Detecting pre-cancerous lesions from high resolution prostate MRI

描述（由申请人提供）： 成功地治疗已经转移的癌症比在癌变过程的早期治疗癌症或癌前状态要困难得多。前列腺癌，如果及早发现，有100%的五年生存率-与许多其他类型的癌症相比，这是一个令人惊讶的积极统计数据。因此，通过筛查对前列腺癌进行早期检测和定位至关重要。最近，我们一直在开发计算机辅助诊断（CAD）工具，用于从高分辨率磁共振（MR）成像（MRI）中检测恶性和癌前病变。由于癌前病变被广泛认为会转化为癌，因此这样的系统将有助于识别和监测具有前列腺癌高风险的患者，并启动早期靶向治疗以使肿瘤过程消退。该项目的广泛长期目标是早期检测癌前病变和恶性前列腺病变，这在以下方面具有极其重要的意义：（1）监测具有前列腺腺癌高风险的患者，（2）早期靶向治疗以使癌前病变和恶性病变消退，以及（3）检测新的组织学组织类别，这可能对理解疾病过程具有重要意义。这项工作的首要目标是早期检测癌前病变和恶性病变，并可能通过CAD在高分辨率离体MR图像上识别新的组织学组织类别。拟议工作共包括3个具体目标和9项任务。由于已知癌前病变经常与前列腺癌共存，因此在本研究中，我们建议仅纳入已诊断为前列腺癌并计划进行前列腺切除术的患者。在目标1下，将获得总共20个匿名患者数据集，包括3个特斯拉（T）离体MRI扫描以及根治性椎间盘切除术后的完整组织切片。纳入组织学数据将允许通过H&E染色和手动分割精确确定癌前病变的存在和程度。目标1还将涉及通过将整个包埋组织切片与相应的离体MRI图像配准来确定离体癌前病变的空间范围（基础事实）。为了检测癌前病变的存在和空间范围，我们采用了一种双管齐下的方法，使用监督和非监督分类技术。首先，在目标2下，我们开发和评估了一种监督CAD方法，用于通过对HGPIN在离体MRI研究中的纹理属性进行明确建模来检测癌前病变。在目标3下，我们开始于有监督的CAD模型，以在离体MRI上区分癌性前列腺病变和良性前列腺病变，然后应用无监督的非线性降维方法来检测新的组织学组织类别，如具有介于良性和恶性之间的特征的组织学组织类别。目标3将提供（i）检测癌前病变的辅助方法，因此可用于评估目标2中开发的监督CAD模型的功效，以及（ii）有助于潜在发现新的组织学类别，这可以促进我们对癌症进展的理解。根据目标2、3提出的方法的有效性将根据来自组织学的基础事实进行评价。该项目将是罗格斯大学和宾夕法尼亚大学（UPENN）研究人员之间的合作。目标1（数据生成）将在UPENN进行，而目标2（肿瘤地面实况生成）和目标3（CAD模型）将在Rutgers进行。

DESCRIPTION (provided by applicant): Successfully treating cancer that has metastasized is considerably more difficult than treating the cancer or precancerous state early in the process of carcinogenesis. Prostate cancer, if caught early, has a 100 percent, five-year survival rate - a surprisingly positive statistic compared to many other types of cancer. For this reason, early detection and localization of prostate cancer through screening is critical. Of late we have been developing computer-aided diagnosis (CAD) tools for detecting malignant and pre-malignant lesions from high resolution Magnetic Resonance (MR) imaging (MRI). Since pre-malignant lesions are widely believed to transform into carcinoma, such a system will help identify and monitor patients with a high risk of prostate cancer and initiate early targeted treatment for regression of the neoplastic process. The broad long term goal of this project is early detection of pre-malignant and malignant prostate lesions, which is extremely significant in (1) Monitoring patients with a high risk of developing prostatic adenocarcinoma, (2) Early targeted treatment for regression of pre-malignant and malignant lesions, and (3) Detection of new histological tissue classes which may be significant in understanding disease processes. The overarching goal of this work is early detection of pre-malignant and malignant lesions and possible identification of new histological tissue classes on high-resolution ex vivo MR imagery via CAD. The proposed work comprises a total of 3 specific aims and 9 tasks. Since it is known that pre-malignant lesions frequently coexist with prostate carcinoma, in this study we propose to only include patients who have been diagnosed with prostate cancer and have been scheduled for a prostatectomy. Under Aim 1 a total of 20 anonymised patient data sets comprising 3 Tesla (T) ex vivo MRI scans with accompanying whole mount histological sections after radical prostatectomy will be obtained. The inclusion of histological data will allow for precise determination of presence and extent of pre-malignant lesions via H&E staining and manual segmentation. Aim 1 will also involve determination of spatial extent of pre-malignant lesions (ground truth) ex vivo by registering the whole mount histological sections with the corresponding ex vivo MRI images. To detect presence and spatial extent of pre-malignant lesions we adopt a two pronged approach using a supervised and unsupervised classification technique. First, under Aim 2 we develop and evaluate a supervised CAD method for detecting pre-cancerous lesions by explicitly modeling textural attributes of HGPIN on ex vivo MRI studies. Under Aim 3 we begin with a supervised CAD model to distinguish cancerous from benign prostate lesions on ex vivo MRI and then apply an unsupervised non-linear dimensionality reduction method to detect new histological tissue classes as those that have characteristics which are intermediate between benign and malignant. Aim 3 will provide (i) a secondary method of detecting pre- cancerous lesions and thus useful in evaluating efficacy of the supervised CAD model developed in Aim 2 and (ii) aid in potential discovery of new histological classes which could facilitate our understanding of cancer progression. The efficacy of the methods proposed under Aims 2, 3 will be evaluated against ground truth derived from histology. This project will be a collaboration between investigators at Rutgers University and the University of Pennsylvania (UPENN). Aim 1 (Data Generation) will be carried out at UPENN while Aims 2 (Tumor ground truth generation) and 3 (CAD model) will be done at Rutgers.