Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules

Gregory T. Kennedy; Feredun S. Azari; Elizabeth Bernstein; Bilal Nadeem; Ashley Chang; Alix Segil; Neil Sullivan; Emmanuel Encarnado; Charuhas Desphande; John C. Kucharczuk; Kaela Leonard; Philip S. Low; Silvia Chen; Aline Criton; Sunil Singhal

doi:10.1007/s00259-022-05868-9

Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules

Gregory T. Kennedy, Feredun S. Azari, Elizabeth Bernstein, Bilal Nadeem, Ashley Chang, Alix Segil, Neil Sullivan, Emmanuel Encarnado, Charuhas Desphande, John C. Kucharczuk, Kaela Leonard, Philip S. Low, Silvia Chen, Aline Criton, Sunil Singhal

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

BACKGROUND: The diagnostic yield of biopsies of solitary pulmonary nodules (SPNs) is low, particularly in sub-solid lesions. We developed a method (NIR-nCLE) to achieve cellular level cancer detection during biopsy by integrating (i) near-infrared (NIR) imaging using a cancer-targeted tracer (pafolacianine), and (ii) a flexible NIR confocal laser endomicroscopy (CLE) system that can fit within a biopsy needle. Our goal was to assess the diagnostic accuracy of NIR-nCLE ex vivo in SPNs.

METHODS: Twenty patients with SPNs were preoperatively infused with pafolacianine. Following resection, specimens were inspected to identify the lesion of interest. NIR-nCLE imaging followed by tissue biopsy was performed within the lesion and in normal lung tissue. All imaging sequences (n = 115) were scored by 5 blinded raters on the presence of fluorescent cancer cells and compared to diagnoses by a thoracic pathologist.

RESULTS: Most lesions (n = 15, 71%) were adenocarcinoma-spectrum malignancies, including 7 ground glass opacities (33%). Mean fluorescence intensity (MFI) by NIR-nCLE for tumor biopsy was 20.6 arbitrary units (A.U.) and mean MFI for normal lung was 6.4 A.U. (p < 0.001). Receiver operating characteristic analysis yielded a high area under the curve for MFI (AUC = 0.951). Blinded raters scored the NIR-nCLE sequences on the presence of fluorescent cancer cells with sensitivity and specificity of 98% and 97%, respectively. Overall diagnostic accuracy was 97%. The inter-observer agreement of the five raters was excellent (κ = 0.95).

CONCLUSIONS: NIR-nCLE allows sensitive and specific detection of cancer cells in SPNs. This technology has far-reaching implications for diagnostic needle biopsies and intraprocedural decision-making.

Original language	English (US)
Pages (from-to)	4194-4204
Number of pages	11
Journal	European Journal of Nuclear Medicine and Molecular Imaging
Volume	49
Issue number	12
DOIs	https://doi.org/10.1007/s00259-022-05868-9
State	Published - Oct 2022

Keywords

Biopsy
NIR-nCLE
Pulmonary nodules
Endoscopic Ultrasound-Guided Fine Needle Aspiration/methods
Multiple Pulmonary Nodules
Humans
Pancreatic Neoplasms/pathology
Microscopy, Confocal/methods
Adenocarcinoma/pathology

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

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10.1007/s00259-022-05868-9

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Kennedy, G. T., Azari, F. S., Bernstein, E., Nadeem, B., Chang, A., Segil, A., Sullivan, N., Encarnado, E., Desphande, C., Kucharczuk, J. C., Leonard, K., Low, P. S., Chen, S., Criton, A., & Singhal, S. (2022). Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules. European Journal of Nuclear Medicine and Molecular Imaging, 49(12), 4194-4204. https://doi.org/10.1007/s00259-022-05868-9

Kennedy, GT, Azari, FS, Bernstein, E, Nadeem, B, Chang, A, Segil, A, Sullivan, N, Encarnado, E, Desphande, C, Kucharczuk, JC, Leonard, K, Low, PS, Chen, S, Criton, A & Singhal, S 2022, 'Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules', European Journal of Nuclear Medicine and Molecular Imaging, vol. 49, no. 12, pp. 4194-4204. https://doi.org/10.1007/s00259-022-05868-9

@article{d76cde1e35974349b4fb0e7e08f7ecc1,

title = "Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules",

abstract = "BACKGROUND: The diagnostic yield of biopsies of solitary pulmonary nodules (SPNs) is low, particularly in sub-solid lesions. We developed a method (NIR-nCLE) to achieve cellular level cancer detection during biopsy by integrating (i) near-infrared (NIR) imaging using a cancer-targeted tracer (pafolacianine), and (ii) a flexible NIR confocal laser endomicroscopy (CLE) system that can fit within a biopsy needle. Our goal was to assess the diagnostic accuracy of NIR-nCLE ex vivo in SPNs.METHODS: Twenty patients with SPNs were preoperatively infused with pafolacianine. Following resection, specimens were inspected to identify the lesion of interest. NIR-nCLE imaging followed by tissue biopsy was performed within the lesion and in normal lung tissue. All imaging sequences (n = 115) were scored by 5 blinded raters on the presence of fluorescent cancer cells and compared to diagnoses by a thoracic pathologist.RESULTS: Most lesions (n = 15, 71%) were adenocarcinoma-spectrum malignancies, including 7 ground glass opacities (33%). Mean fluorescence intensity (MFI) by NIR-nCLE for tumor biopsy was 20.6 arbitrary units (A.U.) and mean MFI for normal lung was 6.4 A.U. (p < 0.001). Receiver operating characteristic analysis yielded a high area under the curve for MFI (AUC = 0.951). Blinded raters scored the NIR-nCLE sequences on the presence of fluorescent cancer cells with sensitivity and specificity of 98% and 97%, respectively. Overall diagnostic accuracy was 97%. The inter-observer agreement of the five raters was excellent (κ = 0.95).CONCLUSIONS: NIR-nCLE allows sensitive and specific detection of cancer cells in SPNs. This technology has far-reaching implications for diagnostic needle biopsies and intraprocedural decision-making.",

keywords = "Biopsy, NIR-nCLE, Pulmonary nodules, Endoscopic Ultrasound-Guided Fine Needle Aspiration/methods, Multiple Pulmonary Nodules, Humans, Pancreatic Neoplasms/pathology, Microscopy, Confocal/methods, Adenocarcinoma/pathology",

author = "Kennedy, {Gregory T.} and Azari, {Feredun S.} and Elizabeth Bernstein and Bilal Nadeem and Ashley Chang and Alix Segil and Neil Sullivan and Emmanuel Encarnado and Charuhas Desphande and Kucharczuk, {John C.} and Kaela Leonard and Low, {Philip S.} and Silvia Chen and Aline Criton and Sunil Singhal",

note = "Funding Information: Dr. Kennedy was supported by the American Philosophical Society and the National Institutes of Health (grant F32 CA254210-01). Dr. Azari was supported by the Society for Thoracic Surgeons. Dr. Singhal was supported by the National Institutes of Health (grant R01 CA193556) and the State of Pennsylvania Health Research Formula Fund. The study funding was provided by Johnson and Johnson. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = oct,

doi = "10.1007/s00259-022-05868-9",

language = "English (US)",

volume = "49",

pages = "4194--4204",

journal = "European Journal of Nuclear Medicine and Molecular Imaging",

issn = "1619-7070",

publisher = "Springer Verlag",

number = "12",

}

TY - JOUR

T1 - Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules

AU - Kennedy, Gregory T.

AU - Azari, Feredun S.

AU - Bernstein, Elizabeth

AU - Nadeem, Bilal

AU - Chang, Ashley

AU - Segil, Alix

AU - Sullivan, Neil

AU - Encarnado, Emmanuel

AU - Desphande, Charuhas

AU - Kucharczuk, John C.

AU - Leonard, Kaela

AU - Low, Philip S.

AU - Chen, Silvia

AU - Criton, Aline

AU - Singhal, Sunil

N1 - Funding Information: Dr. Kennedy was supported by the American Philosophical Society and the National Institutes of Health (grant F32 CA254210-01). Dr. Azari was supported by the Society for Thoracic Surgeons. Dr. Singhal was supported by the National Institutes of Health (grant R01 CA193556) and the State of Pennsylvania Health Research Formula Fund. The study funding was provided by Johnson and Johnson. Publisher Copyright: © 2022, The Author(s).

PY - 2022/10

Y1 - 2022/10

N2 - BACKGROUND: The diagnostic yield of biopsies of solitary pulmonary nodules (SPNs) is low, particularly in sub-solid lesions. We developed a method (NIR-nCLE) to achieve cellular level cancer detection during biopsy by integrating (i) near-infrared (NIR) imaging using a cancer-targeted tracer (pafolacianine), and (ii) a flexible NIR confocal laser endomicroscopy (CLE) system that can fit within a biopsy needle. Our goal was to assess the diagnostic accuracy of NIR-nCLE ex vivo in SPNs.METHODS: Twenty patients with SPNs were preoperatively infused with pafolacianine. Following resection, specimens were inspected to identify the lesion of interest. NIR-nCLE imaging followed by tissue biopsy was performed within the lesion and in normal lung tissue. All imaging sequences (n = 115) were scored by 5 blinded raters on the presence of fluorescent cancer cells and compared to diagnoses by a thoracic pathologist.RESULTS: Most lesions (n = 15, 71%) were adenocarcinoma-spectrum malignancies, including 7 ground glass opacities (33%). Mean fluorescence intensity (MFI) by NIR-nCLE for tumor biopsy was 20.6 arbitrary units (A.U.) and mean MFI for normal lung was 6.4 A.U. (p < 0.001). Receiver operating characteristic analysis yielded a high area under the curve for MFI (AUC = 0.951). Blinded raters scored the NIR-nCLE sequences on the presence of fluorescent cancer cells with sensitivity and specificity of 98% and 97%, respectively. Overall diagnostic accuracy was 97%. The inter-observer agreement of the five raters was excellent (κ = 0.95).CONCLUSIONS: NIR-nCLE allows sensitive and specific detection of cancer cells in SPNs. This technology has far-reaching implications for diagnostic needle biopsies and intraprocedural decision-making.

AB - BACKGROUND: The diagnostic yield of biopsies of solitary pulmonary nodules (SPNs) is low, particularly in sub-solid lesions. We developed a method (NIR-nCLE) to achieve cellular level cancer detection during biopsy by integrating (i) near-infrared (NIR) imaging using a cancer-targeted tracer (pafolacianine), and (ii) a flexible NIR confocal laser endomicroscopy (CLE) system that can fit within a biopsy needle. Our goal was to assess the diagnostic accuracy of NIR-nCLE ex vivo in SPNs.METHODS: Twenty patients with SPNs were preoperatively infused with pafolacianine. Following resection, specimens were inspected to identify the lesion of interest. NIR-nCLE imaging followed by tissue biopsy was performed within the lesion and in normal lung tissue. All imaging sequences (n = 115) were scored by 5 blinded raters on the presence of fluorescent cancer cells and compared to diagnoses by a thoracic pathologist.RESULTS: Most lesions (n = 15, 71%) were adenocarcinoma-spectrum malignancies, including 7 ground glass opacities (33%). Mean fluorescence intensity (MFI) by NIR-nCLE for tumor biopsy was 20.6 arbitrary units (A.U.) and mean MFI for normal lung was 6.4 A.U. (p < 0.001). Receiver operating characteristic analysis yielded a high area under the curve for MFI (AUC = 0.951). Blinded raters scored the NIR-nCLE sequences on the presence of fluorescent cancer cells with sensitivity and specificity of 98% and 97%, respectively. Overall diagnostic accuracy was 97%. The inter-observer agreement of the five raters was excellent (κ = 0.95).CONCLUSIONS: NIR-nCLE allows sensitive and specific detection of cancer cells in SPNs. This technology has far-reaching implications for diagnostic needle biopsies and intraprocedural decision-making.

KW - Biopsy

KW - NIR-nCLE

KW - Pulmonary nodules

KW - Endoscopic Ultrasound-Guided Fine Needle Aspiration/methods

KW - Multiple Pulmonary Nodules

KW - Humans

KW - Pancreatic Neoplasms/pathology

KW - Microscopy, Confocal/methods

KW - Adenocarcinoma/pathology

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U2 - 10.1007/s00259-022-05868-9

DO - 10.1007/s00259-022-05868-9

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AN - SCOPUS:85133454233

SN - 1619-7070

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JO - European Journal of Nuclear Medicine and Molecular Imaging

JF - European Journal of Nuclear Medicine and Molecular Imaging

IS - 12

ER -

Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules

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