Park Juyeon, Shin Su-Jin, Shin Jeongwon, Lee Ariel J, Lee Moosung, Lee Mahn Jae, Kim Geon, Heo Ji Eun, Suk Lee Kwang, Park YongKeun
Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea.
Biomed Opt Express. 2023 Feb 7;14(3):1071-1081. doi: 10.1364/BOE.484092. eCollection 2023 Mar 1.
Clear cell renal cell carcinoma (ccRCC) is a common histopathological subtype of renal cancer and is notorious for its poor prognosis. Its accurate diagnosis by histopathology, which relies on manual microscopic inspection of stained slides, is challenging. Here, we present a correlative approach to utilize stained images and refractive index (RI) tomography and demonstrate quantitative assessments of the structural heterogeneities of ccRCC slides obtained from human patients. Machine-learning-assisted segmentation of nuclei and cytoplasm enabled the quantification at the subcellular level. Compared to benign regions, malignant regions exhibited a considerable increase in structural heterogeneities. The results demonstrate that RI tomography provides quantitative information in synergy with stained images on the structural heterogeneities in ccRCC.
透明细胞肾细胞癌(ccRCC)是肾癌常见的组织病理学亚型,其预后较差,声名狼藉。依靠对染色玻片进行人工显微镜检查的组织病理学对其进行准确诊断具有挑战性。在此,我们提出一种关联方法,利用染色图像和折射率(RI)断层扫描,并展示对从人类患者获取的ccRCC玻片结构异质性的定量评估。机器学习辅助的细胞核和细胞质分割实现了亚细胞水平的定量分析。与良性区域相比,恶性区域的结构异质性显著增加。结果表明,RI断层扫描与染色图像协同提供了关于ccRCC结构异质性的定量信息。