Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions
Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions
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DOI：
10.1088/0031-9155/52/9/018
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发表时间：
2007-05-07
影响因子：
3.5
通讯作者：
Marchesini, R.
中科院分区：
文献类型：
--
作者：
Carrara, M.;Bono, A.;Marchesini, R.
已经开发了基于色素皮肤病变图像的采集和加工的各种仪器，试图在体内确定病变是否是黑色素瘤。尽管令人鼓舞，但这些文书的反应，例如落光显微镜、反射分光光度法和荧光成像目前无法取代成熟的诊断程序。然而，最近的研究表明，仪器应该重现临床专家对是否需要切除病变的评估，而不是用仪器评估病变诊断的方法。本研究的目的是评估分光光度系统模拟此类决策的性能。该研究连续招募了 1794 名患者，其中 1966 个可疑皮肤色素病变被切除以进行组织病理学诊断，另外 348 名患者有 1940 个未切除病变，因为临床上令人放心。所有这些病变的图像都是通过多光谱成像系统获得的。数据集被随机分为训练集（802个放心病灶和1003个需要切除病灶，包括139个黑色素瘤）、验证集（464个放心病灶和439个需要切除病灶，包括72个黑色素瘤）和测试集（674个放心病灶和524个需要切除病灶，包括76个黑色素瘤）。根据临床专家对如何处理每个检查病变的决定，建立了人工神经网络（ANN（1））来将病变分类为需要切除或放心的。在独立测试集中，系统能够模拟临床医生，灵敏度为 88%，特异性为 80%。在 462 个被正确分类为需要切除的病变中，72 个（95%）是黑色素瘤。在测试集和训练/验证集之间没有发现接收器操作特性曲线有重大变化。在同一数据集上，然后构建了另一个人工神经网络（ANN（2）），以根据组织学诊断将病变分类为黑色素瘤或非黑色素瘤。将识别黑色素瘤的灵敏度设置为 95% 后，ANN(1) 在令人放心的病变分类方面明显优于 ANN2。这项研究表明，多光谱图像分析和人工神经网络可用于支持初级保健医生或全科医生识别需要进一步研究的色素性皮肤病变。
Various instruments based on acquisition and elaboration of images of pigmented skin lesions have been developed in an attempt to in vivo establish whether a lesion is a melanoma or not. Although encouraging, the response of these instruments, e.g. epiluminescence microscopy, reflectance spectrophotometry and fluorescence imaging, cannot currently replace the well-established diagnostic procedures. However, in place of the approach to instrumentally assess the diagnosis of the lesion, recent studies suggest that instruments should rather reproduce the assessment by an expert clinician of whether a lesion has to be excised or not. The aim of this study was to evaluate the performance of a spectrophotometric system to mimic such a decision. The study involved 1794 consecutively recruited patients with 1966 doubtful cutaneous pigmented lesions excised for histopathological diagnosis and 348 patients with 1940 non- excised lesions because clinically reassuring. Images of all these lesions were acquired in vivo with a multispectral imaging system. The data set was randomly divided into a train (802 reassuring and 1003 excision- needing lesions, including 139 melanomas), a verify (464 reassuring and 439 excision-needing lesions, including 72 melanomas) and a test set (674 reassuring and 524 excision- needing lesions, including 76 melanomas). An artificial neural network (ANN(1)) was set up to perform the classification of the lesions as excision- needing or reassuring, according to the expert clinicians' decision on how to manage each examined lesion. In the independent test set, the system was able to emulate the clinicians with a sensitivity of 88% and a specificity of 80%. Of the 462 correctly classified as excision-needing lesions, 72 ( 95%) were melanomas. No major variations in receiver operating characteristic curves were found between the test and the train/ verify sets. On the same data set, a further artificial neural network (ANN(2)) was then architected to perform classification of the lesions as melanoma or nonmelanoma, according to the histological diagnosis. Having set the sensitivity in recognizing melanoma to 95%, ANN(1) resulted to be significantly better in the classification of reassuring lesions than ANN2. This study suggests that multispectral image analysis and artificial neural networks could be used to support primary care physicians or general practitioners in identifying pigmented skin lesions that require further investigations.