BACKGROUND Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK. METHODS The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk. RESULTS The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3-25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0-13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006). CONCLUSIONS Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients.

中文翻译：

---

完整的基于幻灯片图像的机器学习方法可预测导管原位癌（DCIS）的复发风险。

背景技术乳腺导管原位癌（DCIS）约占筛查检测到的乳腺癌的20％。保乳手术治疗的DCIS患者的总体风险几乎完全来自局部复发。尽管乳房切除术或辅助放疗可以降低复发风险，但对于患者过度/治疗不足仍存在重大担忧。当前的临床病理标记不足以准确评估复发风险。为了解决这个问题，我们开发了一种新颖的机器学习（ML）管道，可以使用数字化的完整幻灯片图像（WSI）和来自回顾性收集的DCIS患者组（n = 344）的临床病理学长期结局数据来预测同侧复发的风险在英国诺丁汉大学医院接受肿块切除术。方法将研究对象按病例分为训练组（n = 159，复发10年的31名）和验证组（n = 185，10年复发的26岁的）。用H＆E对原发肿瘤的切片进行染色，然后数字化并通过管道进行分析。第一步，将病理学家手动训练的分类器应用于数字幻灯片，以注释基质，正常/良性导管，癌导管，密集的淋巴细胞区域和血管的区域。第二步，对来自注释区域的八个选定的建筑和空间组织组织特征进行了复发风险分类器训练，以预测复发风险。结果复发分类器可显着预测培训中的10年复发风险[危险比（HR）= 11.6；95％置信区间（CI）5.3-25.3，准确度（Acc）= 0.87，敏感性（Sn）= 0.71，特异性（Sp）= 0.91]和独立验证[HR = 6.39（95％CI 3.0-13.8），p <0.0001； Acc = 0.85，Sn = 0.5，Sp = 0.91]。尽管我们的队列有局限性，并且在某些情况下敏感性表现较差，但相对于测试的临床病理变量，我们的工具显示出更高的准确性，特异性，阳性预测值，一致性和风险比（p <0.0001）。此外，它显着确定了可能会从其他治疗中受益的患者（验证队列p = 0.0006）。结论我们基于机器学习的模型满足了准确预测经肿块切除术治疗的DCIS患者的复发风险的未满足的临床需求。Sn = 0.5，Sp = 0.91]。尽管我们的队列有局限性，并且在某些情况下敏感性表现较差，但相对于测试的临床病理变量，我们的工具显示出更高的准确性，特异性，阳性预测值，一致性和风险比（p <0.0001）。此外，它显着确定了可能会从其他治疗中受益的患者（验证队列p = 0.0006）。结论我们基于机器学习的模型满足了准确预测经肿块切除术治疗的DCIS患者的复发风险的未满足的临床需求。Sn = 0.5，Sp = 0.91]。尽管我们的队列有局限性，并且在某些情况下敏感性表现较差，但相对于测试的临床病理变量，我们的工具显示出更高的准确性，特异性，阳性预测值，一致性和风险比（p <0.0001）。此外，它显着确定了可能会从其他治疗中受益的患者（验证队列p = 0.0006）。结论我们基于机器学习的模型满足了准确预测经肿块切除术治疗的DCIS患者的复发风险的未满足的临床需求。相对于测试的临床病理变量的风险比和风险比，预测复发（p <0.0001）。此外，它显着确定了可能会从其他治疗中受益的患者（验证队列p = 0.0006）。结论我们基于机器学习的模型满足了准确预测经肿块切除术治疗的DCIS患者的复发风险的未满足的临床需求。相对于测试的临床病理变量的风险比和风险比，预测复发（p <0.0001）。此外，它显着确定了可能会从其他治疗中受益的患者（验证队列p = 0.0006）。结论我们基于机器学习的模型满足了准确预测经肿块切除术治疗的DCIS患者的复发风险的未满足的临床需求。