Concentrated Thermomics for Early Diagnosis of Breast Cancer

Thermography has been employed broadly as a corresponding diagnostic instrument in breast cancer diagnosis. Among thermographic techniques, deep neural networks show an unequivocal potential to detect heterogeneous thermal patterns related to vasodilation in breast cancer cases. Such methods are used to extract high-dimensional thermal features, known as deep thermomics. In this study, we applied convex non-negative matrix factorization (convex NMF) to extract three predominant bases of thermal sequences. Then, the data were fed into a sparse autoencoder model, known as SPAER, to extract low-dimensional deep thermomics, which were then used to assist the clinical breast exam (CBE) in breast cancer screening. The application of convex NMF-SPAER, combining clinical and demographic covariates, yielded a result of 79.3% (73.5%, 86.9%); the highest result belonged to NMF-SPAER at 84.9% (79.3%, 88.7%). The proposed approach preserved thermal heterogeneity and led to early detection of breast cancer. It can be used as a noninvasive tool aiding CBE.

Effects Of Acclimation, Population, And Sex On Behavioral Thermoregulation, CTMax, Symptoms Of Heat Stress, And Gene Expression of Melanoplus Differentialis, A Generalist Grasshopper - Does Temporal Thermal Heterogeneity Prepare Populations For A Warming World?

Insects thermoregulate using both canalized and plastic mechanisms. Populations of insects utilize these mechanisms to different extents, and while it is posited that the degree of thermal fluctuation a population experiences can determine the optimal combination of mechanisms to utilize, this is still being elucidated. We used three populations of the generalist grasshopper, Melanoplus differentialis (Thomas, 1856), from sites experiencing different degrees of thermal heterogeneity to test for correlations between thermal heterogeneity and 1) behavioral thermoregulation, 2) upper temperature tolerance, 3) the ability to thermally acclimate, and 4) gene expression. We found that 1) behavioral thermoregulation did not differ among sites, 2) CTMax of males, but not females, was higher at more thermally heterogeneous sites, 3) there was acclimation in some of the tested traits, but thermally heterogeneous sites did not always have the most plastic individuals, and 4) there were differences in gene expression among sites, but these differences were not between the most and least thermally heterogeneous sites. We concluded that thermal heterogeneity may play a selective role in some, but not all, of the measured thermoregulatory traits and their plasticity.

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.

Assessing Stream Thermal Heterogeneity and Cold-Water Patches from UAV-Based Imagery: A Matter of Classification Methods and Metrics

Understanding stream thermal heterogeneity patterns is crucial to assess and manage river resilience in light of climate change. The dual acquisition of high-resolution thermal infrared (TIR) and red–green–blue-band (RGB) imagery from unmanned aerial vehicles (UAVs) allows for the identification and characterization of thermally differentiated patches (e.g., cold-water patches—CWPs). However, a lack of harmonized CWP classification metrics (patch size and temperature thresholds) makes comparisons across studies almost impossible. Based on an existing dual UAV imagery dataset (River Ovens, Australia), we present a semi-automatic supervised approach to classify key riverscape habitats and associated thermal properties at a pixel-scale accuracy, based on spectral properties. We selected five morphologically representative reaches to (i) illustrate and test our combined classification and thermal heterogeneity assessment method, (ii) assess the changes in CWP numbers and distribution with different metric definitions, and (iii) model how climatic predictions will affect thermal habitat suitability and connectivity of a cold-adapted fish species. Our method was successfully tested, showing mean thermal differences between shaded and sun-exposed fluvial mesohabitats of up to 0.62 °C. CWP metric definitions substantially changed the number and distance between identified CWPs, and they were strongly dependent on reach morphology. Warmer scenarios illustrated a decrease in suitable fish habitats, but reach-scale morphological complexity helped sustain such habitats. Overall, this study demonstrates the importance of method and metric definitions to enable spatio-temporal comparisons between stream thermal heterogeneity studies.

Coronary plaque rupture is associated with carotid thermal heterogeneity in patients presenting with acute myocardial infarction

Introduction Vulnerable plaques of the coronary arteries, share common characteristics such as the thin cap fibrous cap, that make the prone to rupture in the presence of stimulus such as shear stress or inflammation. Optical coherence tomography (OCT) is an imaging method, by which the fibrous cap and the presence of plaque rupture can be accurately in vivo visualized. Recent studies have shown an association between increased carotid temperature heterogeneity (ΔT) detected by microwave radiometry (MWR) and cardiovascular events. Purpose To evaluate the impact of carotid temperature heterogeneity on the culprit plaque morphology on patients presenting with acute myocardial infarction. Method A total of 37 patients undergoing percutaneous coronary intervention (PCI) for an acute myocardial infarction who had an identifiable de novo culprit lesion in a native coronary artery, were enrolled in this study. All patients underwent PCI and Optical Coherence Study (OCT) within 12 hours since symptom onset. The culprit lesion of the angiogram was clearly identified by a combination of ECG, wall motion abnormalities seen in cardiac ultrasound, and coronary angiogram. The OCT study was performed using the LightLab OCT wire, and acquired images were analyzed by 2 independent investigators using previously validated criteria for OCT plaque characterization. After the completion of the PCI all patients underwent MWR of both carotid arteries and ΔT was defined as maximal temperature detected along each carotid artery minus minimum. Results Thirty four patients with acute myocardial infarction 21 with STEMI (61.76%) and 13 (38.23%) with NSTEMI were included in the study. STEMI patients had more ruptured plaques compared to NSTEMI patients (71.41 versus 38.46%, p=0.053). Thin cap fibroatheroma (TCFA) was present in 31 patients (91.1%), while all ruptured plaques had a TCFA compared to 11 TCFA (78.57%) observed in plaques that had no rupture (p=0.03). HsCRP was significantly increased in ruptured plaques compared to non ruptured ones (14.41±4.02 versus 9.9±2,5, p<0.005). Mean ΔT was significantly increased in ruptured plaques compared to no ruptured ones (1.01±0.31 versus 0.51±0.14°C, p<0.005), as well as in plaques with TCFA compared to those without a TCFA (0.82±0.37 versus 0.60±0.05°C, p=0.001). In the multivariate analysis, STEMI, hsCRP, and ΔT were entered from which hsCRP (OR 1.51; 95% CI 0.99–2.28; P=0.051) and ΔT ((OR for 0.1°C increase 3.40; 95% CI 1.29–8.96; P=0.013) remained in the final model, with ΔT being the only variable independently associated with the presence of rupture. Conclusions Carotid thermal heterogeneity is associated with the presence of plaque rupture in patients with acute myocardial infarction. Further studies are needed in order to assess the possible prognostic impact of carotid ΔT on such population. Funding Acknowledgement Type of funding source: None

Thin cap fibroatheroma and plaque rupture is associated with carotid thermal heterogeneity in patients presenting with acute myocardial infarction

Microwave radiometry (MWR) has been applied successfully in the evaluation of carotid atherosclerosis, measuring reliably temperature heterogeneity of atherosclerotic plaques. Recent studies have shown an association between increased carotid temperature heterogeneity (ΔT) detected by MWR and cardiovascular events. Vulnerable plaques of the coronary arteries, share common characteristics such as the thin cap fibrous cap, that make the prone to rupture in the presence of stimulus such as shear stress or inflammation. Optical coherence tomography (OCT) is an imaging method, by which the fibrous cap and the presence of plaque rupture can be accurately in vivo visualized. Purpose To evaluate the impact of carotid temperature heterogeneity on the culprit plaque morphology on patients presenting with acute myocardial infarction. Method A total of 37 patients undergoing percutaneous coronary intervention (PCI) for an acute myocardial infarction who had an identifiable de novo culprit lesion in a native coronary artery, were enrolled in this study. All patients underwent PCI and Optical Coherence Study (OCT) within 12 hours since symptom onset. The OCT study was performed according to the standard techniques and acquired images were analyzed by 2 independent investigators., After the completion of the PCI all patients underwent MWR of both carotid arteries and ΔT was defined as maximal temperature detected along each carotid artery minus minimum. Results Thirty four patients with acute myocardial infarction 21 with STEMI (61.76%) and 13 (38.23%) with NSTEMI were included in the study. Thin cap fibroatheroma (TCFA) was present in 31 patients (91.1%), while all ruptured plaques had a TCFA compared to 11 TCFA (78.57%) observed in plaques that had no rupture (p=0.03). HsCRP was significantly increased in ruptured plaques compared to non ruptured ones (14.41±4.02 versus 9.9±2.5, p<0.005). Mean ΔT was significantly increased in ruptured plaques compared to no ruptured ones (1.01±0.31 versus 0.51±0.14°C, p<0.005), as well as in plaques with TCFA compared to those without a TCFA (0.82±0.37 versus 0.60±0.05°C, p=0.001). In the multivariate analysis DM, hsCRP, and ΔT were entered from which DM (OR 4.12; 95% CI 0.77–22.07; P=0.07) and ΔTau ((OR for 0.1°C increase 1.43; 95% CI 1.03–1.98; P=0.03) remained in the final model, with ΔT being the only variable independently associated with the presence of TCFA. Similarly regarding plaque rupture, STEMI, hsCRP, and ΔT were entered in the multivariate analysis from which hsCRP (OR 1.51; 95% CI 0.99–2.28; P=0.051) and ΔTau ((OR for 0.1°C increase 3.40; 95% CI 1.29–8.96; P=0.013) remained in the final model, with ΔT being the only variable independently associated with the presence of rupture. Conclusions Carotid thermal heterogeneity is associated with TCFA and plaque rupture in patients with acute myocardial infarction. Funding Acknowledgement Type of funding source: None

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3–81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.