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Background Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. Methods We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. Results Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P  =  .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P  =  .019), similar false positive rate (33% vs 35%; P  =  .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model. Conclusions Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification. [ABSTRACT FROM AUTHOR]

Background: The capability of bronchoscopy in the diagnosis of peripheral pulmonary nodules (PPNs) remains limited. Despite decades of effort, evidence suggests that the diagnostic accuracy for electromagnetic navigational bronchoscopy (EMN) and radial endobronchial ultrasound (EBUS) approach only 50%. New developments in robotic bronchoscopy (RB) may offer improvements in the assessment of PPNs.Methods: A prospective single-blinded randomized controlled comparative study to assess success in localization and puncture of PPNs, using an ultrathin bronchoscope with radial EBUS (UTB-rEBUS) vs EMN vs RB in a human cadaver model of PPNs < 2 cm, was performed. The primary end point was the ability to successfully localize and puncture the target nodule, verified by cone-beam CT comparing RB and EMN. Secondary end points included needle to target position "miss" distance, and UTB-rEBUS comparisons.Results: Sixty procedures were performed to target 20 PPNs over the study period. Implanted PPNs were distributed across all lobes, with 80% located within the lung periphery. The target PPN mean diameter was 16.5 ± 1.5 mm, with 50% noted to have a CT bronchus sign. The rate of successful PPN localization and puncture was superior when using RB, compared with EMN (80% vs 45%; P = .02). Among unsuccessful needle passes, the median needle to target "miss" distance was significantly different when comparing UTB-rEBUS, EMN, and RB (P = .0014).Conclusions: In a cadaver model, use of RB significantly increased the ability to localize and successfully puncture small PPNs when compared with existing technologies. This study demonstrates the potential of RB to precisely reach, localize, and puncture small nodules in the periphery of the lung. [ABSTRACT FROM AUTHOR]

Drawing from insights from communication science and behavioral economics, the University of Pennsylvania Telehealth Research Center of Excellence (Penn TRACE) is designing and testing telehealth strategies with the potential to transform access to care, care quality, outcomes, health equity, and health-care efficiency across the cancer care continuum, with an emphasis on understanding mechanisms of action. Penn TRACE uses lung cancer care as an exemplar model for telehealth across the care continuum, from screening to treatment to survivorship. We bring together a diverse and interdisciplinary team of international experts and incorporate rapid-cycle approaches and mixed methods evaluation in all center projects. Our initiatives include a pragmatic sequential multiple assignment randomized trial to compare the effectiveness of telehealth strategies to increase shared decision-making for lung cancer screening and 2 pilot projects to test the effectiveness of telehealth to improve cancer care, identify multilevel mechanisms of action, and lay the foundation for future pragmatic trials. Penn TRACE aims to produce new fundamental knowledge and advance telehealth science in cancer care at Penn and nationally., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Natural language processing (NLP) tools can automate the identification of cancer patients eligible for specific pathways. We developed and validated a cancer agnostic, rules-based NLP framework to extract the dimensions and measurements of several concepts from pathology and radiology reports. This framework was then efficiently and cost-effectively deployed to identify patients eligible for breast, lung, and prostate cancers clinical pathways.