1
|
Li C, Mi J, Wang Y, Zhang Z, Guo X, Zhou J, Hu Z, Tian J. New and effective EGFR-targeted fluorescence imaging technology for intraoperative rapid determination of lung cancer in freshly isolated tissue. Eur J Nucl Med Mol Imaging 2023; 50:494-507. [PMID: 36207638 DOI: 10.1007/s00259-022-05975-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/15/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE During lung cancer surgery, it is very important to define tumor boundaries and determine the surgical margin distance. In previous research, systemically application of fluorescent probes can help medical professionals determine the boundaries of tumors and find small tumors and metastases, thereby improving the accuracy of surgical resection. However, there are very few safe and effective probes that can be applied to clinical trials up to now, which limits the clinical application of fluorescence imaging. Here we developed a new technology that can quickly identify the tumor area in the resected lung tissue during the operation and distinguish the tumor boundary and metastatic lymph nodes. EXPERIMENTAL DESIGN For animal studies, a PDX model of lung cancer was established. The tumors, lungs, and peritumoral muscle tissues of tumor-bearing mice were surgically removed and incubated with a probe targeting epidermal growth factor receptor (EGFR) for 20 min, and then imaged by a closed-field near-infrared two-zone (NIR-II) fluorescence imaging system. For clinical samples, ten surgically removed lung tissues and 60 lymph nodes from 10 lung cancer patients undergoing radical resection were incubated with the targeting probe immediately after intraoperative resection and imaged to identify the tumor area and distinguish the tumor boundary and metastatic lymph nodes. The accuracy of fluorescence imaging was confirmed by HE staining and immunohistochemistry. RESULTS The ex vivo animal imaging experiments showed a fluorescence enhancement of tumor tissue. For clinical samples, our results showed that this new technology yielded more than 85.7% sensitivity and 100% specificity in identifying the tumor area in the resected lung tissue. The average fluorescence tumor-to-background ratio was 2.5 ± 1.3. Furthermore, we also used this technique to image metastatic lymph nodes intraoperatively and showed that metastatic lymph nodes have brighter fluorescence than normal lymph nodes, as the average fluorescence tumor-to-background signal ratio was 2.7 ± 1.1. Calculations on the results of the fluorescence signal in relation to the number of metastatic lymph nodes yielded values of 77.8% for sensitivity and 92.1% for specificity. We expect this new technology to be a useful diagnostic tool for rapid intraoperative pathological detection and margin determination. CONCLUSIONS By using fluorescently labeled anti-human EGFR recombinant antibody scFv fragment to incubate freshly isolated tissues during surgery, the probes can quickly accumulate in lung cancer tissues, which can be used to quickly identify tumor areas in the resected lung tissues and distinguish tumor boundaries and find metastases in lymph nodes. This technology is expected to be used for rapid intraoperative pathological detection and margin determination.
Collapse
Affiliation(s)
- Changjian Li
- School of Engineering Medicine, Beihang University, Beijing, 100191, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, People's Republic of China
| | - Jiahui Mi
- Department of Thoracic Surgery, Peking University People's Hospital, No.11, Xi Zhi Men South Avenue, Beijing, 100190, People's Republic of China
| | - Yueqi Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Zeyu Zhang
- School of Engineering Medicine, Beihang University, Beijing, 100191, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, People's Republic of China
| | - Xiaoyong Guo
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Department of Gastroenterology, The Third Medical Centre, Chinese PLA General Hospital, Beijing, 100190, People's Republic of China
| | - Jian Zhou
- Department of Thoracic Surgery, Peking University People's Hospital, No.11, Xi Zhi Men South Avenue, Beijing, 100190, People's Republic of China.
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, People's Republic of China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, People's Republic of China.
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China.
- Zhuhai Precision Medical Center, Zhuhai People's Hospital, Affiliated With Jinan University, Zhuhai, 519000, People's Republic of China.
| |
Collapse
|