Spatial and temporal patterns of Cenozoic dynamic topography around Australia

[1] Despite its importance, the spatial and temporal pattern of dynamic topography generated by mantle convective circulation is poorly known. We present accurate estimates of dynamic topography from oceanic basins and continental margins surrounding Australia. Our starting point is measurement of residual depth anomalies on the oldest oceanic floor adjacent to the continental shelf. These anomalies were determined from a combined dataset of ~200 seismic reflection and wide-angle images of well-sedimented oceanic crust. They have amplitudes of between −1 km and +0.5 km, and their spatial variation is broadly consistent with long-wavelength free-air gravity and shallow seismic tomographic anomalies. Along the Northwest Shelf, a regional depth anomaly of −300 to −700 m intersects the adjacent continental shelf. The temporal evolution of this anomaly was determined by analyzing the stratigraphic architecture of an extensive carbonate platform, which fringes the shelf and records a dramatic switch from progradation to aggradation during Neogene times. Three-dimensional seismic mapping calibrated by boreholes was used to calculate water-loaded subsidence histories at rollover points of clinoforms along the shelf. At 9 ± 3 Ma, the rate of subsidence increases from 5 to up 75 m Myr−1, generating a subsidence anomaly of −300 to −700 m. The amplitude of this anomaly varies along the shelf and cannot be generated by glacio-eustatic sea-level variation. Instead, we propose that a regional subsidence episode, which affects both the proximal shelf and the distal oceanic basin, was generated by convective drawdown. By combining our results with other published estimates of uplift and subsidence, a map of Australia, which shows the spatial and temporal pattern of dynamic topography is presented. Most, but not all, of Australia's epeirogeny can be attributed to rapid northward motion of the Australian plate over a pre-existing pattern of convective circulation

Imaging Biomarkers for Precision Medicine in Locally Advanced Breast Cancer

Guidelines from the American National Comprehensive Cancer Network (NCCN)recommend neoadjuvant chemotherapy (NAC) to patients with locally advanced breast cancer (LABC) to downstage tumors before surgery. However, only a small fraction (15-17%) of LABC patients achieve complete pathologic response (pCR), i.e. no residual tumor in the breast, after treatment. Measuring tumor response during 53 neoadjuvant chemotherapy can potentially help physicians adapt treatment thus, potentially improving the pCR rate. Recently, imaging biomarkers that are used to measure the tumor’s functional and biological features have been studied as pre-treatment markers for pCR or as an indicator for intra-treatment tumor response. Also, imaging biomarkers have been the focus of intense research to characterize tumor heterogeneity as well as to advance our understanding of the principle mechanisms behind chemoresistance. Advances in investigational radiology are moving rapidly to high-resolution imaging, capturing metabolic data, performing tissue characterization and statistical modelling of imaging biomarkers, with an endpoint of personalized medicine in breast cancer treatment. In this commentary, we present studies within the framework of imaging biomarkers used to measure breast tumor response to chemotherapy. Current studies are showing that significant progress has been made in the accuracy of measuring tumor response either before or during chemotherapy, yet the challenges at the forefront of these works include translational gaps such as needing large-scale clinical trials for validation, and standardization of imaging methods. However, the ongoing research is showing that imaging biomarkers may play an important role in personalized treatments for LABC

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography

© 2019 The Authors Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy

Ultrasound imaging of apoptosis: high-resolution non-invasive monitoring of programmed cell death in vitro, in situ and in vivo

A new non-invasive method for monitoring apoptosis has been developed using high frequency (40 MHz) ultrasound imaging. Conventional ultrasound backscatter imaging techniques were used to observe apoptosis occurring in response to anticancer agents in cells in vitro, in tissues ex vivo and in live animals. The mechanism behind this ultrasonic detection was identified experimentally to be the subcellular nuclear changes, condensation followed by fragmentation, that cells undergo during apoptosis. These changes dramatically increase the high frequency ultrasound scattering efficiency of apoptotic cells over normal cells (25- to 50-fold change in intensity). The result is that areas of tissue undergoing apoptosis become much brighter in comparison to surrounding viable tissues. The results provide a framework for the possibility of using high frequency ultrasound imaging in the future to non-invasively monitor the effects of chemotherapeutic agents and other anticancer treatments in experimental animal systems and in patients. © 1999 Cancer Research Campaig

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning

ObjectiveNeoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT).Materials and methodsSeveral deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes.ResultsAmongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network.ConclusionDeep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism

Focused Ultrasound Stimulation of Microbubbles in Combination With Radiotherapy for Acute Damage of Breast Cancer Xenograft Model

Objective: Several studies have focused on the use of ultrasound-stimulated microbubbles (USMB) to induce vascular damage in order to enhance tumor response to radiation. Methods: In this study, power Doppler imaging was used along with immunohisto- chemistry to investigate the effects of combining radiation therapy (XRT) and USMB using an ultrasound-guided focused ultrasound (FUS) therapy system in a breast cancer xenograft model. Specifically, MDA-MB-231 breast cancer xenograft tumors were induced in severe combined immuno-deficient female mice. The mice were treated with FUS alone, ultrasound and microbubbles (FUS + MB) alone, 8 Gy XRT alone, or a combined treatment consisting of ultrasound, microbubbles, and XRT (FUS + MB + XRT). Power Doppler imaging was conducted before and 24 h after treatment, at which time mice were sacrificed and tumors assessed histolog- ically. The immunohistochemical analysis included terminal deoxynucleotidyl transferase dUTP nick end labeling, hematoxylin and eosin, cluster of differentiation-31 (CD31), Ki-67, carbonic anhydrase (CA-9), and ceramide labeling. Results: Tumors receiving treat- ment of FUS + MB combined with XRT demonstrated significant increase in cell death (p = 0.0006) compared to control group. Furthermore, CD31 and Power Doppler analysis revealed reduced tumor vascularization with combined treatment indicating (P \u3c .0001) and (P = .0001), respectively compared to the control group. Additionally, lesser number of proliferating cells with enhanced tumor hypoxia, and ceramide content were also reported in group receiving a treatment of FUS + MB + XRT. Conclusion: The study results demonstrate that the combination of USMB with XRT enhances treatment outcomes