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Computer aided diagnosis of cancer metastases in the brain

计算机辅助诊断脑部癌症转移

基本信息

批准号：
10163013
负责人：
Geoffrey Young
金额：
$ 26.85万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-06 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10163013
关键词：
3-Dimensional Address Algorithms Anatomy Angiogenesis Inhibition Appearance Award Blinded Blood - brain barrier anatomy Blood Vessels Brain Brain Neoplasms Brain scan Cancer Detection Cancer Etiology Cancer Patient Characteristics Clinic Clinical Colon Carcinoma Color Complement Computational Technique Computer software Computer-Assisted Diagnosis Cranial Irradiation Data Detection Development Diagnosis Diagnostic Diagnostic Neoplasm Staging Digital Imaging and Communications in Medicine Disease Disease remission Disseminated Malignant Neoplasm Dura Mater Early Diagnosis Edema Event Functional Magnetic Resonance Imaging Goals Human Image Imaging Techniques Immunotherapy Intervention Label Leptomeninges Lesion Life Longevity Magnetic Resonance Imaging Malignant Epithelial Cell Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of ovary Malignant neoplasm of pancreas Metastatic malignant neoplasm to brain Methods Mind Modality Morbidity - disease rate Morphology Neoplasm Metastasis Neuraxis Patient imaging Patient-Focused Outcomes Patients Performance Peripheral Personal Satisfaction Physicians Play Process Quality of life Radiation Radiation therapy Radiology Specialty Radiosurgery Reading Renal carcinoma Research Resolution Savings Scanning Shapes Signal Transduction Skin Cancer Speed Structure Techniques Testing Time accurate diagnosis base brain parenchyma cancer cell cancer diagnosis cancer therapy chemotherapy clinical Diagnosis contrast enhanced dashboard diagnosis design diagnostic accuracy imaging modality improved malignant breast neoplasm melanoma novel outcome forecast parallel computer radiologist segmentation algorithm tool tumor user-friendly

项目摘要

The overarching goal of this project is to improve the accuracy in diagnosing cancer metastases in the brain through the development of a novel computer-aided diagnosis (CAD) technique. In today’s cancer treatment, it is often not the primary cancer but the metastasized cancer that causes fatality. Many cancer, including lung, kidney, ovarian, and breast cancer, and melanoma, have a tendency metastasizing to the brain and the number of brain metastases is as high as 170,000 a year in the US alone. Therefore, accurate diagnosis of brain metastases is of utmost importance in saving lives and improving patient’s well-being. Magnetic resonance imaging (MRI) is the most widely used modality to scan brain for potential metastases but diagnosing metastases is a very challenging task that has a considerable rate of false-negatives. The first difficulty in diagnosing metastases is that, at early stage, metastases are asymptomatic. The second difficulty is that metastases manifest as weak signal intensity changes on MRI and their appearance is often highly similar to normal brain structures, such as small blood vessels, meaning that one must visualize in his/her mind whether an observed object is a metastasis or a blood vessel. Missing a metastasis has a severe consequence as the patient will not be called for further treatment. The benefit of accurate diagnosis of metastases, on the other hand, can have a significant benefit to the patient as treatment like stereotactic radiosurgery (SRS) can completely eliminate the metastasized tumor in many cases and extend patient’s life span by three to four years in most cases. CAD can play a key role in improving the accuracy in diagnosing brain metastases by identifying abnormal signal intensity changes and mark them for radiologists to examine. In this process, CAD will function as an aid tool to complement human’s expertise in interpreting brain MRI. However, despite the importance of finding and treating brain metastases, there currently is lacking a CAD approach to this problem. Many existing computational techniques on brain MRI were tailored to MRI data acquired in a research setting that often involves many other MRI techniques such as DWI, DTI, and functional MRI. But in clinics only anatomic MRI like T1- and T2-weighted MRI are used to scan a patient, therefore, a CAD approach must be tailored to the clinical setting to assist radiologists in reading the brain MRI. In this project we propose a CAD design that is based on novel computational techniques and integrated with routine clinical MRI acquisition. The CAD design features minimum user intervention and parameter selection, high robustness, and user-friendliness. We will also take advantage of the availability of graphics processing unit (GPU) in implementation to speed up the computations. We expect the proposed CAD approach will improve the accuracy of diagnosing brain metastases, and in turn, save lives and benefit patients’ well-being.

该项目的首要目标是提高诊断脑中癌症转移的准确性通过一种新的计算机辅助诊断（CAD）技术的发展。在当今的癌症治疗中，通常不是原发性癌症，而是导致死亡的转移性癌症。许多癌症，包括肺癌，肾癌、卵巢癌、乳腺癌和黑色素瘤有向大脑转移的趋势，仅在美国，脑转移的数量就高达每年17万。因此，准确诊断脑转移对于挽救生命和改善患者的健康至关重要。磁共振成像（MRI）是扫描脑以寻找潜在转移的最广泛使用的方式，诊断转移是一项非常具有挑战性的任务，其具有相当大的假阴性率。第一诊断转移的困难在于，在早期阶段，转移是无症状的。第二个困难转移瘤在MRI上表现为弱信号强度变化，并且它们的外观通常高度类似于正常的大脑结构，如小血管，这意味着一个人必须在他/她的脑海中想象观察对象是转移瘤还是血管。错过一个转移有严重的后果因为患者不会被要求进一步治疗。准确诊断转移瘤的益处，另一方面，可以对患者有显著的益处，因为像立体定向放射外科手术（SRS）这样的治疗可以在许多情况下完全消除转移的肿瘤，并延长患者的寿命三到四年在大多数情况下，几年。 CAD可通过识别脑转移瘤的异常，提高脑转移瘤的诊断准确性信号强度的变化，并标记它们供放射科医生检查。在这个过程中，CAD将起到辅助作用这是一个补充人类在解释大脑MRI方面的专业知识的工具。然而，尽管发现以及治疗脑转移瘤，目前缺乏针对该问题的CAD方法。许多现有脑MRI的计算技术是根据研究环境中获得的MRI数据量身定制的，涉及许多其他MRI技术，如DWI、DTI和功能性MRI。但在临床上，如T1和T2加权MRI用于扫描患者，因此，CAD方法必须根据患者的临床环境，以帮助放射科医生阅读脑部MRI。在这个项目中，我们提出了一个CAD设计，基于新的计算技术并与常规临床MRI采集相结合。cad设计具有最少的用户干预和参数选择、高鲁棒性和用户友好性。我们将还利用图形处理单元（GPU）的可用性来加速实现计算。我们期望提出的CAD方法将提高诊断大脑的准确性转移，并反过来挽救生命，造福患者的福祉。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantification of retinal blood leakage in fundus fluorescein angiography in a retinal angiogenesis model.

视网膜血管生成模型中眼底荧光素血管造影的视网膜血液泄漏的定量。

DOI：
10.1038/s41598-021-99434-2
发表时间：
2021-10-06
期刊：
Scientific reports
影响因子：
4.6
作者：
Comin CH;Tsirukis DI;Sun Y;Xu X
通讯作者：
Xu X

A neural network approach to segment brain blood vessels in digital subtraction angiography.

DOI：
10.1016/j.cmpb.2019.105159
发表时间：
2020-03
期刊：
Computer methods and programs in biomedicine
影响因子：
6.1
作者：
Zhang M;Zhang C;Wu X;Cao X;Young GS;Chen H;Xu X
通讯作者：
Xu X

That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data

DOI：
10.48550/arxiv.2210.06565
发表时间：
2022-10
期刊：
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
影响因子：
0
作者：
Denis Jered McInerney;Geoffrey S. Young;Jan-Willem van de Meent;Byron Wallace
通讯作者：
Denis Jered McInerney;Geoffrey S. Young;Jan-Willem van de Meent;Byron Wallace

LED Phototherapy with Gelatin Sponge Promotes Wound Healing in Mice.

LED 光疗与明胶海绵促进小鼠伤口愈合