Carrara M, Bono A, Bartoli C, Colombo A, Lualdi M, Moglia D, Santoro N, Tolomio E, Tomatis S, Tragni G, Santinami M, Marchesini R
Department of Medical Physics, Fondazione IRCSS, Istituto Nazionale dei Tumori, Milan, Italy.
Phys Med Biol. 2007 May 7;52(9):2599-613. doi: 10.1088/0031-9155/52/9/018. Epub 2007 Apr 17.
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 non-melanoma, 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 ANN(2). 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.
为了在活体状态下确定色素沉着性皮肤病变是否为黑色素瘤，人们研发了各种基于获取和分析此类病变图像的仪器。尽管诸如表皮透光显微镜、反射分光光度法和荧光成像等仪器的反馈令人鼓舞，但目前它们还无法取代已确立的诊断程序。然而，近期研究表明，仪器不应采用评估病变诊断的方法，而应模拟专家临床医生对病变是否需要切除的评估。本研究的目的是评估一种分光光度系统模拟这种决策的性能。该研究纳入了1794例连续招募的患者，他们共有1966个可疑的皮肤色素沉着性病变被切除以进行组织病理学诊断，另有348例患者的1940个病变因临床诊断可靠而未被切除。所有这些病变的图像均通过多光谱成像系统在活体状态下获取。数据集被随机分为训练集（802个诊断可靠和1003个需要切除的病变，包括139例黑色素瘤）、验证集（464个诊断可靠和439个需要切除的病变，包括72例黑色素瘤）和测试集（674个诊断可靠和524个需要切除的病变，包括76例黑色素瘤）。根据专家临床医生对每个检查病变的处理决策，建立了一个人工神经网络（ANN(1)）来将病变分类为需要切除或诊断可靠。在独立测试集中，该系统能够以88%的灵敏度和80%的特异性模拟临床医生的判断。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。测试集与训练集/验证集之间的受试者操作特征曲线没有发现重大差异。然后，在同一数据集上，构建了另一个人工神经网络（ANN(2)），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定为95%后，ANN(1)在诊断可靠病变的分类方面明显优于ANN(2)。本研究表明，多光谱图像分析和人工神经网络可用于辅助初级保健医生或全科医生识别需要进一步检查的色素沉着性皮肤病变。
