bluuebunny/crossref_metadata_2025_split huggingface dataset:
A transfer learning-based graph neural network model using mIF images to predict neoadjuvant immunochemotherapy response in patients with gastrointestinal cancer.
Authors: Li Shenglong , Wang Yanan , Su Lan , Wang Jun , Zhou Jiaolin 
Published: May 2024

e13592 Background: Neoadjuvant immunochemotherapy has great potential to revolutionize the treatment of gastrointestinal cancer. However, effective biomarkers to classify responders from non-responders are still lacking. We aimed to build a response prediction model using multiplex immunofluorescence (mIF) images from patients with gastrointestinal cancer. Methods: We proposed a graph neural network-based (GNN) approach based on the pre-treatment mIF samples collected from 77 patients, including 30 gastric cancer (GC) patients, 34 colorectal cancer (CRC) patients, and 13 esophageal cancer (ESCA) patients. All the tissue samples were stained by two panels of mIF agents, namely Panel-A (CD3, CD8, PD1, PD-L1) and Panel-B (CD56, CD68, CD163). Cell coordinates, agent densities, and cell morphology features were used to generate cell graphs for model training. Results: Initially, we trained a pan-cancer GNN model for each panel and assessed its performance on the patch- and patient-level using five-fold cross-validation. The Panel-A model achieved higher areas under the receiver operating characteristic curve (AUROC) (patch-level: 0.71 ± 0.036, patient-level: 0.65 ± 0.010) than the Panel-B model (patch-level: 0.59 ±0.017, patient-level: 0.62 ± 0.021) and conventional mIF-based markers (i.e., positive cell ratios/counts of fluorescence agents, AUROC: 0.34 ~ 0.54). Specifically, the performance of CRC was better in the Panel-A model (patch-level: 0.75± 0.053, patient-level: 0.76 ± 0.053) than that in Panel-B (patch-level: 0.52 ± 0.027, patient-level: 0.60 ± 0.028), while the performance of GC was better in Panel-B (patch-level: 0.64 ± 0.030, patient-level: 0.71 ± 0.034) than that in Panel-A (patch-level: 0.53 ± 0.019, patient-level: 0.51 ± 0.018). The AUROCs of both panels of ESCA were less than 0.5, reflecting feature differences associated with treatment response between ESCA and GC/CRC. Furthermore, discrepancies of GC and CRC between Panel-A and -B motivated us to further optimize the performance for individual cancer types using transfer learning (TL). The GC TL-model performance (patch-level: 0.691±0.025, patient-level: 0.751 ± 0.046) was improved compared with the pan-cancer model, but not for the CRC TL-model (patch-level: 0.636 ± 0.066, patient-level: 0.697 ± 0.039). Moreover, the best patient-level specificity and sensitivity under the optimal Youden-Index were 70% and 88% for CRC, and 93% and 62% for GC. Conclusions: The GNN-based approach using mIF images demonstrated promising potential in predicting the immunochemotherapy response of gastrointestinal cancer patients, despite the limited number of samples were curated. The performance variation among cancer types indicated the cancer heterogeneity and the necessity of building single-cancer models when more data becomes available.
Automated survivorship care plan generation using large language models.
Authors: Pradeepkumar Jathurshan , Liebovitz David , Patel Jyoti D. , Sun Jimeng 
Published: May 2024

e13595 Background: Cancer survivorship, particularly from post-treatment to end-of-life, faces challenges such as communication gaps and coordination issues, leading to suboptimal care. A survivorship care plan (SCP) is a summarized record of patient cancer history and treatments, along with follow-up care recommendations. Despite extensive survivorship care guidelines from national cancer organizations, their adoption remains limited. Moreover, creating SCPs is time-intensive and burdensome for healthcare providers due to workforce limitations, hindering efficient personalization of care plans. Our study aims to address these issues by employing large language models (LLMs) to generate personalized SCPs. Methods: We introduce SurvGPT, a customized LLM designed to generate personalized SCPs focusing on its two main sections: the treatment summary and the follow-up care plan. Initially, our method extracts information from the structured and unstructured patient records to generate the treatment summary. Subsequently, it retrieves relevant context from a survivorship knowledge base crafted from existing medical literature and guidelines to generate a customized follow-up care plan. To mitigate LLM-related challenges such as hallucinations, our method ensures that the generated follow-up care plan is grounded on existing medical knowledge with a self-checking mechanism. Results: We created synthetic patient data based upon clinical inputs by a medical oncologist and an internist. We conducted a preliminary study using synthetic lung cancer patient EHR data to evaluate our approach. The evaluation focused on several key dimensions to validate the quality of the generated SCPs, including comprehensiveness, personalization, clarity, and actionable recommendations. The results presented in the table suggest promise for this approach yet also need for additional refinements both in degree of details extracted for ongoing care and then in formatting for effective use. The results also illustrate the complexity of generating synthetic cases for this work whose similarity to real patient care improved with iteration yet remained on occasion perceptibly different from current practice as noted by our clinical oncologist team member. Conclusions: In conclusion, our study demonstrates the potential of SurvGPT for generating personalized SCPs. The results of the preliminary results suggest that this approach is technically feasible with further validation required using real patient data. [Table: see text]
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.