Automatic optical biopsy for colorectal cancer using hyperspectral imaging and artificial neural networks

Collins, Toby; Bencteux, Valentin; Benedicenti, Sara; Moretti, Valentina; Mita, Maria Teresa; Barbieri, Vittoria; Rubichi, Francesco; Altamura, Amedeo; Giaracuni, Gloria; Marescaux, Jacques; Hostettler, Alex; Diana, Michèle; Viola, Massimo; Barberio, Manuel

doi:10.1007/s00464-022-09524-z

Cited by 15 publications

(8 citation statements)

References 49 publications

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“…Using a CNN to automatically detect CRC in the HIS images they obtained a sensitivity of 87% and specificity of 90% for cancer detection. Their approach could be used for objectively assessing tumor margins during surgery ( 24 ).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Avram

Lazăr²,

Mariş³

et al. 2023

Front. Oncol.

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BackgroundA considerable number of recent research have used artificial intelligence (AI) in the area of colorectal cancer (CRC). Surgical treatment of CRC still remains the most important curative component. Artificial intelligence in CRC surgery is not nearly as advanced as it is in screening (colonoscopy), diagnosis and prognosis, especially due to the increased complexity and variability of structures and elements in all fields of view, as well as a general shortage of annotated video banks for utilization.MethodsA literature search was made and relevant studies were included in the minireview.ResultsThe intraoperative steps which, at this moment, can benefit from AI in CRC are: phase and action recognition, excision plane navigation, endoscopy control, real-time circulation analysis, knot tying, automatic optical biopsy and hyperspectral imaging. This minireview also analyses the current advances in robotic treatment of CRC as well as the present possibility of automated CRC robotic surgery.ConclusionsThe use of AI in CRC surgery is still at its beginnings. The development of AI models capable of reproducing a colorectal expert surgeon’s skill, the creation of large and complex datasets and the standardization of surgical colorectal procedures will contribute to the widespread use of AI in CRC surgical treatment.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Avram

Lazăr²,

Mariş³

et al. 2023

Front. Oncol.

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“…Consequently, there was a strong isolation between model development and performance evaluation. The CNN design was the same as used in our prior animal study on HSI-based tissue recognition [ 46 ] and our human study on HSI-based colorectal cancer recognition [ 59 ]. All training parameters were similar to Collins et al’s [ 59 ], and they were not modified in this study’s data.…”

Section: Methodsmentioning

confidence: 99%

Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology

et al. 2022

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Complete mesocolic excision (CME), which involves the adequate resection of the tumor-bearing colonic segment with “en bloc” removal of its mesocolon along embryological fascial planes is associated with superior oncological outcomes. However, CME presents a higher complication rate compared to non-CME resections due to a higher risk of vascular injury. Hyperspectral imaging (HSI) is a contrast-free optical imaging technology, which facilitates the quantitative imaging of physiological tissue parameters and the visualization of anatomical structures. This study evaluates the accuracy of HSI combined with deep learning (DL) to differentiate the colon and its mesenteric tissue from retroperitoneal tissue. In an animal study including 20 pig models, intraoperative hyperspectral images of the sigmoid colon, sigmoid mesentery, and retroperitoneum were recorded. A convolutional neural network (CNN) was trained to distinguish the two tissue classes using HSI data, validated with a leave-one-out cross-validation process. The overall recognition sensitivity of the tissues to be preserved (retroperitoneum) and the tissues to be resected (colon and mesentery) was 79.0 ± 21.0% and 86.0 ± 16.0%, respectively. Automatic classification based on HSI and CNNs is a promising tool to automatically, non-invasively, and objectively differentiate the colon and its mesentery from retroperitoneal tissue.

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“…Given the potential clinical impact of enhanced intraoperative visualization, there has been increasing interest in developing ‘dye-free’ ways of analysing tissue texture and character via multispectral analysis 111 . Such hyperspectral imaging is technically possible and generates huge amounts of information, which again may benefit from computational interpretation 112 . Early clinical use is proving encouraging, but it remains unclear as to how useful it will prove and whether it is best as an adjunct rather than alternative to NIR fluorescence.…”

Section: Future Opportunities and Horizon Scanningmentioning

confidence: 99%

Fluorescence-guided surgery: comprehensive review

Sutton

Dam

et al. 2023

BJS Open

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Background Despite significant improvements in preoperative workup and surgical planning, surgeons often rely on their eyes and hands during surgery. Although this can be sufficient in some patients, intraoperative guidance is highly desirable. Near-infrared fluorescence has been advocated as a potential technique to guide surgeons during surgery. Methods A literature search was conducted to identify relevant articles for fluorescence-guided surgery. The literature search was performed using Medical Subject Headings on PubMed for articles in English until November 2022 and a narrative review undertaken. Results The use of invisible light, enabling real-time imaging, superior penetration depth, and the possibility to use targeted imaging agents, makes this optical imaging technique increasingly popular. Four main indications are described in this review: tissue perfusion, lymph node assessment, anatomy of vital structures, and tumour tissue imaging. Furthermore, this review provides an overview of future opportunities in the field of fluorescence-guided surgery. Conclusion Fluorescence-guided surgery has proven to be a widely innovative technique applicable in many fields of surgery. The potential indications for its use are diverse and can be combined. The big challenge for the future will be in bringing experimental fluorophores and conjugates through trials and into clinical practice, as well as validation of computer visualization with large data sets. This will require collaborative surgical groups focusing on utility, efficacy, and outcomes for these techniques.

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Automatic optical biopsy for colorectal cancer using hyperspectral imaging and artificial neural networks

Cited by 15 publications

References 49 publications

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology

Fluorescence-guided surgery: comprehensive review

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