bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trajectories of machine learning-predicted mortality risk among patients with cancer and associated end-of-life utilization.
Authors: Liu Manqing , Chen Jinbo , Li Eric , Li Runze , Parikh Ravi Bharat 
Published: June 2021

1553 Background: Machine learning (ML) algorithms outperform traditional tools used for prognostication and may facilitate earlier discussions between oncologists and patients (pts) about hospice enrollment and treatment modification. Identifying longitudinal trajectories of mortality risk may help clinicians and health systems understand which populations such algorithms are likely to benefit. Methods: We identified trajectories of mortality risk and their association with existing metrics of end-of-life care quality, using electronic health and registry data from a prospective cohort of 3,280 pts with cancer who were seen in 18 tertiary or community medical oncology practices within a large academic health system between January 2018 and May 2020 and died prior to November 2020. A validated ML algorithm (c-statistic 0.89; Parikh et al, JAMA Oncol, 2020) prospectively generated mortality risk predictions prior to all encounters. Functional principal component analysis (FPCA) identified modes of variation for all patient-level mortality risk predictions associated with encounters prior to death. Adjusted logistic regression analyses tested associations between mortality risk trajectory and metrics of high-quality end-of-life care. Results: FPCA revealed 2 trajectories that represented 36% and 64% of all pts in the cohort. The first cluster (“unpredictable”) consisted of pts whose ML-predicted mortality risk rose sharply within 30 days of death. The second cluster (“predictable”) consisted of pts whose ML-predicted mortality risk was higher at baseline and rose gradually until death. Individuals with predictable mortality risk trajectories were more likely to have worse performance status, high comorbidity burden, and gastrointestinal (GI) malignancies (Table). Predictable trajectories were associated with higher hospice enrollment (adjusted odds ratio [aOR] 1.87, 95% CI 1.48-2.37), less inpatient death (aOR 0.72, 95% CI 0.56-0.92), less end-of-life intensive care unit admissions in the last 30 days of life (aOR 0.74, 95% CI 0.57-0.95), and less chemotherapy in the last 14 days of life (aOR 0.77, 95% CI 0.55-1.08). Conclusions: Over one-third of deaths among pts with cancer follow an unpredictable trajectory. For pts with unpredictable trajectories, overreliance on ML predictions could perpetuate aggressive end-of-life care.[Table: see text]
Clinical accuracy of information extracted from prostate needle biopsy pathology reports using natural language processing.
Authors: Wong Risa Liang , Sagar Medha , Hoffman Jacob , Huang Claire , Lerma Angelica , Kanabolo Diboro , Caldwell Joshua , Gore John L. 
Published: June 2021

1557 Background: Patients with prostate cancer are diagnosed through a prostate needle biopsy (PNB). Information contained in PNB pathology reports is critical for informing clinical risk stratification and treatment; however, patient comprehension of PNB pathology reports is low, and formats vary widely by institution. Natural language processing (NLP) models trained to automatically extract key information from unstructured PNB pathology reports could be used to generate personalized educational materials for patients in a scalable fashion and expedite the process of collecting registry data or screening patients for clinical trials. As proof of concept, we trained and tested four NLP models for accuracy of information extraction. Methods: Using 403 positive PNB pathology reports from over 80 institutions, we converted portable document formats (PDFs) into text using the Tesseract optical character recognition (OCR) engine, removed protected health information using the Philter open-source tool, cleaned the text with rule-based methods, and annotated clinically relevant attributes as well as structural attributes relevant to information extraction using the Brat Rapid Annotation Tool. Text pre-processing for classification and extraction was done using Scispacy and rule-based methods. Using a 75:25 train:test split (N = 302, 101), we tested conditional random field (CRF), support vector machine (SVM), bidirectional long-short term memory network (Bi-LSTM), and Bi-LSTM-CRF models, reserving 46 training reports as a validation subset for the latter two models. Model-extracted variables were compared with values manually obtained from the unprocessed PDF reports for clinical accuracy. Results: Clinical accuracy of model-extracted variables is reported in the Table. CRF was the highest performing model, with accuracies of 97% for Gleason grade, 82% for percentage of positive cores ( < 50% vs. ≥50%), 90% for perineural or lymphovascular invasion, and 100% for presence of non-acinar carcinoma histology. On manual review of inaccurate results, model performance was limited by PDF image quality, errors in OCR processing of tables or columns, and practice variability in reporting number of biopsy cores. Conclusions: Our results demonstrate successful proof of concept for the use of NLP models in accurately extracting information from PNB pathology reports, though further optimization is needed before use in clinical practice.[Table: see text]
Improving tumor mutational burden calibration in non-European patients.
Authors: Adib Elio , Nassar Amin , Akl Elie , Abou Alaiwi Sarah , El Zarif Talal , El-Khoury Habib , Ananda Guruprasad , Choueiri Toni K. , Gusev Alexander 
Published: June 2021

1559 Background: Tumor mutational burden (TMB), has been recently granted FDA approval as a biomarker for ICI treatment in tumors with a high mutation load (≥ 10/MB). To leverage this biomarker in the clinical setting, it is necessary to evaluate its application in diverse patient populations. In particular, tumor-only sequencing may overestimate TMB in non-EUR populations for which reference panels are small or unavailable, and thus have poorer predictive performance. Herein, we investigate the effect of TMB overestimation in non-EURs on patient diagnosis and clinical outcomes in a real world patient cohort. Methods: TMB was computed using a tumor-only NGS platform (Oncopanel) for 8349 cancer patients (pts) of 7 cancer types (Table). Genetic ancestry was inferred directly from tumor sequencing data and confirmed for a subset of pts using germline SNP arrays. TMB was compared using Wilcoxon rank-sum test between European (EUR) and non-EUR pts. TMB percentile rank by ancestral group (East Asian, African, European) and tumor histology was computed. In non-EUR pts, TMB was calibrated by reassigning its value to the corresponding percentile rank in EUR pts having the same cancer (AH-TMB). Tumors with raw TMB ≥ 10/MB were assigned as TMB-high (TMBH; with the rest referred to as TMBL for TMB-low) and those with calibrated AH-TMB ≥ 10/MB as AH-TMBH. A subset of pts treated with ICIs at DFCI and of EUR ancestry were analyzed. TMB was intentionally mis-calibrated in EURs (MC-TMB), to mimic the TMB overestimation observed in non-EURs. Associations between TMBH status and overall survival (OS) was assessed using Cox regression. Results: Uncalibrated TMB was significantly higher in tumors from non-EUR pts overall (p <0.0001, Table) as anticipated, whereas this difference was not observed in tumors with matched germline filtering. We reassigned non-EUR pts into low/high groups using calibrated AH-TMB, which changed the assignment of 67/670 non-EURs, most of which (65/67) were downgraded from TMBH to AH-TMBL. In the ICI-treated cohort, there was a strong association between TMBH status and OS (p = 2e-11, HR = 0.6) whereas MC-TMBH (mimicking the miscalibration in non-EUR samples) had a weaker association with OS (p = 0.0013, HR = 0.8). Importantly, pts assigned to TMB-H due to miscalibration (90 TMB-L/MC-TMBH pts) had shorter OS compared to the true TMBH group (p = 3e-6, HR = 1.9). Conclusions: In this analysis, we showed that tumor-only sequencing platforms can overestimate TMB in non-EUR pts, impacting treatment decisions and outcomes. Replacing raw TMB counts with an ancestry-adjusted measure could optimize TMB application in the clinical setting when germline sequencing is not available. Accurate ancestry inference can be performed using tumor-only sequencing.[Table: see text]
A machine learning tool to predict mortality risk among patients with metastatic cancer in outpatient oncology care.
Authors: Butler Brandon , Tayiab Nadaa , Phu Serra , Hoang Susan Nga , Turnwald Brian , Garey Jody S. , He Bo , Hoverman John Russell 
Published: June 2021

1560 Background: End-of-life management is a well-known challenging aspect of cancer care. In particular, timely hospice enrollment is a leading quality metric in the Oncology Care Model that has substantial room for improvement. An automated algorithmic tool that can incorporate the wealth of available EHR data and rapidly identify patients with a high risk of imminent mortality could be a valuable asset to supplement important clinical decisions and improve timely hospice care. Methods: A retrospective study cohort was formed using patients with metastatic cancer from US Oncology Network (USON) practices participating in the Oncology Care Model (OCM) between January 1, 2017 and June 30, 2019. Patients were required to have at least one record for lab values and vital signs in the EHR database. Patients were excluded from the study cohort if they were not enrolled in the OCM program or did not have a diagnosis for metastatic cancer. The patients satisfying the selection criterion were used to train and optimize the model. The training dataset was also used for internal validation and hyperparameter tuning until the final model was produced. As external validation, the final model was independently tested on 3 separate holdout datasets including OCM patients between July 1, 2019 and March 31, 2020. To avoid bias, all holdout datasets used for validation were excluded from the model. Results: A multivariable model to predict 90-day mortality was developed using a retrospective dataset derived from EHR data and Medicare claims data. A logistic regression algorithm using L1 (lasso) regularization yielded the best performance compared to other model candidates. The performance on the training cohort was given by a cross-validated AUC score of 0.85 (95% CI, 0.84 to 0.86). Further, external validation conducted using 3 independent holdout datasets demonstrated impressive generalizability marked by stable performance scores across multiple time periods (AUC between 0.84 and 0.85). Conclusions: This study builds upon previous work and further establishes the utility of machine learning to predict risk of imminent mortality for advanced cancer patients using available EHR data. A data-driven tool that estimates the probability of 90-day mortality could be leveraged as a powerful supplementary aid to clinicians managing end-of-life care at oncology practices.
Oncology trial enrollment trends following the first wave of the COVID-19 pandemic.
Authors: Lamont Elizabeth W. , Blum Kevin , Katriel Ron G. , Ensign Lisa G 
Published: June 2021

1565 Background: The first wave of the COVID-19 pandemic was associated with a negative effect on global oncology clinical research and development including documented decreases in new trial launches and patients enrolling on existing trials. We sought to evaluate secular trends in patient enrollment on global oncology trials of drugs and/or biological agents coincident with trends in the pandemic through 10/31/2020. Methods: This time-series study of global oncology clinical trial patient enrollment relied on data from the Medidata Enterprise Data Store, which comprises studies using the RAVE electronic data capture platform, and eCDC. We quantified the number of patients enrolling on oncology trials according to contiguous pandemic waves between 01/05/2020 and fixed right censoring at 10/31/2020. For this study, we defined pandemic waves empirically through review of peaks and nadirs of global case counts/week from eCDC data: wave 1 (01/05/2020-05/02/2020), wave 2 (05/03/2020-08/22/2020), wave 3 (08/23/2020-10/31/2020). We used negative binomial regressions to evaluate associations between time periods (i.e., waves) and counts of new trial enrollees and time periods and counts of new COVID-19 patients. Results: A total of 54,752 patients enrolled in 1,176 oncology trials world-wide during the observation period: 14,888 patients during wave 1, 19,631 patients during wave 2, and 20,233 patients during wave 3 of the pandemic. Compared to wave 1, the incidence of new trial enrollees increased by 10% in wave 2 (incidence rate ratio [IRR] 1.10, 95% confidence interval [CI] 0.95-1.26) and by 29% in wave 3 (IRR 1.29, 95% CI: 1.10-1.52). Over the same period, 47,336,995 people were diagnosed with COVID-19 infection: 3,481,165 patients during wave 1, 20,205,244 patients during wave 2, and 23,650,586 patients during wave 3. Compared to wave 1, the incidence of new COVID-19 patients increased by 617% in wave 2 (IRR 6.17, 95% CI: 2.76-13.77) and by 1,155% (IRR 11.55, 95% CI: 4.61-28.95) in wave 3. Conclusions: Despite substantial increases in the incidence of COVID-19 following the first wave of the pandemic, the number of patients enrolled in oncology clinical trials increased notably during the same period. Future research seeks to understand the mechanism through which the oncology research and development enterprise adapted to the shock of the COVID-19 pandemic. eCDC Source: European Centre for Disease Prevention and Control. Data provided subject to license available at: https://www.ecdc.europa.eu/en/copyright
AI-based imaging biomarker in mammography for prediction of tumor invasiveness.
Authors: Nam Hyeonseob , Kim Ki Hwan , Ock Chan-Young 
Published: June 2021

1568 Background: The preoperative diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) can be upstaged in the final pathology, and this possibility is linked to the controversy over whether axillary staging is necessary in primary operation. In this study, we developed an artificial intelligence (AI)-powered Imaging Biomarker in Mammography (IBM) that can predict tumor invasiveness in preoperative mammography and evaluated its performance in an external validation cohort. Methods: A total of 151,764 exams of 4-view mammograms were collected from five institutions of three countries to develop the AI algorithm for breast cancer detection, where 31,776 were cancer exams. In previous studies, the performance of this breast cancer detection algorithm has already been evaluated, and in this study, we further developed the AI-powered IBM for predicting tumor invasiveness on top of the AI algorithm for breast cancer detection. To develop the AI-powered IBM for predicting invasiveness, final diagnosis information was collected for 8,251 cancer exams (472 DCIS, 388 ductal carcinoma in situ (DCIS-MI), and 7,391 invasive ductal carcinoma (IDC)), and 886 cancer exams (44 DCIS, 51 DCIS-MI, 791 IDC) were additionally collected for internal validation. The AI-powered IBM was developed via two stages of training – 1) training with diagnosis labels (cancer vs non-cancer), followed by 2) fine-tuning with invasiveness labels (DCIS, DCIS-MI, IDC). The AI-powered IBM also tested in an external validation cohort of 699 cancer exams (68 DCIS, 19 DCIS-MI, 612 IDC) and all the exams were confirmed by surgical biopsy. Results: The AI-powered IBM showed an area under the curve (AUC) values of 0.968 for breast cancer detection and it successfully distinguished IDC from DCIS and DCIS-MI with AUC values of 0.898 and 0.851, respectively (Table). In addition, the AUC value in terms of discriminating between DCIS and DCIS-MI was 0.752. When the AI-powered IBM was tested in the external cohort, it could detect breast cancer with the AUC of 0.952 and, its performance in terms of invasiveness prediction was similar that of the internal validation (IDC vs DCIS, 0.810; IDC vs DCIS-MI, 0.846), which supports the AI-powered IBM is applicable to the unseen mammography exam. Conclusions: The AI-powered IBM can distinguish IDC from DCIS and DCIS-MI in mammography. The results support that the AI-powered IBM can be used as a biomarker to help determine the surgical plan that includes whether or not to perform the axillary dissection.[Table: see text]
Superior overall survival (OS) and disease-free survival (DFS) predictions for patients with glioblastoma multiforme (GBM) using Cellworks Singula: myCare-022-03.
Authors: Wen Patrick Y. , Castro Michael , Watson Drew , Kapoor Shweta , Agrawal Ashish , Alam Aftab , Roy Kunal Ghosh , Rajagopalan Swaminathan , Basu Kabya , Lala Deepak Anil , Mundkur Nirjhar , Christie Jim , Pampana Anusha , Basu Sayani , Sahu Diwyanshu , Narvekar Yugandhara , Singh Divya , Nair Prashant , Ahluwalia Manmeet Singh 
Published: June 2021

2017 Background: The Cellworks Singula Therapeutic Response Index (TRI) has been developed to assist clinicians and GBM patients in choosing between competing therapeutic options. In contrast to approaches that consider single aberrations, which often yield limited benefit, Cellworks utilizes an individual patient’s next generation sequencing results and a mechanistic multi-omics biology model, the Cellworks Omics Biology Model (CBM), to biosimulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. For any individual patient and alternative therapy, Cellworks integrates this biologically modeled multi-omics information into a continuous Singula TRI Score, scaled from 0 (low therapeutic benefit) to 100 (high therapeutic benefit). We demonstrate that Singula is strongly associated with OS and DFS beyond standard clinical factors, including patient age, patient gender, and physician prescribed treatments (PPT). Methods: In this study, Singula’s ability to predict response was evaluated in a retrospective cohort of 100 GBM patients with OS and DFS data from The Cancer Genome Atlas (TCGA) project, treated with PPT. As a primary analysis of the CBM and TRI Score, Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used to assess the hypothesis that Singula is predictive of OS and DFS above and beyond patient age, patient gender, and PPT. A p-value < 0.05 for the corresponding likelihood ratio statistic was required to be considered significant. Results: Multivariate analyses were performed to assess the performance of the Singula Therapy Response Index after adjusting for the contribution of standard clinical factors. The same Singula TRI algorithm and clinical cutoffs were used for all clinical outcome measures. These analyses, shown in the table, suggests that the proposed Singula TRI provides predictive value of OS and DFS above and beyond patient age, patient gender, and PPT. Conclusions: The Singula TRI Score provides a continuous measure scaled from 0 (low benefit) to 100 (high benefit) for alternative GBM therapeutic options. In this retrospective cohort, Singula was strongly predictive of OS and DFS and provided predictive value beyond PPT, patient age and gender. These results will be further validated in larger scale, prospectively designed clinical studies.[Table: see text]
Efficacy finding cohort of a cancer peptide vaccine, TAS0313, in treating recurrent glioblastoma.
Authors: Arakawa Yoshiki , Okita Yoshiko , Narita Yoshitaka 
Published: June 2021

2038 Background: TAS0313 is a cancer vaccine cocktail comprising three long peptides with a total of 12 cytotoxic T lymphocyte (CTL) epitope peptides. These peptides were derived from eight cancer-associated antigens (EGFR, KUA, LCK, MRP3, PTHRP, SART2, SART3, and WHSC2) that are overexpressed in various cancer types including glioblastoma (GBM). We performed a single-arm, multicenter efficacy confirmatory cohort study as part of the TAS0313 Phase I/II study in patients with recurrent GBM. Methods: The enrolled patients with histologically or cytologically confirmed grade IV GBM (including gliosarcoma, giant cell glioblastoma, and epithelioid glioblastoma) had at least one of the following HLA types: HLA-A*02:01, -A*02:06, -A*02:07, -A*11:01, -A*24:02, -A*31:01, or -A*33:03. Eligible patients were those who received standard radiotherapy and temozolomide, had a confirmed first or second recurrence, or progression with measurable disease outcomes with good performance status (Karnofsky Performance Status score > 70). TAS0313 (27mg) was subcutaneously administered on days 1, 8, and 15 of cycles 1 and 2 and day 1 of cycle 3 or later in 21-day cycles until disease progression or unacceptable toxicity occurred. Tumor response was evaluated using The Response Assessment in Neuro-Oncology criteria. The antigen-specific CTL and immunoglobulin G (IgG) were analyzed by ELISPOT assay and Luminex assay, respectively, before and after treatment. The primary objective was to evaluate the efficacy of TAS0313. Secondary and exploratory methods were used to evaluate the safety and immune response of TAS0313. Results: As of 10th September 2020, 10 patients have been treated with TAS0313 (eight with GBM and two with gliosarcoma). The median age of participants was 56.5 [range, 33–69 years], and methylation status of the MGMT promoter was methylated in four patients, unmethylated in four, and unknown in two. The best overall response was partial response 1/10 (10.0%) and stable disease 4/10 (40.0%). One case showed 69.1% tumor shrinkage. The treatment of two patients was ongoing for over 7 months. The 6-month progression-free survival (PFS) rate was 25.0%, and the median PFS was 2.3 months. The most common adverse drug reactions (ADRs) were grade 1–2 injection site reactions and pyrexia. There were no grade 4 or 5 ADRs. In some patients, TAS0313 treatment was confirmed to increase CTL and IgG levels compared with pre-treatment samples. Conclusions: TAS0313 showed promising efficacy with expected immune responses and favorable safety and tolerability in patients with recurrent GBM. Further investigation of TAS0313 is warranted to validate our findings. Clinical trial information: JapicCTI-183824.