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诊断脑部癌症转移的准确性 通过开发一种新颖的计算机辅助诊断（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，包括肺癌， 肾癌、卵巢癌、乳腺癌和黑色素瘤有转移到大脑和全身的倾向 仅在美国，每年脑转移瘤的数量就高达 17 万例。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。磁的 磁共振成像 (MRI) 是扫描大脑是否存在潜在转移的最广泛使用的方法，但 诊断转移瘤是一项非常具有挑战性的任务，具有相当大的假阴性率。第一个 诊断转移的困难在于，在早期阶段，转移是无症状的。第二个难点 转移瘤在 MRI 上表现为微弱的信号强度变化，并且其外观通常高度 类似于正常的大脑结构，例如小血管，这意味着人们必须在他/她的头脑中进行想象 观察对象是转移瘤还是血管。错过转移会产生严重后果 因为患者不会被要求接受进一步治疗。准确诊断转移的好处 另一方面，可以给患者带来显着的好处，因为立体定向放射外科 (SRS) 等治疗可以 在许多情况下完全消除转移的肿瘤，并延长患者三至四年的寿命 大多数情况下是几年。 CAD 可通过识别异常情况在提高脑转移诊断准确性方面发挥关键作用 信号强度变化并进行标记以供放射科医生检查。在此过程中，CAD将起到辅助作用 补充人类在解释大脑 MRI 方面的专业知识的工具。然而，尽管发现很重要 以及治疗脑转移，目前缺乏解决这个问题的 CAD 方法。许多现有的 脑 MRI 的计算技术是根据在研究环境中获取的 MRI 数据量身定制的，这些数据通常 涉及许多其他 MRI 技术，如 DWI、DTI 和功能 MRI。但在临床上只有解剖 MRI 就像 T1 和 T2 加权 MRI 用于扫描患者一样，因此 CAD 方法必须适合患者 临床环境可协助放射科医生读取脑部 MRI。在这个项目中，我们提出了一个 CAD 设计，即 基于新颖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 还利用实施中图形处理单元（GPU）的可用性来加速 计算。我们预计所提出的 CAD 方法将提高诊断大脑的准确性 转移，进而挽救生命并造福患者的福祉。