Intra-tumor heterogeneity (ITH) is a fundamental characteristic of breast cancer (BC), influencing tumor progression, prognosis, and therapeutic responses. However, the complexity of ITH in BC makes its accurate characterization challenging. This study leverages deep learning (DL) techniques to comprehensively evaluate ITH in early-stage luminal BC and provide a nuanced understanding of its impact on tumor behavior and patient outcomes. A large cohort (n= 2561) of early-stage luminal BC was evaluated using whole slide images (WSIs) of hematoxylin and eosin-stained slides of excision specimens. Morphological features of both the tumor and stromal components were meticulously annotated by a panel of pathologists in a subset of cases. A DL model was applied to develop an algorithm to assess the degree of heterogeneity of various morphological features per individual case utilizing defined patches. The results of extracted features were used to generate an overall heterogeneity score that was correlated with the clinicopathological features and outcome. Overall, 162 features were quantified and a significant positive correlation between these features was identified. Specifically, there was a significant association between a high degree of intra-tumor heterogeneity and larger tumor size, poorly differentiated tumors, highly proliferative tumors, tumors of no special type (NST), and those with low estrogen receptor (ER) expression. When all features are considered in combination, a high overall heterogeneity score was significantly associated with parameters characteristic of aggressive tumor behavior, and it was an independent predictor of poor patient outcome. In conclusion, DL models can be used to accurately decipher the complexity of ITH and provide extra information for outcome prediction.
肿瘤内异质性（ITH）是乳腺癌（BC）的基本特征，影响肿瘤进展、预后及治疗反应。然而，乳腺癌中ITH的复杂性使其准确表征具有挑战性。本研究利用深度学习（DL）技术全面评估早期管腔型乳腺癌的ITH，并深入理解其对肿瘤行为及患者预后的影响。通过切除标本的苏木精-伊红染色切片全玻片图像（WSIs），对大型队列（n=2561）的早期管腔型乳腺癌进行了评估。在部分病例中，由病理学家小组对肿瘤及间质成分的形态学特征进行了精细标注。应用深度学习模型开发算法，利用预设图像块评估每个病例中多种形态学特征的异质性程度。提取的特征结果用于生成总体异质性评分，并与临床病理特征及预后进行关联分析。研究共量化了162个特征，并发现这些特征间存在显著正相关性。具体而言，高度肿瘤内异质性与较大肿瘤体积、低分化肿瘤、高增殖性肿瘤、非特殊类型（NST）肿瘤以及低雌激素受体（ER）表达肿瘤显著相关。当综合所有特征时，高总体异质性评分与侵袭性肿瘤行为特征参数显著相关，且是患者不良预后的独立预测因子。综上所述，深度学习模型可用于准确解析肿瘤内异质性的复杂性，并为预后预测提供额外信息。
Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence
肿瘤(癌症)患者之家