Expert consensus on the evaluation and management of high-risk indeterminate pulmonary nodules

Yang Dawei; Stephan Lam; Kai Wang; Zhou Jian; Zhang Xiaoju; Wang Qi; Zhou Chengzhi; Zhang Lichuan; Bai Li; Wang Yuehong; Li Ming; Sun Jiayuan; Li Yang; Fengming Kong; Haiquan Chen; Ming Fan; Xuan Jianwei; Fred R. Hirsch; Charles A. Powell; Bai Chunxue

Clinical eHealth (Feb 2024)

Expert consensus on the evaluation and management of high-risk indeterminate pulmonary nodules

Yang Dawei,
Stephan Lam,
Kai Wang,
Zhou Jian,
Zhang Xiaoju,
Wang Qi,
Zhou Chengzhi,
Zhang Lichuan,
Bai Li,
Wang Yuehong,
Li Ming,
Sun Jiayuan,
Li Yang,
Fengming Kong,
Haiquan Chen,
Ming Fan,
Xuan Jianwei,
Fred R. Hirsch,
Charles A. Powell,
Bai Chunxue

Affiliations

Yang Dawei: Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian province, China; Shanghai Engineering Research Center for the Respiratory Medical Internet of Things, China
Stephan Lam: British Columbia Cancer Agency and the University of British Columbia, Vancouver, BC, Canada
Kai Wang: Department of Respiratory and Critical Care Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China; Corresponding authors at: Department of Pulmonary and Critical Care Medicine of Zhongshan Hospital, Fudan University, Shanghai Engineering Research Center for the Respiratory Medical Internet of Things, Shanghai Respiratory Research Institute, Shanghai, China (C. Bai). Department of Pulmonary Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China (K. Wang). Division of Pulmonary, Critical care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, NY, USA (C. Powell).
Zhou Jian: Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian province, China
Zhang Xiaoju: Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
Wang Qi: Department of Respiratory Medicine, The Second Hospital, Dalian Medical University, Dalian, China
Zhou Chengzhi: Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, Guangzhou, China
Zhang Lichuan: Zhongshan Hospital Affiliated of Dalian University, Dalian, Liaoning, China
Bai Li: Department of Respiratory Critical Care Medicine, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China
Wang Yuehong: Department of Respiratory Disease, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
Li Ming: Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
Sun Jiayuan: Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
Li Yang: Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
Fengming Kong: Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong 518053, China; Department of Clinical Oncology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
Haiquan Chen: Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
Ming Fan: Department of Radiation Oncology, Fudan University Cancer Hospital, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
Xuan Jianwei: Health Economic Research Institute, School of Pharmacy, Sun Yat-Sen University, Guangzhou, Guangdong Province, China
Fred R. Hirsch: Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, NY, USA
Charles A. Powell: Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, NY, USA; Corresponding authors at: Department of Pulmonary and Critical Care Medicine of Zhongshan Hospital, Fudan University, Shanghai Engineering Research Center for the Respiratory Medical Internet of Things, Shanghai Respiratory Research Institute, Shanghai, China (C. Bai). Department of Pulmonary Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China (K. Wang). Division of Pulmonary, Critical care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, NY, USA (C. Powell).
Bai Chunxue: Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Engineering Research Center for the Respiratory Medical Internet of Things, China; Corresponding authors at: Department of Pulmonary and Critical Care Medicine of Zhongshan Hospital, Fudan University, Shanghai Engineering Research Center for the Respiratory Medical Internet of Things, Shanghai Respiratory Research Institute, Shanghai, China (C. Bai). Department of Pulmonary Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China (K. Wang). Division of Pulmonary, Critical care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, NY, USA (C. Powell).

Journal volume & issue: Vol. 7
pp. 27 – 35

Abstract

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Background: The most effective method for improving the prognosis of lung cancer is the application of low-dose computed tomography (LDCT) for pulmonary nodule screening in populations at high risk. Timely diagnosis and treatment of early-stage lung cancer can contribute to higher long-term survival rates. However, it remains difficult to differentiate malignant from benign pulmonary nodules measuring 8–15 mm, and avoid overtreatment on the one hand and delayed diagnosis on the other hand. In this consensus paper, we aimed to clarify the definition of “high-risk indeterminate pulmonary nodules (IPNs)” and discuss appropriate evaluation and management to facilitate timely diagnosis of lung cancer to improve lung cancer outcome. Direction for future research was discussed. Methods: A multi-disciplinary panel of doctors and IT experts from Asia, Canada and the U.S. were invited to participate. Published evidence and consensus guidelines were used to develop this consensus was clarified. Their evaluation and management were discussed. Findings: The experts believed that the prevalence of pulmonary nodules was very high, and it that was difficult to diagnose early-stage lung cancer due to the small size of the nodules, often leading to delayed diagnosis or overtreatment. To address this issue and to improve long-term outcome, the panel considered important to revise the classification of high-risk IPNs, (1) as pulmonary nodules that cannot be clearly diagnosed with non-surgical biopsy procedures, but is highly suspicious for early-stage lung cancer. The panel also recommends the most responsible should arrange imaging evaluations and follow-ups, taking new technologies into account. Artificial intelligence (AI) assessment based on the Medical Internet of Things (MIoT) can be combined with expert opinion to form a human–computer multidisciplinary team (MDT) that can fully implement the three core procedures of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing. This will help to upgrade the non-standard diagnosis and treatment, the so-called “handicraft workshop model”, to a modern assembly-line model that meets international standards. The MIoT technology, which has the potential to realize “simplification of complex problems, digitalization of simple problems, programming of digital problems, and systematization of programming problems”, can promote the homogeneous evaluation of pulmonary nodules by enhancing both the sensitivity and the specificity of detecting early-stage lung cancer, in order to avoid delayed diagnosis and overtreatment. Conclusion: To optimize the evaluation of early-stage lung cancer, and to avoid delayed diagnosis and overtreatment, it is necessary to propose and promote the concept of “high-risk IPNs”. The application of current technologies, AI, and a human–computer MDT, will facilitate improvement in nodule evaluation, transforming the current diagnosis and treatment model, which is akin to production in handicraft workshops, into a modern assembly-line model that meets international standards, and will ultimately result in better prognosis.

Published in Clinical eHealth

ISSN: 2588-9141 (Online)
Publisher: KeAi Communications Co., Ltd.
Country of publisher: China
LCC subjects: Medicine
Website: https://www.keaipublishing.com/en/journals/clinical-ehealth/

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