Computer vision detects subtle histological effects of dutasteride on benign prostate.
Computer vision detects subtle histological effects of dutasteride on benign prostate.
复制标题
计算机视觉检测度他雄胺对良性前列腺的微妙组织学影响。
DOI：
10.1111/bju.14172
复制
发表时间：
2018
影响因子：
4.5
通讯作者：
Gann,PeterH
中科院分区：
文献类型：
--
作者：
Sethi,Amit;Sha,Lingdao;Kumar,Neeraj;Macias,Virgilia;Deaton,RyanJ;Gann,PeterH
目的确定基于计算机视觉的方法应用于血红素和伊红（H&E）前列腺活检图像是否可以区分杜他雄胺治疗组织和安慰剂，并确定与5α还原酶抑制剂（5ARI）治疗反应程度相关的特征。研究对象和方法我们的研究人群包括100名在REDUCE试验中接受杜他雄胺或安慰剂治疗的无前列腺癌男性，他们有强制性第4年活检的切片。一半的男性还提供了1 - 2年的活检切片。我们获得了20倍的整张幻灯片图像，并使用专门的软件从包含超像素和几种核的物体中生成1,300个上皮和基质特征库，包括每个层次之间和内部物体之间的空间关系。我们使用惩罚逻辑回归和五重交叉验证来寻找第4年活检中组织学特征的最佳组合。2年活检的特征数据被拟合到最终模型中进行独立验证。两名对治疗不知情的病理学家对每张图像的局灶性萎缩和先前与5AR1治疗相关的组织学特征进行评分。结果病理学家的共识分类获得了与偶然性相当的鉴别准确率。21个特征的计算机视觉模型在第4年活检曲线下的交叉验证面积为0.97(95%置信区间[CI] 0.95-0.99)，在预留的第2年活检曲线下的交叉验证面积为0.79 （95% CI: 0.65-0.92）。组织学评分与前列腺特异性抗原水平、血清二氢睾酮水平或腺体体积的变化无关。与杜他雄胺治疗相关的主要特征包括间质形状和颜色更均匀，上皮核不规则聚集，管腔形状变化更大。目前的研究结果表明，计算机视觉方法可以检测到杜他雄胺引起的细微组织学影响，从而对药物的反应性进行连续测量，最终可用于预测BPH治疗或癌症化学预防背景下的个体患者反应。
ObjectiveTo determine whether a computer vision‐based approach applied to haematoxylin and eosin (H&E) prostate biopsy images can distinguish dutasteride‐treated tissue from placebo, and identify features associated with degree of responsiveness to 5α‐reductase inhibitor (5ARI) therapy.Subjects and MethodsOur study population comprised 100 treatment‐adherent men without prostate cancer assigned to dutasteride or placebo in the REDUCE trial, who had slides available from mandatory year‐4 biopsies. Half of the men also provided slides from a year‐2 biopsy. We obtained 20× whole‐slide images and used specialized software to generate a library of 1 300 epithelial and stromal features from objects comprising superpixels and several types of nuclei, including spatial relations among objects between and within each hierarchical level. We used penalized logistic regression and fivefold cross‐validation to find optimal combinations of histological features in the year‐4 biopsies. Feature data from the year‐2 biopsies were fitted to a final model for independent validation. Two pathologists, blinded to treatment, scored each image for focal atrophy and histological features previously linked to 5AR1 treatment.ResultsConsensus classification by pathologists obtained a discrimination accuracy equivalent to chance. A 21‐feature computer vision model gave a cross‐validation area under the curve of 0.97 (95% confidence interval [CI] 0.95–0.99) in the year‐4 biopsies and 0.79 (95% CI: 0.65–0.92) in the set‐aside year‐2 biopsies. Histology scores were not correlated with change in prostate‐specific antigen level, serum dihydrotestosterone level or gland volume. Key features associated with dutasteride treatment included greater shape and colour uniformity in stroma, irregular clustering of epithelial nuclei, and greater variation in lumen shape.ConclusionThe present findings show that a computer vision approach can detect subtle histological effects attributable to dutasteride, resulting in a continuous measure of responsiveness to the drug that could eventually be used to predict individual patient response in the context of BPH treatment or cancer chemoprevention.