Detection of surgical margins in oral cavity cancer: the role of dynamic optical contrast imaging

Pellionisz, P.; Badran, Karam W.; Grundfest, Warren S.; John, Maie A. St.

doi:10.1097/moo.0000000000000444

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…We have previously designed Dynamic Optical Contrast Imaging (DOCI), a novel imaging modality that acquires temporally dependent measurements of tissue autofluorescence. [5][6][7][8][9] Furthermore, we demonstrated that DOCI can distinguish HNSCC from adjacent healthy tissue with a high degree of accuracy. 5 DOCI images are captured in real time, offering an operatively wide field of view.…”

Section: Introductionmentioning

confidence: 72%

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ICG-Functionalized Gold Nanostars As An Effective Contrast Agent For Real-time Tumor Localization with Dynamic Optical Contrast Imaging (DOCI) and Enhanced Radiation Therapy

Alhiyari,

Liu,

Mukdad

et al. 2023

Preprint

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The primary management of head and neck squamous cell carcinoma relies on complete surgical resection of the tumor. However, the establishment of negative margin complete resection is often difficult given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. Positive margin status is associated with significantly decreased survival. Currently, surgeons determine where the tumor cuts are made, by palpating the edges of the tumor and using prior imaging. After a tumor is presumed to be removed in its entirety, the surrounding tissues are sampled by frozen section histologic pathology to ensure that no microscopic disease is left behind, the efficacy of which varies and is subject to sampling error. The methodology by which frozen sections are collected whether tumor bed driven or specimen driven can also alter margin outcome. Thus, improving intraoperative detection of tumor margins is key to optimizing treatment and outcomes. Our group has developed a possible solution for this unmet clinical need. We have previously designed Dynamic Optical Contrast Imaging (DOCI), a novel imaging modality that acquires temporally dependent measurements of tissue autofluorescence. Furthermore, we demonstrated that DOCI can distinguish HNSCC from adjacent healthy tissue with a high degree of accuracy. DOCI images are captured in real time and offer an operatively wide field of view. With the addition of ICG conjugated gold nanostars (GNS) we can improve DOCI image contrast between tumors vs normal tissues as well as use the GNS for CT imaging and radiotherapeutic treatment.

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Section: Introductionmentioning

confidence: 72%

“…[3][4][5] The methodology by which frozen section margins are collected-whether from the tumor bed or specimen itself-can also alter margin outcome. 6,7 Thus, improving intraoperative detection of tumor margins is critical to optimizing oncologic surgical treatment and outcomes.…”

Section: Introductionmentioning

confidence: 99%

ICG-Functionalized Gold Nanostars As An Effective Contrast Agent For Real-time Tumor Localization with Dynamic Optical Contrast Imaging (DOCI) and Enhanced Radiation Therapy

Alhiyari,

Liu,

Mukdad

et al. 2023

Preprint

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“…Spectroscopic methods such as elastic scattering spectroscopy and Raman spectroscopy have been also reported for in vivo discrimination of OSCC from surrounding normal tissue ( 11, 12 ), and fluorescence lifetime imaging (FLIM)–derived parameters combined with machine learning classifiers have shown promise in discriminating healthy margin tissue from OSCC ( 19 ). Another emerging approach is dynamic optical contrast imaging, which is another configuration of FLIM and maps fluorescence lifetime variations between OSCCs and margins ( 13 ). Microscopic imaging approaches include high-resolution micro-endoscopy, optical coherence tomography (OCT), and ex vivo fluorescence confocal microscopy ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

A Prospective Double-Blinded Comparison of Reflectance Confocal Microscopy with Conventional Histopathology for In Vivo Assessment in Oral Cancer

Zanoni,

Demétrio De Souza França,

Valero

et al. 2024

Clinical Cancer Research

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Purpose: We investigated reflectance confocal microscopy (RCM) as a possible non-invasive approach for the diagnosis of cancer and real-time assessment of surgical margins. Patients and methods: In a phase I study on 20 patients, we established the RCM imaging morphological features that distinguish OSCC from normal tissue with a newly developed intra-oral RCM probe. Our subsequent phase II prospective double-blinded study in 60 patients tested the diagnostic accuracy of RCM against histopathology. Five RCM videos from the tumor and five from normal surrounding mucosa were collected on each patient, followed by a 3-mm punch biopsy of the imaged area. An experienced RCM reader, who was blinded to biopsy location and histological diagnosis, examined the videos from both regions and classified each as “tumor” or “not-tumor” based on RCM features established in phase I. Hematoxylin and eosin slides from the biopsies were read by a pathologist who was blinded to RCM results. Using histology as the gold standard, we calculated the sensitivity and specificity of RCM. Results: We report a high agreement between the blinded readers (95% for normal tissue and 81.7% for tumors), high specificity (98.3%) and negative predictive values (96.6%) for normal tissue identification, and high sensitivity (90%) and positive predictive values (88.2%) for tumor detection. Conclusions: RCM imaging is a promising technology for non-invasive in vivo diagnosis of OSCC and for real-time intraoperative evaluation of mucosal surgical margins. Its inherent constraint, however, stems from the diminished capability to evaluate structures located at more substantial depths within the tissue.

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“…As has already been demonstrated by our previous studies and in the current one, DOCI reliably differentiates head and neck cancer tissue from surrounding normal tissue on the basis of fluorescence decay information. [5][6][7][8][9][10] The performance of the DOCI system is enhanced by the CNN-based classification model for automated classification of tumor and nontumor tissue of DOCI-acquired images. As the CNN model continues to acquire further DOCIacquired image samples, we anticipate that the tumor region confidence will continue to improve above the current 76.02% confidence mark.…”

Section: Discussionmentioning

confidence: 99%

“…Our group has developed and previously described Dynamic Optical Contrast Imaging (DOCI), a novel imaging modality that acquires temporally-dependent measurements of tissue autofluorescence. [5][6][7][8][9] Additionally, in fresh ex-vivo experiments, our group showcased that the DOCI system reliably differentiates HNSCC from surrounding normal tissue and facilitates specific tumor localization [Figure 1]. 5 DOCI images are captured in real-time, do not require administration of contrast agents, and offer an operatively wide field of view.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Classification of Head and Neck Tumors using Convolutional Neural Networks with Dynamic Optical Contrast Imaging

Odion

Mukdad²,

Alhiyari³

et al. 2022

Preprint

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Background: Recent advances in artificial intelligence (AI) in the field of imaging have resulted in new opportunities for automated tumor detection and margin assessment. In particular, AI deep learning techniques such as the Convolutional Neural Network (CNN) have greatly advanced the field of computer vision. Here we introduce the application of a CNN model for use with Dynamic Optical Contrast Imaging (DOCI), an imaging technique developed by our group that creates a unique molecular signature on tissue targets by obtaining the autofluorescence decay of several spectral bands in the UV-Vis range. Methods: 21 patients undergoing surgical resection for tonsillar squamous cell carcinoma (SCC) were identified on a prospective basis. DOCI images were analyzed and compared to the pathology results as ground truth. A CNN model was used to segment sections of DOCI images and provide a percentage chance of tumor presence, allowing for automated tumor margin delineation without a-priori knowledge of the tumor tissue composition. Results: CNN outputs yielded a 99.98% confidence in classifying non-tumor tissue and 76.02% confidence in classifying tumor tissue. Conclusions: Our results indicate that a CNN-based classification model for DOCI allows for real-time analysis of tissue, providing improved sensitivity and accuracy of determining true margins and thus enabling the head and neck cancer surgeon to save healthy tissue and improve patient outcomes.

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Detection of surgical margins in oral cavity cancer: the role of dynamic optical contrast imaging

Cited by 5 publications

References 32 publications

ICG-Functionalized Gold Nanostars As An Effective Contrast Agent For Real-time Tumor Localization with Dynamic Optical Contrast Imaging (DOCI) and Enhanced Radiation Therapy

ICG-Functionalized Gold Nanostars As An Effective Contrast Agent For Real-time Tumor Localization with Dynamic Optical Contrast Imaging (DOCI) and Enhanced Radiation Therapy

A Prospective Double-Blinded Comparison of Reflectance Confocal Microscopy with Conventional Histopathology for In Vivo Assessment in Oral Cancer

AI-Enabled Classification of Head and Neck Tumors using Convolutional Neural Networks with Dynamic Optical Contrast Imaging

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