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Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography

Liu, Hung-Chang, Lin, Miao-Hui, Chang, Wei-Chin, Zeng, Rui-Cheng, Wang, Yi-Min, Sun, Chia-Wei·2023·Cancers
DOI 10.3390/cancers15225388PMC PMC10670228
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Abstract

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human–machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion’s location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

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  • The AI-enhanced system's performance was compared with conventional frozen sections, using final paraffin-embedded pathology as the reference standard.

    “Its performance was compared with conventional frozen sections, using final paraffin-embedded pathology as the reference standard.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [52]

  • The proposed AI system could offer faster and more reliable intraoperative diagnostic support, potentially improving surgical outcomes.

    “These findings indicate that the proposed system could offer faster and more reliable intraoperative diagnostic support, potentially improving surgical outcomes [52].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [52]

  • The AI approach demonstrated markedly superior discrimination among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal lung tissue, achieving an overall accuracy of 84.9%, whereas frozen sections reached only 20%.

    “The AI approach demonstrated markedly superior discrimination among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal lung tissue, achieving an overall accuracy of 84.9%, whereas frozen sections reached only 20%.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [52]

  • Liu et al. developed an interactive human-machine interface (HMI) that incorporates a mobile optical coherence tomography (OCT) platform, deep learning techniques, and attention modules to address the difficulty in defining resection margins when tumor histology is not known before surgery.

    “Liu et al. introduce an interactive human–machine interface (HMI) that incorporates a mobile optical coherence tomography (OCT) platform, deep learning techniques, and attention modules to address a key limitation of frozen-section analysis—namely, the difficulty in defining resection margins when tumor histology is not known before surgery.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [52]

  • Sensitivity and specificity were higher for MIA (89% and 82.7%) and IA (94% and 80.6%) with the AI approach.

    “Sensitivity and specificity were also higher for both MIA (89% and 82.7%) and IA (94% and 80.6%), respectively.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [52]

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Cited by

Sorted by citations

Authors
Liu, H.-C., Lin, M.-H., Chang, W.-C., Zeng, R.-C., Wang, Y.-M., & Sun, C.-W.
Affiliations
  • Section of Thoracic Surgery, Mackay Memorial Hospital, Taipei City 10449, Taiwan
  • Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan
  • Department of Pathology, Mackay Memorial Hospital, New Taipei City 25160, Taiwan
Venue
Cancers
Publisher
MDPI AG
Published
2023-11-13
Volume
15
Issue
22
Pages
5388
ISSN
2072-6694
DOI
10.3390/cancers15225388
PMC
PMC10670228
Cited by
9
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • interdisciplinary academic research corporation of National Yang Ming Chiao Tung University and Mackay Memorial Hospital (MMH-CT-10808, MMH-CT-10906, MMH-CT-11001, MMH-CT-11104, 109-2221-E-009-018-MY3, 111-2221-E-A49-047-MY3, 111-2221-E-195-002, 111-2221-E075-002, 111-2314-B-561-001, 111-2321-B-A49-003, 111-2314-B-A49-078)
  • National Science and Technology Council (MMH-CT-10808, MMH-CT-10906, MMH-CT-11001, MMH-CT-11104, 109-2221-E-009-018-MY3, 111-2221-E-A49-047-MY3, 111-2221-E-195-002, 111-2221-E075-002, 111-2314-B-561-001, 111-2321-B-A49-003, 111-2314-B-A49-078)

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