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首页> 外文期刊>Medical image analysis >Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.
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Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.

机译：多核图嵌入用于通过MRI / MRS检测前列腺癌的Gleason分级。

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Even though 1 in 6 men in the US, in their lifetime are expected to be diagnosed with prostate cancer (CaP), only 1 in 37 is expected to die on account of it. Consequently, among many men diagnosed with CaP, there has been a recent trend to resort to active surveillance (wait and watch) if diagnosed with a lower Gleason score on biopsy, as opposed to seeking immediate treatment. Some researchers have recently identified imaging markers for low and high grade CaP on multi-parametric (MP) magnetic resonance (MR) imaging (such as T2 weighted MR imaging (T2w MRI) and MR spectroscopy (MRS)). In this paper, we present a novel computerized decision support system (DSS), called Semi Supervised Multi Kernel Graph Embedding (SeSMiK-GE), that quantitatively combines structural, and metabolic imaging data for distinguishing (a) benign versus cancerous, and (b) high- versus low-Gleason grade CaP regions from in vivo MP-MRI. A total of 29 1.5Tesla endorectal pre-operative in vivo MP MRI (T2w MRI, MRS) studies from patients undergoing radical prostatectomy were considered in this study. Ground truth for evaluation of the SeSMiK-GE classifier was obtained via annotation of disease extent on the pre-operative imaging by visually correlating the MRI to the ex vivo whole mount histologic specimens. The SeSMiK-GE framework comprises of three main modules: (1) multi-kernel learning, (2) semi-supervised learning, and (3) dimensionality reduction, which are leveraged for the construction of an integrated low dimensional representation of the different imaging and non-imaging MRI protocols. Hierarchical classifiers for diagnosis and Gleason grading of CaP are then constructed within this unified low dimensional representation. Step 1 of the hierarchical classifier employs a random forest classifier in conjunction with the SeSMiK-GE based data representation and a probabilistic pairwise Markov Random Field algorithm (which allows for imposition of local spatial constraints) to yield a voxel based classification of CaP presence. The CaP region of interest identified in Step 1 is then subsequently classified as either high or low Gleason grade CaP in Step 2. Comparing SeSMiK-GE with unimodal T2w MRI, MRS classifiers and a commonly used feature concatenation (COD) strategy, yielded areas (AUC) under the receiver operative curve (ROC) of (a) 0.89±0.09 (SeSMiK), 0.54±0.18 (T2w MRI), 0.61±0.20 (MRS), and 0.64±0.23 (COD) for distinguishing benign from CaP regions, and (b) 0.84±0.07 (SeSMiK),0.54±0.13 (MRI), 0.59±0.19 (MRS), and 0.62±0.18 (COD) for distinguishing high and low grade CaP using a leave one out cross-validation strategy, all evaluations being performed on a per voxel basis. Our results suggest that following further rigorous validation, SeSMiK-GE could be developed into a powerful diagnostic and prognostic tool for detection and grading of CaP in vivo and in helping to determine the appropriate treatment option. Identifying low grade disease in vivo might allow CaP patients to opt for active surveillance rather than immediately opt for aggressive therapy such as radical prostatectomy.
机译：尽管在美国有六分之一的男性在其一生中预计会被诊断出患有前列腺癌（CaP），但据估计，只有三分之一的男性会因此而死亡。因此，在许多被确诊为CaP的男性中，最近的趋势是，如果在活检中被诊断出格里森评分较低，则倾向于主动监视（等待观察），而不是立即寻求治疗。一些研究人员最近在多参数（MP）磁共振（MR）成像（例如T2加权MR成像（T2w MRI）和MR光谱学（MRS））上确定了低级和高级CaP的成像标记。在本文中，我们提出了一种新型的计算机决策支持系统（DSS），称为半监督多核图嵌入（SeSMiK-GE），该系统定量地结合了结构和代谢成像数据以区分（a）良性与癌性，以及（b ）从体内MP-MRI获得高格里森级和低格里森级CaP区域。这项研究共纳入了29例1.5特斯拉直肠内手术前体内MP MRI（T2w MRI，MRS）研究。通过在术前成像上疾病程度的注释，通过将MRI与离体全组织组织学标本进行视觉关联，获得了评估SeSMiK-GE分类器的基础。 SeSMiK-GE框架包含三个主要模块：（1）多核学习，（2）半监督学习和（3）降维，这些模块可用于构建不同成像的集成低维表示和非成像MRI协议。然后，在此统一的低维表示中构建用于CaP的诊断和格里森分级的分层分类器。分层分类器的步骤1将随机森林分类器与基于SeSMiK-GE的数据表示和概率成对的马尔可夫随机场算法（允许施加局部空间约束）结合使用，以生成基于体素的CaP存在分类。随后在步骤2中将在步骤1中确定的感兴趣的CaP区域分类为高或低Gleason级CaP。将SeSMiK-GE与单峰T2w MRI，MRS分类器和常用特征级联（COD）策略进行比较，得出面积（在（a）0.89±0.09（SeSMiK），0.54±0.18（T2w MRI），0.61±0.20（MRS）和0.64±0.23（COD）的接收者操作曲线（ROC）下，用于区分良性和CaP区域， （b）0.84±0.07（SeSMiK），0.54±0.13（MRI），0.59±0.19（MRS）和0.62±0.18（COD）使用留一法交叉验证策略来区分高品位和低品位CaP在每个体素的基础上进行评估。我们的结果表明，经过进一步严格的验证，SeSMiK-GE可以发展成为功能强大的诊断和预后工具，用于体内CaP的检测和分级以及帮助确定合适的治疗方案。在体内识别低度疾病可能会使CaP患者选择积极的监测，而不是立即选择积极的治疗，例如根治性前列腺切除术。

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