Top

Surgery Today

Published in:

24-06-2023 | Lung Cancer | Original Article

Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks

Authors: Yuya Ishikawa, Takaaki Sugino, Kenichi Okubo, Yoshikazu Nakajima

Published in: Surgery Today | Issue 12/2023

Abstract

Objectives

The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces.

Materials and methods

We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons.

Results and conclusions

Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically.

Arbour KC. Riely GJ Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review. JAMA. 2019;322:764–74. https://doi.org/10.1001/jama.2019.11058.CrossRefPubMed

Miller M, Hanna N. Advances in systemic therapy for non-small cell lung cancer. BMJ. 2021. https://doi.org/10.1136/bmj.n2363.CrossRefPubMedPubMedCentral

Postmus PE, Kerr KM, Oudkerk M, Senan S, Waller DA, Vansteenkiste J, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017;28:iv1–21. https://doi.org/10.1093/annonc/mdx222.CrossRefPubMed

Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, et al. NCCN guidelines insights: non-small cell lung cancer, version 2.2021. J Natl Compr Canc Netw. 2021;19:254–66. https://doi.org/10.6004/jnccn.2021.0013.CrossRefPubMed

Maconachie R, Mercer T, Navani N, McVeigh G. Lung cancer: diagnosis and management: summary of updated NICE guidance. BMJ. 2019;364:l1049. https://doi.org/10.1136/bmj.l1049.CrossRefPubMed

Louie BE, Wilson JL, Kim S, Cerfolio RJ, Park BJ, Farivar AS, et al. Comparison of video-assisted thoracoscopic surgery and robotic approaches for clinical stage I and stage II non-small cell lung cancer using the Society of Thoracic Surgeons Database. Ann Thorac Surg. 2016;102:917–24. https://doi.org/10.1016/j.athoracsur.2016.03.032.CrossRefPubMedPubMedCentral

Ujiie H, Gregor A, Yasufuku K. Minimally invasive surgical approaches for lung cancer. Expert Rev Respir Med. 2019;13:571–8. https://doi.org/10.1080/17476348.2019.1610399.CrossRefPubMed

Lui TK, Tsui VW, Leung WK. Accuracy of artificial intelligence–assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest Endosc. 2020;92:821–30. https://doi.org/10.1016/j.gie.2020.06.034.CrossRefPubMed

Paderno A, Holshinger FC, Piazza C. Videomics: bringing deep learning to diagnostic endoscopy. Curr Opin Otolaryngol Head Neck Surg. 2021;29:143–8. https://doi.org/10.1097/MOO.0000000000000697.CrossRefPubMed

10.

Zhang R, Zheng Y, Poon CC, Shen D, Lau JY. Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recognit. 2018;83:209–19. https://doi.org/10.1016/j.patcog.2018.05.026.CrossRefPubMedPubMedCentral

11.

Pacal I, Karaboga D. A robust real-time deep learning based automatic polyp detection system. Comput Biol Med. 2021;134:104519. https://doi.org/10.1016/j.compbiomed.2021.104519.CrossRefPubMed

12.

Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018;21:653–60. https://doi.org/10.1007/s10120-018-0793-2.CrossRefPubMed

13.

Ohmori M, Ishihara R, Aoyama K, Nakagawa K, Iwagami H, Matsuura N, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc. 2020;91:301–9. https://doi.org/10.1016/j.gie.2019.09.034.CrossRefPubMed

14.

Tamashiro A, Yoshio T, Ishiyama A, Tsuchida T, Hijikata K, Yoshimizu S, et al. Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks. Dig Endosc. 2020;32:1057–65. https://doi.org/10.1111/den.13653.CrossRefPubMed

15.

Quin K, Li J, Fang Y, Xu Y, Wu J, Zhang H, et al. Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis. Surg Endosc. 2022;36:16–31. https://doi.org/10.1007/s00464-021-08689-3.CrossRef

16.

Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 779–788.

17.

Redmon J, Farhadi A. YOLOv3: an incremental improvement. arXiv:1804.02767 [Preprint]. 2018. Available from: https://doi.org/10.48550/arXiv.1804.02767

18.

Bochkovskiy A, Wang CY, Liao HYM. YOLOv4: optimal speed and accuracy of object detection. arXiv:2004.10934 [Preprint]. 2020. Available from: https://doi.org/10.48550/arXiv.2004.10934

19.

Wang CY, Bochkovskiy A, Liao HYM. Scaled-YOLOv4: scaling cross stage partial network. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). 2021. p. 13029–13038.

20.

Zhang J, Xia Y, Cui H, Zhang Y. Pulmonary nodule detection in medical images: a survey. Biomed Signal Process Control. 2018;43:138–47. https://doi.org/10.1016/j.bspc.2018.01.011.CrossRef

21.

Zhang C, Sun X, Dang K, Li K, Guo XW, Chang J, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncol. 2019;24:1159–65. https://doi.org/10.1634/theoncologist.2018-0908.CrossRef

22.

Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, et al. The medical imaging interaction toolkit: challenges and advances: 10 years of open-source development. Int J Comput Assist Radiol Surg. 2013;8:607–20. https://doi.org/10.1007/s11548-013-0840-8.CrossRefPubMed

23.

Russakovskym O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52. https://doi.org/10.1007/s11263-015-0816-y.CrossRef

24.

Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13:1–17. https://doi.org/10.1186/s13073-021-00968-x.CrossRef

25.

Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92:807–12. https://doi.org/10.1016/j.gie.2020.06.040.CrossRefPubMed

26.

Alexandre LA, Nobre N, Casteleiro J. Color and position versus texture features for endoscopic polyp detection. Proc Int Conf Biomed Eng Inform. 2008;2:38–42. https://doi.org/10.1109/BMEI.2008.246.CrossRef

27.

Iwahori Y, Hattori A, Adachi Y, Bhuyan MK, Woodham RJ, Kasugai K. Automatic detection of polyp using hessian filter and HOG features. Procedia Comput Sci. 2015;60:730–9. https://doi.org/10.1016/j.procs.2015.08.226.CrossRef

28.

Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep learning in medical image analysis. Adv Exp Med Biol. 2020;1213:3–21. https://doi.org/10.1007/978-3-030-33128-3_1.CrossRefPubMedPubMedCentral

29.

van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021;27:775–84. https://doi.org/10.1038/s41591-021-01343-4.CrossRefPubMed

Title: Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks
Authors: Yuya Ishikawa
Takaaki Sugino
Kenichi Okubo
Yoshikazu Nakajima
Publication date: 24-06-2023
Publisher: Springer Nature Singapore
Keywords: Lung Cancer
Lung Cancer
Lung Cancer
Published in: Surgery Today / Issue 12/2023
Print ISSN: 0941-1291
Electronic ISSN: 1436-2813
DOI: https://doi.org/10.1007/s00595-023-02708-7

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks

Abstract

Objectives

Materials and methods

Results and conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Materials and methods

Results and conclusions

Please log in to get access to this content

Other articles of this Issue 12/2023

Transarterial chemoembolization for liver metastases of a pancreatic neuroendocrine neoplasm: a single-center experience

Postoperative cerebral infarction and arrhythmia after pulmonary lobectomy in Japan: a retrospective analysis of 77,060 cases in a national clinical database

Anastomotic time was associated with postoperative complications: a cumulative sum analysis of thoracoscopic repair of tracheoesophageal fistula in a single surgeon’s experience

Gastrectomy reduces pancreatic secretory function via pancreatic atrophy

Single-incision transgastric resection for gastric gastrointestinal stromal tumors in anatomically challenging locations

Efficacy of left colic artery preservation with D3 lymph node dissection in laparoscopic surgery for advanced sigmoid and rectal cancer