvement in an understanding of oncology careers (pre, 3.9; post, 4.5), interest in pursuing a related career (pre, 3.1; post, 3.8), and recognition of available educational resources within their communities (pre, 3.9; post 4.3). Confidence in overcoming educational access barriers slightly decreased (pre-4.1; post-3.8) due to the increased awareness of the length, cost and process of medical training. Most students described the Future Gen Program as helping them identify career goals and a path forward. Conclusions: The Future Gen program helped solidify minority high school students’ desire to become leaders in cancer care and may provide a path forward to achieve this goal. We will track students’ educational and research achievements over time to measure the program’s impact. The establishment of a successful local model can provide a framework for a nationwide program.