Optics and Publishing Department, Chernivtsi National University, 2 Kotsiubynskyi Str., Chernivtsi, 58012, Ukraine.
Optoelectronics and Measurement Techniques Laboratory, University of Oulu, 90014, Oulu, Finland.
Sci Rep. 2021 Mar 4;11(1):5162. doi: 10.1038/s41598-021-83986-4.
Prostate cancer is the second most common cancer globally in men, and in some countries is now the most diagnosed form of cancer. It is necessary to differentiate between benign and malignant prostate conditions to give accurate diagnoses. We aim to demonstrate the use of a 3D Mueller matrix method to allow quick and easy clinical differentiation between prostate adenoma and carcinoma tissues with different grades and Gleason scores. Histological sections of benign and malignant prostate tumours, obtained by radical prostatectomy, were investigated. We map the degree of depolarisation in the different prostate tumour tissues using a Mueller matrix polarimeter set-up, based on the superposition of a reference laser beam with the interference pattern of the sample in the image plane. The depolarisation distributions can be directly related to the morphology of the biological tissues. The dependences of the magnitude of the 1st to 4th order statistical moments of the depolarisation distribution are determined, which characterise the distributions of the depolarisation values. To determine the diagnostic potential of the method three groups of histological sections of prostate tumour biopsies were formed. The first group contained 36 adenoma tissue samples, while the second contained 36 carcinoma tissue samples of a high grade (grade 4: poorly differentiated-4 + 4 Gleason score), and the third group contained 36 carcinoma tissue samples of a low grade (grade 1: moderately differentiated-3 + 3 Gleason score). Using the calculated values of the statistical moments, tumour tissues are categorised as either adenoma or carcinoma. A high level (> 90%) accuracy of differentiation between adenoma and carcinoma samples was achieved for each group. Differentiation between the high-grade and low-grade carcinoma samples was achieved with an accuracy of 87.5%. The results demonstrate that Mueller matrix mapping of the depolarisation distribution of prostate tumour tissues can accurately differentiate between adenoma and carcinoma, and between different grades of carcinoma. This represents a first step towards the implementation of 3D Mueller matrix mapping for clinical analysis and diagnosis of prostate tumours.
前列腺癌是全球男性第二大常见癌症,在某些国家现已成为最常见的癌症诊断类型。有必要对良性和恶性前列腺疾病进行区分,以便做出准确的诊断。我们旨在展示一种 3D Mueller 矩阵方法的应用,该方法可快速、轻松地对不同分级和 Gleason 评分的前列腺腺瘤和癌组织进行临床区分。我们研究了通过根治性前列腺切除术获得的良性和恶性前列腺肿瘤的组织学切片。我们使用基于参考激光束与样品在图像平面上的干涉图案叠加的 Mueller 矩阵偏振计设置来绘制不同前列腺肿瘤组织中的去极化程度。去极化分布可以直接与生物组织的形态相关。确定了去极化分布的 1 阶到 4 阶统计矩的大小依赖性,这些依赖性表征了去极化值的分布。为了确定该方法的诊断潜力,我们将前列腺肿瘤活检的组织学切片分为三组。第一组包含 36 个腺瘤组织样本,第二组包含 36 个高级别(分级 4:低分化-4+4 Gleason 评分)癌组织样本,第三组包含 36 个低级别(分级 1:中分化-3+3 Gleason 评分)癌组织样本。使用计算出的统计矩值,将肿瘤组织分类为腺瘤或癌。对于每组,都实现了对腺瘤和癌样本的高度 (>90%)区分准确性。对高级别和低级别癌样本的区分准确率达到了 87.5%。结果表明,前列腺肿瘤组织的 Mueller 矩阵去极化分布映射可以准确地区分腺瘤和癌,以及不同分级的癌。这代表了朝着对前列腺肿瘤的 3D Mueller 矩阵映射进行临床分析和诊断迈出的第一步。