bluuebunny/crossref_metadata_2025_split huggingface dataset:
HER2 in situ hybridization testing in breast cancer: Applying algorithm-assisted assessment to reduce interobserver variability in difficult cases.
Authors: Gough Madeline , Liu Cheng , Srinivasan Bhuvana , Wilkinson Lisa , Dunk Louisa , Yang Yuanhao , Tuffaha Haitham , Kryza Thomas , Hooper John D. , Lakhani Sunil R. , Snell Cameron Edward 
Published: June 2023

e13561 Background: Accurate assessment of HER2 expression by HER2 immunohistochemistry and in-situ hybridization (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines defined 5 subgroups based on HER2 expression and copy number. Few reports have investigated interobserver variability when applying the revised guidelines. Digital algorithms may have a role in assisting pathologists in assessing cases with equivocal and uncommon patterns of HER2 amplification. This study was designed to evaluate interobserver variability in the gold standard manual assessment of HER2 ISH, particularly in less common HER2 ISH groups. Subsequently, we evaluated whether a new digital image analysis algorithm (Roche uPath HER2 Dual ISH image analysis) could reduce interobserver variability and potentially improve patient selection for targeted anti-HER2 therapy. Methods: Of 2982 breast cancer cases, we derived an enriched cohort of 99 patient cases with HER2 IHC 2+ or 3+ and selective ISH results (groups 2-4, evidence of HER2 heterogeneity, borderline negative group 5 and borderline positive group 1). As per guidelines, a minimum of 20 tumour cells per slide were manually and independently counted by 4 anatomical pathologists. After a washout period and second round of de-identification, the slides were scanned (DP200; Roche) and re-scored using the digital algorithm. Results: Pathologist reporting of patient HER2 status (HER2 positive vs. HER2 negative) using manual microscopy demonstrated significant interobserver variability, with a Fleiss’s kappa value of 0.471 (95% CI: 0.378-0.564; P= 1.24E-21) indicating fair-moderate agreement. Interobserver variability in manual scoring of HER2 ISH at the level of HER2 group designation (groups 1-5) showed poor-moderate reliability between pathologists, with an intraclass correlation coefficient (ICC) of 0.526 (95% CI: 0.407-0.641; P= 9.28E-21). Repeat analysis of the cohort using algorithm-assisted quantification resulted in moderate-substantial concordance in HER2 patient status reporting, with a Fleiss’s kappa value of 0.666 (95% CI: 0.572-0.759; P= 1.70E-40). Algorithm-assisted quantification resulted in moderate-good agreement of HER2 group designation, with an ICC of 0.763 (95% CI: 0.683-0.832; P= 8.76E-48). In sub-group analysis, use of the algorithm improved concordance particularly in groups 2,4 and 5. Time to report cases was also significantly reduced with the use of the algorithm. Conclusions: Our study demonstrates the potential to improve the concordance of HER2 amplification status reporting in less common HER2 subgroups using a digital algorithm and potentially improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.
Machine learning prediction of patient-reported outcomes associated with systemic therapy from medical records.
Authors: Lin Frank Po-Yen , Joshua Anthony M. , Epstein Richard J. 
Published: June 2023

e13564 Background: Measures of patient-reported outcome (PRO) are important metrics in cancer care. However, PROs are poorly captured in routine practice, and data collection may incur undue burden on patients. It remains unclear if electronic medical records (EMR) contain sufficient information to dynamically monitor PROs in patients undergoing intensive systemic therapy. Methods: Over a 6 week period, consecutive patients attending an outpatient chemotherapy unit completed two questionnaires (EORTC QLQ-C30 and NCI PRO-CTCAE) at each visit. The corresponding EMR, authored by oncologists and allied health providers, were extracted up to 4 weeks prior to the questionnaire date. Random forest algorithm was used to predict each dichotomized PRO class using bag-of- n-grams extracted from the text. The classification performance was assessed by 8-fold cross-validation, using the area under receiver operating characteristic curve (AUC) as the metric. This study is part of the ePERSIST (electronic phenotyping, event retrieval, and case summarisation in solid tumours) project, exploring methods for extracting structured variables from EMR to support oncology research. Results: 115 patients (157 visits to the chemotherapy unit) completed up to 6 sets of questionnaires each. Overall, the median quality-of-life (QOL) score of QLQ-C30, QL2, was 67 (IQR 50 to 83). Machine learning predicted low QL2 score with mean AUC of 0.710 (95% CI 0.630 to 0.791). At the Bonferroni threshold of 0.00025, machine learning significantly predicted several components of QLQ-C30: PF2 (mean AUC 0.722, 0.633 to 0.812), AP (0.725, 0.630 to 0.819), CO (0.686, 0.607 to 0.764), FI (0.677, 0.598 to 0.755), PA (0.696, 0.603 to 0.787), FA (0.686, 0.584 to 0.787), and RF2 (0.717, 0.595 to 0.839). Conversely, EMR could only be used to significantly predict 4 of 13 PRO-CTCAE categories: cutaneous (0.745, 0.659 to 0.832), oral (0.734, 0.630 to 0.838), cardiovascular (0.675, 0.593 to 0.758), and gastrointestinal (GI, 0.742, 0.625 to 0.860), or 15 of 79 (19%) of PRO-CTCAE items related to adverse events including cutaneous (radiation burns, hypohidrosis, rash), oral (anorexia, dysphagia, dysgeusia), and GI symptoms (constipation, bloating), and others (anxiety, hoarseness, pain, erectile dysfunction). An exploratory analysis revealed that specific keywords linked to the PRO items were present in only one (11%) QLQ-C30 and 4 (5%) PRO-CTCAE items. Conclusions: The use of machine learning on clinical text predicts QOL and certain patient-reported adverse events with moderate accuracy in individuals undergoing intensive systemic treatment, suggesting its potential utility in syndromic monitoring where PRO is not formally assessed. Given many items cannot be accurately extracted, however, obtaining information directly from patients through questionnaires may still be required because of limited documentation by clinicians.
Evaluating clinical trial inclusion/exclusion criteria from claims using generative artificial intelligence.
Authors: Mueller Judith , Parikh Ravi Bharat , Noble Andrew 
Published: June 2023

e13566 Background: Increasing enrollment in oncology clinical trials (CTs) requires identification of trial-eligible patients from routine health data. However, identification of CT-eligible patients is limited by inadequate access to EHR and its relevant clinical features to evaluate inclusion/exclusion (I/E) criteria in large populations. Artificial Intelligence (AI) applied to large-scale administrative claims may accelerate pre-screening of large patient cohorts against common I/E criteria related to stage and performance status (PS). Methods: This retrospective cohort study included 92,895 adult patients from ConcertAI’s real-world data: linked claims and deeply curated de-identified EHRs sourced from data-sharing agreements with 900+ community oncology clinics in the US. Patients had solid malignancies and an oncology encounter between January 6, 2014, and August 12, 2022. Patients with ≥1 claim record within one week of a PS entry and ≥25 claims within three years of a clinical encounter were included. We trained a Generative AI Transformer Model to predict good PS (ECOG 0-1 or Karnofsky 70-100) and stage IV disease at baseline, both of which are common inclusion criteria for therapeutic CTs in advanced malignancies. Model features included 7,745 timestamped claim codes and 8 time-independent demographic and socioeconomic variables. A state-of-the-art Generative AI Pre-trained Transformer Model was fine-tuned via compute-efficient transfer-learning to obtain predictive models for both PS and stage at baseline. The train/validation/test patient split was 95%/2%/3% with 3,039 patients in the PS test set and 2,718 in the stage IV test set. A comparator model was built to predict stage IV at baseline if a C77-9 ICD-100CM secondary malignancy code or cross-walked ICD-9-CM was in the claim record. Results: Among 92,895 patients, 48,331 (52%) were women, 63,620 (68%) were ≥65 years old, 54,495 (59%) were White, and 6,603 (7%) were Black. 38,229 (41%) were confirmed stage IV at baseline and 64,177 (69%) had good PS. In the test set predicting good PS, area under the receiver operating characteristic curve (AUROC) was 0.8 and under the precision-recall curve (AUPRC) was 0.89 for the Generative AI model. This corresponded to an Equal Error Rate (EER) operating point with good precision and recall (both 0.82). Performance metrics for the stage IV endpoint were also acceptable (AUROC 0.84, AUPRC 0.87, EER operating point 0.77). Comparator stage IV precision was 0.73 and recall was 0.47. Conclusions: Generative AI can leverage claims-only data to evaluate common pre-screening I/E criteria in large cohorts and outperforms models based on limited diagnosis codes. Rather than building one model per criterion per indication, Generative AI enables a systematic and reliable solution that can be readily scaled in future large-scale CT screening.
Individualized treatment estimates to inform on personalized cancer care decisions for treatment selection and treatment management.
Authors: Walsh David , Srivatsa Sumana , George Renee , Ellis Ben , Aljehani Mayada , Gross Mitchell E. , Basho Reva K. , Matasci Naim , Swisher Christine 
Published: June 2023

e13571 Background: Clinical trials report average effects of treatments at a population level. However, clinicians must balance treatment benefits against risks to personalize therapy. Here, we present a statistical framework which incorporates real-world data to estimate efficacy and symptom burden for an individual patient. We then assess the potential impact of continuing immunotherapy beyond progression in patients with metastatic solid malignancies on immune-related adverse events, progression, and survival. Methods: We corrected for sampling biases with inverse propensity score weighting (IPTW), and used the IPTW-transformed data to train predictive models of patient outcomes under each option. The models utilize a peer-based similarity based on 222 routinely-collected patient characteristics, such as cancer and treatment specific factors, laboratory tests, demographics, vitals, stage, comorbidities and symptoms, which are extracted from the electronic health record or natural language processing of clinician narratives, and Social Determinants of Health from Census data. We developed our methods on “Dataset A”: 5,919 breast and genitourinary (GU) cancer patients at a tertiary cancer center. We computed individualized treatment effect (ITE) estimates on “Dataset B”: 5,486 breast, GU, and thoracic cancer patients who received immunotherapy. Results: Our approach eliminated biases in Dataset A: the IPTW-transformation reduced the between-group standardized mean difference across identified confounders from an average of 0.225 to an average of 0.022. The similarity model exhibited strong discrimination (ROC-AUC > 0.75), and identified associations between patient characteristics and outcomes that matched clinicians’ expectations. Validations are ongoing to assess the ITE estimates on a held-out subset of Dataset B. These include subgroup analyses for metastatic triple-negative breast cancer and non-small cell lung cancer. Conclusions: We introduce a method to estimate the efficacy and toxicity of interventions, actions, or treatment decisions, which replicates the statistical robustness of a clinical trial while obtaining individualized insights. This framework provides causal estimates of symptom burden and risks of progression and survival to inform clinical decisions, including continuation of immunotherapy beyond progression. After IPTW, confounders in Dataset A were balanced between the treatment groups, with weighted standardized mean differences <0.1. [Table: see text]
Augmented curation of disease diagnoses and medications for patients with hepatocellular carcinoma.
Authors: Ip Wui , Pawlowski Colin , Mathew Vineet , Choudhary Mayank , Niesen Michiel , Anand Akash , Mao Allen , Peddinti Ananth , Kancharla Chetan , Kunz Isaac , Bonner Joseph D. , Boyle Matthew , Jinadu Olamilekan , Pozhitkov Alex , Xia Xiaoyu , Sarmiento Raul , Berger Stacy , Kirkpatrick Tarrah , Soundararajan Venky , Courdy Samir 
Published: June 2023

e13573 Background: Unstructured EHR data contains nuanced rich insights for Real World Evidence (RWE) studies, however harnessing this unstructured data at scale can be challenging. Here, we evaluated two Natural Language Processing-based curation (“augmented curation”) models to identify medical conditions and medications from clinical notes for patients with hepatocellular carcinoma at City of Hope National Medical Center. Methods: We deployed two augmented curation models originally trained on de-identified EHR data from the nference nSights platform, a federated data platform comprising clinical data from several Academic Medical Centers, on clinical notes found in the City of Hope POSEIDON platform, an oncology insights engine. We assessed their performances using manual chart review as reference. We then calculated an Enrichment Factor (EF) score for augmented curation model extraction and its 95% confidence intervals. The EF score was defined as the number of patients captured by either augmented curation or structured data divided by the number of patients captured by structured data alone. Results: The augmented curation models captured conditions and medications from clinical notes with F1 scores of 0.93 and 0.95, respectively. Compared to structured EHR data alone, the disease diagnosis model captured significantly more individuals with signs and symptoms such as vomiting (EF: 22.8, 95% CI: [15.2, 41.1]), weight loss (EF: 5.0, 95% CI: [4.1, 6.6]), nausea (EF: 4.3, 95% CI: [3.1, 6.9]), and edema (EF: 3.1, 95% CI: [2.7, 3.7]). Similarly, the medication use augmented curation model captured significantly more individuals with medications such as interferon, aspirin, and pembrolizumab. From a Cox proportional hazards model analysis, we found that a survival model using augmented curation-based features and structured data achieved a model concordance of 0.745 (95% CI: [0.741, 0.748]), compared to 0.722 (95% CI: [0.717, 0.726]) for the model based on structured data alone. In addition, the augmented curation-based survival model identified jaundice (HR: 2.0, 95% CI: [1.5, 2.7]) as a significant risk factor for mortality, which was not picked up by the structured data-based survival model. Conclusions: Overall, this study shows that augmented curation models can be used to accurately capture comorbidities and medications from unstructured clinical notes, and these extracted covariates are correlated with clinically meaningful endpoints such as survival. We recommend using augmented curation as a standard insight generation approach in RWE study protocols.
Performance analysis of four machine learning algorithms for the accurate prediction of metastatic disease in cutaneous squamous cell carcinoma.
Authors: Andrew Tom William , Bolnykh Iakov , Bowes Amy Louise , Fernando Suhari Arahliya Serendibsha Grace , Nair Ashvati , Martin Sabrina Noor Pia , Maan Balraj , Sloan Philip , Lovat Penny , Rose Aidan 
Published: June 2023

e13579 Background: Cutaneous squamous cell carcinoma (cSCC) are the most common form of metastasising skin cancer. Whilst rates of metastatic cSCC are low, they account for a significant proportion of skin cancer related morbidity and mortality, particularly within elderly cohorts, which poses a significant burden to healthcare services. Established cSCC tumour staging systems perform poorly at predicting metastatic risk. Additionally, we lack clinically validated prognostic biomarkers – highlighting the unmet need for novel risk stratification tools to guide clinical practice and improve outcomes for patients with advanced disease. We aimed to train four recognised machine learning (ML) algorithms on a large clinic-pathological dataset of primary cSCC, with the objective of optimising an ML strategy and developing a reliable and clinically useful risk stratification tool capable of accurately predicting metastatic events following primary cSCC. Methods: A dataset of primary cSCC registrations was derived from the Northern Cancer Registry, UK. This identified 7003 histologically confirmed primary cSCC registered between 2010–2020; providing a minimum of 2 years clinical follow-up. We conducted a retrospective analysis of standardised pathology datasets, recording clinical-pathological features. Primary outcome measure was regional and/or distant metastasis. Four machine learning algorithms, were trained based on these features, including: a Logistic Regression Trainer, a Decision Tree Classifier, a Random Forest Classifier and a fully connected artificial neural network (ANN). The algorithms were optimised on training data using five-fold cross validation. Subgroup analysis was performed using mean Shapley additive explanations (SHAP). Results: Accuracy scoring identified the ANN as the optimal predictor of metastasis (0.94), followed by: Logistic Regression Trainer (0.82), Random Forest Classifier (0.80), and Decision Tree Classifier (0.71). Preliminary subgroup analysis identified immunosuppression as most sensitive risk factor for developing metastatic disease (SHAP = 0.122). Conclusions: Significant heterogeneity in current morbidity and mortality data has limited the capacity of traditional statistical models and tumour staging systems to identify very high-risk cSSC. Our findings demonstrate that ML algorithms can accurately predict metastatic events in cSSC populations. Further development of a model user-interface is necessary to support the development of a useful risk stratification tool to guide clinical practice.
Association of presenting symptoms with COVID-19 disease severity and outcomes among hospitalized patients with cancer and COVID-19.
Authors: Ali Noman , Kheder Ed , Dickson Kodwo , Rubio David , Koom-Dadzie Kwame , Edelkamp Paul , Lin Heather Y. , Muthu Mayoora , Mohammed Alyssa , Leung Cerena , Odaro Orhue , Sibille Michelle , George Marina C. , Halm Josiah , Manzano Joanna-Grace Mayo ,   UT MD Anderson Hospitalist
Published: June 2023

e13581 Background: COVID-19 has led to several waves of outbreaks over the last 2 years. Cancer patients are among the most vulnerable and have high morbidity and mortality rates. We aimed to determine the association between presenting symptoms and symptom burden and COVID hospitalization outcomes among cancer patients. Methods: We conducted a retrospective cohort study on all adult hospitalized cancer patients during their first admission at our cancer center from March 2020 to May 2021. We reviewed medical charts to identify reported symptoms. We used the Foundry data platform to compile and analyze data, including patient and hospitalization characteristics and outcomes. We used the chi-square test to test differences in categorical variables. Results: Our cohort included 595 patients, 272 with hematologic malignancies (45.7%) and 323 with solid tumor malignancies (54.3%). 52.1% were male, with an age range of 18 to 91 years (median age: 62). 64.2% self-identified as Caucasian, 16.1% as other, 14.3% as African American, and 3.5% as Asian. After univariate analysis, we found that female patients had higher rates of abdominal pain (21.4% vs. 14.5%, p=0.03), GI symptoms (36% vs. 27.4%, p=0.03), and sore throat (14.4% vs. 8.4%, p=0.02) than male patients. Smoking status or Charlson comorbidity score were not associated with any presenting symptoms. Solid tumor patients had higher rates of abdominal pain (26.6% vs. 7.4%, p <0.01) than patients with hematologic malignancies. Patients with hematologic malignancies had higher rates of fever (59.2% vs. 42.7%, p<0.01), cough (66.2% vs. 50.8%, p<0.01), and shortness of breath (53% vs. 41.8%, p<0.01) than solid tumor patients. Solid tumor patients more frequently reported more than 3 symptoms (52% vs. 39.2%, p<0.01) than patients with hematologic malignancies. In terms of COVID outcomes, patients with cough (34.4% vs. 12%, p<0.01), shortness of breath (38.5% vs. 12.9%, p <0.01), and fever (29.1% vs. 20.6%, p=0.02) were more likely to have severe COVID-19. Patients with more than 3 symptoms were more likely to have severe COVID-19 (31.7% vs. 21.3% vs. 7.8%, p< 0.01) than those with 1-3 symptoms or no symptoms. Patients with cough (15.1% vs. 6.4%, p<0.01) and shortness of breath (15.5% vs. 7.9%, p< 0.01) had higher inpatient mortality. Patients with chills (5.8% vs. 12.9%, p< value 0.03) and headache (4.4% vs. 12.7%, p=0.02) had lower inpatient mortality. Conclusions: Our study showed that certain presenting COVID-19 symptoms are associated with patient and clinical characteristics, severity of disease, and inpatient mortality among adult hospitalized cancer patients.
Impact of life-sustaining therapies on critically ill patients with cancer.
Authors: Shah Kanan , Matos Joao , Struja Tristan , Svoboda Josef , Celi Leo , Sauer Christopher Martin 
Published: June 2023

e13590 Background: Advances in diagnosis and treatment have led to large reductions in mortality rates for patients with cancer, resulting in a steady increase in patients admitted to intensive care units (ICUs). However, there is conflicting evidence supporting the benefit of common life-sustaining therapies for critically ill patients, within this cohort. We examined differences in treatment effects of life-sustaining therapies between critically ill septic patients with and without cancer. Methods: Adults aged 18+ with a principal cancer diagnosis from 2008 - 2019, admitted to the ICU for sepsis, were extracted from publicly accessible ICU databases: Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Logistic regression estimated the likelihood of receiving mechanical ventilation (MV), renal replacement therapy (RRT) or vasopressors (VP) during ICU admission. Additionally targeted maximum likelihood estimation (TMLE) models estimated average treatment effects of MV, VP, and RRT on mortality. As TMLE uses machine learning models, it accommodates large numbers of covariates with complex, non-linear relationships. The method outputs Average treatment effect (ATE), an odds ratio representing the mean difference in outcomes in a hypothetical world in which all patients received treatment, compared to a hypothetical world in which no patients received treatment. Models were adjusted for age, sex, ethnicity, Charlson Comorbidity Index, SOFA score, hypertension, heart failure, asthma, COPD, CKD, and code status at admission. Results: A total of 7,120 adults met inclusion criteria. Septic patients with cancer did not have a significantly different likelihood of receiving MV (aOR [95%CI], 0.94 [0.6 - 1.46]), RRT (0.79, [0.33 - 1.93]), or VP (1.09, [0.74, 1.9]) than septic patients without cancer. Among patients with low SOFA scores, those with cancer were more likely than those without cancer to benefit from RRT. Otherwise, there was no statistically significant difference in mortality with the use of MV, RRT, and VP between patients with and without cancer (p>0.05). Conclusions: To our knowledge this is the first study to utilize contemporary, nationwide critical care data to establish a causal relationship between mortality and the use of common life-sustaining therapies for patients with cancer. Our study highlights the tremendous advances in cancer treatment over the last decade, leading to similar effects of common critical care interventions on mortality, regardless of cancer diagnosis. [Table: see text]