Chandramouli Sacheth, Leo Patrick, Lee George, Elliott Robin, Davis Christine, Zhu Guangjing, Fu Pingfu, Epstein Jonathan I, Veltri Robert, Madabhushi Anant
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
Department of Anatomic Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA.
Cancers (Basel). 2020 Sep 21;12(9):2708. doi: 10.3390/cancers12092708.
In this work, we assessed the ability of computerized features of nuclear morphology from diagnostic biopsy images to predict prostate cancer (CaP) progression in active surveillance (AS) patients. Improved risk characterization of AS patients could reduce over-testing of low-risk patients while directing high-risk patients to therapy. A total of 191 (125 progressors, 66 non-progressors) AS patients from a single site were identified using The Johns Hopkins University's (JHU) AS-eligibility criteria. Progression was determined by pathologists at JHU. 30 progressors and 30 non-progressors were randomly selected to create the training cohort D ( = 60). The remaining patients comprised the validation cohort D ( = 131). Digitized Hematoxylin & Eosin (H&E) biopsies were annotated by a pathologist for CaP regions. Nuclei within the cancer regions were segmented using a watershed method and 216 nuclear features describing position, shape, orientation, and clustering were extracted. Six features associated with disease progression were identified using D and then used to train a machine learning classifier. The classifier was validated on D. The classifier was further compared on a subset of D ( = 47) against pro-PSA, an isoform of prostate specific antigen (PSA) more linked with CaP, in predicting progression. Performance was evaluated with area under the curve (AUC). A combination of nuclear spatial arrangement, shape, and disorder features were associated with progression. The classifier using these features yielded an AUC of 0.75 in D. On the 47 patient subset with pro-PSA measurements, the classifier yielded an AUC of 0.79 compared to an AUC of 0.42 for pro-PSA. Nuclear morphometric features from digitized H&E biopsies predicted progression in AS patients. This may be useful for identifying AS-eligible patients who could benefit from immediate curative therapy. However, additional multi-site validation is needed.
在本研究中，我们评估了诊断性活检图像中细胞核形态的计算机化特征预测主动监测（AS）患者前列腺癌（CaP）进展的能力。改善AS患者的风险特征描述可以减少低风险患者的过度检查，同时将高风险患者导向治疗。使用约翰霍普金斯大学（JHU）的AS入选标准，从单一机构中识别出191例AS患者（125例病情进展者，66例非病情进展者）。病情进展由JHU的病理学家确定。随机选择30例病情进展者和30例非病情进展者组成训练队列D（n = 60）。其余患者组成验证队列D（n = 131）。病理学家对数字化苏木精和伊红（H&E）活检切片中的CaP区域进行标注。使用分水岭法对癌区域内的细胞核进行分割，并提取描述位置、形状、方向和聚集情况的216个核特征。使用训练队列D识别出与疾病进展相关的六个特征，然后用于训练机器学习分类器。该分类器在验证队列D上进行验证。在训练队列D的一个子集（n = 47）上，将该分类器与前列腺特异性抗原（PSA）的一种同工型——前列腺特异性抗原前体（pro-PSA，与CaP的关联更强）进行进一步比较，以预测病情进展。通过曲线下面积（AUC）评估性能。核空间排列、形状和无序特征的组合与病情进展相关。使用这些特征的分类器在验证队列D中的AUC为0.75。在有pro-PSA测量值的47例患者子集中，该分类器的AUC为0.79，而pro-PSA的AUC为0.42。数字化H&E活检切片的核形态计量学特征可预测AS患者的病情进展。这可能有助于识别可能从立即根治性治疗中获益的符合AS条件的患者。然而，还需要进行额外的多机构验证。
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