Rapid Histological Assessment of Prostate Specimens in the Three-dimensional Space by Hydrophilic Tissue Clearing and Confocal Microscopy. (August 2022)

• Record Type: Journal Article
• Authors: Peng, Yu-Ching; Lin, Yu-Chieh; Hung, Yu-Ling; Fu, Chien-Chung; Chang, Margaret Dah-Tsyr; Lin, Yen-Yin; Chou, Teh-Ying
• Is Part Of: Journal of histochemistry and cytochemistry. Volume 70:Number 8(2022)
• Page Start: 597
• Page End: 608
• Publication Date: 2022-08
• Subjects: confocal imaging -- histology -- prostate cancer -- screening; Histochemistry -- Periodicals; Cytochemistry -- Periodicals; Histocytochemistry -- methods; Histological Techniques; 572.05
• DOI: 10.1369/00221554221116936
• Languages: English
• ISSNs: 0022-1554

Abstract

Microscopic examination of biopsied and resected prostatic specimens is the mainstay in the diagnosis of prostate cancer. However, conventional analysis of hematoxylin and eosin (H&E)-stained tissue is time-consuming and offers limited two-dimensional (2D) information. In the current study, we devised a method—termedP rostateR apidO ptical examination for cancerSTAT us (proSTAT)—for rapid screening of prostate cancer using high-resolution 2D and three-dimensional (3D) confocal images obtained after hydrophilic tissue clearing of 100-µm-thick tissue slices. The results of the proSTAT method were compared with those of traditional H&E stains for the analysis of cores ( n =15) obtained from radical prostatectomy specimens ( n =5). Gland lumen formation, consistent with Gleason pattern 3, was evident following tracking of multiple optical imaging sections. In addition, 3D rendering allowed visualizing a tubular network of interconnecting branches. Rapid 3D fluorescent labeling of tumor protein p63 accurately distinguished prostate adenocarcinoma from normal tissue and benign lesions. Compared with conventional stains, the 3D spatial and molecular information extracted from proSTAT may significantly increase the amount of available data for pathological assessment of prostate specimens. Our approach is amenable to automation and—subject to independent validation—can find a wide spectrum of clinical and research applications.