School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
The Affiliated Qingdao Central Hospital of Qingdao University, Qingdao, Shandong, 266042, China.
Photodiagnosis Photodyn Ther. 2023 Sep;43:103708. doi: 10.1016/j.pdpdt.2023.103708. Epub 2023 Jul 22.
Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents.
This investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500-675 nm band range.
The classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data.
Our findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus.
皮肤黑色素瘤是一种极其侵袭性的皮肤癌，在皮肤癌的恶性程度和死亡率方面均位居首位。在早期，由于恶性黑色素瘤和良性色素痣在病理上具有相似的特征，因此很难将其区分开来。传统的皮肤组织学检查技术主要依赖于光学显微镜成像，提供的信息量有限，对比度低，因此需要快速有效的早期诊断方法。作为一种非接触、非电离、无标记的成像工具，高光谱成像在辅助病理学家进行无造影剂识别程序方面具有潜力。
本研究利用高光谱相机来确定恶性黑色素瘤和色素痣组织的光学特性并捕获其光谱特征，旨在促进早期病理诊断应用。我们进一步通过将迁移学习与深度卷积网络相结合，来对高光谱病理图像中的黑色素瘤和色素痣进行分类，从而增强诊断过程。该研究包括了 50 例黑色素瘤和 50 例色素痣患者的病理切片。为了准确表示不同组织之间的光谱差异，我们采用了反射率校准，结果表明最显著的光谱差异出现在 500-675nm 波段范围内。
一维样本数据的色素性肿瘤和色素痣分类准确率为 89%，二维样本数据的分类准确率为 98%。
我们的研究结果有可能加快病理诊断，提高诊断精度，并为黑色素瘤和痣的鉴别提供新的研究视角。
