Near-Infrared Spectroscopy Combined With Skin Impedance for Detection of Skin Cancer in Primary Care.
The established method in primary care to distinguish skin cancer from benign lesions is clinical examination, with or without dermoscopy. The experience among primary care physicians in assessing skin tumours varies, as does the accessibility to teledermoscopy. To enhance diagnostic performance, improved methods for skin tumour assessment are warranted. The aim of this study was to investigate the diagnostic performance of a non-invasive method that combines near-infrared spectroscopy with skin impedance measurement (NIRIMP) to detect skin cancer in primary care.
NIRIMP measurements were collected prospectively from patients seeking primary care for skin lesion examination. The measurements were compared to the true lesion diagnosis using several machine learning methods, to determine the best machine learning methods to use and to determine the diagnostic performance of NIRIMP in distinguishing skin cancer from benign lesions.
Eighty participants with 109 lesions were included. Among these, 50 skin cancers or in situ cancers were detected: eight melanomas/in situ melanomas, four in situ squamous cell carcinomas, and 38 basal cell carcinomas. The ability of NIRIMP to distinguish any skin cancer/in situ cancer, as illustrated by the area under the receiver operating characteristics curve (AUC), was 0.776 and for melanomas/in situ melanomas alone the AUC was 0.911. When detecting any skin cancer, the AUC was slightly higher for NIR alone (0.826) compared to NIRIMP (0.776), whereas for IMP alone it was slightly lower (0.693).
Near infrared spectroscopy appears to be a promising bioengineering technique to detect skin cancer in primary care settings, of potential benefit for future skin lesion assessment. However, there was no compelling evidence supporting the benefit of adding skin impedance to improve diagnostic performance.
NIRIMP measurements were collected prospectively from patients seeking primary care for skin lesion examination. The measurements were compared to the true lesion diagnosis using several machine learning methods, to determine the best machine learning methods to use and to determine the diagnostic performance of NIRIMP in distinguishing skin cancer from benign lesions.
Eighty participants with 109 lesions were included. Among these, 50 skin cancers or in situ cancers were detected: eight melanomas/in situ melanomas, four in situ squamous cell carcinomas, and 38 basal cell carcinomas. The ability of NIRIMP to distinguish any skin cancer/in situ cancer, as illustrated by the area under the receiver operating characteristics curve (AUC), was 0.776 and for melanomas/in situ melanomas alone the AUC was 0.911. When detecting any skin cancer, the AUC was slightly higher for NIR alone (0.826) compared to NIRIMP (0.776), whereas for IMP alone it was slightly lower (0.693).
Near infrared spectroscopy appears to be a promising bioengineering technique to detect skin cancer in primary care settings, of potential benefit for future skin lesion assessment. However, there was no compelling evidence supporting the benefit of adding skin impedance to improve diagnostic performance.