Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 andn = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan–Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.

To investigate local haemodynamics in the setting of acute coronary plaque rupture and erosion. Intracoronary optical coherence tomography performed in 37 patients with acute coronary syndromes caused by plaque rupture (n = 19) or plaque erosion (n = 18) was used for 3D reconstruction and computational fluid dynamic simulation. Endothelial shear stress (ESS), spatial ESS gradient (ESSG), and oscillatory shear index (OSI) were compared between plaque rupture and erosion through mixed-effects logistic regression. Lipid, calcium, macrophages, layered plaque, and cholesterol crystals were also analysed. By multivariable analysis, only high ESSG (odds ratio [OR] 5.29, 95% confidence interval [CI] 2.57-10.89, p < 0.001), lipid (OR 12.98, 95% CI 6.57-25.67 p < 0.001), and layered plaque (OR 3.17, 95% CI 1.82-5.50, p < 0.001) were independently associated with plaque rupture. High ESSG (OR 13.28, 95% CI 6.88-25.64, p < 0.001), ESS (OR 2.70, 95% CI 1.34-5.42, p = 0.005) and OSI (OR 2.18, 95% CI 1.33-3.54, p = 0.002) independently associated with plaque erosion. ESSG was higher at rupture sites than erosion sites (median (interquartile range): 5.78 (2.47, 21.15) versus 2.62 (1.44, 6.18) Pa/mm, p = 0.009), OSI was higher at erosion sites than rupture sites (1.04x10⁻² (2.3x10⁻³, 4.74x10⁻²) versus 1.29x10⁻³ (9.39x10⁻⁵, 3.0x10⁻²), p < 0.001), but ESS was similar (p = 0.29). High ESSG is independently associated with plaque rupture while high ESSG, ESS, and OSI associate with plaque erosion. While ESSG is higher at rupture sites than erosion sites, OSI is higher at erosion sites and ESS was similar. These results suggest that ESSG and OSI may play critical roles in acute plaque rupture and erosion, respectively. Plaque rupture and erosion are distinct pathological and clinical entities with possibly different optimal treatments. This study demonstrates that high endothelial shear stress gradient is independently associated with site of both rupture and erosion, and is significantly higher in rupture. High oscillatory shear index is independently associated with the site of erosion only, and is higher in erosion than rupture. Larger studies are necessary to determine whether these indices may detect and distinguish plaque rupture and erosion in a clinical setting or to assess overall risk for acute coronary syndromes.

We developed and evaluated an automatic stent strut detection method in intravascular optical coherence tomography (IVOCT) pullback runs. Providing very high resolution images, IVOCT has been rapidly accepted as a coronary imaging modality for the optimization of the stenting procedure and its follow-up evaluation based on stent strut analysis. However, given the large number of struts visible in a pullback run, quantitative three-dimensional analysis is only feasible when the strut detection is performed automatically. The presented method first detects the candidate pixels using both a global intensity histogram and the intensity profile of each A-line. Gaussian smoothing is applied followed by specified Prewitt compass filters to detect the trailing shadow of each strut. Next, the candidate pixels are clustered using the shadow information. In the final step, several filters are applied to remove the false positives such as the guide wire. Our new method requires neither a priori knowledge of the strut status nor the lumen/vessel contours. In total, 10 IVOCT pullback runs from a 1-year follow-up study were used for validation purposes. 18,311 struts were divided into three strut status categories (malapposition, apposition or covered) and classified based on the image quality (high, medium or low). The inter-observer agreement is 95 %. The sensitivity was defined as the ratio of the number of true positives and the total number of struts in the expert defined result. The proposed approach demonstrated an average sensitivity of 94 %. For malapposed, apposed and covered stent struts, the sensitivity of the method is respectively 91, 93 and 94 %, which shows the robustness towards different situations. The presented method can detect struts automatically regardless of the strut status or the image quality, and thus can be used for quantitative measurement, 3D reconstruction and visualization of the stents in IVOCT pullback runs.

Background: The relationship between gut microbiota and in vivo coronary plaque characteristics has not been reported. This study was conducted to investigate the relationship between gut microbiota and coronary plaque characteristics in patients with coronary artery disease. Methods and Results: Patients who underwent both optical coherence tomography and intravascular ultrasound imaging and provided stool and blood specimens were included. The composition of gut microbiota was evaluated using 16S rRNA sequencing. A total of 55 patients were included. At the genus level, 2 bacteria were associated with the presence of thin‐cap fibroatheroma, and 9 bacteria were associated with smaller fibrous cap thickness. Among them, some bacteria had significant associations with inflammatory/prothrombotic biomarkers. Dysgonomonas had a positive correlation with interleukin‐6, Paraprevotella had a positive correlation with fibrinogen and negative correlation with high‐density lipoprotein cholesterol, Succinatimonas had positive correlations with fibrinogen and homocysteine, and Bacillus had positive correlations with fibrinogen and high‐sensitivity C‐reactive protein. In addition, Paraprevotella , Succinatimonas , and Bacillus were also associated with greater plaque volume. Ten bacteria were associated with larger fibrous cap thickness. Some were associated with protective biomarker changes; Anaerostipes had negative correlations with trimethylamine N‐oxide, tumor necrosis factor α, and interleukin‐6, and Dielma had negative correlations with trimethylamine N‐oxide, white blood cells, plasminogen activator inhibitor‐1, and homocysteine, and a positive correlation with high‐density lipoprotein cholesterol. Conclusions: Bacteria that were associated with vulnerable coronary plaque phenotype and greater plaque burden were identified. These bacteria were also associated with elevated inflammatory or prothrombotic biomarkers. Registration: URL: https://www.umin.ac.jp/ctr/ ; Unique identifier: UMIN000041692.

An important application of intravascular optical coherence tomography (IVOCT) for atherosclerotic tissue analysis is using it to estimate attenuation and backscatter coefficients. This work aims at exploring the potential of the attenuation coefficient, a proposed backscatter term, and image intensities in distinguishing different atherosclerotic tissue types with a robust implementation of depth-resolved (DR) approach. Therefore, the DR model is introduced to estimate the attenuation coefficient and further extended to estimate the backscatter-related term in IVOCT images, such that values can be estimated per pixel without predefining any delineation for the estimation. In order to exclude noisy regions with a weak signal, an automated algorithm is implemented to determine the cut-off border in IVOCT images. The attenuation coefficient, backscatter term, and the image intensity are further analyzed in regions of interest, which have been delineated referring to their pathology counterparts. Local statistical values were reported and their distributions were further compared with a two-sample t-test to evaluate the potential for distinguishing six types of tissues. Results show that the IVOCT intensity, DR attenuation coefficient, and backscatter term extracted with the reported implementation are complementary to each other on characterizing six tissue types: mixed, calcification, fibrous, lipid-rich, macrophages, and necrotic core.