Table_1_Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.DOCX

The abundance and/or location of tumor infiltrating lymphocytes (TILs), especially CD8+ T cells, in solid tumors can serve as a prognostic indicator in various types of cancer. However, it is often difficult to select an appropriate threshold value in order to stratify patients into well-defined risk groups. It is also important to select appropriate tumor regions to quantify the abundance of TILs. On the other hand, machine-learning approaches can stratify patients in an unbiased and automatic fashion. Based on immunofluorescence (IF) images of CD8+ T lymphocytes and cancer cells, we develop a machine-learning approach which can predict the risk of relapse for patients with Triple Negative Breast Cancer (TNBC). Tumor-section images from 9 patients with poor outcome and 15 patients with good outcome were used as a training set. Tumor-section images of 29 patients in an independent cohort were used to test the predictive power of our algorithm. In the test cohort, 6 (out of 29) patients who belong to the poor-outcome group were all correctly identified by our algorithm; for the 23 (out of 29) patients who belong to the good-outcome group, 17 were correctly predicted with some evidence that improvement is possible if other measures, such as the grade of tumors, are factored in. Our approach does not involve arbitrarily defined metrics and can be applied to other types of cancer in which the abundance/location of CD8+ T lymphocytes/other types of cells is an indicator of prognosis.

肿瘤浸润淋巴细胞（TILs）及其中的CD8+ T细胞在实体瘤中的丰度与/或分布，对于多种类型癌症的预后具有指示意义。然而，在将患者分层为明确的风险组时，往往难以选择合适的阈值值。同时，选择合适的肿瘤区域以量化TILs的丰度亦至关重要。另一方面，机器学习方法能够以无偏见且自动化的方式对患者进行分层。基于CD8+ T淋巴细胞和癌细胞免疫荧光（IF）图像，我们开发了一种机器学习方法，能够预测三阴性乳腺癌（TNBC）患者复发的风险。来自9名预后不良患者和15名预后良好患者的肿瘤切片图像被用作训练集。来自独立队列的29名患者的肿瘤切片图像被用于测试我们算法的预测能力。在测试队列中，29名患者中预后不良组的6名患者均被我们的算法正确识别；对于预后良好组的23名患者，其中17名被正确预测，且有迹象表明，如果考虑其他指标，如肿瘤分级等，则改善的可能性增加。我们的方法不涉及任意定义的指标，且可应用于其他类型的癌症，在这些癌症中，CD8+ T淋巴细胞/其他类型细胞的丰度与/或分布是预后指标。