Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.
Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.
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DOI：
10.1186/s13014-016-0718-3
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发表时间：
2016-11-10
期刊：
影响因子：
--
通讯作者：
Madabhushi A
中科院分区：
文献类型：
--
作者：
Shiradkar R;Podder TK;Algohary A;Viswanath S;Ellis RJ;Madabhushi A
放射组学或计算机提取的纹理特征已被证明在针对前列腺癌（PCa）病变方面比单独的多参数MRI （mpMRI）信号强度具有更好的性能。放射组学与可变形的共配准工具可用于制定框架，以产生靶向局灶放射治疗计划。Rad-TRaP框架包括三个不同的模块。首先，一个基于放射组学的模块，通过一个特征机器学习分类器在mpMRI上检测PCa病变。第二个模块包括一个多模态可变形的协同配准方案，将组织、器官和勾画的目标体积从MRI映射到CT。最后，第三个模块涉及生成基于放射组学的剂量计划，用于近距离治疗的MRI和用于EBRT的CT，使用从MRI转移到CT的目标描绘。Rad-TRaP框架采用来自两个不同机构的23例患者研究的回顾性队列进行评估。来自第一家机构的11名患者被用于训练放射组学分类器，该分类器用于检测来自第二家机构的12名患者的肿瘤区域。训练机器学习分类器的真实癌症描述是由经验丰富的放射肿瘤学家使用mpMRI，活检位置知识和放射学报告进行的。将检测到的肿瘤区域用于mpMRI生成近距离治疗方案，并将肿瘤区域从MRI映射到CT生成相应的EBRT治疗方案。对于EBRT和近距离放疗的每一种，都产生了3种剂量计划——全腺体均匀（），这是目前的临床标准，基于放射组学的局灶（），以及基于放射组学的局灶增强（）。与常规的比较显示，靶向局灶近距离治疗将导致桨叶剂量的显着减少，同时确保规定的剂量被传递到病变。导致桨叶剂量仅略有增加。在EBRT的情况下观察到类似的趋势。提出了一种放射治疗计划框架，以产生有针对性的局部治疗计划。使用该框架生成的局部治疗计划显示，对有风险器官的剂量减少，对癌变病灶的剂量增加。
Radiomics or computer – extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous () which is the current clinical standard, radiomics based focal (), and whole gland with a radiomics based focal boost (). Comparison of against conventional revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. resulted in only a marginal increase in dosage to the OARs compared to . A similar trend was observed in case of EBRT with and compared to . A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.
域限制的变形（DOCD）模型，用于共同注册前后的前列腺MRI。
影响因子：
6
作者：
Toth, Robert;Traughber, Bryan;Ellis, Rodney;Kurhanewicz, John;Madabhushi, Anant
通讯作者：
Madabhushi, Anant