STTR Phase II: Automated system for creating custom three-dimensional radiofrequency ablation lesion geometries in post-lumpectomy margin ablation breast cancer treatment

这项小型企业技术转移（STTR）二期项目的更广泛影响/商业潜力将集中在设计第一个完全自动化的乳房组织射频消融（RFA）系统上。在美国，每年大约有20万乳腺癌患者选择乳房肿瘤切除术，其中超过20%的患者由于缺乏清晰的边缘而必须再次手术。此外，许多接受乳房肿瘤切除术的乳腺癌患者需要昂贵的辅助放射治疗，这种辅助放射治疗可能持续长达7周，导致患者不得不离开工作和家庭。幸运的是，术中对乳房肿瘤切除术腔进行射频消融术已被证明可以通过对肿瘤边缘进行消毒来减少再次手术和放射治疗的需要。然而，没有RFA装置是为乳房肿瘤切除术后的乳房腔或乳房组织设计的。该项目开发的射频消融术系统将自动完成乳房肿瘤切除术后的射频消融术，根据局部组织特性和所需消融深度在三维空间中准确准确地输送能量，从而为每位患者定制治疗方案。该系统的最终商业化可以为早期乳腺癌患者提供新的治疗选择，提高生活质量，减轻护理负担，降低成本，同时提供乳腺癌复发控制。该项目旨在开发一个系统（控制单元和设备），以实现最佳的乳房肿瘤切除术后RFA。该装置被设计为机械贴合乳房肿瘤切除术后的腔体，以实现近乎完美的组织接触。控制单元包括先进的算法，利用机器学习来创建每个边缘的三维消融状态图（消融与未消融），以控制消融。该组合系统允许外科医生定制和监测三维消融轮廓，并自动提供治疗，以确保准确，精确的消融结果。目标是收集设备要求，确定自动化RFA程序的关键任务，并通过收集尸体和预防性乳房切除术标本的训练数据来改进系统算法。提出的智能优点是：(1)在嵌入式微处理器和co上实现的自动烧蚀系统。FPGA处理器能够对烧蚀形状进行估计和控制；(2)通过成功消融人体组织和外科医生可用性来证明临床相关性的系统。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project will focus on the design of the first completely automated radio frequency ablation (RFA) system for breast tissue. Of the approximately 200,000 breast cancer patients in the US who elect lumpectomy annually, over 20% must undergo re-operation due to lack of clear margins. Furthermore, many breast cancer patients who are treated with lumpectomy require expensive adjuvant radiation therapy that can last up to 7 weeks causing time away from work and family. Fortunately, performing intraoperative RFA of the lumpectomy cavity has been shown to decrease the need for re-operations and radiation therapy by sterilizing tumor margins. However, no RFA devices are designed for post-lumpectomy breast cavities or breast tissue. The RFA system being developed in this project will automate post-lumpectomy RFA, accurately and precisely delivering energy based on local tissue properties and desired ablation depth in three dimensions thus customizing treatment for each patient. Eventual commercialization of this system could provide early stage breast cancer patients new treatment options that improve quality of life, reduce burdens of care, and costs while providing breast cancer recurrence control. The proposed project aims to develop a system (control unit and device) for optimal post-lumpectomy RFA. The proposed device is designed to mechanically fit the post-lumpectomy cavity for near-perfect tissue contact. The control unit includes advanced algorithms that utilize machine learning to create a three-dimensional ablation status map of each margin (ablation vs in-ablated) for controlling ablation. The combined system allows the surgeon to customize and monitor the three-dimensional ablation profile and automates therapy delivery to ensure accurate, precise ablation results. The objectives propose gathering device requirements, identifying critical tasks of an automated RFA procedure, and improving the system algorithm by collecting training data in cadaveric and prophylactic mastectomy specimens. The intellectual merits proposed are: (1) an automated ablation system implemented on an embedded microprocessor and co?]processor FPGA capable of ablation shape estimation and control; (2) a system that demonstrates clinical relevance through successful ablation in human tissue and surgeon usability.