Optical coherence tomography for the diagnosis of malignant skin tumors: a meta-analysis

Xiong, Yiquan; Mo, Yun; Wen, Yu-Qi; Cheng, Mingji; Huo, Shuting; Chen, Xue-Jiao; Chen, Qing

doi:10.1117/1.jbo.23.2.020902

Cited by 40 publications

(25 citation statements)

References 61 publications

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“…Addressing the in vivo use of OCT Xiong et al carried out a meta-analysis to assess the accuracy of OCT for the diagnosis of skin cancer, in the in vivo, non-invasive setting and not on excised tissue samples. This meta-analysis reported that OCT appeared to be useful for the detection of BCC and SCC and was a valuable diagnostic method when screening for early skin cancers [26]. In the future, the combination of ex vivo and in vivo CLSM in just one device could be a perfect diagnostic tool for the pre-and intraoperative setting.…”

Section: Discussionmentioning

confidence: 98%

Simple 3‐criteria‐based ex vivo confocal diagnosis of basal cell carcinoma

Hartmann¹,

Krammer²,

Bachmann³

et al. 2018

Journal of Biophotonics

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Section: Discussionmentioning

confidence: 98%

Simple 3‐criteria‐based ex vivo confocal diagnosis of basal cell carcinoma

Hartmann¹,

Krammer²,

Bachmann³

et al. 2018

Journal of Biophotonics

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“…12–20 Additionally, OCT can accurately detect depth. 21–23 However, each technology, when used independently, cannot provide comprehensive information to guide both diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Combined Reflectance Confocal Microscopy–Optical Coherence Tomography Device for Detection and Depth Assessment of Basal Cell Carcinoma

et al. 2018

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IMPORTANCE The limited tissue sampling of a biopsy can lead to an incomplete assessment of basal cell carcinoma (BCC) subtypes and depth. Reflectance confocal microscopy (RCM) combined with optical coherence tomography (OCT) imaging may enable real-time, noninvasive, comprehensive three-dimensional sampling in vivo, which may improve the diagnostic accuracy and margin assessment of BCCs.OBJECTIVE To determine the accuracy of a combined RCM-OCT device for BCC detection and deep margin assessment. DESIGN, SETTING, AND PARTICIPANTSThis pilot study was carried out on 85 lesions from 55 patients referred for physician consultation or Mohs surgery at Memorial Sloan Kettering Skin Cancer Center in Hauppauge, New York. These patients were prospectively and consecutively enrolled in the study between January 1, 2017, and December 31, 2017. Patients underwent imaging, with the combined RCM-OCT probe, for previously biopsied, histopathologically confirmed BCCs and lesions clinically or dermoscopically suggestive of BCC. Only patients with available histopathologic examination after imaging were included. MAIN OUTCOMES AND MEASURESImprovements in sensitivity, specificity, and diagnostic accuracy for BCC using the combined RCM-OCT probe as well as the correlation between OCT-estimated depth and histopathologically measured depth were investigated. RESULTSIn total, 85 lesions from 55 patients (27 [49%] were female and 28 [51%] were male with a median [range] age of 59 [21-90] years) were imaged. Imaging was performed on 25 previously biopsied and histopathologically confirmed BCCs and 60 previously nonbiopsied but clinically or dermoscopically suspicious lesions. Normal skin and BCC features were correlated and validated with histopathologic examination. In previously biopsied lesions, residual tumors were detected in 12 of 25 (48%) lesions with 100% sensitivity (95% CI, 73.5%-100%) and 23.1% specificity (95% CI, 5.0%-53.8%) for combined RCM-OCT probe. In previously nonbiopsied and suspicious lesions, BCCs were diagnosed in 48 of 60 (80%) lesions with 100% sensitivity (95% CI, 92.6%-100%) and 75% specificity (95% CI, 42.8%-94.5%). Correlation was observed between depth estimated with OCT and depth measured with histopathologic examination: the coefficient of determination (R 2 ) was 0.75 (R = 0.86; P < .001) for all lesions, 0.73 (R = 0.85; P < .001) for lesions less than 500 μm deep, and 0.65 (R = 0.43; P < .001) for lesions greater than 500 μm deep. CONCLUSIONS AND RELEVANCECombined RCM-OCT imaging may be prospectively used to comprehensively diagnose lesions suggestive of BCC and triage for treatment. Further validation of this device must be performed on a larger cohort.

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“…Dubois et al [39] have used line-field confocal optical coherence tomography (LC-OCT) for high resolution and non-invasive imaging of human skin, which significantly improves accuracy for diagnosis of skin tumors resulting in a reduced number of biopsies. Xiong et al [40] used optical coherence tomography (OCT), imaging tool for non-invasive diagnosis of skin diseases i.e., basal cell carcinoma (BCC), squamous cell carcinoma (SCC), actinic keratosis, and malignant melanoma. The authors in [41] have used a real-time magneto-motive optical Doppler tomography (MM-ODT), imaging method for detection of superparamagnetic iron oxide (SPIO) magnetic nanoparticles implanted into melanoma tissue melanoma tissues and used multi-threaded programming techniques for real-time imaging and optimized the signal path.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

et al. 2019

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This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 %

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Optical coherence tomography for the diagnosis of malignant skin tumors: a meta-analysis

Cited by 40 publications

References 61 publications

Simple 3‐criteria‐based ex vivo confocal diagnosis of basal cell carcinoma

Simple 3‐criteria‐based ex vivo confocal diagnosis of basal cell carcinoma

Evaluation of a Combined Reflectance Confocal Microscopy–Optical Coherence Tomography Device for Detection and Depth Assessment of Basal Cell Carcinoma

Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

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