Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning.
Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning.
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DOI：
10.1117/1.jbo.27.6.066002
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发表时间：
2022-06
影响因子：
3.5
通讯作者：
Jo, Javier A.
中科院分区：
文献类型：
--
作者：
Vasanthakumari, Priyanka;Romano, Renan A.;Rosa, Ramon G. T.;Salvio, Ana G.;Yakovlev, Vladislav;Kurachi, Cristina;Hirshburg, Jason M.;Jo, Javier A.
关键词：
准确的早期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不必要的良性活检。开发并验证一种基于多光谱自身荧光寿命成像（maFLIM）皮肤镜的无标签、客观的图像引导策略，用于临床评估可疑色素皮肤病变。我们验证了maflim衍生的自身荧光全局特征可以用于机器学习（ML）模型来区分恶性和良性色素皮肤病变的假设。在组织活检取样之前，获得了30例患者的41例良性和19例恶性色素皮损的临床广角maFLIM皮肤镜成像。提取了三种不同的全球图像级maFLIM特征池：多光谱强度、时域双指数和频域相量特征。通过训练二次判别分析（QDA）分类模型，并采用留一例患者的交叉验证策略，评估每个特征池区分良性与恶性色素皮肤病变的分类潜力。无偏特征选择后获得的分类性能估计如下：相量特征池68%的灵敏度和80%的特异性，双指数特征池84%的灵敏度和71%的特异性，强度特征池84%的灵敏度和32%的特异性。使用相量和双指数特征训练的QDA模型的集合组合产生了84%的灵敏度和90%的特异性，优于所有考虑的其他模型。基于从maFLIM皮肤镜图像中提取的时间分辨（双指数和相量）自身荧光全局特征的简单分类ML模型有可能提供恶性和良性色素病变的客观区分。ml辅助的maFLIM皮肤镜检查可以潜在地帮助临床评估可疑病变，并识别那些从活检检查中获益最多的患者。
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.