In vivo reflectance confocal microscopy of wounds: feasibility of intraoperative basal cell carcinoma margin assessment

Shavlokhova, Veronika; Vollmer, Michael; Vollmer, Andreas; Gholam, Patrick; Saravi, Babak; Hoffmann, Jürgen; Engel, Michael; Elsner, Jens P.; Neumeier, Florian; Freudlsperger, Christian

doi:10.21037/atm-21-3462

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Potentially, our imaging approach could be extended to intraoperative search for residual cancer [ 29 , 30 ], and postoperative monitoring for local recurrence with the current limit of a not sterilizable HH-RCM probe with the common methods of sterilization used for surgical devices. Integration of RCM/OCT imaging in Mohs surgery could be considered in a presurgical stage potentially able to save time by reducing the required number of Mohs stages.…”

Section: Discussionmentioning

confidence: 99%

Combining Reflectance Confocal Microscopy, Optical Coherence Tomography, and Ex-Vivo Fluorescence Confocal Microscopy for Margin Assessment in Basal Cell Carcinoma Excision

Michelini,

Mandel,

Ardigò

et al. 2024

Dermatology Practical &Amp; Conceptual

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Introduction: Recent developments of noninvasive, high-resolution imaging techniques, such as reflectance confocal microscopy (RCM) and optical coherence tomography (OCT), have enhanced skin cancer detection and precise tumor excision particularly in highly aggressive and poorly defined basal cell carcinomas (BCCs) Objectives: The aim of this pilot study is to assess feasibility and reproducibility of a systematic clinical workflow combining noninvasive (RCM-OCT) and invasive fluorescence confocal microscopy (FCM) imaging modalities in pre- and intra-surgical evaluations of lateral and deep margins of BCC. Methods: Superficial incisions were made 2 mm beyond the clinical-dermoscopic BCC margins. Lateral margins were then explored with OCT and RCM. In positive margins, a further cut was made 2 mm distal from the previous. A final RCM/OCT-based double-negative margin was drawn around the entire perimeter of the lesion before referring to surgery. The freshly excised specimen was then examined with FCM (ex-vivo) for the evaluation of the deep margin. Histopathologic examination eventually confirmed margin involvement. Results: The study included 22 lesions from 13 patients. At the end of the study, 146 margins—106 negative (73%) and 40 positive (27%) at RCM/OCT—were collected. RCM/OCT margin evaluation showed an overall sensitivity of 100%, a specificity of 96.3%. The overall positive margins diagnostic accuracy was 98.2%. Reproducibility was evaluated on recorded images and the raters showed a substantial inter-observer agreement on both RCM (κ =0.752) and OCT images (κ =0.724). Conclusions: The combined RCM/OCT/FCM ex-vivo approach noninvasively facilitates the presurgical and intrasurgical lateral and deep margin assessment of poorly defined BCCs.

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Section: Discussionmentioning

confidence: 99%

Combining Reflectance Confocal Microscopy, Optical Coherence Tomography, and Ex-Vivo Fluorescence Confocal Microscopy for Margin Assessment in Basal Cell Carcinoma Excision

Michelini,

Mandel,

Ardigò

et al. 2024

Dermatology Practical &Amp; Conceptual

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“…Meanwhile, the tissue taken in disc herniation surgery, for example, is considered biological waste and discarded. When more effective, cost-efficient, and fast diagnostic methods, such as in vivo reflectance confocal microscopy [52], are available in the future, the prediction could be made intraoperatively, and further management can be translated directly from the available data sets of each patient ("precision medicine"). Overall, the extensive amount of research that has been performed within the last few years indicates the possibility of reliable textual data processing for multimodal hybrid deep learning models.…”

Section: Textual Data Conversion Methods For Deep Learning Approachesmentioning

confidence: 99%

“…Vasile et al, for example, used x-ray images, symptoms, and clinical and biological variables within an ensemble of deep learning models to predict the severity of COVID-19 diagnosis [57]. In combination with imaging techniques such as in vivo or ex vivo reflectance confocal microscopy or ultrasound imaging, this multimodal approach could solve prediction tasks in real-time, such as chairside applications [52,59]. Furthermore, Yuan et al recently introduced a general architecture for Hybrid deep neural networks supporting mixed inputs reporting that the Hybrid model reached higher accuracies with classical MLP and CNN models [60].…”

Section: Multi-input Mixed Data Deep Learning Modelsmentioning

confidence: 99%

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

Saravi

Hassel²,

Ülkümen

et al. 2022

JPM

Self Cite

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Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

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Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation

et al. 2023

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Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.

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In vivo reflectance confocal microscopy of wounds: feasibility of intraoperative basal cell carcinoma margin assessment

Cited by 4 publications

References 37 publications

Combining Reflectance Confocal Microscopy, Optical Coherence Tomography, and Ex-Vivo Fluorescence Confocal Microscopy for Margin Assessment in Basal Cell Carcinoma Excision

Combining Reflectance Confocal Microscopy, Optical Coherence Tomography, and Ex-Vivo Fluorescence Confocal Microscopy for Margin Assessment in Basal Cell Carcinoma Excision

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation

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