A comprehensive computational framework for facilitating the radiological and surgical management of breast cancer

诊断和治疗一直是医学的两大支柱，前者的准确性对后者的成功至关重要。特别是，在癌症研究中，在疾病的早期阶段诊断往往与更好的预后和更长的生存期相关。出于这个原因，在过去的几十年里，已经做出了相当大的努力来开发肿瘤疾病的早期诊断的准确方法。在这些方法和工具中，医学成像已经发展成为现代医疗保健的关键组成部分，目前实体癌症的诊断基于此。在这种情况下，目前研究计划的主要重点是开发先进的、针对问题量身定做的医学图像处理方法，使临床医生能够以更有效的方式管理肿瘤疾病。该计划的基本理念源于这样一个事实，即从临床角度来看，实体癌症的诊断和治疗是两个紧密交织的过程，涉及由相同的客观信息支持的相互决策，例如由医学成像提供的信息。因此，该计划不是针对放射学或治疗学的需求设计单独的解决方案，而是旨在开发一个联合的计算和可视化平台，双方可以共享该平台，以促进双方的互动，以改善健康结果。*作为一个特定的应用程序，本计划侧重于乳腺癌，这仍然是加拿大女性中最常被诊断为恶性肿瘤的疾病。目前，乳腺癌的放射治疗和外科治疗都依赖于几种成像手段，其中包括X射线乳腺摄影和磁共振成像(MRI)。然而，基于不同的物理现象，这些模式产生了不同的、往往是补充性质的图像。出于这个原因，一些临床应用需要对这些图像的内容进行适当的“融合”。众所周知，这种核聚变是一个困难的问题，还没有找到有效的解决方案。此外，乳腺MRI的超高敏感性仍然被其相对较低的特异性所抵消，这往往会导致多余的乳腺活检，以及相关的生理和经济负担。为了解决这一问题，需要使用尖端成像技术，如扩散磁共振成像(DMRI)。然而，尽管dMRI已经证明了区分良、恶性肿瘤的能力，但它仍然存在一些伪影，需要通过进一步的研究来解决。最后，先进的成像技术仍然需要引入手术室，通过更准确的术前计划来提高乳房手术的成功率。基于可视化技术的最新进展，这一想法的实际实现构成了本研究计划的另一个目标。

Diagnosis and treatment have always been the two defining pillars of medicine, with the fidelity of the former being essential to the success of the latter. In particular, in cancer research, diagnosing the disease at its early stages is often associated with better prognosis and prolonged survival. For this reason, considerable efforts have been extended over the last few decades to develop accurate means of early diagnosis of oncological disorders. Among such methods and tools, medical imaging has evolved into a key component of modern health care upon which diagnosis of solid cancers is currently based. That being the case, the main focus of the present research program is to develop advanced, problem-tailored methods of medical image processing that enable clinicians to manage oncological disorders in a more effective way. The fundamental idea of the program stems from the fact that, from a clinical perspective, diagnosis and treatment of solid cancers are two intimately interwoven processes, which involve mutual decision making supported by the same objective information, such as that provided by medical imaging. Consequently, instead of devising separate solutions catering to the needs of either radiology or therapeutics, this program aims at developing a joint computational and visualization platform, which can be shared by both sides to facilitate their interaction towards improved health outcomes.******As a particular application, the present program focuses on breast cancer, which remains the most frequently diagnosed malignancy among Canadian women. Presently, both radiological and surgical management of breast cancer depend on several imaging modalities, among which are X-ray mammography and Magnetic Resonance Imaging (MRI). Based on different physical phenomena, however, the modalities produce images of a distinct and often supplementary nature. For this reason, several clinical applications require the content of such images to be properly “fused”. Such fusion is known to be a difficult problem, effective solutions to which are yet to be found. Moreover, the sublime sensitivity of breast MRI is still counterpoised by its relatively low specificity, which often results in redundant breast biopsies, with their associated phycological and economic burdens. To address this problem calls for the use of cutting-edge imaging technologies, such as diffusion MRI (dMRI). However, having demonstrated a proven ability to discriminate between benign and malignant tumours, dMRI still suffers from a number of artifacts which need to be resolved through further research. Finally, advanced imaging is still to be brought to the operating room, where it is much needed to improve the success of breast surgery through more accurate presurgical planning. A practical realization of this idea based on recent advances in visualization technologies constitutes another goal of this research program.