Prospective evaluation of oral premalignant lesions using a multimodal imaging system: a pilot study

Yang, Eric C.; Vohra, Imran S.; Badaoui, Hawraa; Schwarz, Richard A.; Cherry, Katelin D.; Jacob, Jovitta; Rodriguez, Jessica; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards‐Kortum, Rebecca

doi:10.1002/hed.25978

Cited by 9 publications

(9 citation statements)

References 30 publications

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“…In clinical practice, histopathology can be complemented by immunochemistry and special stains (e.g., alcian blue and reticulum stain) when the diagnosis cannot solely be made based on histological sections. Currently, new optical technologies are under evaluation combining conventional morphological imaging with molecular imaging (such as specific imaging and metabolic imaging) or multimodal imaging to provide supportive data [43]. Recently, dynamic FFOCT technology has been shown to highlight a low level of backscattered light from structures in the tissue [44].…”

Section: Discussionmentioning

confidence: 99%

Value of Full‐Field Optical Coherence Tomography Imaging for the Histological Assessment of Head and Neck Cancer

et al. 2020

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Background and Objectives In head and neck surgery, intraoperative and postoperative evaluation of tumor margins is achieved by histopathological assessment, which is a multistep process. Intraoperative analysis of tumor margins to obtain a preliminary diagnosis is usually carried out on frozen sections. Analysis of frozen sections is challenging due to technical difficulties in processing. Full‐field optical coherence tomography (FFOCT) provides ex vivo images of fresh tissue samples at a microscopic scale without tissue processing. The objectives of our study were to define the diagnostic criteria required to interpret head and neck FFOCT images and to evaluate the reliability of a histological diagnosis made on an “optical biopsy” produced by head and neck FFOCT imaging compared with conventional histology. Study Design/Materials and Methods First, we established an atlas of comparative images (FFOCT/standard histology) and defined the diagnostic criteria based on FFOCT images. Two pathologists subsequently performed a blinded review on 57 FFOCT images (32 patients). Specificity and sensitivity were measured by comparison with the standard histological diagnosis. The primary endpoint was major concordance, defined as two classifications leading to the same therapeutic decision (treatment/no treatment). Results Pathologists identified four main criteria for tissue diagnosis on FFOCT images: heterogeneous cell distribution, stromal reaction, coiling, and keratinization abnormalities. The correlation study showed good results, with sensitivity from 88% to 90% and specificity from 81% to 87%, regardless of whether the FFOCT image review was performed by a pathologist with or without previous experience in optical imaging. Conclusions Our results demonstrate that FFOCT images can be used by pathologists for differential diagnosis, and that high‐resolution FFOCT imaging can provide an assessment of microscopic architecture in head and neck tissues without tissue processing requirements. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

Value of Full‐Field Optical Coherence Tomography Imaging for the Histological Assessment of Head and Neck Cancer

et al. 2020

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“…Included studies (Table 1) were published between 2006 and 2020, and they were focused on the Autofluorescence technique (n:14) [28][29][30][31][32][33][34][35][36][37][38][39][40][41], HRME (n:5) [42][43][44][45][46], Optical spectroscopy (n:7) [10, [47][48][49][50][51][52], NBI (n:2) [53,54], and Vital Stain Colorants (n:6) [55][56][57][58][59][60]. In total, 34 studies were performed in adults (18 years of age or older).…”

Section: Study Characteristicsmentioning

confidence: 99%

“…According to the Newcastle-Ottawa scale (NOS) on cross-sectional (n:24) [28,29,31,35,37,39,40,[42][43][44][45][46][48][49][50][54][55][56]59,60,[73][74][75][76]; case-control (n:10) [10,32,47,[51][52][53][77][78][79][80] and cohort studies (n:7) [30,34,36,38,41,57,58] the authors evaluated the qualities of all included studies based on object selection, comparability, and exposure. A star was described as an appropriate entry, with each star representing one point.…”

Section: Total Score=mentioning

confidence: 99%

In Vivo Imaging-Based Techniques for Early Diagnosis of Oral Potentially Malignant Disorders—Systematic Review and Meta-Analysis

Mazur

Ndokaj

Venugopal

et al. 2021

IJERPH

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Objectives: Oral potentially malignant disorders (OPMDs) are lesions that may undergo malignant transformation to oral cancer. The early diagnosis and surveillance of OPMDs reduce the morbidity and mortality of patients. Diagnostic techniques based on medical images analysis have been developed to diagnose clinical conditions. This systematic review and meta-analysis aimed to evaluate the efficacy of imaging-based techniques compared to the gold standard of histopathology to assess their ability to correctly identify the presence of OPMDs. Design: Literature searches of free text and MeSH terms were performed using MedLine (PubMed), Scopus, Google Scholar, and the Cochrane Library (from 2000 to 30 June 2020). The keywords used in the search strategy were: (“oral screening devices” or “autofluorescence” or “chemiluminescence” or “optical imaging” or “imaging technique”) and (“oral dysplasia” or “oral malignant lesions” or “oral precancerosis”). Results: The search strategy identified 1282 potential articles. After analyzing the results and applying the eligibility criteria, the remaining 43 papers were included in the qualitative synthesis, and 34 of these were included in the meta-analysis. Conclusions: None of the analyzed techniques based on assessing oral images can replace the biopsy. Further studies are needed to explore the role of techniques-based imaging analysis to identify an early noninvasive screening method.

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“…[5][6][7][8] Because abnormal nuclear morphology is a key hallmark of oral neoplasia, numerous clinical studies have demonstrated their potential to diagnose oral neoplasia. [9][10][11][12][13][14][15][16] To reduce subjectivity in image interpretation, automated algorithms have been developed to calculate statistical features of nuclei that correlate with neoplasia, like nuclear density, mean nuclear size and variance, nuclear eccentricity, and the nuclear-to-cytoplasm ratio. [17][18][19] These features can then be used as inputs to machine learning algorithms that distinguish neoplastic tissue from benign tissue.…”

Section: Introductionmentioning

confidence: 99%

“… 5 – 8 Because abnormal nuclear morphology is a key hallmark of oral neoplasia, numerous clinical studies have demonstrated their potential to diagnose oral neoplasia. 9 – 16 …”

Section: Introductionmentioning

confidence: 99%

Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images

et al. 2020

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Purpose: In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm 2 , a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.

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Prospective evaluation of oral premalignant lesions using a multimodal imaging system: a pilot study

Cited by 9 publications

References 30 publications

Value of Full‐Field Optical Coherence Tomography Imaging for the Histological Assessment of Head and Neck Cancer

Value of Full‐Field Optical Coherence Tomography Imaging for the Histological Assessment of Head and Neck Cancer

In Vivo Imaging-Based Techniques for Early Diagnosis of Oral Potentially Malignant Disorders—Systematic Review and Meta-Analysis

Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images

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