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Decision support with MRI for targeting, evaluating laser ablation for prostate c

Decision support with MRI for targeting, evaluating laser ablation for prostate c
MRI 决策支持,用于定位、评估激光消融治疗前列腺癌
批准号:
8544441
负责人:
Anant Madabhushi
金额:
$24.11万
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-12 至 2015-08-31

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项目成果

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中文摘要
翻译
描述(由申请人提供):对于越来越多的中低危前列腺癌(CAP)患者,主动监测可能在生物学或心理上是不可取的。然而,与激进的全器官治疗(例如,调强放射治疗)相关的短期和长期并发症和共病仍然与治疗相关的发病率风险有关。在某些患者中,使用激进的全腺体疗法 对于对放射治疗更敏感的人,直肠和膀胱中的高剂量部位可能会导致并发症,包括慢性直肠出血、腹泻和膀胱炎等泌尿症状。激光诱导间质热疗(LITT)是一种新型的可控、靶向的热消融治疗方法,在前列腺局灶性治疗中具有其他消融疗法无法比拟的优势。由于LITT与MRI兼容,与其他手术或消融技术相比,它具有成像优势,这些技术利用经直肠超声来靶向和监测治疗。然而,成功地采用局部疗法治疗前列腺癌将取决于几个关键问题:1)我们能否准确地识别前列腺内的指标性病变和癌症?2)局部疗法患者的适当随访,以及3)如何发现复发/持续性疾病?多参数(MP)MRI(T2W,动态增强(DCE),弥散成像(DWI))的引入使得(1)提高了CAP定位的检测灵敏度和特异性,以及(2)评估了前列腺的治疗反应。然而,需要(1)新的计算图像分析工具来定量地整合MP-MRI参数以改进活体中的帽分类,以及(2)非刚性配准工具来实现靶向治疗和治疗后变化的评估。在这项研究中,我们将使用先进的计算机视觉、图像分析、计算机辅助诊断(CAD)和可变形登记工具,结合MP-MRI结合一项小型临床试验使用,该试验涉及40名有记录的CAP患者,目的是(A)自动勾画治疗前MP-MRI上的肿瘤区域,从而确定需要通过LITT进行消融的特定区域,以及(B)识别和描绘消融区内外的局部复发疾病,以便进行LITT后评估。在LITT前MRI上通过CAD识别的区域将被作为治疗的目标,而在LITT后MP-MRI上,被确定为CAP复发可疑区域(由于MR成像标记物的巨大变化)将通过针芯活检进行评估。在这个项目中开发的工具将被整合到一个实用和可行的治疗范例中,用于低风险局部帽的局部治疗,这将使患者能够避免与根治性全腺体治疗相关的并发症。这个跨学科的翻译项目结合了MP-MRI计算机辅助设计、多模式图像配准和机器学习方面的工程专业知识,以及介入放射学、前列腺MRI和MRI引导的局部治疗方面的临床专业知识。
英文摘要
DESCRIPTION (provided by applicant): For a growing population of low- and intermediate-risk prostate cancer (CaP) patients, active surveillance may be biologically or psychologically undesirable. Yet the short- and long-term complications and co-morbidities associated with radical whole-organ therapies (e.g. intensity modulated radiation therapy) are still associated with a risk of treatment-related morbidity. With radical whole gland therapies, in certain patients who are more sensitive to radiation treatment, high dosage spots in the rectum and in the bladder can lead to complications including chronic rectal bleeding, diarrhea, and urinary symptoms such as cystitis. Laser induced interstitial thermal therapy (LITT) is a novel form of controlled, targeted thermal ablation that may offer measurable advantages over other ablative therapies for focal prostate therapy. Because LITT is MRI compatible, it enables an imaging advantage over other surgical or ablation techniques that utilize transrectal ultrasound to target and monitor treatment. However successful adoption of focal therapy for the treatment of CaP will hinge on several critical issues: 1) Can we accurately identify index lesions and cancers within the prostate? 2) Appropriate follow-up of patients treated with focal therapy and 3) How to detect recurrent/persistent disease? The introduction of multi-parametric (MP) MRI (T2w, dynamic contrast enhanced (DCE), Diffusion (DWI)) has allowed for (1) improved detection sensitivity and specificity for CaP localization, and (2) evaluating treatment response in the prostate. However there exists a need for (1) novel computational image analysis tools to quantitatively integrate MP-MRI parameters for improved CaP classification in vivo and (2) non-rigid registration tools for enabling targeted therapy and evaluation of post-treatment changes. In this study we will employ sophisticated computer vision, image analysis, computer assisted diagnostic (CAD) and deformable registration tools in conjunction with MP-MRI to be used in conjunction with a small clinical trial involving 40 patients with documented CaP for (a) automated delineation of tumor regions on pre-treatment MP-MRI to thereby identify the specific regions for ablation via LITT, and (b) identify and delineate locally recurrent disease within and outside the ablation zone for post-LITT evaluation. Regions identified via CAD on pre-LITT MRI will be targeted for therapy, while on post-LITT MP-MRI, regions identified as being suspicious for CaP recurrence (on account of large changes in MR imaging markers) will be evaluated via needle core biopsy. The tools developed in this project will be integrated into a practical and feasible treatment paradigm for focal treatment of low-risk localized CaP which will allow patients to avoid the complications associated with radical whole-gland therapy. This inter-disciplinary, translational project combines engineering expertise in terms of CAD on MP-MRI, multimodal image registration and machine learning and clinical expertise in interventional radiology, prostate MRI, and MRI guided focal therapy.
期刊论文(11)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1371/journal.pone.0150016
发表时间: 2016
期刊: PloS one
影响因子: 3.7
作者: [Toth R, Sperling D, Madabhushi A]
通讯作者: Madabhushi A
DOI: 10.1038/srep32706
发表时间: 2016-09-07
期刊: Scientific reports
影响因子: 4.6
作者: [Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E, Madabhushi A]
通讯作者: Madabhushi A
DOI: 10.1016/j.compmedimag.2016.05.003
发表时间: 2017-04
期刊: COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
影响因子: 5.7
作者: [Janowczyk, Andrew, Basavanhally, Ajay, Madabhushi, Anant]
通讯作者: Madabhushi, Anant
Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.
用于组织病理学图像中细胞核检测的多通道自适应投票
DOI: 10.1038/srep33985
发表时间: 2016-10-03
期刊: Scientific reports
影响因子: 4.6
作者: [Lu C, Xu H, Xu J, Gilmore H, Mandal M, Madabhushi A]
通讯作者: Madabhushi A
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