tbuckley/cabot-search huggingface dataset:
Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer.
Authors: Charles-Éric Rivest, Fatim Ezzahra Ben Abderrazik, Marc-Antoine Côté-Marcoux, Mandy Malick, Robert Hanel, Michel Pavic
Journal: Journal of Clinical Oncology (2024)

e13570 Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer. Background: The Multinational Association for Supportive Care in Cancer (MASCC) score is used to risk-stratify outpatients with febrile neutropenia. Currently, data on the use of the MASCC score are based on studies with a small number of patients. Our primary aim was to determine whether a MASCC score ≥ 21 identifies patients with solid tumors who would evolve without complications. Methods: We conducted a retrospective cohort study of patients admitted with febrile neutropenia and solid cancer at Sherbrooke University Hospital from 2011 to 2022. We collected patients' demographics, type of cancer, current chemotherapy regimen, whether patients were candidates for outpatient treatment, MASCC score, duration of IV antibiotic therapy, complications (intensive care unit admission, hypotension, hypoxemia, acute kidney failure, bacteriemia (persistent or developing after 48h of IV antibiotics) and escalation in antimicrobial regimen after hospital admission), and inpatient deaths. We used chi-square analysis and multivariate analysis to determine factors other than MASCC that predict complications. Results: A total of 290 febrile neutropenia patients with solid tumors were identified. 93 patients had a MASCC score &lt;21 and 196 were identified as low risk (MASCC score ≥ 21). The preliminary results showed that the MASCC score had a specificity of 58,3% [95% CI 48,4%-67,8%], a sensibility of 83,0% [95% CI 76,7%-88,1%], a positive predictive value of 77,0% [95% CI 72,7%-80,9%] and a negative predictive value of 67,0 [95% CI 58,7%-74,4%] to identify complications. A low risk MASCC score showed statistically significative reduction in duration of neutropenia, IV antibiotics, fever and length of hospitalisation. Conclusions: The MASCC score did not demonstrate high enough accuracy to precisely identify patients which would have evolved without complications. However, a low-risk score identified by using the MASCC score was found to have strong association with reduction of duration of hospitalisation burden. Furter data will be collected to increase the strength of the study.
Use of a deep learning model on H&amp;E slides to predict nucleic acid yield for NGS testing.
Authors: Josh Och, Yoni Muller, Kunal Nagpal, Boleslaw Osinski, Sun Hae Hong, Martin C. Stumpe, Nike Beaubier, Ryan D Jones
Journal: Journal of Clinical Oncology (2024)

e13589 Background: Samples sent for NGS may fail testing, leading to treatment delays or missed opportunities to offer patients targeted therapies. A common reason for assay failure is insufficient total nucleic acid (TNA). We developed and validated a model based on a digitized H&amp;E slide and readily available clinical data that predicts whether samples will yield sufficient TNA for NGS testing. This model could lead to faster turnaround times and lower failure rates by helping choose patient blocks most likely to complete sequencing, or routing likely-to-fail samples to specialized extraction methods or smaller panel assays with lower input TNA requirements. We also evaluated the model’s ability to identify samples that yield abundant TNA, which could help pathologists spare tissue for other testing. Methods: We developed our model on samples received for NGS testing from January 1 - June 30, 2023, and tested on a large, real-world cohort of 9,707 samples received from July 1 - September 30, 2023. Cell count, assessed by a previously developed deep learning model on digitized H&amp;E slides, along with the number of slides scraped, sample age, procedure type, and tissue site were used as model inputs. We measured the model’s ability to predict whether a sample yielded less than 100 ng of TNA (the target mass for the NGS assay used) and whether it failed sequencing. We also evaluated the model’s ability to predict whether a sample would have abundant TNA (defined here as yielding more than 1000 ng of TNA, 10x the target mass for the assay). Results: On the validation set, the model predicted samples that yielded TNA &lt; 100 ng with a specificity of 98%, sensitivity of 26% and PPV of 82% (prevalence = 24%). It predicted samples that failed DNA sequencing with a specificity of 96%, sensitivity of 30% and PPV of 58% (prevalence = 15%); and samples that failed either DNA or RNA sequencing with a specificity of 98%, sensitivity of 23%, and PPV of 80% (prevalence = 27%). On the validation set, 742 samples were flagged, 430 of which failed DNA sequencing, and 590 of which failed either DNA or RNA sequencing. When predicting abundant TNA, the model predicted samples yielding TNA &gt; 1000 ng at a specificity of 97%, sensitivity of 54% and PPV of 88% (prevalence = 26%). On the validation set, the model predicted 1544 samples as abundant, only 185 of which yielded TNA &lt; 1000 ng and only 16 with TNA &lt; 100 ng. Conclusions: The developed model holds promise as a tool for use at the beginning of NGS assays. It can be utilized to optimize testing decisions including selection of which samples to sequence, routing likely-to-fail samples to alternate testing methods, and sparing tissue for future assays from samples with abundant tissue.
Natural language processing for automated breast cancer recurrence detection and classification in computed tomography reports.
Authors: Jaimie Lee, Andres Zepeda, Gregory Arbour, Kathryn V. Isaac, Raymond T. Ng, Alan Nichol
Journal: Journal of Clinical Oncology (2024)

e13591 Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]
Use of bispecific antibodies (BsAbs) in community practices: Key attributes to develop logistics and workflow for management of cytokine release syndrome (CRS).
Authors: William Bruce Donnellan, Jonathan Abbas, Jesus G. Berdeja, Michael Byrne, Jason Claud Chandler, Lourenia Cassoli, Shih-Wen Lin, Anthony Masaquel, Lisa Raff
Journal: Journal of Clinical Oncology (2024)

e13575 Background: Establishing workflows to monitor and manage CRS in community practice settings presents unique challenges that may not be encountered in more specialized centers. Real world experience was gathered from OneOncology specialists to understand key steps, common challenges, and best practices to establish CRS workflows in community practices. Methods: Qualitative data were gathered via a 90-minute focus group and follow-up interviews. Participants were MD or PharmD community heme-oncology providers in physician-owned practices with 10-20 physicians. As of January 2024, participants had treated over 30 patients with commercially available BsAbs. Theme matrix techniques facilitated data analysis and presentation. Results: Catalysts for developing CRS workflows were the addition of BsAbs to the practice formulary, identification of practice leaders willing to drive protocol development, and education of clinic and hospital staff who would manage patients treated with BsAbs. Key steps in the workflow included defining lines of communication within the practice during and after office hours, designating leads to oversee staff training, and creating practice-specific management plans. A process for potential inpatient admission for moderate/severe CRS was developed in collaboration with hospital administrators and nursing leaders. Lines of communication between the treating practice, pharmacy, and hospital care staff before, during, and after treatment were established, and methods to ensure availability of necessary medications were identified. Finally, patient support and the logistics of patient/caregiver education on BsAb therapy, symptoms of CRS, and how and when to access care was highlighted. Conclusions: Use of BsAbs in the community requires thoughtful coordination between oncology practices and hospitals. Keys to success include defining flexible, practice-specific workflows for managing CRS, transitioning patients efficiently from clinic to inpatient setting, and careful training of staff, patients, and caregivers. [Table: see text]
Tumor origin identification through machine learning and gene expression profiling.
Authors: Xin Wang, Jun Li, Yanqing Zhou, Yaru Chen, Tianliang Liu, Shifu Chen
Journal: Journal of Clinical Oncology (2024)

e13597 Background: Accurate identification of tumor origin is crucial for effective diagnosis and treatment, particularly in cases of metastatic tumors. Interpretable machine learning models have displayed significant potential in addressing this problem when imaging and immunohistochemistry (IHC) examinations are ineffective. In this study, we developed a panel using gene expression profiles from various tumors and constructed a robust machine learning model for precise identification of tumor origin. Methods: RNA sequencing (RNA-seq) data of 9462 tumor samples originating from 21 different organs were collected from The Cancer Genome Atlas (TCGA). We conducted feature engineering through unsupervised clustering and differential gene expression analysis, selecting a refined panel of 164 genes from a pool of over 60,000 identifiers. Subsequently, a machine learning classifier grounded in Logistic regression (LR) was trained on 9462 samples with the constructed 164-gene panel. To enhance the adaptability of our model to this task, 10-fold cross-validation was employed in the multi-class mode. Two independent test sets, the Primary tumor set (PT, n=3420, including 19 tumor types) and the Metastatic tumor set (MTP, n=100, all originating from the prostate and spread to bone, liver, etc.), were established with samples from Gene Expression Omnibus (GEO) and other published studies. Notably, all samples underwent FPKM normalization and there was no overlap between training and testing samples. Model performance was assessed using accuracy, specificity, and sensitivity metrics. Results: The 164-gene expression panel achieved a cross-validation accuracy of 96.73%. In the assessment of the PT test set, our model achieved an overall 99.62% specificity, 92.31% accuracy and 89.79% sensitivity, exhibiting performance comparable to similar models in other studies. Additionally, our model accurately traced 91% of metastatic tumors in the MTP test set to the prostate, surpassing previous lines of work by a large margin. Conclusions: Gene expression patterns reveal organ-specific characteristics that could be used to identify tumor origin. The combination of a condensed yet comprehensive gene expression panel with a robust machine learning model serves as a promising tool for tumor diagnosis. Ongoing correlative studies aim to extend predictions from tissue organs to cancer subtypes.
Financial impact of fixed-dose versus weight-based dosing approaches for pembrolizumab and nivolumab in the Oncology Care Model for The US Oncology Network (The Network).
Authors: Jennifer Fernandez, Puneeth Indurlal, Jody S. Garey, Lalan S. Wilfong
Journal: Journal of Clinical Oncology (2024)

e13573 Background: PD-1/PD-L1 antagonists, pembrolizumab and nivolumab, are being used in an increasing number of cancer types. The FDA initially approved both agents based on a weight-based dosing (WBD) approach, but subsequently included a therapeutically equivalent fixed-dose (FD) approach for most indications. The Oncology Care Model (OCM) was a value-based care (VBC) program from Medicare that required total cost of care (TCOC) accountability from participating practices. We evaluated the impact of using the FD vs WBD on TCOC in the OCM for both pembrolizumab and nivolumab. Methods: Using Medicare claims data for 14 OCM-participating practices in The Network from July 2016 to June 2022, and corresponding weight data from the Electronic Health Record (EHR), we evaluated the distribution of patient weights, use of FD vs WBD, and the financial impact of the use of FD vs WBD vs weight-based dosing capped at the fixed dose (WBDc). Results: Summary statistics are shown (Table). In most cases, the FD was higher than the WBD for both nivolumab and pembrolizumab. FD led to an increase in drug cost of 8.4% for nivolumab and 27.2% for pembrolizumab, corresponding to a 11.3% increase in TCOC for the episodes where FD nivolumab and pembrolizumab were used. Conclusions: Fixed-dosing approaches for commonly used high-cost anti-neoplastic drugs can drive increases in TCOC. The use of WBD could lead to a decrease in drug cost and 11.3% savings of TCOC within OCM-like models, likely without impacting clinical outcomes. These findings encourage practices in risk-bearing models to reflect on the widespread adoption of FD. The withdrawal of varying sizes of vials can limit the use of WBD. Pharmaceutical manufacturers should assist with WBD by providing multi-use vials and varying sizes. The feasibility of using the WBD for pembrolizumab with an every 6-week regimen remains to be explored. [Table: see text]
Impact of preparation and execution of strategy to mitigate the platinum shortage within a network of community oncology practices.
Authors: Aimee Ginsburg, Andrea Dickens, Robert Thornby, Kaci Dominguez, Josh Howell, Leslie T. Busby
Journal: Journal of Clinical Oncology (2024)

e13572 Background: The US Oncology Network (USON) is made up of over 600 sites of care and treats 1,400,000 patients annually. Drug shortages have a massive impact on patient outcomes, particularly with limited source oncology medications. Proactive mitigation strategies by the national USON Pharmacy &amp; Therapeutics (P&amp;T) committee are examined to assess impact on cisplatin and carboplatin (cis/carbo) shortages, where a phased approach extended supply for patients. Methods: P&amp;T began implementing gradual reductions in cis/carbo allocations in mid-April 2023. This included stewardship to reserve use in patients with limited alternatives, dose rounding, reducing dose/cycle, and extending cycle interval, when clinically appropriate. As supply depletion became imminent, P&amp;T implemented a novel drug ordering process that restricted cis/carbo use criteria to curative intent without alternative treatment options, requiring case-by-case approval. Comprehensive clinical guidance was developed to provide recommendations for alternative therapies. Alternative regimen templates were built into the centrally managed EHR. To evaluate the impact, drug utilization patterns were tracked during the 6-month shortage (April - September 2023) and compared to historical data. Results: Pronounced impacts were seen during acute supply constraints: cisplatin administrations (admins) and mg decreased to 53% and 48% of baseline, carboplatin admins and mg decreased to 84% and 78%. Compared to the prior 6 months, patients receiving cis/carbo was similar pre-shortage (15,368) vs. during (15,296).Mitigation strategies resulted in preservation of cis/carbo admins for diseases with limited alternatives (NSCLC, head &amp; neck, ovarian/uterine); other cancers with alternatives had a decrease in admins (see table). Dose conservation guidance led to a decrease in average mg administered by 5.1% for cisplatin (72.2 vs. 68.5), and 6.2% for carboplatin (385.5 vs. 361.7). New admins of oxaliplatin increased by 11% (4,230 vs 4,714), primarily in GI malignancies. Conclusions: The rigors of stewardship with a phased approach allowed USON to sustain new patients while conserving cis/carbo for those with limited alternatives. Frequent communication about supply status and conservation guidance was critical for transition to alternative treatment strategies. This experience tested USON organizational readiness for future shortages. [Table: see text]
Greening Ireland’s oncology practice: Reducing climate toxicity and improving sustainability in a national chemotherapy formulary.
Authors: Emer Lynch, Philip Hugh Bredin, Catherine Weadick, Simon L Barry, Niamh Dorney, Munzir Hamid, Maeve Hennessy, Nay Myo Oo, Alex J. Bryan, Katie Cooke, Deirdre O'Mahony, Roelof W.F. van Leeuwen, Matthijs Van De Poll, Dearbhaile Catherine Collins, Seamus O'Reilly
Journal: Journal of Clinical Oncology (2024)

e13557 Background: Climate change has been acknowledged by the World Health Organisation (WHO) as a “fundamental threat” to healthcare delivery. However, healthcare now accounts for more than 5% of global emissions. Over 25% of the oncology services contribution to this relates to the development, transport and administration of systemic anticancer therapies (SACT), and represents a modifiable factor. In Ireland, the National Cancer Control Programme (NCCP) determines the standard of care systemic anticancer therapy (SACT) protocols. This study proposes to review existing protocols and assess for and highlight nationally implementable climate toxicity mitigation strategies. Methods: NCCP SACT protocols (available at https://www.hse.ie/eng/services/list/5/cancer/profinfo/chemoprotocols/) were reviewed for impact reduction potential under the following headings: 1) Baseline and regular tests and investigations; 2) Pre-medications; 3) De-escalation, and 4) ESMO Magnitude of Clinical Benefit. Results: A total of N = 333 protocols were reviewed. N = 32 protocols had multiple indications across more than one tumour stream. Overall, N = 702 opportunities were identified where the protocol impact could be reduced, either by consolidation of pre-medication and pre-hydration to oral administration, regimen or investigation de-escalation, dose banding, switching non-oral to equivalent oral SACT alternative or re-considering regimen use where clinical benefit is measured as non-substantial. In addition, there were a further N = 507 opportunities identified where baseline and regular investigations required by the protocol could be incorporated into routine visits, minimising hospital travel and time toxicity to patients. Conclusions: This study considered just one aspect of oncology care in Ireland, the delivery of SACT, and identified over 1200 opportunities across standardised national protocols where associated climate toxicity could be reduced without compromising standard of care. A sustainability matrix to promote identified action points is proposed for inclusion within all protocols and could be used in other jurisdictions. Further work to consider impact reduction opportunities in other areas of cancer care, including radiology and diagnostics should be considered.
Predicting persistent fever in patients with cancer using machine learning.
Authors: Christopher Martin Sauer, Alexander Brehmer, Till Rostalski, Martin H. Schuler, Felix Nensa, Hans Christian Reinhardt, Jens Kleesiek, Gernot Pucher
Journal: Journal of Clinical Oncology (2024)

e13552 Background: Sepsis is the leading cause of hospital readmission and death in the developed world. Cancer patients are at high risk due to their underlying disease and therapies. Meanwhile, antimicrobial resistance increases steadily, warranting a more careful administration of antibiotics. Predicting fever persistence 48 hours after initiation of antibiotics could help reduce antibiotic escalation and chest CT scans in patients where fever is predicted to subside within the next 24 hours. Methods: All cancer patients of the University Hospital Essen (UHE) between 1/1/2020-7/1/2023, receiving broad-spectrum i.v. antibiotics, and having persistent fever after 48 hours of first administration were extracted from UHE’s Fast Health Interoperability Resources clinical database. Vital signs, clinically important covariates, as well as lag features of vital signs and laboratory values were included. The binary target to predict was prevalence of body temperature ≥38 °C after 48-72 hours. Gradient boosting machines, a deep learning neural network, logistic regression (LR), random forest (RF) and a voting ensemble classifier were implemented. Hyperparameter tuning and model validation was performed through five-fold stratified cross validation (80/20% train/test split). Results: Analyzing 2.9 million patient records of UHE we obtained approx. 27,000 hospitalized adults with cancer. Restriction to hematology/oncology wards and intravenous broad-spectrum antibiotics resulted in 1,267 cancer patients. Of these, 264 patients had persistent fever after 48 hours, of which 111 patients had solid cancers, 66 lymphomas, and 87 other hematologic malignancies. 72 hours after antibiotic therapy 160 patients (60.2%) had persistent fever. Based on the area under the precision-recall curve (AUPRC) of 81.7%, the voting classifier model had the highest predictive performance. The model shows a sensitivity of 85%, a specificity of 63%, a Negative Predictive Value (NPV) of 75% and a Positive Predictive Value (PPV) of 73% on a cross-validated test set (Table). Conclusions: Machine learning-based algorithms can predict fever evolution and assist in clinical decision-making, potentially contributing to better patient care by preventing unnecessary antibiotics escalations and CT scans. Based on current model performance, 25% of chest CT scans could be obsolete as patients will not have persistent fever after 48 hours. Additional results will be presented at the meeting. [Table: see text]