Optical radiomic signatures derived from optical coherence tomography images improve identification of melanoma

Turani, Z ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1158/0008-5472.CAN-18-2791
  3. Publisher: American Association for Cancer Research Inc , 2019
  4. Abstract:
  5. The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15–30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluated the performance of the algorithm using several tissue-mimicking phantoms and then tested the OPE algorithm on 69 human subjects. Our data show that benign nevi and melanoma can be differentiated with 97% sensitivity and 98% specificity. These findings suggest that the adoption of OPE algorithm in the clinic can lead to improvements in melanoma diagnosis and patient experience. Significance: This study describes a noninvasive, safe, simple-to-implement, and accurate method for the detection and differentiation of malignant melanoma versus benign nevi. ©2019 American Association for Cancer Research
  6. Keywords:
  7. Article ; Cancer diagnosis ; Cancer prognosis ; Cancer staging ; Classification algorithm ; Diagnostic accuracy ; Human ; Image analysis ; Image processing ; In vivo study ; Optical coherence tomography ; Priority journal ; Sensitivity and specificity ; Unnecessary procedure ; Algorithm ; Classification ; Diagnostic imaging ; Imaging phantom ; Procedures ; Statistical model ; Algorithms ; Humans ; Image Processing, Computer-Assisted ; Melanoma ; Models, Statistical ; Phantoms, Imaging ; Tomography, Optical Coherence
  8. Source: Cancer Research ; Volume 79, Issue 8 , 2019 , Pages 2021-2030 ; 00085472 (ISSN)
  9. URL: https://cancerres.aacrjournals.org/content/79/8/2021