Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of $0.81 \pm 0.02$ on the artery-level, and $0.87 \pm 0.02$ on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.Comment: This work has been accepted to IEEE TMI for publicatio

MicroRNA-214 enriched exosomes from human cerebral endothelial cells (hCEC) sensitize hepatocellular carcinoma to anti-cancer drugs

Hepatocellular carcinoma (HCC) is the most common primary liver tumor worldwide. Current medical therapy for HCC has limited efficacy. The present study tests the hypothesis that human cerebral endothelial cell-derived exosomes carrying elevated miR-214 (hCEC-Exo-214) can amplify the efficacy of anti-cancer drugs on HCC cells. Treatment of HepG2 and Hep3B cells with hCEC-Exo-214 in combination with anti-cancer agents, oxaliplatin or sorafenib, significantly reduced cancer cell viability and invasion compared with monotherapy with either drug. Additionally, the therapeutic effect of the combination therapy was detected in primary tumor cells derived from patients with HCC. The ability of hCEC-Exo-214 in sensitizing HCC cells to anti-cancer drugs was specific, in that combination therapy did not affect the viability and invasion of human liver epithelial cells and non-cancer primary cells. Furthermore, compared to monotherapy with oxaliplatin and sorafenib, hCEC-Exo-214 in combination with either drug substantially reduced protein levels of P-glycoprotein (P-gp) and splicing factor 3B subunit 3 (SF3B3) in HCC cells. P-gp and SF3B3 are among miR-214 target genes and are known to mediate drug resistance and cancer cell proliferation, respectively. In conclusion, the present in vitro study provides evidence that hCEC-Exo-214 significantly enhances the anti-tumor efficacy of oxaliplatin and sorafenib on HCC cells

Efecto de diferentes abonos orgánicos en el arrozal ecológico en la Albufera de Valencia

En una parcela de arrozal ecológico, variedad Montsianell, localizada en el Tancat de Zacarés en La Albufera (El Palmar, Valencia), se ha realizado durante la campaña de 2009 un ensayo con la finalidad de evaluar la eficacia de dos abonos orgánicos comerciales: Naturgan y Labinor. Los tratamientos incluidos en el experimento fueron: T1, testigo (sin fertilización), T2, T3 y T4, Naturgan a dosis de 29, 44 y 59 kg N/ha, y T5, T6 y T7, Labinor a 55, 83 y 110 kg N/ha, respectivamente. Los resultados obtenidos pusieron de manifiesto una respuesta significativa del cultivo a la fertilización orgánica, tanto en desarrollo vegetativo como en la producción. Los rendimientos obtenidos con el abono Labinor fueron superiores a los generados con Naturgan en todas las dosis comparadas. El rendimiento más alto se logró con la dosis alta de Labinor (110 kg N/ha). Asimismo la eficiencia del nitrógeno, evaluada como porcentaje del nitrógeno recuperado por el cultivo, registró con el producto Labinor unos valores entre 28 y 30%, mientras que con Naturgan se obtuvieron unas cifras más bajas (entre 10 y 20%). El contenido de nitrógeno en las hojas bandera dio unos valores subóptimos en todos los tratamientos de Naturgan, en cambio, las tres dosis de Labinor originaron niveles adecuados. Los contenidos de macro y micronutrientes en el grano y la paja de arroz no resultaron afectados de forma significativa por los tratamientos de fertilización. Las mayores cantidades de nutrientes extraídas por las plantas (grano y paja) correspondieron al tratamiento dosis alta de Labinor (110 kg N/ha)