Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.
Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, secondary curative therapy. However, with more patients being diagnosed with early stage and premalignant disease manifesting as large polyps, greater...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 51; no. 10; pp. 3135 - 3149 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Aug2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178837434&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178837434 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Aug2024 vid: 51 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178837434 177770751 10.1007/s00259-024-06731-9 178837434 ppf: 3135 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art. aug: au: Boland, Patrick A. Hardy, N. P. Moynihan, A. McEntee, P. D. Loo, C. Fenlon, H. Cahill, R. A. affil: https://ror.org/05m7pjf47 UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland sug: ab: Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, secondary curative therapy. However, with more patients being diagnosed with early stage and premalignant disease manifesting as large polyps, greater accuracy in diagnostic and therapeutic precision is needed right from the time of first endoscopic encounter. Rapid advancements in the field of artificial intelligence (AI), coupled with widespread availability of near infrared imaging (currently based around indocyanine green (ICG)) can enable colonoscopic tissue classification and prognostic stratification for significant polyps, in a similar manner to contemporary dynamic radiological perfusion imaging but with the advantage of being able to do so directly within interventional procedural time frames. It can provide an explainable method for immediate digital biopsies that could guide or even replace traditional forceps biopsies and provide guidance re margins (both areas where current practice is only approximately 80% accurate prior to definitive excision). Here, we discuss the concept and practice of AI enhanced ICG perfusion analysis for rectal cancer surgery while highlighting recent and essential near-future advancements. These include breakthrough developments in computer vision and time series analysis that allow for real-time quantification and classification of fluorescent perfusion signals of rectal cancer tissue intraoperatively that accurately distinguish between normal, benign, and malignant tissues in situ endoscopically, which are now undergoing international prospective validation (the Horizon Europe CLASSICA study). Next stage advancements may include detailed digital characterisation of small rectal malignancy based on intraoperative assessment of specific intratumoral fluorescent signal pattern. This could include T staging and intratumoral molecular process profiling (e.g. regarding angiogenesis, differentiation, inflammatory component, and tumour to stroma ratio) with the potential to accurately predict the microscopic local response to nonsurgical treatment enabling personalised therapy via decision support tools. Such advancements are also applicable to the next generation fluorophores and imaging agents currently emerging from clinical trials. In addition, by providing an understandable, applicable method for detailed tissue characterisation visually, such technology paves the way for acceptance of other AI methodology during surgery including, potentially, deep learning methods based on whole screen/video detailing. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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