bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.
Can synthetic data accurately mimic oncology clinical trials?
Authors: El Kababji Samer , Mitsakakis Nicholas , Fang Xi , Beltran-Bless Ana-Alicia , Pond Gregory Russell , Vandermeer Lisa , Radhakrishnan Dhenuka , Mosquera Lucy , Clemons Mark J. , El Emam Khaled 
Published: June 2023

1554 Background: There is strong interest by researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data. Reusing data extracts the most utility possible from patient contributions. The majority of patients do want to share their data for secondary research purposes. However, data access for secondary analysis remains a challenge. A key reason why individual-level data is not made directly available to data users by authors and data custodians is concern over breaches of patient privacy. Synthetic data generation (SDG) is an effective way to address privacy concerns that can enable the broader sharing of clinical trial datasets. However, a key question is whether the reproducibility of the generated data is adequate to draw reliable conclusions. Methods: We synthesized datasets from five pragmatic breast cancer clinical trials performed by the REaCT group (https://react.ohri.ca/). A sequential synthesis method, a type of machine learning was performed. The published analysis of each trial was repeated on each synthetic dataset to evaluate reproducibility. We evaluated reproducibility on three criteria: (a) decision agreement: the direction and statistical significance of the primary endpoint effect estimates are the same as the real data, (b) estimate agreement: the parameter estimates from the synthetic data are within the 95% confidence interval of the real data, and (c) the confidence interval overlap between real and synthetic parameters is above 50%. In addition, we evaluated privacy using a membership disclosure metric. This evaluates the ability of an adversary to determine that a target individual was in the original dataset using the synthetic data, computed as an F1 classification accuracy score. Results: Our results show that decision and estimate agreements held true across all five trials, and the confidence interval overlap was high. The risks of membership disclosure are all below the established 0.2 threshold. Conclusions: In this study, we were able to successfully generate synthetic datasets that accurately replicated original data from 5 oncology trials and yielded the same results as in the original published studies, with a very low risk of membership disclosure. With proper modeling techniques, synthetic datasets can play a key role in data democratization and the reuse of oncology clinical trials.[Table: see text]
An intelligent machine learning model for real-time early detection of undesirable cancer events: AIM2REDUCE.
Authors: Grant Robert C. , Kabir Muammar , Yuan Baijiang , Grant Benjamin , Narine Sharon , He Kevin , Ajaj Rami , Zhan Luna Jia , Fawzy Aly , Xu Janine , Zhang Yuhua , Yu Vivien , French Conor , Xu Wei , Krishnan Rahul G. , Gallinger Steven , Krzyzanowska Monika K. , Truong Tran , Liu Geoffrey 
Published: June 2023

1557 Background: Cancer and its treatment cause undesirable cancer events (UCEs). Automated warning systems could reduce the frequency and severity of UCEs by alerting the healthcare team and allocating preventative interventions. Most previous studies predicted single UCEs at the initiation of treatment. In AIM2REDUCE, we developed and evaluated a general-purpose system for predicting UCEs during outpatient systemic anti-cancer therapy. Methods: Each time a patient receives treatment, AIM2REDUCE applies machine learning to the preceding data in the electronic medical record (EMR) to predict future UCEs. We identified patients treated for aerodigestive cancers from the EMR at Princess Margaret Cancer Centre, who were randomly split into development, validation, and test cohorts. Features included cancer diagnosis, treatment sessions with doses, laboratory tests, and patient-reported symptoms. UCEs are listed in the Table. We trained LASSO regression and random forests models in the training cohort and tuned hyperparameters in the validation cohort using Bayesian optimization. We evaluated performance across discrimination, calibration, and net benefit in the test cohort. Results: The cohort included 5,760 patients who received 175,565 treatment sessions, with 13,612,746 unique data points across 102 features. Of these patients, 2,352 (40.8%) were female, the median age was 64.0 years (interquartile range 14.0), the most common diagnoses were lung cancer (2,071, 36.0%) and pancreatic cancer (926, 16.1%), and the most common treatment regimens were weekly gemcitabine (433, 7.5%) and maintenance pemetrexed (417, 7.2%). The Table shows the performance of AIM2REDUCE. Conclusions: We demonstrate that longitudinal machine learning systems trained using EMR data can accurately predict a wide range of UCEs. Based on these results, automated warning systems should be implemented and evaluated in real-time clinical practice. [Table: see text]
Early assessment of sotorasib for treatment of patients with advanced non-small cell lung cancer using real-world data in the United States.
Authors: Aran Dvir , Shahoha Meishar , Rosenberg-Katz Keren , Amar-Farkash Shlomit 
Published: June 2023

1560 Background: Sotorasib is the first small molecule for targeting tumors with KRAS G12C mutations. Despite promising early results which led to expedited approval, recent interim results from the phase III clinical trial suggest similar efficacy to standard-of-care chemotherapy docetaxel. Here we perform the first evaluation of sotorasib for the treatment of non-small cell lung cancer (NSCLC) in real-world setting. Methods: Elevance Health claims data was used. Lung cancer (LC) patients with sotorasib claims between June 2021-August 2022 were identified (assumed to be NSCLC). LC patients treated with docetaxel as second-line+ were used as control group. Potential confounding factors, including social determinant index (SDI), age, gender, Charlson Comorbidity Index (CCI), and metastases profile were addressed using inverse probability treatment weighting (IPTW). Outcomes were hospitalizations and real-world overall survival (rwOS), which was estimated using weighted Kaplan-Meier estimator. For comparison with the clinical trial population-level adjustments were performed. Results: Cohorts included 105 sotorasib-treated and 448 docetaxel-treated patients. Sotorasib-treated were primarily female, of older age and had more comorbidities. Posttreatment all-cause hospitalization rates were similar during the first 3 months (36% in both). Most common hospitalization diagnosis was gastrointestinal (GI) related which was uncommon in the control group (29% vs. 8%, p<.001). No evidence for liver toxicity was identified. For a subset of patients it was possible to perform survival analysis. Adjusted survival curves of RWD and the clinical trial (CodeBreak-100) were indistinguishable following population-level adjustment (p=0.92). Adjusted RWD sotorasib and the control cohorts revealed non-significant difference in rwOS up to 6-months of follow-up (62% sotorasib vs. 55% docetaxel; p=0.1). Conclusions: We present the first analysis of sotorasib in US real-world settings. This early analysis suggests that sotorasib does not provide survival benefit at 6-months follow-up. Sotorasib is associated with increased GI-related severe adverse events. RWD can be used to provide early information in expedited FDA approved treatments. [Table: see text]