3 research outputs found
Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere
Solar Ring (SOR) is a proposed space science mission to monitor and study the
Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic
plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit.
The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second,
S2, 90{\deg} downstream, and the third, S3, completes the configuration. This
design with necessary science instruments, e.g., the Doppler-velocity and
vector magnetic field imager, wide-angle coronagraph, and in-situ instruments,
will allow us to establish many unprecedented capabilities: (1) provide
simultaneous Doppler-velocity observations of the whole solar surface to
understand the deep interior, (2) provide vector magnetograms of the whole
photosphere - the inner boundary of the solar atmosphere and heliosphere, (3)
provide the information of the whole lifetime evolution of solar featured
structures, and (4) provide the whole view of solar transients and space
weather in the inner heliosphere. With these capabilities, Solar Ring mission
aims to address outstanding questions about the origin of solar cycle, the
origin of solar eruptions and the origin of extreme space weather events. The
successful accomplishment of the mission will construct a panorama of the Sun
and inner-heliosphere, and therefore advance our understanding of the star and
the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space
Researc
Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer
Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process