A new commentary published in Cancer, an interdisciplinary journal of the American Cancer Society (ACS), outlines priority areas for advancing imaging technologies across the lung cancer care continuum.
The American Cancer Society National Lung Cancer Roundtable strategic plan: Opportunities in advanced imaging across the lung cancer care continuum
Authors: Daniel C. Sullivan, Michael McNitt-Gray, Denise R. Aberle, Ricardo Avila, Samuel Armato III, Paul M. Bergstrom Jr, Maria Chong, Dianna Cody, Laura P. Coombs, Richard Frank, Robert J. Gillies, Jonathan Goldin, Jayashree Kalpathy-Cramer, Paul Kinahan, Matthew B. Schabath, Robert Smith, David Yankelevitz, and Ella A. Kazerooni
Cancer 12 August 2026 DOI: 10.1002/cncr.70516
The publication was developed as part of the American Cancer Society National Lung Cancer Roundtable’s (ACS NLCRT) strategic plan.
The ACS NLCRT Advanced Imaging Across the Lung Cancer Care Continuum Task Group identified four advanced imaging technology areas as priorities for further development:
- Novel imaging technologies, including approaches for lung cancer screening, staging and monitoring;
- Quantitative imaging methods and imaging biomarkers, aimed at generating more objective and reproducible information from medical images;
- Platforms and infrastructure for sharing image data and algorithms;
- Artificial intelligence-driven imaging solutions.
The commentary provides background on each of these areas and examines clinical problems along the lung cancer care pathway through use-case scenarios. The authors also discuss challenges that may limit the development, implementation, and broader dissemination of advanced imaging technologies.
The broader goals of the task group’s strategic plan include improving the identification of individuals at risk of developing lung cancer, advancing lung cancer screening technologies, improving prediction of malignancy in CT-detected pulmonary nodules, supporting patient-management strategies, and developing better methods for assessing treatment response and monitoring for recurrence.
The group also highlights several barriers to progress. These include the need for additional investment in imaging technology development, standardized approaches to quantitative imaging, larger and more diverse datasets for algorithm development, infrastructure for data sharing, and clearer standards and validation pathways for artificial intelligence and machine-learning tools.
For AI specifically, the strategic plan emphasizes improving algorithm robustness, establishing standards for good machine-learning practices, clarifying validation pathways, and advancing interpretable AI, in which systems can provide greater insight into how their outputs or decisions are generated.
The authors conclude by considering how these technological opportunities and implementation challenges could be addressed to support the integration of advanced imaging throughout the lung cancer care pathway.
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