bluuebunny/crossref_metadata_2025_split huggingface dataset:
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]