Early diagnosis of
malignant skin lesions is critical for prompt
treatment and a clinical prognosis of skin cancers. However, it is
difficult to precisely evaluate the development stage of nonmelanoma
skin cancers because they are derived from the same tissues as a result
of the uncontrolled growth of abnormal squamous keratinocytes in the
epidermis layer of the skin. In the present study, we developed a
linear-kernel support vector machine (LSVM) model to distinguish basal
cell carcinoma (BCC) from actinic keratosis (AK) and Bowen’s
disease (BD). The input parameters of the LSVM model consist of appropriate
lifetime components and entropy values, which were extracted from
two-photon fluorescence lifetime imaging of hematoxylin and eosin
(H&E)-stained biopsy sections. Different features used as inputs
for SVM training were compared and evaluated. In constructing the
SVM models, features obtained from the lifetime (τ2) of the second component were found to be significantly more predictive
than the average fluorescence lifetime (τm) in terms
of diagnostic accuracy, sensitivity, and specificity. The above findings
were confirmed on the basis of the receiver operating characteristic
(ROC) curves of diagnostic models. Shannon entropy was added to the
SVM models as an independent feature to further improve the diagnostic
accuracy. Therefore, fluorescence lifetime analysis and entropy calculations
can provide highly informative features for the accurate detection
of skin neoplasm disorders. In summary, fluorescence lifetime imaging
microscopy (FLIM) combined with the SVM classification exhibited great
potential for developing an effective computer-aided diagnostic criterion
and accurate cancer detection in dermatology.