000 01213 a2200313 4500
005 20250518094844.0
008 ####s 0 0 eng d
022 _a2405-6308
024 7 _a10.1016/j.ctro.2020.03.007
_2doi
040 _aNLM
_beng
_cNLM
100 1 _aLuna, José Marcio
245 0 0 _aMachine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation.
_h[electronic resource]
260 _bClinical and translational radiation oncology
_cMay 2020
300 _a69-75 p.
_bdigital
500 _aPublication Type: Journal Article
700 1 _aChao, Hann-Hsiang
700 1 _aShinohara, Russel T
700 1 _aUngar, Lyle H
700 1 _aCengel, Keith A
700 1 _aPryma, Daniel A
700 1 _aChinniah, Chidambaram
700 1 _aBerman, Abigail T
700 1 _aKatz, Sharyn I
700 1 _aKontos, Despina
700 1 _aSimone, Charles B
700 1 _aDiffenderfer, Eric S
773 0 _tClinical and translational radiation oncology
_gvol. 22
_gp. 69-75
856 4 0 _uhttps://doi.org/10.1016/j.ctro.2020.03.007
_zAvailable from publisher's website
999 _c30841698
_d30841698