Publications
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Publications
With the only FDA-cleared, Medicare-reimbursable solution for AI-powered lung cancer diagnostic support, our technology is built on robust scientific & clinical evidence. Published in leading journals and collaborating with top academics & clinicians working in thoracic care, our publications form the foundation of our platform advancing precision care for lung cancer & lung disease
Dr Lyndsey Pickup
Head of Clinical Evidence
& co-founder
& co-founder
“
I lead the Clinical Evidence team at Optellum. Clinical research and publishing evidence of the value of our innovations plays a crucial part in gaining the trust of clinicians, industry & academic partners, and, ultimately, patients and their families.
I co-founded Optellum because I wanted to bridge the gap between cutting-edge AI technology and real-world clinical impact in lung cancer care. My role leading our Clinical Evidence team inspires me every day to fulfill that vision and drive meaningful advancements in lung cancer care.
I co-founded Optellum because I wanted to bridge the gap between cutting-edge AI technology and real-world clinical impact in lung cancer care. My role leading our Clinical Evidence team inspires me every day to fulfill that vision and drive meaningful advancements in lung cancer care.
Clinical validity & accuracy
Peer-reviewed paperMalignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric...
Piskorski et al 2025
Conference proceedingsIdentifying Patients With Pulmonary Masses From CT Radiology Reports Using Natural Language Processing (NLP)
Dotson et al 2025
Conference proceedingsCan an Artificial Intelligence-supported Incidental Nodule Program Outperform a Lung Cancer Screening Program?
Heideman et al 2025
Conference proceedingsLongitudinal Trend of a Radiomic Lung Cancer Prediction Score for Sub-solid Nodules
Vachani et al 2024
Conference proceedingsPre- and post-operative lung cancer recurrence prediction following curative surgery: A retrospective study using...
Valter et al 2024
Peer-reviewed paperDeep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review...
Wulaningsih et al 2024
Conference proceedingsPrediction of lung cancer risk in ground-glass nodules using deep learning from CT images
Vachani et al 2023
Peer-reviewed paperRecalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung...
Landy et al 2023
Peer-reviewed paperDiagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With...
Weir-McCall et al 2023
Conference proceedingsUtilizing a Deep Learning Radiomic Model for the Quantification of Emphysema
Lester et al 2022
Peer-reviewed paperLung cancer prediction by Deep Learning to identify benign lung nodules
Heuvelmans et al 2021
Conference proceedingsAI Assistance for Pulmonary Nodule Stratification: An Multiple-Reader Multiple-Case Study
Dowd et al 2021
Peer-reviewed paperEvaluation of a novel deep learning–based classifier for perifissural nodules
Han et al 2020
Peer-reviewed paperAssessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules
Massion et al 2020
Peer-reviewed paperExternal validation of a convolutional neural network artificial intelligence tool to predict malignancy in...
Baldwin et al 2020b
Conference proceedingsHow Many Malignancies Present at Baseline That Get Diagnosed in the Next Screening Round?...
Dowson et al 2019
Conference proceedingsEvaluation of a Deep Learning-Based Automatic Classifier for the Classification of Perifissural Nodules
Han et al 2019
Conference proceedingsWhat Is the Impact of Localised Data When Training Deep Neural Networks for Lung...
Devaraj et al 2019
Conference proceedingsRepeated Measures of Risk Based on Radiomics Features of Indeterminate Pulmonary Nodules for the...
Balar et al 2019
Conference proceedingsMulti-Center Independent Validation of AI Risk Stratification for Indeterminate Pulmonary Nodules from CT Images
Gleeson et al 2019
Conference proceedingsDeep Learning for Rule-Out of Unnecessary Follow-Up in Patients with Incidentally Detected, Indeterminate Pulmonary...
Peschl et al 2018
Conference proceedingsNodule Size Measurement: Automatic or Human-Which is Better for Predicting Lung Cancer in a...
Ather et al 2018
Conference proceedingsAutomatic Nodule Size Measurements Can Improve Prediction Accuracy Within a Brock Risk Model
Arteta et al 2018c
Conference proceedingsLung Cancer Prediction Using Deep Learning Software: Validation on Independent Multi-Centre Data
Peschl et al 2018
Conference proceedingsAI Based Malignancy Prediction of Indeterminate Pulmonary Nodules: Robustness to CT Contrast Media
Chometon et al 2018
Conference proceedingsSolid and Part-Solid Lung Nodule Classification Using Deep Learning on the National Lung Screening...
Kadir et al 2018
Conference proceedingsDeep Learning Based Risk Stratification of Patients with Suspicious Nodules
Arteta et al 2018b
Peer-reviewed paperPulmonary nodules: Assessing the imaging biomarkers of malignancy in a "coffee-break"
Talwar et al 2018
Conference proceedingsLung nodule risk stratification using Deep Learning on the complete US National Lung Screening...
Arteta et al 2018a
Conference proceedingsAssessment of CT Texture Analysis as a Tool for Lung Nodule Follow-Up
Ather et al 2017
Conference proceedingsA Comparison of the Imaging Features of Early Stage Primary Lung Cancer in Patients...
Talwar et al 2017b
Conference proceedingsNodule Size Isn't Everything: Imaging Features Other Than Size Contribute to AI Based Risk...
Pickup et al 2017b
Peer-reviewed paperA retrospective validation study of three models to estimate the probability of malignancy in...
Talwar et al 2017a
Clinical validity & accuracy
Peer-reviewed paperTheoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-based Tool...
Kim et al 2026
Peer-reviewed paperEmployment of Artificial Intelligence for Early Lung Cancer Diagnosis: A Retrospective Cohort Study
Hill et al 2025
Peer-reviewed paperRadiomic ‘Stress Test’: Exploration of a deep learning radiomic model in a high-risk prospective...
Xiao et al 2025
Conference proceedingsImpact of Artificial Intelligence (AI) on Follow-up of Incidental Lung Nodules: A US Multi-center...
Bellinger et al 2025
Conference proceedingsTheoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-Based Tool...
Kim et al 2025
Conference proceedingsIncidentals Upon Incidentals: Characterizing and Quantifying Significant Radiologic Findings Within a Centralized Incidental Lung...
Heideman et al 2025
Peer-reviewed paperLeveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a...
Woodhouse et al 2025
Conference proceedingsUtilization of artificial intelligence support for early lung cancer diagnosis in a lung nodule...
Hill et al 2024
Conference proceedingsSuccessful implementation of Artificial Intelligence (AI) to Support a Lung Nodule Program in Driving...
Lee et al 2024
Peer-reviewed paperClinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules
Kim et al 2024
Study protocolDetermining the impact of an artificial intelligence tool on the management of pulmonary nodules...
O'Dowd et al 2024
Conference proceedingsIdentifying Patients With Pulmonary Nodules From CT Radiology Reports Using Natural Language Processing (NLP)
Dotson et al 2023
Peer-reviewed paperEffect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules
Kim et al 2023
Peer-reviewed paperArtificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT
Kim et al 2022
Conference proceedingsAn Artificial Intelligence Lung Cancer Prediction Tool Improves Clinicians' Risk Assessment of Indeterminate Pulmonary...
Kim et al 2021
Peer-reviewed paperThe utility of a convolutional neural network (CNN) model score for cancer risk in...
Tsakok et al 2021
Conference proceedingsAI-Based Computer-Aided Diagnosis (CADx) Improves Stratification Decisions on Indeterminate Pulmonary Nodules: An MRMC Reader...
Dotson et al 2020a
Conference proceedingsThe Utility of a Convolutional Neural Network (CNN) Model Score for Cancer Risk in...
Tsakok et al 2019
Conference proceedingsAcceleration of lung cancer diagnosis: utility study for AI-based stratification of pulmonary nodules
Pickup et al 2019
Clinical validity & accuracy
Peer-reviewed paperCost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules
Godfrey et al 2026
Conference proceedingsArtificial Intelligence-assisted Versus Clinician Only Evaluation of Indeterminate Pulmonary Nodules: A Comparative Effectiveness Study
Godfrey et al 2023
Category
Publication type
Evidence type
Conference proceedingsClinical utilityReal-world integration of artificial intelligence for lung cancer prediction of indeterminate pulmonary nodules
Miller et al 2026
Peer-reviewed paperHealth-economic outcomesCost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules
Godfrey et al 2026
Peer-reviewed paperClinical utilityTheoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-based Tool...
Kim et al 2026
Peer-reviewed paperClinical utilityEmployment of Artificial Intelligence for Early Lung Cancer Diagnosis: A Retrospective Cohort Study
Hill et al 2025
Conference proceedingsClinical utilityImpact of Artificial Intelligence (AI) on Follow-up of Incidental Lung Nodules: A US Multi-center...
Bellinger et al 2025
Conference proceedingsClinical utilityArtificial intelligence-enabled lung nodule clinic to enhance clinical follow-up and early-stage lung cancer detection
Fiscus et al 2025
Peer-reviewed paperClinical utilityRadiomic ‘Stress Test’: Exploration of a deep learning radiomic model in a high-risk prospective...
Xiao et al 2025
Conference proceedingsClinical utilityIncidentals Upon Incidentals: Characterizing and Quantifying Significant Radiologic Findings Within a Centralized Incidental Lung...
Heideman et al 2025
Conference proceedingsClinical validity & accuracyCan an Artificial Intelligence-supported Incidental Nodule Program Outperform a Lung Cancer Screening Program?
Heideman et al 2025
Conference proceedingsClinical utilityTheoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-Based Tool...
Kim et al 2025
Conference proceedingsClinical validity & accuracyIdentifying Patients With Pulmonary Masses From CT Radiology Reports Using Natural Language Processing (NLP)
Dotson et al 2025
Peer-reviewed paperClinical utilityLeveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a...
Woodhouse et al 2025
Peer-reviewed paperClinical validity & accuracyMalignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric...
Piskorski et al 2025
Conference proceedingsClinical utilityUtilization of artificial intelligence support for early lung cancer diagnosis in a lung nodule...
Hill et al 2024
Conference proceedingsClinical utilitySuccessful implementation of Artificial Intelligence (AI) to Support a Lung Nodule Program in Driving...
Lee et al 2024
Peer-reviewed paperClinical utilityClinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules
Kim et al 2024
Conference proceedingsClinical validity & accuracyPre- and post-operative lung cancer recurrence prediction following curative surgery: A retrospective study using...
Valter et al 2024
Peer-reviewed paperClinical validity & accuracyDeep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review...
Wulaningsih et al 2024
Conference proceedingsClinical validity & accuracyLongitudinal Trend of a Radiomic Lung Cancer Prediction Score for Sub-solid Nodules
Vachani et al 2024
Peer-reviewed paperClinical utilityDetermining the impact of an artificial intelligence tool on the management of pulmonary nodules...
O'Dowd et al 2024
Conference proceedingsClinical validity & accuracyPrediction of lung cancer risk in ground-glass nodules using deep learning from CT images
Vachani et al 2023
Conference proceedingsClinical utilityIdentifying Patients With Pulmonary Nodules From CT Radiology Reports Using Natural Language Processing (NLP)
Dotson et al 2023
Conference proceedingsHealth-economic outcomesArtificial Intelligence-assisted Versus Clinician Only Evaluation of Indeterminate Pulmonary Nodules: A Comparative Effectiveness Study
Godfrey et al 2023
Peer-reviewed paperClinical validity & accuracyLongitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary...
Paez et al 2023
Peer-reviewed paperClinical utilityEffect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules
Kim et al 2023
Peer-reviewed paperClinical validity & accuracyRecalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung...
Landy et al 2023
Peer-reviewed paperClinical validity & accuracyDiagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With...
Weir-McCall et al 2023
Peer-reviewed paperClinical utilityArtificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT
Kim et al 2022
Conference proceedingsClinical validity & accuracyUtilizing a Deep Learning Radiomic Model for the Quantification of Emphysema
Lester et al 2022
Peer-reviewed paperClinical validity & accuracyDeveloping an understanding of artificial intelligence lung nodule risk prediction using insights from the...
Chetan et al 2022
Conference proceedingsClinical utilityAn Artificial Intelligence Lung Cancer Prediction Tool Improves Clinicians' Risk Assessment of Indeterminate Pulmonary...
Kim et al 2021
Peer-reviewed paperClinical validity & accuracyLung cancer prediction by Deep Learning to identify benign lung nodules
Heuvelmans et al 2021
Peer-reviewed paperClinical utilityThe utility of a convolutional neural network (CNN) model score for cancer risk in...
Tsakok et al 2021
Conference proceedingsClinical validity & accuracyAI Assistance for Pulmonary Nodule Stratification: An Multiple-Reader Multiple-Case Study
Dowd et al 2021
Peer-reviewed paperClinical validity & accuracyEvaluation of a novel deep learning–based classifier for perifissural nodules
Han et al 2020
Conference proceedingsClinical utilityAI-Based Computer-Aided Diagnosis (CADx) Improves Stratification Decisions on Indeterminate Pulmonary Nodules: An MRMC Reader...
Dotson et al 2020a
Peer-reviewed paperClinical validity & accuracyAssessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules
Massion et al 2020
Peer-reviewed paperClinical validity & accuracyExternal validation of a convolutional neural network artificial intelligence tool to predict malignancy in...
Baldwin et al 2020b
Conference proceedingsClinical validity & accuracyHow Many Malignancies Present at Baseline That Get Diagnosed in the Next Screening Round?...
Dowson et al 2019
Conference proceedingsClinical utilityThe Utility of a Convolutional Neural Network (CNN) Model Score for Cancer Risk in...
Tsakok et al 2019
Conference proceedingsClinical validity & accuracyEvaluation of a Deep Learning-Based Automatic Classifier for the Classification of Perifissural Nodules
Han et al 2019
Conference proceedingsClinical validity & accuracyWhat Is the Impact of Localised Data When Training Deep Neural Networks for Lung...
Devaraj et al 2019
Conference proceedingsClinical utilityAcceleration of lung cancer diagnosis: utility study for AI-based stratification of pulmonary nodules
Pickup et al 2019
Conference proceedingsClinical validity & accuracyRepeated Measures of Risk Based on Radiomics Features of Indeterminate Pulmonary Nodules for the...
Balar et al 2019
Conference proceedingsClinical validity & accuracyMulti-Center Independent Validation of AI Risk Stratification for Indeterminate Pulmonary Nodules from CT Images
Gleeson et al 2019
Conference proceedingsClinical validity & accuracyDeep Learning for Rule-Out of Unnecessary Follow-Up in Patients with Incidentally Detected, Indeterminate Pulmonary...
Peschl et al 2018
Conference proceedingsClinical validity & accuracyNodule Size Measurement: Automatic or Human-Which is Better for Predicting Lung Cancer in a...
Ather et al 2018
Conference proceedingsClinical validity & accuracyAutomatic Nodule Size Measurements Can Improve Prediction Accuracy Within a Brock Risk Model
Arteta et al 2018c
Conference proceedingsClinical validity & accuracyLung Cancer Prediction Using Deep Learning Software: Validation on Independent Multi-Centre Data
Peschl et al 2018
Conference proceedingsClinical validity & accuracyAI Based Malignancy Prediction of Indeterminate Pulmonary Nodules: Robustness to CT Contrast Media
Chometon et al 2018
Conference proceedingsClinical validity & accuracySolid and Part-Solid Lung Nodule Classification Using Deep Learning on the National Lung Screening...
Kadir et al 2018
Conference proceedingsClinical validity & accuracyDeep Learning Based Risk Stratification of Patients with Suspicious Nodules
Arteta et al 2018b
Peer-reviewed paperClinical validity & accuracyPulmonary nodules: Assessing the imaging biomarkers of malignancy in a "coffee-break"
Talwar et al 2018
Conference proceedingsClinical validity & accuracyLung nodule risk stratification using Deep Learning on the complete US National Lung Screening...
Arteta et al 2018a
Conference proceedingsClinical validity & accuracyAssessment of CT Texture Analysis as a Tool for Lung Nodule Follow-Up
Ather et al 2017
Conference proceedingsClinical validity & accuracyA Comparison of the Imaging Features of Early Stage Primary Lung Cancer in Patients...
Talwar et al 2017b
Conference proceedingsClinical validity & accuracyNodule Size Isn't Everything: Imaging Features Other Than Size Contribute to AI Based Risk...
Pickup et al 2017b
Peer-reviewed paperClinical validity & accuracyA retrospective validation study of three models to estimate the probability of malignancy in patients with small pulmonary nodules from a tertiary oncology follow-up centre
Talwar et al 2017a
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Redefining early intervention of lung disease
starting with one of the deadliest, lung cancer
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