101 research outputs found

    Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis

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    Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V5 of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability

    Phase II of the LAMOST-Kepler/K2 survey. I. Time series of medium-resolution spectroscopic observations

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    Phase \RNum{2} of the LAMOST-{\sl Kepler/K}2 survey (LK-MRS), initiated in 2018, aims at collecting medium-resolution spectra (R7,500R\sim7,500; hereafter MRS) for more than 50,00050,000 stars with multiple visits (60\sim60 epochs) over a period of 5 years (2018 September to 2023 June). We selected 20 footprints distributed across the {\sl Kepler} field and six {\sl K}2 campaigns, with each plate containing a number of stars ranging from 2,000\sim2,000 to 3,000\sim 3,000. During the first year of observations, the LK-MRS has already collected 280,000\sim280,000 and 369,000\sim369,000 high-quality spectra in the blue and red wavelength range, respectively. The atmospheric parameters and radial velocities for 259,000\sim259,000 spectra of 21,05321,053 targets were successfully calculated by the LASP pipeline. The internal uncertainties for the effective temperature, surface gravity, metallicity, and radial velocity are found to be 100100\,K, 0.150.15\,dex, 0.090.09\,dex, and 1.001.00\,km\,s1^{-1}, respectively. We found 14,99714,997, 20,09120,091, and 1,5141,514 stars in common with the targets from the LAMOST low-resolution survey (LRS), GAIA and APOGEE, respectively, corresponding to a fraction of 70%\sim70\%, 95%\sim95\% and 7.2%\sim7.2\%. In general, the parameters derived from LK-MRS spectra are consistent with those obtained from the LRS and APOGEE spectra, but the scatter increases as the surface gravity decreases when comparing with the measurements from APOGEE. A large discrepancy is found with the GAIA values of the effective temperature. The comparisons of radial velocities of LK-MRS to GAIA and LK-MRS to APOGEE nearly follow an Gaussian distribution with a mean μ1.10\mu\sim1.10 and 0.730.73\,km\,s1^{-1}, respectively.Comment: 24 pages, 15 figures, 4 tables, ApJS, accepte
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