Tumor location matters, next generation sequencing mutation profiling of left-sided, rectal, and right-sided colorectal tumors in 552 patients.

Despite the introduction of new molecular classifications, advanced colorectal cancer (CRC) is treated with chemotherapy supplemented with anti-EGFR and anti-VEGF targeted therapy. In this study, 552 CRC cases with different primary tumor locations (250 left side, 190 rectum, and 112 right side) were retrospectively analyzed by next generation sequencing for mutations in 50 genes. The most frequently mutated genes were TP53 in left-sided tumors compared to right-sided tumors and BRAF in right-sided tumors compared to left-sided tumors. Mutations in KRAS, NRAS, and BRAF were not detected in 45% of patients with left-sided tumors and in 28.6% of patients with right-sided tumors. Liver metastases were more common in patients with left-sided tumors. Tumors on the right side were larger at diagnosis and had a higher grade (G3) than tumors on the left. Rectal tumors exhibit distinctive biological characteristics when compared to left-sided tumors, including a higher absence rate of KRAS, NRAS, and BRAF mutations (47.4% in rectal versus 42.8% in left-sided tumors). These rectal tumors are also unique in their primary metastasis site, which is predominantly the lungs, and they have varying mutation rates, particularly in genes such as BRAF, FBXW7, and TP53, that distinguish them from tumors found in other locations. Primary tumor location has implications for the potential treatment of CRC with anti-EGFR therapy.

Evaluation of Complete Pathological Regression after Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Patients with BRCA1 Founder Mutation Aided Bayesian A/B Testing Approach.

The aim of this study was to evaluate the probability of pathologic complete regression (pCR) by the BRCA1 gene mutation status in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy. The study involved 143 women (mean age 55.4 ± 13.1 years) with TNBC. The BRCA1 mutation was observed in 17% of the subjects. The most commonly used (85.3%) chemotherapy regimen was four cycles of adriamycine and cyclophosphamide followed by 12 cycles of paclitaxel (4AC + 12T). The differences between clinico-pathological factors by BRCA1 status were estimated. Odds ratios and 95% confidence intervals for pCR vs. non-pCR were calculated using logistic regression. The probability distribution of pCR based on BRCA1 status was estimated using beta distributions. The presence of T3-T4 tumours, cancer in stages II and III, lymphovascular invasion, and the use of chemotherapy schedules other than 4AC + 12T significantly decreased the odds of pCR. It was established that there was a 20% chance that pCR in patients with the BRCA1 mutation was 50% or more times as frequent than in patients without the mutation. Thus, the BRCA1 mutation can be a predictive factor for pCR in patients with TNBC.

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization.

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.

Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms.

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-L1 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-L1 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.