Lin Sherman, Samsoondar Joshua P, Bandari Ela, Keow Samantha, Bikash Binit, Tan Djarren, Martinez-Acevedo Jacobo, Loggie John, Pham Michelle, Wu Nina J, Misra Tanya, Lam Victor H K, Sansano Irene, Cecchini Matthew J
Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada.
Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada.
Mod Pathol. 2023 Mar;36(3):100055. doi: 10.1016/j.modpat.2022.100055. Epub 2023 Jan 10.
Non-small cell lung carcinoma is currently staged based on the size and involvement of other structures. Tumor size may be a surrogate measure of the total number of tumor cells. A recently revised reporting system for adenocarcinoma incorporates high-risk histologic patterns, which may have increased cellular density. Modern digital image analysis tools can be utilized to automate the quantification of cells. In this study, we tested the hypothesis that tumor cellularity can be used as a novel prognostic tool for lung cancer. Digital slides from The Cancer Genome Atlas lung adenocarcinoma (ADC) data set (n = 213) and lung squamous cell carcinoma (SCC) data set (n = 90) were obtained and analyzed using QuPath. The number of tumor cells was normalized with the surface area of the tumor to provide a measure of tumor cell density. Tumor cellularity was calculated by multiplying the size of the tumor with the cell density. Major histologic patterns and grade were compared with the tumor density of the lung ADC and lung SCC cases. The overall and progression-free survival were compared between groups of high and low tumor cellularity. High-grade histologic patterns in the ADC and SCC cases were associated with greater tumor densities compared with low-grade patterns. Cases with lower tumor cellularity had improved overall and progression-free survival compared with cases with higher cellularity. These results support tumor cellularity as a novel prognostic tool for non-small cell lung carcinoma that considers tumor stage and grade elements.
非小细胞肺癌目前是根据肿瘤大小及其他结构的受累情况进行分期。肿瘤大小可能是肿瘤细胞总数的一个替代指标。最近修订的腺癌报告系统纳入了高风险组织学模式，这些模式可能具有更高的细胞密度。现代数字图像分析工具可用于自动进行细胞定量分析。在本研究中，我们检验了肿瘤细胞密度可用作肺癌新的预后工具这一假设。从癌症基因组图谱肺癌腺癌（ADC）数据集（n = 213）和肺鳞状细胞癌（SCC）数据集（n = 90）获取数字切片，并使用QuPath进行分析。用肿瘤表面积对肿瘤细胞数量进行标准化，以提供肿瘤细胞密度的一个指标。通过将肿瘤大小与细胞密度相乘来计算肿瘤细胞密度。将主要组织学模式和分级与肺ADC和肺SCC病例的肿瘤密度进行比较。比较高肿瘤细胞密度组和低肿瘤细胞密度组的总生存期和无进展生存期。与低级别模式相比，ADC和SCC病例中的高级别组织学模式与更高的肿瘤密度相关。与高细胞密度病例相比，低肿瘤细胞密度病例的总生存期和无进展生存期有所改善。这些结果支持将肿瘤细胞密度作为一种考虑肿瘤分期和分级因素的非小细胞肺癌新的预后工具。
