Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.
Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.
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DOI：
10.1186/s13058-016-0682-8
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发表时间：
2016-02-16
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通讯作者：
Caldas C
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作者：
Ali HR;Dariush A;Provenzano E;Bardwell H;Abraham JE;Iddawela M;Vallier AL;Hiller L;Dunn JA;Bowden SJ;Hickish T;McAdam K;Houston S;Irwin MJ;Pharoah PD;Brenton JD;Walton NA;Earl HM;Caldas C
有必要改善对乳腺癌化疗反应的预测，以改善临床管理，这可以通过利用组织病理学的计算指标来实现。我们研究了来自数字病理切片计算分析的定量图像指标与接受新辅助化疗的乳腺癌女性对化疗的反应之间的相关性。我们对768名参加Neo-tAnGo随机对照试验的患者的乳腺肿瘤诊断和手术样本的组织切片进行了数字化。我们对数字图像进行了系统分析，优化了单细胞检测。机器学习方法用于将细胞分类为癌症，基质或淋巴细胞，并使用这些数据计算绝对数量，相对分数和细胞密度的估计值。病理学完全缓解（pCR）是化疗反应的组织学指标，是主要终点。使用单变量和多变量logistic回归测试了15个图像指标与pCR的相关性。在单变量分析（OR 4.46，95% CI 2.34-8.50，p < 0.0001;观察值= 614）和校正临床因素后的多变量分析（OR 2.42，95% CI 1.08-5.40，p = 0.03;观察值= 406）中，中位淋巴细胞密度与pCR最强相关。进一步的探索性分析显示，在大约四分之一的病例中，与诊断性活检相比，手术切除的肿瘤中淋巴细胞密度增加。手术时淋巴细胞密度的降低与pCR密切相关（OR 0.28，95% CI 0.17-0.47，p < 0.0001;观察值= 553）。计算病理学的数据驱动分析揭示淋巴细胞密度是pCR的独立预测因子。特别地，暴露于化疗后淋巴细胞密度的增加与pCR的缺乏相关。计算病理学可以提供客观、定量和可重复的组织指标，并代表了乳腺癌预后预测的可行方法。ClinicalTrials.gov NCT00070278; 03/10/2003本文的在线版本（doi：10.1186/s13058-016-0682-8）包含补充材料，可供授权用户使用。
There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. ClinicalTrials.gov NCT00070278; 03/10/2003 The online version of this article (doi:10.1186/s13058-016-0682-8) contains supplementary material, which is available to authorized users.