Objective: To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Methods: Specimens were collected from 209 breast cancer patients who received NAT at Renmin Hospital of Wuhan University from October 2019 to June 2023. TSR levels in pre-NAT biopsy specimens were automatically computed using a deep learning algorithm and categorized into low stroma (TSR≤30%), intermediate stroma (TSR 30% to ≤60%), and high stroma (TSR>60%) groups. Residual cancer burden (RCB) grading of post-NAT surgical specimens was determined to compare the relationship between TSR expression levels and RCB grades. The correlation of TSR with NAT efficacy was analyzed, and the association between TSR expression and patient prognosis was further investigated. Results: There were 85 cases with low stroma (TSR≤30%), 93 cases with intermediate stroma (TSR 30% to ≤60%), and 31 cases with high stroma (TSR>60%). Different TSR expression levels showed significant differences between various RCB grades (P<0.05). Logistic univariate and multivariate analyses showed that TSR was a risk factor for obtaining a complete pathological remission from neoadjuvant therapy for breast cancer when it was used as a continuous variable (P<0.05); COX regression and survival analyses showed that the lower the percentage of tumorigenic mesenchyme was, the better the prognosis of the patient was (P<0.05). Conclusions: The deep learning-based model enables automatic and accurate quantification of TSR. A lower pre-treatment tumoral stroma is associated with a lower RCB score and a higher rate of pathologic complete response, indicating that TSR can predict the efficacy of neoadjuvant therapy in breast cancer and thus holds prognostic significance. Therefore, TSR may serve as a biomarker for predicting therapeutic outcomes in breast cancer neoadjuvant therapy.
目的： 探讨深度学习自动量化的肿瘤间质比（tumor-stroma ratio，TSR）在接受乳腺癌新辅助治疗患者中的预后价值。 方法： 收集武汉大学人民医院2019年10月至2023年6月接受新辅助治疗的209例乳腺癌患者标本。基于深度学习算法自动计算出新辅助治疗前穿刺标本中的TSR水平，分为低间质组（TSR≤30%）、中间质组（30%<TSR≤60%）、高间质组（TSR>60%）。计算新辅助治疗后乳腺癌患者手术标本的残余肿瘤负荷（residual cancer burden，RCB）分级，比较不同表达程度的TSR与RCB分级之间的关系，分析TSR与新辅助治疗疗效的相关性，并进一步探讨TSR表达与患者预后的关系。 结果： TSR≤30%者85例，30%<TSR≤60%者93例，TSR>60%者31例；不同表达程度的TSR在不同RCB分级之间差异具有统计学意义（P<0.05）；Logistic单因素和多因素分析显示，TSR作为连续变量时是获得乳腺癌新辅助治疗病理完全缓解的危险因素（P<0.05）；COX回归和生存分析显示，肿瘤性间质占比越低，患者预后越好（P<0.05）。 结论： 基于深度学习模型能自动并准确地定量TSR。新辅助治疗前肿瘤性间质越低，RCB分级越低，病理完全缓解率越高，表明TSR能够预测乳腺癌新辅助治疗疗效，具有预后价值，即TSR能够作为预测乳腺癌新辅助治疗疗效的生物标志物。.