bluuebunny/crossref_metadata_2025 huggingface dataset:
Treatment rates and reasons for non-treatment in a real-world cohort of patients with pancreatic cancer.
Authors: Sundaresan Tilak Kumar , Evans Nicole S , Gupta Suraj , Leung Lawrence , Lee Jeffrey , Wong Maily , VanDenEeden Stephen K.
Published: May 2024

e13554

Background: The proportion of patients who receive treatment for pancreatic ductal adenocarcinoma (PDAC) and reasons for non-treatment in real-world patient populations are poorly understood. Our study aims to determine treatment rates, predictors for non-treatment, and the rationale for declining treatment in a well-defined, diverse, community-based patient population. Methods: We identified all adult patients with PDAC (n = 3852) between 2010 and 2020 through the Kaiser Permanente Northern California Cancer Registry. Patients were stratified by age and tumor characteristics to determine rates of oncologic therapy. Multivariable logistic regression was used to assess how covariates were associated with receiving treatment. A detailed chart review of all patients ≤ 65 years and a random sample of 100 patients > 65 years with non-metastatic disease who did not receive treatment was performed to better understand the reasons for non-treatment. A random sample of 20 patients with metastatic disease who did not receive treatment were also reviewed. Results: Of patients with PDAC, 77.7% of patients with non-metastatic and 56.2% with metastatic disease received any cancer-directed treatment. A multivariate analysis showed no statistically significant differences by race/ethnicity or gender. Increasing age was a significant negative predictor of treatment. Charlson comorbidity index (CCI) did not influence treatment rates in patients with non-metastatic PDAC, but in patients with metastatic disease those with CCI 3+ had a decreased odds of receiving treatment. Of patients ≤ 65 years with non-metastatic disease, 90.3% were seen by an oncologist, and of those patients 89.3% were offered treatment. The primary reason for declining treatment was patient/family preference in 80.8%, followed by PDAC-related frailty in 11.5%, and medical comorbidity/other malignancy in 7.69%. Similarly, of patients > 65 years with non-metastatic disease, the majority were also seen by an oncologist, offered treatment, and declined due to personal preference. The same trends were noted in patients with metastatic PDAC. Conclusions: A significant proportion of patients with PDAC did not receive cancer-directed treatment in a real-world, community-based patient population, and this was more likely for patients with metastatic disease. Race and gender did not influence whether patients received treatment in this fully insured patient population and comorbidity had a small effect. Patients of increasing age, however, were less likely to receive treatment. Most patients who did not initiate treatment did so primarily due to patient/family preference despite being offered cancer-directed therapy by their treatment team. These findings may help guide the development and utilization of patient decision-aids in PDAC to improve goal concordant care that accounts for patients’ preferences and values.
Delays in receipt of neoadjuvant chemotherapy compared to upfront surgery in patients with operable breast cancer.
Authors: Wooster Margaux , DeStephano David , Beauchemin Melissa , Wang Shikun , Hur Chin , Wright Jason Dennis , Hershman Dawn L. , Accordino Melissa Kate
Published: May 2024

e13561

Background: Delays in time from breast cancer (BC) diagnosis to BC treatment initiation (BCTI) including upfront breast surgery (UBS) or neoadjuvant chemotherapy (NACT), can negatively impact BC survival. We evaluated odds of delay in BCTI among patients (pts) who received NACT compared to pts who received UBS. Methods: We conducted a retrospective cohort study using the National Cancer Database. Eligibility included age ≥18 years, diagnosis of non-metastatic BC between 1/1/2010 – 12/31/2020, who were treated with NACT followed by surgery or UBS followed by adjuvant chemotherapy (AC). Pts who did not receive NACT or UBS within 180 days of diagnosis were excluded. The cohort was stratified by first BC treatment (NACT or UBS), and time to NACT (TTNACT) or time to surgery (TTS) was recorded for each patient in days. Pts were categorized into sub-cohorts by receptor status and clinical stage: 1) Hormone Receptor positive/HER-2 negative (HR+/HER2-) lymph node (LN)+; 2) HER-2 positive (HER2+) cT2+ or LN+; and 3) triple negative BC (TNBC) cT2+ or LN+. Logistic regression adjusted for demographic, socioeconomic, and tumor characteristics was used to identify delays in BCTI > 60 days. Multiple imputation was used for missing data. Inverse probability treatment weighting (IPTW) was done as a sensitivity analysis. Results: We identified 218,049 eligible pts, 75% were White, 18% were Black, and 8.6% were Hispanic. Median age was 53 years (range 18-90). There were 145,697 (67%) pts who received NACT with median TTNACT of 32 days (range 0-180), and 9.8% had a delay > 60 days (Table). There were 72,353 (33.0%) pts who received UBS followed by AC, with a median TTS of 33 days (range 0-180), and 13% with a delay > 60 days. In multivariable analysis, pts who received UBS had higher odds of BCTI delays compared to NACT, with OR 1.50 (95%CI 1.42-1.57) [HR+/HER2-], OR 1.49 (95%CI 1.41-1.58) [HER2+], and OR 1.26 (95%CI 1.18-1.34) [TNBC]. Results from IPTW analysis were similar, groups were well balanced, and pts who received UBS had higher odds of BCTI OR 1.50 (95%CI 1.44-1.57) [HR+/HER2-], OR 1.75 (95%CI 1.67-1.83) [HER2+], and OR 1.46 (95%CI 1.38-1.53) [TNBC]. Conclusions: Among high clinical risk BC patients who received surgery and chemotherapy, those who underwent NACT were less likely to experience delays in BCTI compared to pts who received UBS first. Among eligible pts, use of NACT may reduce delays in BCTI. [Table: see text]
Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer.
Authors: Rivest Charles-Éric , Ben Abderrazik Fatim Ezzahra , Côté-Marcoux Marc-Antoine , Malick Mandy , Hanel Robert , Pavic Michel
Published: May 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 <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.
Optimizing the oncology ecosystem: Addressing barriers between community and academic oncologists.
Authors: Khalil Ahmed , Kango Ghazal , Graff Stephanie L.
Published: May 2024

e13580

Background: Oncology practice is becoming more complex and collaboration between academic and community practices is necessary to optimize care for patients. This qualitative analysis explores perceived gaps between academic and community practices and proposed solutions to a more cohesive oncology ecosystem for optimal quality and care delivery. Methods: A 10-question mixed-methods quantitative and qualitative survey was developed (SurveyMonkey) to identify practice setting, methods of collaboration, perceived barriers, and strategies for improvement. Both open-ended and multiple-choice questions were used. Survey was distributed via social media outreach on Facebook, X, and email. Individual responses were coded by AK, then compared using two sample z-test for proportions. Results: 68 responses were collected, comprising 45.5% community and 44.1% academic physicians. Non-clinician respondents (2.9%) were excluded. Respondents had been in practice: >20 years (16.42%), 10-20 years (40.3%), 5-9 years (19.4%) and < 5 years (23.9%). Collaboration commonly occurred via conferences (73.68%), shared Electronic Medical Records (EMRs, 59.65%) or research (38.6%). The main barriers perceived are summarized (Table). Community oncologists describe lack of appreciation from academics, time constraints, difficulty communicating. Main barriers to collaboration perceived by academic oncologists include delayed referral, time constraints, difficulty communicating, lack of shared EMRs, fear of patient displacement from the primary referring practice, and administrative red tape. Proposed solutions from both groups include engaging community oncologists in academic tumor boards (n=8/68,11.8%) and regional education events (n=2/68, 2.9%). Improved communication. Additionally, both groups were interested in expanding clinical trials in the community. Both groups noted the need for financial incentives and institutional support for successful cooperation (n=3/68, 4.4%). Conclusions: Our data unveiled the nuances of collaboration. Barriers mainly consist of lack of acknowledgement, poor communication, and barriers in EMR utilization. Both groups struggle with time constraints. This data can inform multi-shareholder groups working for systems-based process improvement, expanding collaboration between community and academic oncology. [Table: see text]
Improving LDCT screening rates within a large multidisciplinary community based practice using integrated ACR accredited imaging centers.
Authors: Lachhar Garry , Greenside Jarett , Staszewski Harry
Published: May 2024

e13582

Background: Lung cancer remains the leading cause of cancer-related death in the US, accounting for about 22% of such deaths. Lung cancer detected by low-dose computed tomography (LDCT) screening is discovered at an early stage 64-85% of the time. The benefits of LDCT screening have been demonstrated in numerous trials, but there has been difficulty in establishing programs that screen a high percentage of eligible patients. We present data from the first 18 months of our lung cancer screening program that is designed to provide the highest quality screening, follow-up and access to prevention, diagnosis, and treatment including clinical trials within their local communities. Methods: New York Cancer and Blood Specialists (NYCBS) is a community practice with over 50 locations across NY. While most lung cancer screening programs are hospital-based, NYCBS has a series of American College of Radiology (ACR) accredited imaging centers that perform these screenings and are closely affiliated with oncology, primary care, Ob-Gyn, nephrology, urology and other specialty medical and surgical practices. These centers are strategically located in areas that have high rates of tobacco use as suggested by heat map data from the NY City Community Health Survey . Upon initial patient presentation, all NYCBS and affiliated sites screen patients with tablet-based questionnaires that prompt additional questions during follow-up appointments. Patients with positive findings are discussed during a monthly, system-wide multidisciplinary lung nodule review conference. Follow-up interventions include referrals to pulmonology, interventional radiology, or further imaging with PET/CT. Per ACR guidelines, patients not requiring acute intervention continue with follow-up CT. Results: In 2023, 29,501 patients were screened with an in-office questionnaire. Of those patients, 3,606 were eligible for screening, 225 declined, and 896 (29%) high risk patients proceeded to undergo LDCT screening. In screened patients, 128 underwent follow-up CT, 36 patients had findings requiring immediate intervention, and 6 were diagnosed with lung cancer. 5 of these patients were diagnosed with stage I and 1 patient was diagnosed with stage IV lung cancer. Conclusions: LDCT screening is a proven method to discover early stage lung cancer and improve the survival rate of diagnosed patients but has not been well adopted in practice. With access to a diverse array of multidisciplinary practices across NY and integrated accredited imaging centers, we have increased the accessibility of lung cancer screening and improved transitions to follow-up care. Nationally, only 4.5% of patients at high risk are screened, however, we found that with our approach to patient care, we have achieved among the highest reported screening rates in eligible individuals.
Improving dynamic prognostication of patients with hepatocellular carcinoma by node fusion algorithm equipped survival path mapping.
Authors: Shen Lujun , Jiang Yiquan , Fan Wei-Jun
Published: May 2024

e13594

Background: Patients with hepatocellular carcinoma (HCC) always require routine surveillance and repeated treatment. Our previous work (1) developed a machine learning methodology called “Survival Path Mapping”, which converted the time series data into a cascading survival map to facilitate dynamic prognosis prediction and treatment planning. However, in previous work, the exponential growth of paths raised challenges of exhausting limited cohort and over-fitting risk. Novel algorithm enhancing data utilization in the Survival Path Mapping was in urgent need. Methods: A node fusion algorithm was designed. First, feature selection algorithm bifurcated each parent-nodes into temporary child-nodes, which constitutes temporary child-nodes set. Then, clustering was performed within temporary child-nodes set according to key features of each child-node. Finally, in each cluster, multiple child-nodes would be merged into one if there was no significant difference in survival. For nodes with bifurcation, a treatment recommendation algorithm was developed. A total of 19446 patients with newly diagnosed primary HCC at Sun Yat-sen University Cancer Center (SYSUCC) between 2008 and 2023 were retrospectively included as internal cohort (divided into internal train and holdout validation cohort); meanwhile 1135 patients from five hospitals were included as multicenter external validation cohort. Dedicated natural language processing program was developed to extract structured features from medical imaging reports. The time series data of patients were converted into data of time slices, with an interval of 3 months. Results: The original survival path mapping (raw-SP) consists of 463 different nodes, while the node fusion survival path mapping (fusion-SP) consisted of 84 nodes (81.8% decrease from raw-SP). The 3-year c-index of fusion-SP were 0.745 (95% CI 0.717-0.772), significantly better (p<0.001) than that of raw-SP: 0.725 (95% CI 0.696-0.755), in the internal holdout validation. Both raw-SP and fusion-SP significantly (p < 0.001) outperformed conventional BCLC and CNLC staging. In the multicenter external validation, the fusion-SP also demonstrated better performance than raw-SP and BCLC staging system. The recommended therapy calculated based fusion-SP system for patients receiving second-line or subsequent treatment had superior or non-inferior efficacy compared to BCLC guideline’s recommendation. Conclusions: The proposed node fusion algorithm solved exponential growth of paths in previous survival path algorithm, improving the accuracy and external validity of dynamic prognosis prediction for HCC patients. The fusion-SP model also retained its transparency and could be used to guide dynamic treatment planning, with its effectiveness in treatment guidance evaluated in prospective trials in the future. 1. Nat Commun 9, 2230; 2018.
Multi-institutional validation survey on Belong.life's conversational artificial intelligence (AI) oncology mentor, "Dave.
Authors: Golan Talia , Purim Ofer , Rosin Danny , Sapir Eli , Gatt Moshe , Charas Tomer , Iofin Ilya , Wolf Ido , Haikin Bozhena , Vorobiof Daniel A.
Published: May 2024

e13596

Background: Belong.life, a global oncology social network for patients and caregivers, recently launched “Dave” the first conversational AI oncology mentor and companion. “Dave’s” objectives are to provide uninterrupted support, clarify relevant clinical issues and guide patients and caregivers with relevant and empathetic information and education in all aspects of cancer, from diagnosis to treatment protocols and side effects management. “Dave” underwent training on Belong’s unique and large datasets of patients to physicians, and patients to patients’ interactions, as well as incorporating high quality information from the latest international cancer guidelines, providing it with a robust up-to-date supportive data and a wide understanding of the patients’ cancer journey. Methods: “Dave’s” responses to inquiries from Belong members were subjected to a validation survey conducted by eight oncologists, each specializing in various solid and haematological cancers and affiliated with several medical institutions. From the social network datasets, 471 questions from patients and caregivers were randomly selected and categorized into groups, including Breast, Gastrointestinal and Pancreatic, Genito-urinary, Bone, Haematological cancers, and Radiation therapy. The oncologists assessed the AI mentor's replies for their relevance and helpfulness, aligning them with evidence-based medicine, current recommendations, and guidelines. Results: Results were categorized into positive or negative assessments. Impressively, 432 out of 471 “Dave’s” responses (91.8%) received positive validation for providing suitable and relevant recommendations. Conversely, 39/471 responses (8.2%) received a negative validation, of which only 5/471 (1%) were graded as not at all helpful or relevant. Minor variations were observed within diagnostic groups, with positive validation rates as follows: Genito-urinary 100%, Breast 98.4%, Musculoskeletal 97%, Radiation therapy 88%, Gastrointestinal and Pancreatic 86%, and Haematological cancers 84%. Conclusions: The documented results on 471 patients’ posts are very promising, indicating that “Dave” provides valuable and reliable recommendations, achieving a positive validation rate of nearly 92%, with only 1% graded as not at all helpful or relevant. The findings also offer insights into the AI mentor's performance across distinct medical domains. This validation study provides a solid foundation and adds confirmation that the addition of an AI oncology mentor and companion, like “Dave”, improves patients’ knowledge and coping mechanisms and provides helpful and relevant guidance during their cancer journey, while supporting physicians in the daily management of their cancer patients.
Tumor origin identification through machine learning and gene expression profiling.
Authors: Wang Xin , Li Jun , Zhou Yanqing , Chen Yaru , Liu Tianliang , Chen Shifu
Published: May 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.
Artificial intelligence–aided lung cancer screening in routine clinical practice: A pilot of LungFlag at Geisinger.
Authors: Facktor Matthew A. , Jin Yue , Maff Rebecca , Mitchell Elliot , Ton Thanh G. N. , Olghi Nicolò , Obradovic Milan
Published: May 2024

e13604

Background: Use of low-dose computed tomography (LDCT) in lung cancer screening is recommended for early detection and can reduce mortality. LungFlag (Medial-EarlySign) is a machine-learning risk prediction model that identifies individuals at high risk of non-small cell lung cancer so their care team may recommend LDCT screening. The LungFlag model was developed using data from Kaiser Permanente Southern California and is being implemented in a pilot at Geisinger. Here we provide an overview of the clinical workflow for the LungFlag pilot. Methods: Geisinger has implemented LungFlag into routine clinical practice via integration with the electronic medical record system. This pilot is examining the performance and clinical impact of LungFlag in the first year. LungFlag runs every month to provide risk evaluation for individuals 50 to 80 years old who are eligible for annual lung cancer screening according to US Preventive Services Task Force recommendations. Eligible individuals have a 20 pack-year smoking history, no lung cancer history, and currently smoke or quit within the past 15 years. Patients with cancer history in the past 5 years and those screened or scheduled for screening in the past year are excluded. LungFlag identifies the top 30 individuals with the highest LungFlag scores per month (high-risk patients) and refers them for targeted outreach from a lung cancer screening program nurse navigator. A positive LungFlag result prompts nurse navigator chart review, patient outreach, and coordination with the primary care provider. Flagged individuals are contacted (up to 3 phone calls) and encouraged to receive lung cancer screening. Individuals not flagged by the model (non–high-risk patients) receive usual care consisting of reminders for lung cancer screening during primary care visits without additional targeted outreach and high-risk prioritization. Results: The LungFlag pilot was initiated in August 2023 and will continue until August 2024. As of January 2024, risk profiles of >12,000 patients were analyzed and 180 high-risk patients were identified. Conclusions: This pilot is the first evaluation of the implementation process for the LungFlag model in routine clinical practice. After the pilot, a retrospective observational study is planned to further assess the impact of LungFlag and the associated clinical workflow in identifying high-risk individuals and increasing compliance with LDCT screening among high-risk individuals.
AI-based quantification of TILs using hematoxylin and eosin and immunohistochemistry-stained slides in triple-negative breast cancer.
Authors: Kobayashi Takuma , Tomioka Nobumoto , Hatanaka Kanako C , Konishi Teppei , Grynkiewicz Mateusz , Komura Daisuke , Ishikawa Shumpei , Watanabe Kenichi , Takahashi Masato , Hatanaka Yutaka
Published: May 2024

e13608

Background: The importance of accurately understanding the status of tumor-infiltrating lymphocytes (TILs) in the preoperative chemotherapy context, for triple-negative breast cancer is underscored, highlighting the dual role of TILs in gauging treatment efficacy and prognostication. However, the difficulty in quantitatively and objectively assessing TILs in clinical practice is acknowledged, noting the barrier to its adoption as a standard diagnostic end-point. This study proposes a novel AI-based method to overcome these challenges, by quantitatively assessing TILs using hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining, and exploring their prognostic relationship. Methods: In this study, 68 cases of breast cancer with axillary lymph node metastasis at the time of resection, were selected from a total of 3,902 resections conducted between 2002 and 2016. To quantitatively assess TILs, we developed a quantitative assessment method, integrating an AI-based epithelial detection model and non-rigid image registration techniques to align H&E and IHC-stained images for precise measurement of immune markers (CD3, CD8, FOXP3, PD-L1) within the tumor stroma. Using an AI-based model trained on 12,273 annotated epithelial images, the stroma, excluding the epithelium, was identified. The tumor bed was also defined by a pathologist. Finally, we assessed the prognostic significance of the TILs for disease-free survival (DFS) through ROC analysis and log-rank tests. Results: Optimal thresholds for CD3 (0.152 mm²), CD8 (4.8 mm²), FOXP3 (0.25 mm²), and PD-L1 (0.115 mm²) were identified, with corresponding AUCs of 0.678, 0.625, 0.746, and 0.659. Patients with cell counts or areas above these thresholds demonstrated improved DFS (P<0.05 for all markers). Conclusions: These findings suggest that the quantification of immune cells within the tumor bed provides reliable prognostic indicators for recurrence. The AI-based TILs quantification offers a significant advancement in the prognostic assessment of triple-negative breast cancer. In addition, it improves the objectivity and clinical utility of pathological evaluations. This precise measurement of TILs could be transformative in patient prognostication and the formulation of treatment plans.
A digital pathology AI model to predict immune-oncology biomarker status and pembrolizumab response in a real-world cohort of patients with colorectal and breast cancer.
Authors: Chen Ming , Lama Norsang , Cheng Yating , Amidi Eghbal , Xiu Joanne , Ribeiro Jennifer , Oberley Matthew James , Spetzler David , Sledge George W.
Published: May 2024

e13617

Background: Deep learning (DL) algorithms have garnered significant attention for prediction of biomarker status in digitized tissue slides. Here, we trained and validated a digital pathology algorithm to predict microsatellite instability (MSI)/mismatch repair deficiency (MMRd) in colorectal cancer (CRC) and PD-L1 status in breast cancer (BC) using a large real-world patient cohort. Methods: H&E and immunohistochemistry (IHC) slides of CRC and BC from Caris Life Sciences (Phoenix, AZ) with known biomarker status were digitally scanned (CRC: N=27,494, 6.6% MSI-high by next-generation sequencing [NGS]; N=25,788, 6.4% MMRd by IHC; BC: N=16,069, 15.8% PD-L1+ by IHC). A transformer-based DL model (Wagner, Sophia J., et al., 2023) was trained to predict biomarker status, with 5-fold cross-validation. Accuracy, F1 score, area under the curve (AUC), and other metrics were calculated comparing the model to NGS/IHC “truth”. Risk stratification was performed on holdout datasets of pembrolizumab-treated CRC (N=422-454) and triple negative breast cancer (TNBC; N=513) pts. Post-treatment overall survival (OS) was calculated from start of pembrolizumab to last contact, per insurance claims data. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated by Cox-Proportional hazards and compared between model and “truth”. Results: When applied to holdout datasets for biomarker prediction, the validated model achieved AUC of 0.78-0.95. In CRC and BC, performance was better among primary specimens than metastatic. For MSI-H/MMRd risk stratification in pembrolizumab-treated CRC, HR for OS were comparable between model and “truth” for primary and metastatic cohorts. When applied to the TNBC holdout dataset, the model performed better than “truth” for PD-L1 risk stratification in pembrolizumab-treated pts. Conclusions: A digital pathology DL model predicts biomarker status with high accuracy and is non-inferior to NGS/IHC for risk stratification of pembrolizumab-treated CRC and TNBC pts. These results support further investigation of AI algorithms developed from large datasets as possible rapid screening tools for biomarker detection in molecular testing programs. [Table: see text]
Automating the detection of treatment progression in patients with lung cancer using large language models.
Authors: Sivarajkumar Sonish , Edupuganti Subhash , Bhattacharya Manisha , Lazris David , Davis Michael , Huang Yufei , Wang Yanshan
Published: May 2024

e13620

Background: Accurate assessment of treatment response is fundamental to advancing cancer patient care, particularly in lung cancer, where treatment modalities are diverse and complex. The extraction of treatment response, particularly disease progression information, from Electronic Health Records (EHRs) is essential for several reasons: It facilitates generation of real-world evidence from real-world data, enables personalized treatment planning, and contributes to a broader understanding of cancer therapeutics. However, much of treatment response information is documented in free-text EHR notes and traditional methods of extracting such information from notes are labor-intensive, error-prone, and inefficient, presenting significant barriers to timely and accurate real-world evidence studies. The advancement of Natural Language Processing (NLP) technologies, especially Large Language Models (LLMs), presents a transformative opportunity to automate the extraction of treatment responses. Methods: We used a cohort of 1,953 primary lung cancer patients identified from UPMC Hillman Cancer Center cancer registry and retrieved ~113,000 clinical notes from UPMC clinical data warehouse. We focused on extracting disease progression information, following the RECIST guidelines which define progression as an increase in tumor size or cancer markers after therapy. We selected ~50 notes to perform manual annotations by an experienced oncologist and created a gold standard dataset to validate the NLP models. We fine-tuned a state-of-the-art open-source LLM named LLAMA-2 and compared against a traditional rule-based NLP system for the automated extraction of disease progression from notes. The process of fine-tuning involved adjusting the model's parameters specifically to improve its ability to recognize and classify instances of disease progression. Results: Our analysis demonstrated a significant enhancement in performance with the LLM compared to the traditional rule-based NLP system. LLM exhibited a remarkable increase in sensitivity by ~37%, indicating its superior ability to accurately identify disease progression. Additionally, the model maintained high specificity and PPV, achieving scores nearly comparable to the rule-based system but with a notable improvement in the F1-score by ~14%. Conclusions: Our research highlights the transformative potential of LLM-based NLP algorithms in automating the extraction of treatment responses from free-text EHRs. This methodology not only provides a scalable and efficient mechanism for processing large volumes of clinical text but also significantly enhances the accuracy of lung cancer treatment response assessments. [Table: see text]
Novel approach to implementing natural language processing for clinical staging of non-small-cell lung cancer.
Authors: Shah Manan P , Prichard Laura , Glaspy John A , Vu Vu , Wang Jason , Gelvezon Alex V , Cummings Amy Lauren
Published: May 2024

e13624

Background: Cancer staging is instrumental in driving clinical management and trial enrollment, but staging data are generally unreliable and unstructured in the electronic health record (EHR). Advances in natural language processing (NLP) may facilitate clinical staging and documentation [1], but challenges to real-world implementation include (1) automatically identifying appropriate patients and reports from the EHR and (2) developing an unbiased dataset for training and validation [2]. We describe our institution’s novel approach to overcome these barriers while building an in-house NLP pipeline for clinical tumor staging of non-small-cell lung cancer (NSCLC). Methods: We identified patients by searching our EHR (Epic) for a molecular analysis test ordered specifically for pathological diagnoses of NSCLC at our institution. We used the test order date as the diagnosis proxy date (DPD). For each patient, we extracted imaging reports up to 16 weeks before and 6 weeks after the DPD. To derive primary tumor size, we analyzed the CT Chest or PET/CT report closest to the DPD using an oncology-trained NLP text extraction and labeling tool (John Snow Labs). We cleaned all extracted tumor size entities and identified the largest measurement linked to the lungs. We compared primary tumor measurements from the NLP pipeline to those in a preexisting, manually compiled cancer registry (CNEXT). We manually analyzed discrepancies through chart review. Results: 542 patients with a DPD between 11/2016 - 9/2023 were processed through the NLP pipeline. Of 443 patients with valid values in both the pipeline and CNEXT, 53% (234) were exact matches, and 20% (90) had a close match (within 0-5mm), yielding a 73% accuracy rate for values within 5mm. When mismatched values were manually reviewed, several cases in CNEXT were found to have a DPD differing by more than 3 months and tumor sizes derived from external reports. When these cases were excluded, 320 of the remaining 349 patients had valid values in both the pipeline and the updated manual review. In this refined population, 66% (213) were exact matches, and 15% (48) had a close match, yielding an 82% accuracy rate for values within 5mm. Conclusions: To our knowledge, this is the first report of a pathology-based method to automatically and reliably identify patients with NSCLC and their relevant imaging reports directly from the EHR. We used a prebuilt NLP tool to derive primary tumor sizes with relatively high accuracy and found that adding flags for timeline discrepancies and external reports can further improve validity. As we near completion of analogous pipelines for node and metastasis staging, we will develop methodology to identify subgroups of patients that can be clinically staged with near-perfect accuracy, ultimately aiming to substantially limit manual staging of uncomplicated cases. 1. Puts 2023. 2. Wang 2022.
Advancing precision medicine in cutaneous oncology through 3D high-frequency ultrasound imaging.
Authors: Henke Julian , Jeong Jiwoong Jason , Yang Xiaofeng , Khan Mohammad Khurram
Published: May 2024

e13625

Background: Clinical assessment of tumor depth and shape often requires biopsy, especially when evaluating skin cancers. High Frequency Ultrasound (HFUS) is a non-invasive clinical tool that can quickly produce high resolution images ideally suited for evaluating topical tumors. In comparison to other imaging modalities, HFUS do not always provide the spatial data required to model 3-dimensional (3D) tumors. Currently, 3D-images are an amalgamation of many segmented image slices. However, in outpatient clinics it is unrealistic for HFUS devices and clinicians to gather large sets of image data. In our present original research, we aimed to overcome limited number of HFUS and dermatoscopic images by constructing a simple framework to process sparse and non-spatial data into working 3D skin tumor models. Methods: We analyzed two different skin tumors, the dermal duct tumor (DDT) and basal cell carcinoma (BCC), each with different number of HFUS images at 30Hz and 50Hz respectively. 1 set of 6 parallel images were obtained for the BCC, while 2 orthogonal sets of 6 parallel images were obtained for the DDT. For each tumor, a single dermatoscopic photo and sets of bitmap HFUS images with a resolution of 1024 x 512 were taken. Our study was IRB-approved. Mathematica and MATLAB, both with accessible documentation, were used to process images. Results: We developed a novel algorithmic approach to produce 3D tumor models with limited medical data. Both tumors appear corporally hypoechoic under HFUS. Mathematica was used to first reduce HFUS image noise and binarize the tumor region with a bilateral Gaussian filter. The user-friendly MATLAB Image Segmenter application manually filled the region of interest. Next, Mathematica was used to orient and plot tumor borders in 3D. Finally, we encompassed the borders with a 3D convex hull to conservatively predict tight margins around the tumor. Conclusions: Our simplified approach allows clinicians and scientists to produce 3D renderings of tumor margins. Margin control must always be balanced with a positive surgical outcome. By approaching our 3D-model with a convex hull around sampled border cross-sections, we chose a conservative approach that focuses skin tumor margin evaluation. Our results promote precision medicine and planning into the current standard of skin cancer treatment. Our findings can allow tracking tumor progression or treatment possible via non-invasive evaluation of tumor depth and contour. An aim of our present research is to lower the barrier of 3D construction in clinical settings. Simple algorithms, such as shown in our work, transform orthogonal and sparse data into usable data that can be implemented to train machine learning and artificial intelligent programs. Future research tailoring this approach may allow instant programming of HFUS images to non-invasively render cutaneous tumor borders and depth in real time during clinical examination.
Performance of three commercially available large language models and one locally fine-tuned model at preparing formal letters to appeal medical insurance denials of radiotherapy services.
Authors: Kiser Kendall , Waters Michael , Reckford Jocelyn , Lundeberg Christopher , Abraham Christopher
Published: May 2024

e13630

Background: As many as 60% of prior authorization requests are denied, yet coverage approval occurs for more than 60% of appeals for some therapies. Appeal processes encumber providers and increase burnout, but large language models (LLMs) may aid providers by drafting appeal letters. We evaluated LLM performance at this task for radiotherapy denials. Methods: Three commercially accessible LLMs were evaluated: generative pre-trained transformer 3.5 (GPT3.5), GPT4, and GPT4+web with internet search capacity (OpenAI, Inc., San Francisco, CA). A fourth LLM, GPT3.5-FT, was developed by fine-tuning GPT3.5 in a HIPAA-complaint local environment. The fine-tuning training data comprised 53 insurance denial appeal letters prepared by radiation oncologists and paired prompts describing the clinical history and appeal intent. Training data were enriched in appeal letters for proton radiotherapy, stereotactic body radiotherapy, and image-guided radiotherapy for myriad clinical scenarios. Twenty prompts, each requesting a letter for a simulated patient history, were programmatically presented to the LLMs. Three radiation oncologists, who were blinded to the LLM source, scored letter outputs across four domains: language syntax and semantics, clinical detail inclusion, clinical reasoning validity, and overall readiness for insurer submission. Additionally, one radiation oncologist scored the authenticity and relevance of literature sources cited in output letters, which were requested by several test prompts. Interobserver agreement between radiation oncologist scores was determined by Cohen’s kappa coefficient. Scores were compared between LLMs with non-parametric statistical tests. Results: Agreement between radiation oncologists’ scores was moderate-to-excellent across all domains (median κ = 0.68, minimum κ = 0.41). GPT3.5, GPT4, and GPT4+web drafted letters that, by mode average, were semantically and syntactically clear, included all provided clinical history without confabulation, clinically reasoned with few necessary revisions, and overall were submissible to an insurer with minor revisions. GPT4 and GPT4+web clinically reasoned better than GPT3.5 (p values < 0.001). In contrast, GPT3.5-FT performance was inferior to other LLMs across all domains (p values < 0.001). LLMs were poor at identifying, citing, and summarizing relevant literature unless provided in the prompt. Conclusions: LLMs can draft insurance appeal letters for radiotherapy services that require few revisions yet are poor at referencing relevant literature. Contrary to our hypothesis, fine-tuning with data from our department compromised LLM performance.
A prospective study comparing AI-based clinical trial eligibility screening with traditional EMR-based screening.
Authors: Joseph-Thomas Jiby , Green Cristina , Tibrewal Abhishek , Dupont Frank , Flach Jen , Goldstein Amelia , Vaidya Vivek Prabhakar , Kulkarni Rajeev , Parikh Ravi Bharat
Published: May 2024

e13636

Background: For clinical trials of novel oncology therapies, screening patients for eligibility relies on research staff manually abstracting complex eligibility criteria, which is time-consuming and subject to inconsistent interpretations. AI algorithms based on structured data fields may facilitate automated eligibility criteria extraction, assure more complete screening for the benefit of all patients, improve workflow for research staff, and assure no bias in who is reviewed. AI algorithms can support abstraction and imputation approaches to assure the greatest number of clinical records get reviewed. Prospective validation of such algorithms and approaches for sponsored registrational clinical trials is lacking from literature. Methods: We conducted a prospective controlled observational study of an AI model to prioritize potentially eligible patients, as part of a phase 3, randomized, multi-site therapeutic trial for Relapsed or Refractory Multiple Myeloma. In this observational study, we studied eligibility criteria focused on measurable disease and prior anti-myeloma treatments. The AI eligibility model extracted structured eligibility criteria to predict likelihood of meeting study eligibility. A single CRC (Clinical Research Coordinator) allocated 40 hours to screening in both control and intervention phases of the study. In the control phase, the CRC screened myeloma patients’ electronic health records in alphabetical order by last name. In the intervention phase, the CRC screened a different set of patients ranked by the eligibility likelihood model. Descriptive statistics were used to compare primary outcomes of efficiency (percentage of patients watch-listed) and speed (average time to screen a patient) between control and intervention phase. Patients marked as watch-listed meet eligibility criteria but need additional testing or disease progression before becoming eligible for the study. Results: The AI Eligibility method captured the same share (19%1) of screened patients as the EMR method, with a 3.3x (12.5 versus 41 minutes) improvement per screened patient. Conclusions: The AI eligibility method allows research staff to screen three times more patients with similar outcomes as compared to routine CRC EMR screening. Future work can leverage AI to identify and predict patient progression, further enhancing screening efficiency. [Table: see text]
Implementing a natural language processing pipeline to expedite clinical chart review to assess germline genetic testing in veterans with breast cancer.
Authors: Patil Vikas , Mcshinsky Richard , Halwani Ahmad Sami , Morreall Deborah , Li Chunyang , Rasmussen Kelli M , Yong Christina , Burningham Zachary R. , Menendez Carolyn S. , Chiba Akiko , Moss Haley , Colonna Sarah Violet , Rowe Kerry G. , Kelley Michael J.
Published: May 2024

e13640

Background: The Making Advances in Mammography and Medical Options for Veterans Act of 2022 mandated that the Department of Veterans Affairs (VA) perform a study of germline genetic testing in Veterans with breast cancer (BC). Assessing whether a Veteran with BC was offered germline genetic testing based on electronic health records requires time-consuming review of unstructured clinical notes by clinical experts who are also extremely busy providing care. To expedite this review process, we implemented a productionized natural language processing (NLP) pipeline to identify relevant concepts and highlight them within a custom-made chart review interface. Methods: Clinical subject-matter experts (SMEs) were convened to identify keywords suggestive of genetic testing. These were categorized into 3 sets: gene-related concepts, VA-specific services, and exams and tests. We then used entity recognition and entity linking to link these concepts to a knowledge base. These were enriched with entities similarly extracted from an open-source reference clinical text on germline genetic testing in BC. The resulting concepts were visualized as a word cloud and reviewed by the SMEs. We implemented a Power BI and Power Apps interface to display clinical notes from 200 randomly selected Veterans, with relevant concepts highlighted. Each Veteran’s notes were reviewed independently by 2 of the 5 SMEs, who assessed whether the Veteran was offered testing, referred to a genetic counseling service, or otherwise had evidence of testing. Disagreements were adjudicated by the whole team. Results: 11622 notes (mean 58/Veteran, 3028 characters/note) were reviewed. Table shows the most prevalent entities among genes, mutations, and exam names. Of the 5 reviewers, 3 annotated 80 patients each in a single session, lasting less than 50 minutes on average. Reviewers had an inter-annotator agreement of 85.5%. Mention of target concepts was associated with germline genetic testing with an accuracy of 80.4%, precision of 76.4%, recall of 99.1%, and F1 of 86.3%. Germline genetic testing rates are detailed in a separate submission. Conclusions: Modern informatics tools such as productionized NLP, the Microsoft Power Platform, and high-performance computing environments allow for rapid implementation of health information technology tools that can enable clinicians to gain critical insights quickly and efficiently from electronic health record data. [Table: see text]