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Background: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making., Methods: For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic., Findings: From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger., Interpretation: The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool., Funding: ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health., Competing Interests: Declaration of interests BHK reports research support from the US National Institutes of Health (NIH; grant number K08DE030216-01) and the Radiological Society of North America, honoraria from Dedham Group, and stock options in Monogram Orthopedics. JDS reports research support paid to their institution from Merck, Bristol Myers Squibb (BMS), Regeneron, Debiopharm, and Merck KGA; consulting, scientific advisory board, and travel fees from Castle Biosciences, Genentech, Immunitas, Debiopharm, BMS, Nanobiotix, Tilos, AstraZeneca, LEK Consulting, Catenion, ACI Clinical, Astellas, Stimit, and Merck KGA; expert witness fees; stock options in Immunitas; and equity in Doximity. RH reports payment for consulting from Celgene, Nanobiotix, ISA, Merck, Eisai, BMS, AstraZeneca, Pfizer, Loxo, Genentech, Immunomic Therapeutics, GlaxoSmithKline, Gilead Sciences, Vaccinex, Emmanuel Merck Darmstadt (EMD), Serono, BioNTech, Achilles Therapeutics, Bayer, Coherus Biosciences, Boehringer Ingelheim, and Mirati Therapeutics; and research funding from Boehringer Ingelheim, Merck, BMS, Celgene, AstraZeneca, Genentech, Pfizer, Kura Oncology, EMD, and Serono. SA reports research support from the Agency for Healthcare Research and Quality, National Cancer Institute, National Science Foundation, American Cancer Society, American Society of Clinical Oncology, Amazon, and Patterson Trust; fees from American Society of Clinical Oncology, HighCape Capital, Emmerson Collective; and stock options in Prophet Brand Strategy. HJWLA reports research support from NIH (grant numbers U24CA194354, U01CA190234, U01CA209414, and R35CA22052), and the European Union—European research Council (grant number 866504) and stock and other ownership interests in Onc.Ai, Love Health, Health-AI, and Bayerische Motoren Werke AG. BAB reports a consulting or advisory role with Merck, Debiopharm Group, CUE Biopharma, Maverick Therapeutics, Rakuten Medical, Nanobiotix, Macrogenics, ALX Oncology, IO Biotech, Ipsen, Genentech/Roche, Kura Oncology, Merck KGA, Pharmaceutical Product Development Global, and Exelixis; research funding to their institution from Merck, Aduro Biotech, Formation Biologics, BMS, and CUE Biopharma; and travel, accommodations, or expenses from Merck and Debiopharm Group. RLF reports stock and other ownership interests in Novasenta; a consulting or advisory role with Merck, Pfizer, EMD Serono, Numab, Macrogenics, Aduro Biotech, Novasenta, Sanofi, Zymeworks, and BMS; and research funding from Bristol Myers Squibb, AstraZeneca/MedImmune, Merck, Tesaro, and Novasenta. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained using 147,497 x-ray images of 40,643 individuals and tested in three independent cohorts comprising 15,976 individuals. We found that CXR Lung-Risk showed a graded association with lung disease mortality after adjustment for risk factors, including age, smoking, and radiologic findings (Hazard ratios up to 11.86 [8.64-16.27]; p < 0.001). Adding CXR Lung-Risk to a multivariable model improved estimates of lung disease mortality in all cohorts. Our results demonstrate that deep learning can identify individuals at risk of lung disease mortality on easily obtainable x-rays, which may improve personalized prevention and treatment strategies., (© 2023. The Author(s).)

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions. A significant portion of the scientific literature on AI for radiology lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Here, the authors offer a blueprint for transparent AI pipelines on cloud platforms, focusing on lung cancer prediction and biomarker discovery. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Assistant (DL-ODA), a fully automated, end-to-end clinical platform that enables AI interventions for any disease site featuring an automated model-training pipeline, autosegmentations, and QA reporting. Materials and methods: We developed, tested, and prospectively deployed the DL-ODA system at a large university affiliated hospital center. Medical professionals activate the DL-ODA via two pathways (1): On-Demand, used for immediate AI decision support for a patient-specific treatment plan, and (2) Ambient, in which QA is provided for all daily radiotherapy (RT) plans by comparing DL segmentations with manual delineations and calculating the dosimetric impact. To demonstrate the implementation of a new anatomy segmentation, we used the model-training pipeline to generate a breast segmentation model based on a large clinical dataset. Additionally, the contour QA functionality of existing models was assessed using a retrospective cohort of 3,399 lung and 885 spine RT cases. Ambient QA was performed for various disease sites including spine RT and heart for dosimetric sparing. Results: Successful training of the breast model was completed in less than a day and resulted in clinically viable whole breast contours. For the retrospective analysis, we evaluated manual-versus-AI similarity for the ten most common structures. The DL-ODA detected high similarities in heart, lung, liver, and kidney delineations but lower for esophagus, trachea, stomach, and small bowel due largely to incomplete manual contouring. The deployed Ambient QAs for heart and spine sites have prospectively processed over 2,500 cases and 230 cases over 9 months and 5 months, respectively, automatically alerting the RT personnel. Discussion: The DL-ODA capabilities in providing universal AI interventions were demonstrated for On-Demand contour QA, DL segmentations, and automated model training, and confirmed successful integration of the system into a large academic radiotherapy department. The novelty of deploying the DL-ODA as a multi-modal, fully automated end-to-end AI clinical implementation solution marks a significant step towards a generalizable framework that leverages AI to improve the efficiency and reliability of RT systems. [ABSTRACT FROM AUTHOR]