bluuebunny/crossref_metadata_2025 huggingface dataset:
Exchanging genomics reports between pathology labs and medical centers using the Minimal Clinical Oncology Data Element (mCODE) FHIR implementation guide.
Authors: Terry May , Anton Howard , Casagni Michelle , Chamala Srikar , Chen James Lin
Published: June 2023

e13575

Background: The automated transfer of discrete molecular data from pathology labs into the electronic health record (EHR) is rapidly becoming commonplace and is designed to facilitate clinical decision making. Simple genomic changes are represented in existing EHR data transfer standards, but current data standards lack clinical fields that correspond to treatment insights such as associated medications and potential clinical trials. To address this, the mCODE effort defines a consensus oncology data standard that facilitates lightweight transmission of data from patients with cancer. mCODE’s support of genomic data leverages efforts by the HL7 Clinical Genomics Working Group (HL7 CGWG) focusing on oncology. Here, we describe pilot efforts involving both a pathology partner and academic medical centers to extend mCODE FHIR (Fast Healthcare Interoperability Resources) implementation guide (IG) support of genomic data for data interchange and clinical decision support use cases. Methods: From 2021-2022, validation of the mCODE FHIR IG genomic data elements was performed in CodeX, a FHIR accelerator program. These elements encompassed: lab facility, test order information, pathologist, patient demographics, specimen, molecular studies and therapeutic implications. Genomic FHIR data elements were selected for their conciseness and clinical relevance for inclusion in the mCODE FHIR IG under the supervision of two oncology informaticians. As part of the mCODE FHIR IG pilot, synthetic solid tumor molecular patient reports were generated by Tempus Labs. These synthetic molecular reports were transformed into mCODE-conformant FHIR files for transfer. The mCODE IG can be found at http://hl7.org/fhir/us/mcode/ . Results: Over 50 Tempus data elements were analyzed and found to be readily mapped to mCODE genomics elements. Two categories of data elements required additional transformation: 1. Data elements requiring additional genomic database annotations; 2. FHIR syntax constraints like the concatenation of medications in the same drug class. The mCODE file was transmitted to Vanderbilt and the Children's Hospital LA from Tempus for decoding and downstream uses. This effort further informed the HL7 CGWG of needed IG clarifications for compatibility with other standardization efforts and genomic use cases. Conclusions: mCODE’s support of genomic data elements provides a lightweight genomics interchange standard compatible with an existing pathology lab report. Mapping from existing molecular pathology reports to mCODE required nominal alterations. Furthermore, mCODE’s alignment with HL7 genomics efforts ensure FHIR-based compatibility in other clinical domains and with developing international standards. Continued pathology lab and academic medical center collaboration is essential for mCODE refinement and adoption.
Analyzing free-text clinical narratives for veterans with lymphoid malignancies using natural language processing (NLP).
Authors: He Lu , Moldenhauer Matthew , Zheng Kai , Ma Helen
Published: June 2023

e13576

Background: Free-text clinical narratives contain rich patient information, which is labor-intensive to extract through chart review. We developed an NLP pipeline to enable automatic extraction of performance status (PS), staging, and diagnosis from clinical narratives from Veterans Affairs (VA) patients with lymphoid malignancies (LM). Methods: The rule-based NLP algorithm was developed and iteratively refined using a development corpus of 287 notes independently annotated by two clinicians. The F1-score for PS was 95.8 (precision 98.6, recall 93.2), 92.7 for staging (precision 94.0, recall 81.6), and 67 (precision 80.2, recall 57.9) for diagnosis. The NLP pipeline was then externally validated using an evaluation corpus of 97 notes from another group of 100 veterans with T-cell LM. Results: The results are reported. In the 97 notes, primary diagnosis was most routinely documented, with 2.76 mentions per note. In comparison, staging was most sparsely documented with only 34 mentions (note that 11 patients with large granular lymphocytic leukemia were not staged). The NLP pipeline performed relatively well in extracting PS and staging (F1-scores were 0.74 and 0.72, respectively). It also achieved high precision in extracting diagnosis information (precision 0.93). However, recall (0.44) for diagnosis was poor, likely due to the complexities and inconsistencies of how diagnoses are documented for LM. Frequency of documentation and performance in the external validation set. Conclusions: The pipeline shows promising performance on the external validation set, demonstrating the feasibility of using NLP to extract information from notes of patients with LM for clinical research. The NLP pipeline generally has lower recall than precision, indicating that the pipeline may miss clinical information that should be captured. FPs incorrectly capture entities that are easily confused with the clinical entities of interest, such as nutritional status versus performance status. Future work includes capturing more lexical variations and indicators of documentation, as well as contextual information, such as in which sections of notes elements are likely documented. In addition, we describe how diagnosis may exist as primary, secondary, and in a differential, and we are building an NLP-based classifier to distinguish between these types of diagnoses. We will use results from the rule-based NLP pipeline as labels to fine-tune transformer-based, weak-supervised models to further enhance the performance. [Table: see text]
Explainable AI and machine learning algorithms to predict treatment failures for patients with cancer.
Authors: Farooq Muddassar , Shahid Muhammad Usamah
Published: June 2023

e13577

Background: Cancer patients may undergo lengthy and painful chemotherapy treatments, comprising several successive regimens or plans. Treatment inefficacy and other adverse events can lead to discontinuation (or failure) of these plans, or prematurely changing them, which results in a significant amount of physical, financial, and emotional toxicity to the patients and their families. In this research work, we build AI driven treatment failure models that utilize the real-world evidence gathered from patients’ profiles available in an oncology EMR/EHR system, with a goal of predicting the likelihood of a plan being discontinued at the time of its prescription. The selected AI models achieve a prediction accuracy of more than 80% and also provide reasons for their inference. Methods: Inclusion and Exclusion Criteria: Deidentified and anonymized electronic health records of patients, with their prescribed chemotherapies, for five different primary cancer diagnoses - ICD10 codes C18, C34, C50, C61 and C90 - that have the highest plan discontinuation rates between the years 2015 and 2022 are analyzed. All patients of other cancer types are excluded. AI Models: Unique features, that influence the treatment failure, for each cancer type are engineered by using therapeutic classification of drugs, diagnoses codes, comorbidity scores, tumor and biomarker information that is extracted from the notes and lab tests. We only use features that are available at the time of selecting a treatment plan. Several machine learning classifiers are investigated, and three tree ensembles - random forests, Xgboost and boosted forests - are further evaluated on the validation set to fine tune learning parameters with an objective to reduce the complexity of decision trees for providing better interpretability without significantly compromising the accuracy. Results: Our pilot studies reveal that boosted forests comprising of 5 random forests, each with 5 trees of depth 10 offer the best compromise between performance and interpretability. The models once trained are evaluated on unseen datasets and four performance measures of AI models are reported. On average, 15 rules are autonomously generated for a treatment failure inference for each cancer type and generally 6 of them have a significant support of 30 samples or greater. Conclusions: Machine learning algorithms for predicting treatment efficacy of chemotherapy regimens by deriving inference from the patients’ EMR/EHR data is an emerging yet challenging research domain. Our studies demonstrate that AI models like boosted forests provide the optimal models for treatment failure use case. In future, we want to validate the system in controlled clinical trials with the help of oncologists. [Table: see text]
Deep learning-based ensemble model using hematoxylin and eosin (H&E) whole slide images (WSIs) for the prediction of MET mutations in non-small cell lung cancer (NSCLC).
Authors: Choi Yoon-La , Park Sehhoon , Jung Hyun Ae , Sun Jong-Mu , Lee Se-Hoon , Ahn Jin Seok , Ahn Myung-Ju , Lee Taebum , Shin Seunghwan , Park Jongchan , Ma Minuk , Pereira Sergio , Park Gahee , Kim Seulki , Ro Juneyoung , Jung Wonkyung , Yoo Donggeun , Ock Chan-Young
Published: June 2023

e13578

Background: The MET proto-oncogene is involved in tumorigenesis and progression and has emerged as an attractive targetable genomic alteration in NSCLC. However, it has not been actively tested in NSCLC due to the lack of a cost-effective screening tool. We developed a deep-learning based model to predict genomic profiles from H&E WSI and applied it to MET pathologic mutations in NSCLC. The prediction model is based on an ensemble model trained not solely on H&E images but also on multi-layered semantic contents that were produced by a pre-trained AI analyzer, Lunit SCOPE IO. Methods: The model was trained on the MET aberration (exon 14 skipping, n=191; pathogenic mutation, n=109) and paired wild-type dataset (n=500). To extract deep features from raw H&E images, a self-supervised vision transformer was used, and an AI-based pathology profiling analyzer extracted semantic contents such as the spatial information of tumor cells, lymphocytes, cancer epithelium, and cancer stroma. A set of classifiers was trained based on the two features, and the ensemble of these features was used to improve robustness. Following cross-validation, the model was applied to an independent clinical dataset with MET sequencing results from The Cancer Genome Atlas (TCGA) LUAD and LUSC datasets (n=914), Samsung Medical Center (SMC, n=361), and Chonnam National University Hospital (CNUH, n=54). Results: The best cross-validation performances of the models predicting MET aberration measured by mean area under the receiver operating characteristic curve (AUROC) were 0.772 when trained by only H&E images (HE-only), 0.788 by AI semantic content with MLP classifier (AISC-MLP), and 0.803 by AISC with random forest (AI-RF), respectively. An ensemble of the three models showed an increased AUROC of 0.837 in the training dataset by cross-validation. These models were applied to the external validation dataset (n=1,329), including 20 (1.5%) MET exon 14 skipping and known pathologic mutations. The mean AUROC to predict MET aberration by the ensemble model was 0.817 with 95% sensitivity, 64.7% specificity. The AUROC of TCGA, SMC, and CNUH cohorts were 0.815, 0.802, and 0.812, respectively. Conclusions: An AI-based ensemble model combining H&E images with semantic contents extracted from pre-developed AI models significantly improved the accuracy and robustness of MET pathogenic mutation prediction using an H&E sample in NSCLC. These findings allow for cost-effective screening for MET alterations.
Use of an artificial intelligence (AI) –based pre-screening tool for patients with bladder cancer with fibroblast growth factor receptor (FGFR) alteration.
Authors: Shi Guobang , Yu Yanfei , Wang Aodi , Liu Ye , Shi Xiaoliang
Published: June 2023

e13580

Background: Over 25% of urothelial carcinoma have actionable genetic alterations in FGFR. The clinical trial of the pan-FGFR inhibitor requires thousands of patients with FGFR alterations to be screened; however, the cost is prohibitively expensive. A deep learning model can help predict the occurrence of specific genetic alterations and effectively screen patients with FGFR actionable alterations in bladder cancer. Methods: The prediction model of FGFR oncogenic alterations was trained by using 440 H&E-stained bladder cancer diagnostic slides (from 373 patients) from the OrigiMed Database and validated with 448 slides (from 376 patients) from the additional TCGA-BLCA dataset. The model employs an attention-based, weakly supervised learning method to identify tissue subregions most likely to have the mutations of FGFR1-4, so as to classify the entire slide. Every patch is assigned an attention score, which represents the corresponding contribution to slide classification. The slide-level aggregation rule of attention-based pooling computes a final attention score by weighted average. Results: The FGFR oncogenic alteration predictive AI (FGFRonaP) model was trained, validated and tested on the OrigiMed real-world cohort. The FGFRonaP model could identify patients with FGFR1-4 gene oncogenic aberration on the test set accurately (Precision = 60.0%, Recall = 69.2%, AUROC = 0.839). When the model was tested on the additional TCGA independent cohort, 11 negative samples were included mistakenly while 323 true negative slide and 19 true positive slides were identified correctly. The FGFRonaP model showed an acceptable classification precision (63.3%), much higher than the occurrence rate (26.9%) of FGFR oncogenic alterations, in spite of an imperfect AUROC (0.636) and recall (0.167). Conclusions: FGFRonaP is feasible to serve as an in-house or monocentric application to pre-screen the potential FGFR-altered patients. It could be useful and efficient in preliminary screening to reduce workloads of genetic testing, and thus help pharmaceutical companies improve the proportion of candidates with FGFR-alterations in all patients who undergo genetic testing and cut expensive costs for enrolling patients.
Application of natural language processing to assess the performance status documentation quality metric in patients with non–small-cell lung cancer.
Authors: Maghsoudi Arash , Sada Yvonne H. , Zhu Huili , Yarlagadda Sudha , Guffey Danielle , Nowakowski Sara , Li Ang , Razjouyan Javad
Published: June 2023

e13582

Background: Performance status (PS) assessment is important for cancer prognosis and treatment decision-making. PS documentation for stage IV non-small cell lung cancer (NSCLC) is a quality measure for ASCO Quality Oncology Practice Initiative (QOPI) Certification Program. We used natural language processing (NLP) to extract PS from unstructured electronic medical records (EMR) clinical notes in patients with metastatic NSCLC to verify PS documentation compliance and disparities in PS documentation. Methods: We identified oncology progress notes of stage IV NSCLC patients from Harris Health System, which is an integrated comprehensive healthcare system for underserved patients in Houston, TX. All progress notes within 60 days of first oncology clinic visit date were filtered based on visit type and author type. A rule-based regular expression NLP algorithm was used to extract the PS, namely Eastern Standard Cooperative Group (ECOG) or Karnofsky Performance Status (KPS). A total of 400 oncology notes were randomly selected from 400 distinct patients and annotated by two clinician reviewers, and discrepancies were resolved by a senior clinician judge. The annotated notes were split randomly into 200 training notes and 200 validation notes, and the performance of the NLP algorithm was evaluated. We used logistic regression to analyze potential disparity in PS documentation. Results: We identified 15,239 progress notes from 757 stage IV NSCLC patients. The NLP algorithm accuracy validated on the test data was 99% with corresponding positive predictive values (PPVs) for ECOG PS 0, 1, 2, 3, 4 of 100%, 100%, 95%, 94%, 94%, respectively. The NLP algorithm identified 85% of patients with appropriate PS documentation (sensitivity 100%, specificity 91%, positive predictive value 98%, negative predictive value 100%). Logistic regression analyses showed no significant differences in association between PS documentation and race/ethnicity, language, or sex. Conclusions: We developed an internally validated NLP algorithm to extract PS from EMR clinical notes. Despite potential language barriers, the QOPI metric of >75% PS documentation was met in our hospital system without significant disparities in clinical care. [Table: see text]
Comparative assessment of manual chart review and patient-level adverse drug event identification using artificial intelligence in evaluating immunotherapy-related adverse events (irAEs).
Authors: Zitu Md Muntasir , Li Lang , Elsaid Mohamed I. , Gatti-Mays Margaret Elena , Manne Ashish , Shendre Aditi
Published: June 2023

e13583

Background: IrAEs monitoring in patients undergoing Immune checkpoint inhibitor (ICI) treatments relies on analyzing clinical notes and structure data from electronic health records. As IrAEs are not yet fully captured in the ICD claims, they could be under reported. On the other hand, IrAEs are usually recorded in the clinical notes. A natural language processing (NLP) tool is highly desirable to extract them from clinical notes. In this study, we aim to compare the performance of our machine learning (ML) NLP model in IrAE extraction against 1) manual chart reviews and 2) ICD claims data. Methods: The study validation set included 194 newly diagnosed stage I-VI gastrointestinal cancer patients treated with ICIs at the Ohio State University Comprehensive Cancer Center. An irAE was defined as the occurrence of gastrointestinal, pulmonary, dermatological, or endocrine adverse events post-ICI infusion. Our NLP ML model was trained on 1230 clinical notes from 20 positive (who had ADEs) and 10 negative (who did not have ADEs) patients who received ICIs. ICD irAE method is based on ICD-9 and ICD-10 codes, which identifies irAEs post-ICI infusions. All manual reviews of ICI induced irAEs were done by a medical oncologist specializing in gastrointestinal cancers and included patient charts, labs, and imaging post-ICI infusions. Results: In this 194 patient cohort, the median age was 61 years, 68% males, 89% White non-Hispanics, and 29% had colorectal cancer. The study included 8,500 medical notes with a median 29 notes per patient and 1,283 words per note. An estimated 4,726 (55.6%) were progress notes, 1,178 (13.9%) patient Instructions, and 430 (5.1%) consults. The incidences of irAEs were 13.9%, 25.8%, and 27.3%, respectively, using manual reviews, the ML model, and ICD claims, respectively. Compared to the manual reviews, the ML model had a 78% sensitivity, 83% specificity, 0.17 false positive rate (FPR), and 0.80 (95% CI, 0.72 to 0.89) area under the curve (AUC). In contrast, ICD claims had a poor agreement compared to manual reviews. 52% sensitivity, 77% specificity, 0.23 FPR, and AUC 0.64 (95% CI, 0.54 to 0.74). Conclusions: Using clinical notes, ML can identify patients with irAEs with a great potential to infer causality in real time clinical practice. Our irAE manual review analysis further indicates that ML model performance can be further improved if including lab and imaging data.
Deriving breast cancer chemotherapy patterns from real-world data.
Authors: London Jack W. , ORourke Julia , Warnick Jeff , Doole John , De Keyser Luc , Drebert Zuzanna , Wan Olivia , Thompson Courtney , Palchuk Matvey
Published: June 2023

e13586

Background: Real World Data (RWD) collected during routine medical practice can help in clinical trial design, planning and execution. In addition, it can provide a compelling picture of safety of medical products, and account for all potential adverse events that can be encountered in routine clinical practice. However, raw drug information about chemotherapy in cancer care is not readily understandable in the format it is collected. Analytical techniques need to be applied to extract regimen information, which includes drugs, dosage, number of cycles and cycle length. Methods: A retrospective study was performed using data on 7,798 breast cancer patients from the TriNetX Network, a federated network of de-identified, HIPAA-compliant, health data from 21 healthcare organizations across North America as of May 2022. We investigated a method built on rule-based algorithm and clustering analysis to extract regimens and their patterns of administration and align them into lines of treatment (LOT). To derive drug administration patterns, we clustered administration time periods using three features: total number of drug administrations, median number of days between drug administrations, and standard deviation of the days between drug administrations. Results: The patterns of administration that correspond to the two most common regimens for Erb-B2 receptor tyrosine kinase 2 negative group (ERBB2-) stages 1, 2 and 3 are shown. We looked at patients with Hormone Receptor (HR+) positive and Triple Negative (TN) breast cancer. Results of our analysis were in a close agreement with NCCN Guidelines. However, regimen administration patterns varied. This information can be useful to characterize patients based on the adherence to the expected chemotherapy regimens. It can also provide meaningful insight into burden of illness, such that patients who have higher variability of the drug administration might be faced with clinical problems such as drug tolerability or side effects. The variability might also signify access to care challenges. Conclusions: Understanding regimen LOT and administration patterns is central to research based on RWD, but it can also be useful for clinical trial design, execution, as well as site selection and patient recruitment for clinical trials. [Table: see text]
Digital Trial Solutions eScreening: Software to rank patients by their predicted clinical trial eligibility using real-world data and machine learning.
Authors: Bhandari Sudip , Levonian Zachary , Annand Robert , Baxter Jo-Zahn , Cain Jericho , Flach Jen , Joseph-Thomas Jiby , Mueller Judith
Published: June 2023

e13587

Background: The recruitment process for clinical trials is time-consuming and labor-intensive. Electronic screening tools aim to facilitate patient recruitment by focusing the recruiters’ efforts on patients most likely to be eligible. These tools are required to produce useful estimates of patient eligibility despite large amounts of missingness in Real World Data (RWD). Digital Trial Solutions (DTS) eScreening—unlike other proposed tools that use only top-level Inclusion/Exclusion (I/E) criteria—queries for all computable criteria and imputes missing data using Machine Learning (ML). Our ML models use the ConcertAI data from DTS clinical sites. Methods: DTS eScreening provides an eligibility score for each patient in a clinical trial cohort based on how many I/E criteria are fulfilled, weighted by criteria relevance as assessed by clinical experts. If information is missing in the patient record, DTS eScreening predicts the probability of passing the criterion using a binary classifier trained for each criterion. We evaluated classifier performance on a held-out test set. We evaluated the utility of DTS eScreening by collecting quantitative and qualitative data from users of DTS eScreening. Results: All evaluations were done on a multi-site clinical trial with 49 digitized criteria. The cohort was 10,156 patients with the appropriate cancer indication. Model performance: twenty-three criteria had complete data and so no ML models were built. To avoid biased model estimates, we avoided modeling ten criteria with greater than 99% missingness or 1000:1 imbalance. Seventeen LightGBM classifiers were built on the full cohort for criteria such as ECOG, stage, labs, and vitals. The model performance showed an average precision of >99% for most models, while the lowest precision was 83%. DTS eScreening utility: using our tool, clinical screeners reviewed 114 patients with the highest eligibility scores of the 10,156-patient cohort. Of the 114 patients, 38 were determined to meet all digitized eligibility criteria. The precision on reviewed patients was 33%, higher than other reports in the literature. Feedback from semi-structured interviews with 3 users indicated the tool was useful for reducing review time. A screener said: “It makes screening easier for me because the system already filters the most eligible patients at the top.”. Conclusions: DTS eScreening tool demonstrates the ability to conduct patient-trial matching using computable I/E criteria while addressing missingness in RWD. Positive initial feedback from users highlights improvements in the efficiency of patient recruitment in clinical trials. We believe our solution is scalable and can be applied beyond oncology. Future improvements include relaxing restrictions for modeling at low counts to include additional I/E criteria and incorporating unstructured data.
Machine learning prediction of progression events in oncology RECIST 1.1 clinical trials.
Authors: Bowman Erin , Banuprakash Shyam , Nguyen Kim , Marini Matthew
Published: June 2023

e13589

Background: Oncology clinical trials often have an imaging based surrogate endpoint such as Progression Free Survival (PFS). Using PFS as a surrogate for survival has the advantage of achieving trial completion more quickly which reduces cost and risk to patients. These trials are frequently structured to require a certain number of events to achieve statistical significance. The use of machine learning models which can make predictions about progression events from data obtained after the initiation of therapy is a novel idea in this field. It is well understood that many factors play a role in whether a patient will ultimately progress on a certain therapy. While certain statistical predictions can be made given known factors such as tumor genetics and patient history, these are made prior to the patient initiating therapy and do not consider on-therapy response. Machine learning models can be used during trial conduct, incorporate patient response, and better predict progression outcomes. Methods: Machine learning models were trained on over 115,000 RECIST 1.1 datapoints. The iterative training process included inputs from the medical science team which included a lead radiologist with expertise in diagnostic radiology and molecular imaging; and a lead medical informaticist responsible for data quality. Models were trained using 56 raw and derived features. Examples of derived features include number of lesions, time between evaluations, and change in responses over time. The features were trained using Random Forest, AdaBoost, Gradient Boost, XGBoost model types and compared to determine which one performed best. These model types identified the top predictors and created tree-based visuals, which provided insights into the model’s decision-making process. Confusion matrices were reviewed by the team to iteratively address false positive and false negative records. Results: Initial results of the machine learning model demonstrated a 75.1% specificity and a 66.6% accuracy for prediction of progression events. With continuous improvement efforts, as of writing, the model demonstrated 94.4% specificity, and 82.2% accuracy. This process identified the top predictors of progression to be the primary RECIST 1.1 features. Given our scientific and AI expertise results are expected to improve over time. Conclusions: Outcomes have shown the ability to predict RECIST 1.1 progression. Such a model could successfully predict when the required number of progression events are present within a population, at which point enrollment could be ceased and decrease overall trial length. Given enough data and rigorous testing, this technology could predict complete response vs partial response, adverse events, and time to events. Data suggests that AI machine learning within Clinical Trials could have a significant impact and should be considered a priority for continued evaluation given the possible benefits to trials.
Different efficacy of pembrolizumab in overall survival between Asians and non-Asians with advanced cancers: A systematic review and meta-regression analysis.
Authors: Peng Shang-Hsuan , Lin Ching-Hung , Chen I-Chun , Shen Ying-Chun , Chang Dwan-Ying , Chen Tom Wei-Wu , Huang Shu-Min , Hu Fu-Chang , Lu Yen-Shen
Published: June 2023

e13597

Background: Immune checkpoint inhibitors have revolutionized the treatment landscape of malignancy. However, disproportionate enrollment among different races/ethnicities in global trials placed the generalizability in doubt. This meta-analysis aimed to estimate the relative efficacy of pembrolizumab between Asians and non-Asians with advanced cancers. Methods: A systematic review was conducted in PubMed, Cochrane Library, Embase, and Web of Science. The meta-analysis included phase 3 randomized controlled trials investigating pembrolizumab in advanced cancers with available subgroup analysis for Asians and non-Asians. The experimental strategies involved monotherapy versus standard-of-care (SOC) or best supportive care, an add-on to SOC, and combined with multikinase inhibitor versus standard chemotherapy, regardless of the setting. The primary and secondary effect measures were the mean difference (MD) in the natural logarithm (i.e., log) of the hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS) between the two subgroups [MD=log(HRAsians)−log(HRNon-Asians)=log(HRAsians/HRNon-Asians)]. We used the random-effects meta-analysis to calculate the pooled ratio of HRs (i.e., exp(MD)) and implemented a meta-regression analysis to identify significant covariates. The heterogeneity among the selected studies was evaluated using the Chi-squared Q test and the I2 statistic. Results: A total of 17 and 11 trials were selected in the meta-analysis for OS and PFS, respectively. These trials reported available subgroup results on Asians and non-Asians (patient numbers 2,732 [25.49%] and 7,000 [65.32%] for OS; 1,438 [22.50%] and 4,129 [64.61%] for PFS). The pooled ratio of HRs for OS was 0.87 (95% confidence interval [CI], 0.76−0.99; p = 0.0391; Chi-squared Q test Χ2 = 20.84, p = 0.29; I2 = 23.9%), favoring Asians, and no significant difference was found in PFS (the pooled ratio of HRs 0.93; 95% CI, 0.82−1.07; p = 0.2391; Chi-squared Q test Χ2 = 7.37, p = 0.77; I2 ≈ 0%). Both linear meta-regression analyses revealed an open-label design as an important covariate accounting for the observed difference, favoring non-Asians (estimated ratios of HR, 1.6318 for OS [95% CI, 1.1442-2.3273]; 1.3429 for PFS [95% CI, 1.0007−1.8020]). The sensitivity analysis showed that the percentage of subsequent treatment or post-progression programmed cell death 1 (PD-1)/ligand 1 (PD-L1) inhibitors had no significant impact. Conclusions: Asians receiving pembrolizumab for advanced cancers may have a superior OS advantage than non-Asians. Although the results warranted further exploration, this meta-analysis provided insight into clinical research design.
Accrual of adolescents and young adults (AYA) into cancer clinical trials in Canada.
Authors: Jafri Mariam , Willman Corey , Urton Alison May , Henning Jan-Willem , Abbott Lesleigh S. , Davison Kelly , Akra Mohamed A. , Seymour Lesley , Dancey Janet Ellen , Hay Annette E.
Published: June 2023

e13598

Background: Historically, accrual rates of AYA (aged 16-39) to cancer clinical trials have been low. This is suggested to contribute to lesser improvements in their outcomes compared with older and younger populations. We sought to describe the impact of collaborative initiatives launched by the Canadian Cancer Trials Group (CCTG), NCTN and C17 to address this. Methods: All CCTG trials open to accrual from 01/01/2004 to 31/12/2022 were evaluated. Trials with eligibility criteria limited to age > 39, or for which CCTG did not have access to individual patient data on age, were excluded. Accrual rates were assessed by age (<30, 30-39, >40 yrs), cancer type, and compared with 2022 Canadian Cancer Society (CCS) prevalence data. Results: 379 studies were open to accrual. 21 trials were excluded as they had no patients aged 16-39 within their inclusion criteria. 2 trials were excluded as they were either COVID related or a trial sub-study. 230 trials were excluded owing to patient age data being unavailable to CCTG. 126 CCTG and NCTN led trials were included. They involved the following disease sites: brain (3), breast (13), GI (12), GU (11), gynecology (6), head & neck (3), hematology (10), lung (11), melanoma (4), sarcoma (2) novel investigational agents (45) and symptom control (6) – the latter two spanned multiple tumor types. Patients aged 16-18 were excluded from many trials limiting accrual opportunities in this subgroup. AYA patients were recruited into trials at a higher level than expected by prevalence data except for brain, lung and head & neck patients. This may be due to younger, fitter patients being recruited onto trials. GU trials, hematology and sarcoma trials recruited better than expected as there were trials geared to diseases common in younger individuals (e.g. germ cell cancers) and incorporating intensive therapy e.g. stem cell transplantation in lymphoma. 3.5% of patients accrued on trials were <40 in 2004-2013 c.f. 8.7% of patients 2014-2022. Conclusions: Limited data is available on AYA aged 16-18 due to trial entry criteria. Currently, most CCTG trials include patients aged ≥14. Trial accrual in the AYA population was higher than predicted by CCS data indicating that clinical trials are acceptable to AYA patients. This supports ongoing efforts to improve trial access to AYA. [Table: see text]
Phase I solid tumor trials (PHIstt) in the era of precision oncology (PO): A systematic survey.
Authors: Stockton Shannon , Ayers G. Dan , Lee Cody L. , Laferriere Heather E. , Berlin Jordan , Das Satya (Nanu)
Published: June 2023

e13603

Background: In the era of PO, systemic therapies for patients (pts) with solid tumors have fundamentally changed from chemotherapy (CT) to targeted therapy (TT) and immunotherapy (IO). No study to the best of our knowledge has described PHIstt in this era. In this systematic survey, we describe the evolving features of PHIstt which began enrollment between 2010-2020, focusing on type of dose escalation scheme (DES) utilized, use of expansion cohorts (ECs) and nature of adverse events (AEs). Methods: The primary aim was to estimate the proportion of PHIstt which used rule-based DES. A literature search was performed by a biomedical librarian to identify PHIstt in adult patients published between January 1, 2000 – December 31, 2020 from a selection of 12 journals that publish PHIstt. However, we included studies enrolling between 2010-2020 to better capture agents in the PO era. We included trials with both single and multiple agents, and single and multiple modalities if the systemic therapy tested was being escalated. We excluded studies which only included intra-tumoral therapy, radiation therapy, or supportive care. Two reviewers assessed studies for inclusion and performed data abstraction; a 3rd reviewer established concordance. Results: Of 10,744 studies retrieved, 10,195 were non-topical or began enrollment prior to 2010; 437 studies in pts aged ≥ 18 years with advanced solid tumors were included. A median of 31 pts were enrolled per study (IQR 19-50), with median age 60 years in escalation cohorts. In studies reporting pt sex, 45.7% were female, and 53.9% were male. The most common classes of therapeutics were TT (47.6%), IO (22%), and CT (6.9%). Only 40.1% of studies reported ECs in the primary paper, and 46.6% of ECs used genomic selection. Rule-based designs (which assign patients to dose levels based upon prespecified rules of actual AE observations) were used in 89% of PHIstt; a 3+3 DES was used in 80.5% of these studies. With regards to grade 3/4 AE reporting, 56.6% of studies reported treatment-related AEs, 21.3% reported treatment-emergent AEs and 10.7% did not specify how they were reported. The most common class of grade 3/4 AEs was hematologic (37.1%). The most common dose-limiting toxicity classes were other (64.6%) and gastrointestinal (16.9%). Of all the drugs tested in PHIstt, 37.5% were tested subsequently in phase II while 10.3% garnered licensure. Conclusions: There is room for improvement in the trial design of PHIstt which may lead to more regulatory success. Some areas we identified include the more regular adoption of genomically selected ECs, more consistent reporting of AEs and diversification beyond rule-based designs to better assess safety/efficacy profiles of non-CT agents. Dose-Escalation Designs. [Table: see text]
Primary care provider perceptions of patient participation in a multi-cancer early detection trial.
Authors: Myers Ronald , Hallman Mie , Davis Kaitlyn , DiCarlo Melissa , Daskalakis Constantine , Shimada Ayako , Shenoda Botros , Danoff Robert , Dong Karen , Chambers Christopher
Published: June 2023

e13607

Background: Randomized clinical trials of multi-cancer early detection (MCED) tests are being planned, and primary care providers (PCPs) are likely to encounter patients who are considering participation. Little information has been reported about provider support for patient participation in such trials. Methods: We surveyed PCPs from 4 practices in a large health system to learn about their views on patient participation in a hypothetical MCED test trial. The PCPs received a link to view a patient-oriented infographic that described a trial in which participants would undergo serial blood draws for MCED testing and would be randomly assigned either to standard care or to receive MCED test results and follow-up, as needed. After viewing the infographic, respondents were asked to complete a brief survey that included statements related to factors that would affect their receptivity to patient participation in the trial, using a 5-point Likert type response scale that ranged from 1 = Strongly Disagree to 5 = Strongly Agree. The survey also included a single item that asked respondents if they would support patient participation in the trial and provided an opportunity to report the main reason for their answer. Finally, the survey contained a background characteristics section. We computed frequency distributions of survey responses. Results: Twenty-seven of 37 (73%) invited primary care providers completed the survey. About half of the respondents were women and two thirds were white; 81% were physicians while 19% were nurse practitioners, 54% were in practice for less than 20 years, all were board certified, and 74% had a hospital affiliation. Receptivity scores regarding a future MCED trial were high, with a mean of 3.8 and standard deviation of 0.6. Overall, 25 providers (93%) reported that they would support patient participation in an MCED test trial. The most commonly reported reasons for supporting patient participation were the belief that early cancer detection is important and the belief that research to develop new cancer screening tests is needed. The most commonly cited reasons for not supporting participation were concern about managing patients diagnosed with cancer in the trial and worry that trial participants would require too many practice resources. Conclusions: The overwhelming majority of respondents supported patient participation in an MCED test trial, but many were concerned about trial impact on their capacity to meet patient needs. Further research is needed in a larger study to validate these results and learn more about factors likely to influence PCP support for patient participation in MCED test trials and actual participation.
Bridging the gap: FDA diversity-related postmarketing requirements.
Authors: Kormanik Natasha L. , Carioti Theresa , Crai Kayla , Kanapuru Bindu , Richardson Nicholas C. , Gormley Nicole
Published: June 2023

e13608

Background: Historically, pre-market clinical trials have lacked enrollment of racial and ethnic minority patients that represent the population intended to be treated with the approved drug in the US. The Food and Drug Administration (FDA) has several programs and initiatives designed to enhance diversity in oncology clinical trials, such as the Oncology Center of Excellence’s (OCE) Project Equity, FDA Guidance documents, and submission of Diversity Plans from industry. Under the code of federal regulations and/or statute, the FDA can issue post-marketing requirements (PMR) to fulfill accelerated approval requirements for confirmatory studies; for assessing safety signals; or for conducting dedicated studies in pediatric patients. FDA may also issue post-marketing commitments (PMC) to generate additional clinical evidence. PMRs and PMCs have been used to obtain additional information on under-represented populations. The objectives of this analysis were to identify oncology PMRs/PMCs issued with a specific focus on racial and ethnic minority populations and examine the trend over the 10-year period. Methods: We searched FDA’s publicly available database of PMR/PMCs, inclusive of drugs and biologic products, and internal agency databases for PMR/PMCs issued between 2012-2022. The PMR/PMC search was further narrowed down by keywords to capture all PMRs/PMCs relevant to races and ethnicity. The PMRs/PMCs were then manually reviewed for duplication and applicability to malignant indications. Results: In a 10-year period, 34 PMR/PMCs focused on race and ethnicity were issued across 461 oncology approvals (initial marketing approvals = 164, new indications = 297). Twenty-two were issued with the initial marketing approval and 12 with supplemental efficacy approvals. All PMR/PMCs were issued in the last 3 years (2019-2022). PMCs accounted for 55.9% (n = 19) and PMRs 44.1% (n = 15). Nine of the 15 PMRs requesting additional information on underrepresented populations were included with the accelerated approval PMRs. Six of the 34 PMR/PMCs were issued to obtain additional PK or dosing information in the underrepresented population. The most common PMR/PMC indications issued were lymphoma (n = 13, 38.2%), multiple myeloma (n = 8, 23.5%), and non-small cell lung cancer (n = 4, 11.8%). Although PMR/PMCs that mentioned a specific race were limited, African American/Black (n = 8) and Asian (n = 1) were occasionally identified. All studies identified in this analysis are ongoing except for one, for which the product was withdrawn. Conclusions: Our analysis highlights the trend and increased emphasis on race and ethnicity in post approval oncology studies. While progress has been made to promote studies that are representative of the US population through these PMRs/PMCs, diversity plans, and FDA guidance over the course of the past 10 years, more efforts are needed to better enhance diversity in clinical trials moving forward.
Comparative effectiveness trial of two supportive cancer care (SCC) delivery models at 24 clinics: An ongoing cluster randomized trial.
Authors: Medrano Hector S. , Wood Emily H. , Kapphahn Kris , Agarwal Madhuri , Perez Cynthia C. , Matthew Marika , Fuller Keely E. , Patel Manali I.
Published: June 2023

e13617

Background: Many patients with cancer receive care that is misaligned with their goals, values and preferences and have undertreated symptoms. Proactive integration of advance care planning and symptom management -- Supportive Cancer Care (SCC) -- through team-based and technology-based approaches can overcome these deficiencies. Yet, it is unknown which approach is more effective. Methods: This 24-clinic cluster-randomized trial (NCT05297734) will evaluate whether a team-based SCC delivery approach using community health workers more effectively improves health-related quality of life (HrQOL) than a technology-based approach utilizing the electronic health record. We are enrolling 2996 patients across 24 clinics (12 randomized into each group) in the United States including integrated community clinics, academic, Kaiser, VA, and safety-net centers. Eligible patients are: ≥ 21 years; newly diagnosed with cancer; English or Spanish speakers. Those unable to consent, enrolled in competing trials, or planning to move within 12 months are excluded. The primary outcome is change in HrQOL using the Functional Assessment of Cancer Therapy-General survey from baseline to 3-months post-enrollment. Secondary outcomes include changes in patient activation, satisfaction, and healthcare use (emergency department visits, hospitalizations, chemotherapy, radiation, surgery, palliative care, hospice use) from baseline to 3-, 6-, and 12-months post-enrollment. Results: Enrollment began June 2022. As of December 2022, 24 clinics obtained IRB approval and 11 of 24 clinics screened a total of 1075 patients and enrolled a total of 165 participants. The majority (n=146; 77%) are male; 89 (53.9%) reported less than college degree; 45 (27.3%) reported annual household income ≤ $49,999. Conclusions: This ongoing study will inform the most effective SCC delivery approach and strategies to improve SCC across a variety of clinical settings in the United States. Clinical trial information: NCT05297734 . [Table: see text]
Use of secondary subgroup analysis of clinical trials across solid malignancies.
Authors: Alzubi Marah , Odeh Nada Wael , A.l. Zyoud Muntaser , Hamadneh Yazan , Alrawabdeh Jawad , A.l. Hadidi Samer
Published: June 2023

e13618

Background: The use of secondary (subgroup) analyses in medical literature is increasing. Findings from subgroup analyses may be misleading given they are observational in nature and do not resemble a randomized comparisons. Our study aimed to characterize the use of post hoc subgroup analysis in interventional clinical trials across solid malignancies. Methods: We searched OVID database for articles subgroup analyses from the year 2000 until Feb 15, 2022. Some of the used search terms were post-hoc, subgroup, secondary, cancer, tumor, breast, colorectal. 11641 articles were screened to include all secondary analyses of interventional solid tumor clinical trials. We excluded original trials, secondary analyses of cohort/observational studies. Results: Our final analysis included 1491 articles. Most studied malignancy was gastrointestinal cancers (22.6%), followed by genitourinary cancers (21%), and breast cancer (19%). The number of papers published per year increased steadily over the years with a peak in 2021 (16% of all articles). About 78% of articles reported receiving funding, with 40% reporting pharma-related funding. 29% of articles reported the usage of a medical writer. 55% of articles reported that authors had conflicts of interest (COI). Pharma-related funding was significantly associated with usage of a medical writer (p < 0.001). The mean percentage of authors with COI was 31.8 ± 0.95%. Articles that reported usage of a medical writer had a higher percentage of authors with COI (p < 0.0001). Women represented 32.7% and 25.9% of first and last authors, respectively. About 44% of both first and last authors resided in North America. Half of the articles explicitly indicated that they were secondary analyses in their titles. [Table]. Conclusions: Our study indicated that the use of secondary subgroup analysis in solid malignancies clinical trials is increasing. Half of the studies did not clarify the nature of them being secondary analyses in the title. Given their increased use, caution must be entertained when interpreting the results of secondary analysis. [Table: see text]