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

Ultrasound-Stimulated Microbubble Radiation Enhancement of Tumors: Single-Dose and Fractionated Treatment Evaluation

The use of ultrasound-stimulated microbubble therapy has successfully been used to target tumor vasculature and enhance the effects of radiation therapy in tumor xenografts in mice. Here, we further investigate this treatment using larger, more clinically relevant tumor mod- els. New Zealand white rabbits bearing prostate tumor (PC3) xenografts received a single treatment of either ultrasound-stimulated microbubbles (USMB), ionizing radiation (XRT; 8Gy), or a combination of both treatments (USMB+XRT). Treatment outcome was evalu- ated 24 hours after treatment using histopathology, immunolabeling, 3D Doppler ultrasound and photoacoustic imaging. A second cohort of rabbits received multiple treatments over a period of three weeks, where USMB treatments were delivered twice weekly with daily XRT treatments to deliver a fractionated 2Gy dose five days per week. A significant decrease in vascular function, observed through immunolabeling of vascular endothelial cells, was observed in tumors receiving the combined treatment (USMB+XRT) compared to control and single treatment groups. This was associated with an increase in cell death as observed through in situ end labeling (ISEL), a decrease in vascular index measured by Power Dopp- ler imaging, and a decrease in oxygen saturation. In rabbits undergoing the long-term fractionated combined treatment, a significant growth delay was observed after 1 week and a significant reduction in tumor size was observed after 3 weeks with combined therapy. Results demonstrated an enhancement of radiation effect and superior anti-tumor effect of the combination of USMB+XRT compared to the single treatments alone. Tumor growth was maximally inhibited with fractionated radiotherapy combined with the ultrasound-stimulated microbubble-based therapy

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

Overview of Therapeutic Ultrasound Applications and Safety Considerations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135598/1/jum2012314623.pd

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Results: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. Conclusion: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori

Quantitative ultrasound imaging of therapy response in bladder cancer in vivo.

Background and aimsQuantitative ultrasound (QUS) was investigated to monitor bladder cancer treatment response in vivo and to evaluate tumor cell death from combined treatments using ultrasound-stimulated microbubbles and radiation therapy.MethodsTumor-bearing mice (n=45), with bladder cancer xenografts (HT- 1376) were exposed to 9 treatment conditions consisting of variable concentrations of ultrasound-stimulated Definity microbubbles [nil, low (1%), high (3%)], combined with single fractionated doses of radiation (0 Gy, 2 Gy, 8 Gy). High frequency (25 MHz) ultrasound was used to collect the raw radiofrequency (RF) data of the backscatter signal from tumors prior to, and 24 hours after treatment in order to obtain QUS parameters. The calculated QUS spectral parameters included the mid-band fit (MBF), and 0-MHz intercept (SI) using a linear regression analysis of the normalized power spectrum.Results and conclusionsThere were maximal increases in QUS parameters following treatments with high concentration microbubbles combined with 8 Gy radiation: (ΔMBF = +6.41 ± 1.40 (±SD) dBr and SI= + 7.01 ± 1.20 (±SD) dBr. Histological data revealed increased cell death, and a reduction in nuclear size with treatments, which was mirrored by changes in quantitative ultrasound parameters. QUS demonstrated markers to detect treatment effects in bladder tumors in vivo

Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pretreatment Diffuse Optical Spectroscopic-Texture Analysis

Purpose: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pre-treatment DOS functional maps for predicting LABC response to NAC. Methods: LABC patients (n = 37) underwent DOS-breast imaging before starting neoadjuvant chemotherapy. Breast-tissue parametric maps were constructed and texture analyses were performed based on grey level co-occurrence matrices (GLCM) for feature extraction. Ground-truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS-textural features computed on volumetric tumour data before the start of treatment (i.e. “pre-treatment”) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naïve Bayes, and k-nearest neighbour (k-NN) classifiers. Results: Data indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5 and 89.0%, respectively and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn/%Sp = 78.0/81.0% and an accuracy of 79.5%. Conclusions: This study demonstrated that pre-treatment tumour DOS-texture features can predict breast cancer response to NAC and potentially guide treatments

Spatial and temporal uplift history of South America from calibrated drainage analysis

A multidisciplinary approach is used to analyze the Cenozoic uplift history of South America. Residual depth anomalies of oceanic crust abutting this continent help to determine the pattern of present-day dynamic topography. Admittance analysis and crustal thickness measurements indicate that the elastic thickness of the Borborema and Altiplano regions is ≤₁₀ km with evidence for sub-plate support at longer wavelengths. A drainage inventory of 1827 river profiles is assembled and used to investigate landscape development. Linear inverse modeling enables river profiles to be fitted as a function of the spatial and temporal history of regional uplift. Erosional parameters are calibrated using observations from the Borborema Plateau and tested against continent-wide stratigraphic and thermochronologic constraints. Our results predict that two phases of regional uplift of the Altiplano plateau occurred in Neogene times. Regional uplift of the southern Patagonian Andes also appears to have occurred in Early Miocene times. The consistency between observed and predicted histories for the Borborema, Altiplano, and Patagonian plateaux implies that drainage networks record coherent signals that are amenable to simple modeling strategies. Finally, the predicted pattern of incision across the Amazon catchment constrains solid sedimentary flux at the Foz do Amazonas. Observed and calculated flux estimates match, suggesting that erosion and deposition were triggered by regional Andean uplift during Miocene times