Computer aided diagnosis of cancer metastases in the brain

计算机辅助诊断脑部癌症转移

• 批准号: 9216187
• 负责人: Xiaoyin Xu
• 金额: $ 46.18万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-06 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9216187
• 关键词: Address; Algorithms; Anatomy; Angiogenesis Inhibition; Appearance; Blinded; Blood - brain barrier anatomy; Blood Vessels; Brain; Brain Neoplasms; Brain scan; Breast; Cancer Etiology; Cancer Patient; Characteristics; Clinic; Clinical; Colon; Color; Communication; Complement; Computational Technique; Computer software; Computer-Assisted Diagnosis; Computers; Cranial Irradiation; Data; Detection; Development; Diagnosis; Diagnostic; 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 Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Medicine; Metastatic malignant neoplasm to brain; Methods; Mind; Modality; Morbidity - disease rate; Neoplasm Metastasis; Neuraxis; Pancreas; Patient-Focused Outcomes; Patients; Performance; Peripheral; Personal Satisfaction; Physicians; Play; Process; Quality of life; Radiation; Radiation therapy; Radiology Specialty; Radiosurgery; Reading; Renal Cell Carcinoma; Renal carcinoma; Research; Resolution; Scanning; Shapes; Signal Transduction; Skin; Speed; Staging; Structure; Techniques; Testing; Time; Weight; accurate diagnosis; base; brain parenchyma; cancer cell; cancer therapy; chemotherapy; clinical Diagnosis; dashboard; diagnosis design; diagnostic accuracy; digital imaging; imaging modality; improved; killings; malignant breast neoplasm; melanoma; novel; outcome forecast; parallel computer; radiologist; tool; tumor; user-friendly

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.

该项目的总体目标是提高大脑诊断性癌症转移的准确性 通过开发新型的计算机辅助诊断（CAD）技术。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， 肾脏，卵巢癌和乳腺癌和黑色素瘤具有向大脑和大脑的转移趋势 仅在美国，脑转移的数量每年高达170,000。因此，准确的诊断 脑转移在挽救生命和改善患者的福祉方面至关重要。磁的 共振成像（MRI）是最广泛使用的方式，用于扫描大脑的潜在转移，但 诊断转移是一项非常挑战的任务，具有相当大的假阴性率。第一个 诊断转移的困难是，在早期，转移是无症状的。第二个困难 是转移表现为MRI信号强度的变化，其外观通常很高 类似于正常的大脑结构，例如小血管，这意味着必须在他/她的思想中可视化 观察到的物体是转移还是血管。缺少转移有严重的后果 因为不会要求患者进一步治疗。准确诊断转移的好处 另一方面，由于立体定向放射外科（SRS）等治疗可以对患者产生重大好处 在许多情况下，完全消除了转移的肿瘤，并将患者的寿命延长三到四个 在大多数情况下。 CAD可以通过识别异常来提高诊断性脑转移的准确性来发挥关键作用 信号强度会改变并标记它们以供放射科医生检查。在此过程中，CAD将充当援助 补充人类在解释大脑MRI方面的专业知识的工具。但是，要查找的重要性 以及治疗脑转移，目前缺乏解决此问题的CAD方法。许多存在 脑MRI上的计算技术是根据经常在研究环境中获取的MRI数据量身定制的 涉及许多其他MRI技术，例如DWI，DTI和功能性MRI。但是在诊所只有解剖学MRI 像T1-和T2加权MRI一样用于扫描患者，因此，必须将CAD方法量身定制 临床环境，以帮助放射性者阅读大脑MRI。在这个项目中，我们提出了一个CAD设计 基于新颖的计算技术，并与常规临床MRI获取集成。 CAD设计 具有最低用户干预和参数选择，高鲁棒性和用户友好性。我们将 还利用了实施中图形处理单元（GPU）的可用性来加快 计算。我们预计提出的CAD方法将提高诊断大脑的准确性 转移，然后挽救生命并使患者的福祉受益。