Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.
University of São Paulo, São Carlos Institute of Physics, São Paulo, Brazil.
J Biomed Opt. 2022 Jun;27(6). doi: 10.1117/1.JBO.27.6.066002.
Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones.
To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy.
We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy.
Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered.
Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.
准确的早期恶性皮肤病变诊断对于提供充分和及时的治疗至关重要；不幸的是，对外观相似的良性和恶性皮肤病变的初步临床评估可能导致恶性病变的漏诊和良性病变的不必要活检。
开发和验证一种无标记和客观的基于多光谱荧光寿命成像（maFLIM）皮肤镜的可疑色素性皮肤病变临床评估的图像引导策略。
我们测试了基于 maFLIM 衍生的自发荧光全局特征可用于机器学习（ML）模型来区分恶性和良性色素性皮肤病变的假设。在组织活检采样之前，对来自 30 名患者的 41 个良性和 19 个恶性色素性皮肤病变进行了临床宽场 maFLIM 皮肤镜成像。从多光谱强度、时域双指数和频域相向量特征中提取了三种不同的全局图像级 maFLIM 特征池。通过训练二次判别分析（QDA）分类模型并应用患者外留一交叉验证策略，评估每个特征池区分良性和恶性色素性皮肤病变的分类潜力。
经过无偏特征选择后获得的分类性能估计值如下：相向量特征池的敏感性为 68%，特异性为 80%，双指数特征池的敏感性为 84%，特异性为 71%，强度特征池的敏感性为 84%，特异性为 32%。基于相向量和双指数特征训练的 QDA 模型的集成组合产生了 84%的敏感性和 90%的特异性，优于考虑的所有其他模型。
基于从 maFLIM 皮肤镜图像中提取的时间分辨（双指数和相向量）自发荧光全局特征的简单分类 ML 模型有可能提供恶性与良性色素病变的客观鉴别。基于 ML 的 maFLIM 皮肤镜检查有可能辅助可疑病变的临床评估，并确定最受益于活检检查的患者。
