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首页> 外文学位 >Correlating Histopathology and Radiologic Imaging to Validate Prostate Cancer Detection
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Correlating Histopathology and Radiologic Imaging to Validate Prostate Cancer Detection

机译：将组织病理学和放射影像学相关联以验证前列腺癌的检测

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摘要

Radiologic imaging is entrenched in standard practice for screening men with suspicion of prostate cancer (PCa) and to inform treatment decisions. Correlating in-vivo imaging to the radical prostatectomy (RP) specimen, however, is a challenging task due to the vast differences in resolution, scale, and deformation of the tissue. Microscopic examination of the RP specimen represents the gold-standard for diagnosis of PCa, so correlation with radiologic imaging is necessary in order to validate PCa detection competency.;In this work, a methodology and program are presented with three aims: one, match histopathology slices to their corresponding radiologic images; two, register histopathology images to their corresponding radiologic image; and three, validate PCa detection in the radiologic image based on the registration with histopathology. Diffusion weighted imaging (DWI) (b50-b2000) was used in this work for proof of concept.;Histopathology slides were scanned and processed in order to be compatible with a novel registration program. A slice matching method was established and a program, process_H, was created in MatLab (v2017b. Natick, MA) in order to register reconstructed histopathology slices to their closest corresponding DWI slice. A cohort of three RP patients, 29 total histopathological slices, with pre-operative multi-parametric magnetic resonance images (mpMRIs) was identified. Using this cohort, a set of histological images was prepared and process_H registered the respective images, creating validation sets for the registration as well as binary pixel-by-pixel data sets defining PCa by Gleason Score (GS).;These pixel-by-pixel data sets were then used to validate PCa detection from the in-vivo DWIs by means of receiver operating characteristic (ROC) curves. This work specifically compares the diffusion coefficient (D), distributed diffusion coefficient (DDC), and a heterogeneity index (?) to GS. Our results show utility for our methodology and program in the validation of PCa. Further development will make this tool a valuable addition for validating new imaging sequences and those in current use.
机译：放射成像技术已根深蒂固，用于筛查怀疑患有前列腺癌（PCa）的男性并为治疗决策提供依据。然而，由于分辨率，规模和组织变形的巨大差异，将体内成像与前列腺癌根治术（RP）标本相关联是一项艰巨的任务。 RP标本的显微检查代表了诊断PCa的金标准，因此必须与放射影像学相关以验证PCa的检测能力。在这项工作中，提出了一种方法和程序，其目标是三个：一是匹配组织病理学切片为其相应的放射学图像；第二，将组织病理学图像注册到其相应的放射学图像；第三，基于与组织病理学的配准，验证放射影像中的PCa检测。在这项工作中使用了弥散加权成像（DWI）（b50-b2000）进行概念验证。扫描和处理了组织病理学幻灯片，以便与新型注册程序兼容。建立了切片匹配方法，并在MatLab（v2017b。Natick，MA）中创建了一个程序process_H，以将重建的组织病理切片登记到与其最接近的对应DWI切片。鉴定出三名RP患者，共29例组织病理切片，并进行了术前多参数磁共振成像（mpMRI）。使用此队列，准备了一组组织学图像，然后process_H注册了相应的图像，创建了用于注册的验证集以及通过格里森评分（GS）定义PCa的二进制逐像素数据集。像素数据集随后用于通过接收器工作特性（ROC）曲线从体内DWI验证PCa检测。这项工作专门比较了GS的扩散系数（D），分布扩散系数（DDC）和异质性指数（η）。我们的结果显示了我们的方法和程序在PCa验证中的实用性。进一步的发展将使该工具成为验证新的成像序列和当前使用的成像序列的宝贵补充。

著录项

  • 作者

    Caldwell, Brandon M.;

  • 作者单位

    University of Illinois at Chicago.;

  • 授予单位 University of Illinois at Chicago.;
  • 学科 Biomedical engineering.;Medical imaging.
  • 学位 M.S.
  • 年度 2018
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遥感技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:51

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