Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance.
Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance.
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DOI：
10.3390/cancers12092708
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发表时间：
2020-09-21
期刊：
影响因子：
5.2
通讯作者：
Madabhushi A
中科院分区：
文献类型：
--
作者：
Chandramouli S;Leo P;Lee G;Elliott R;Davis C;Zhu G;Fu P;Epstein JI;Veltri R;Madabhushi A
主动监测(AS)前列腺癌患者与选择根治性治疗的患者相比，生活质量较低，焦虑和抑郁的风险增加，疾病进展的风险增加。目前AS患者的纳入标准不能准确地识别进展风险增加的患者，因此需要一种风险分层技术来识别疾病进展风险更高的患者。在这项工作中，我们利用定量组织形态计量学(QH)特征来描述来自初始H&E活检图像的核位置、形状、取向和聚集，以准确地识别符合条件的疾病进展的高风险患者。我们的发现表明，QH特征与AS合格患者的临床进展风险相关，并且能够优于基于Gleason评分和PSA原等临床变量的判断。在这项工作中，我们评估了来自诊断活检图像的核形态的计算机化特征预测主动监测(AS)患者的前列腺癌(CAP)进展的能力。改善AS患者的风险特征可以减少对低风险患者的过度测试，同时指导高危患者进行治疗。使用约翰·霍普金斯大学(JHU)的资格标准，来自同一地点的总共191名患者(125名进展者，66名非进展者)被确定为患者。进展是由JHU的病理学家确定的。随机抽取30名进步者和30名非进步者组成训练队列D1(n=60)。其余患者包括验证队列D2(n=131)。数字化苏木精-伊红(H&E)活检由病理学家对帽区进行注释。使用分水岭方法分割癌区域内的核，提取了216个描述位置、形状、方向和聚集的核特征。使用D1识别与疾病进展相关的六个特征，然后使用这些特征来训练机器学习分类器。该分类器在D2上进行了验证。在预测进展方面，进一步将该分类器在D2(n=47)的子集上与前列腺特异性抗原(PSA)的前PSA进行比较，PSA是前列腺特异性抗原(PSA)的一种亚型，与CAP更相关。用曲线下面积(AUC)评价治疗效果。核的空间排列、形状和无序特征的组合与进展有关。使用这些特征的分类器在D2中产生0.75的AUC。在47名有PSA前测定的患者亚组中，分类器产生的AUC为0.79，而PSA前的AUC为0.42。数字化H&E活检的核形态计量学特征预测AS患者的进展。这可能有助于识别符合条件的患者，他们可以从立即的治疗中受益。然而，还需要额外的多站点验证。
Active surveillance (AS) prostate cancer patients suffer from a lower quality of life, increased risk of anxiety and depression, and an increased risk of disease progression compared to patients who opt for curative treatment. The current inclusion criteria for AS patients is unable to accurately identify patients with increased risk of progression, and therefore there is a need for a risk stratification technique that can identify patients with a higher risk of disease progression. In this work, we leverage quantitative histomorphometric (QH) features describing nuclear position, shape, orientation, and clustering from initial H&E biopsy images to accurately identify AS-eligible patients who are at high risk for disease progression. Our findings indicate that QH features were correlated with the risk of clinical progression in AS-eligible patients and was able to out-perform judgements based on clinical variables such as Gleason score and pro-PSA. 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 D1 (n = 60). The remaining patients comprised the validation cohort D2 (n = 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 D1 and then used to train a machine learning classifier. The classifier was validated on D2. The classifier was further compared on a subset of D2 (n = 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 D2. 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.
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影响因子：
2
作者：
Loeb S;Tosoian JJ
通讯作者：
Tosoian JJ
DOI：
10.1038/s41374-018-0095-7
发表时间：
2018-11
期刊：
Laboratory investigation; a journal of technical methods and pathology
影响因子：
--
作者：
Lu C;Romo-Bucheli D;Wang X;Janowczyk A;Ganesan S;Gilmore H;Rimm D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
4.8
作者：
Heidegger, I.;Klocker, H.;Bektic, J.
通讯作者：
Bektic, J.
影响因子：
23.4
作者：
Mottet, Nicolas;Bellmunt, Joaquim;Cornford, Philip
通讯作者：
Cornford, Philip
影响因子：
4.2
作者：
Evans, Richard;Loeb, Aram;Martin, Emily T.
通讯作者：
Martin, Emily T.