Knowledge about tumor bed localization and its shape analysis is a crucial factor for preventing irradiation of healthy tissues during supportive radiotherapy and as a result, cancer recurrence. The localization process is especially hard for tumors placed nearby soft tissues, which undergo complex, nonrigid deformations. Among them, breast cancer can be considered as the most representative example. A natural approach to improving tumor bed localization is the use of image registration algorithms. However, this involves two unusual aspects which are not common in typical medical image registration: the real deformation field is discontinuous, and there is no direct correspondence between the cancer and its bed in the source and the target 3D images respectively. The tumor no longer exists during radiotherapy planning. Therefore, a traditional evaluation approach based on known, smooth deformations and target registration error are not directly applicable. In this work, we propose alternative artificial deformations which model the tumor bed creation process. We perform a comprehensive evaluation of the most commonly used deformable registration algorithms: B-Splines free form deformations (B-Splines FFD), different variants of the Demons and TV-L optical flow. The evaluation procedure includes quantitative assessment of the dedicated artificial deformations, target registration error calculation, 3D contour propagation and medical experts visual judgment. The results demonstrate that the currently, practically applied image registration (rigid registration and B-Splines FFD) are not able to correctly reconstruct discontinuous deformation fields. We show that the symmetric Demons provide the most accurate soft tissues alignment in terms of the ability to reconstruct the deformation field, target registration error and relative tumor volume change, while B-Splines FFD and TV-L optical flow are not an appropriate choice for the breast tumor bed localization problem, even though the visual alignment seems to be better than for the Demons algorithm. However, no algorithm could recover the deformation field with sufficient accuracy in terms of vector length and rotation angle differences.
Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues’ irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.
Introduction: The article presents the results of the management of patients with breast cancer treated in the Holycross Cancer Centre during the period 2008-2012. In all the patients, invasive breast cancer clinically node negative was diagnosed and multidisciplinary treatment with sentinel node biopsy was applied. Material and methods: The study included a group of 213 women who had previously undergone surgery, breast-conserving and/or mastectomy. In 206 patients, the sentinel lymph node was identified. Due to metastasis to the sentinel lymph node in 32 patients axillary lymphadenectomy was performed and additionally in 7 patients due to the failure of sentinel lymph node identification. Due to the higher tumor burden 10 patients were subjected to more extensive surgical treatment-mastectomy. After surgical treatment the patients were qualified for adjuvant therapy. The mean time of observation of patients after treatment was 61 months. Results: Relapse of the disease was noted in 7 patients, 5 patients died (4 patients due to the spread of the disease, 1 due to the second carcinoma-gastric cancer). Recurrence in the axillary region was observed in 1 patient, metastases to the lungs-in 1 patient, metastases to the liver-in 1, metastases to the ovary-in 1, and in 3 patients metastases to the bones. Based on analysis of the Kaplan-Meier estimator of the survival function, it was found that the probability of survival for 5 years without symptoms of the disease was 96.2%, whereas the probability of overall 5-year survival was 96.4%. Conclusions: The outcome of patients after sentinel lymph node biopsy was excellent. In breast cancer patients sentinel lymph node biopsy is safe and effective.
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