Page 15 - AIH-1-4
P. 15

Artificial Intelligence in Health                                      Radiomics in early-stage lung cancer



            References                                            of a prediction model for overall survival after stereotactic
                                                                  body  radiation  therapy  for primary non-small  cell
            1.   Chang JY, Mehran RJ, Feng L,  et al. Stereotactic ablative   lung cancer using radiomics analysis.  Cancers (Basel).
               radiotherapy for operable stage I non-small-cell lung   2022;14(16):3859.
               cancer (revised STARS): Long-term results of a single-arm,
               prospective trial with prespecified comparison to surgery.      doi: 10.3390/cancers14163859
               Lancet Oncol. 2021;22(10):1448-1457.            12.  Luo LM, Huang BT, Chen CZ,  et al. A  combined model
               doi: 10.1016/S1470-2045(21)00401-0                 to improve the prediction of local control for lung cancer
                                                                  patients undergoing stereotactic body radiotherapy based on
            2.   Li C, Wang L, Wu Q,  et al. A  meta-analysis comparing   radiomic signature plus clinical and dosimetric parameters.
               stereotactic body radiotherapy vs conventional radiotherapy   Front Oncol. 2022;11:819047.
               in inoperable stage I non-small cell lung cancer. Medicine
               (Baltimore). 2020;99(34):e21715.                   doi: 10.3389/fonc.2021.819047
               doi: 10.1097/MD.0000000000021715                13.  Isoyama-Shirakawa Y, Yoshitake T, Ninomiya K,  et al.
                                                                  Combination of clinical factors and radiomics can predict
            3.  Jameson  JL,  Longo  DL.  Precision  medicine--   local recurrence and metastasis after stereotactic body
               personalized, problematic, and promising.  N  Engl J Med.   radiotherapy for non-small cell lung cancer. Anticancer Res.
               2015;372(23):2229-2234.
                                                                  2023;43(11):5003-5013.
               doi: 10.1056/NEJMsb1503104
                                                                  doi: 10.21873/anticanres.16699
            4.   Bertolini M, Trojani V, Botti A, et al. Novel harmonization   14.  Lafata KJ, Hong JC, Geng R,  et al. Association of pre-
               method for multi-centric radiomic studies in non-small cell   treatment radiomic features with lung cancer recurrence
               lung cancer. Curr Oncol. 2022;29(8):5179-5194.
                                                                  following stereotactic body radiation therapy. Phys Med Biol.
               doi: 10.3390/curroncol29080410                     2019;64(2):025007.
            5.   Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics:      doi: 10.1088/1361-6560/aaf5a5
               Extracting more information from medical images using
               advanced feature analysis. Eur J Cancer. 2012;48(4):441-446.  15.  Li Q, Kim J, Balagurunathan Y, Qi J,  et al. CT imaging
                                                                  features associated with recurrence in non-small cell lung
               doi: 10.1016/j.ejca.2011.11.036                    cancer patients after stereotactic body radiotherapy. Radiat
            6.   Fave X, Cook M, Frederick A, et al. Preliminary investigation   Oncol. 2017;12(1):158.
               into sources of uncertainty in quantitative imaging features.      doi: 10.1186/s13014-017-0892-y
               Comput Med Imaging Graph. 2015;44:54-61.
                                                               16.  Na F, Wang J, Li C, Deng L, Xue J, Lu Y. Primary
               doi: 10.1016/j.compmedimag.2015.04.006             tumor standardized uptake value measured on F18-
            7.   Zhao B, Tan Y, Tsai WY, et al. Reproducibility of radiomics   Fluorodeoxyglucose  positron  emission  tomography  is  of
               for  deciphering  tumor  phenotype  with imaging.  Sci   prediction value for survival and local control in non-small-
               Rep. 2016;6:23428.                                 cell lung cancer receiving radiotherapy: Meta-analysis.
                                                                  J Thorac Oncol. 2014;9(6):834-842.
               doi: 10.1038/srep23428
                                                                  doi: 10.1097/JTO.0000000000000185
            8.   Mackin  D,  Fave  X,  Zhang  L,  et al.  Measuring  computed
               tomography scanner variability of radiomics features. Invest   17.  Agarwal M, Brahmanday G, Bajaj SK, Ravikrishnan KP,
               Radiol. 2015;50(11):757-65.                        Wong CY. Revisiting the prognostic value of preoperative
                                                                  (18)F-fluoro-2-deoxyglucose  ((18)F-FDG)  positron
               doi: 10.1097/RLI.0000000000000180                  emission tomography (PET) in early-stage (I & II) non-
            9.   Huynh E,  Coroller TP, Narayan V,  et  al. Associations of   small cell lung cancers (NSCLC).  Eur J  Nucl  Med Mol
               radiomic data extracted from static and respiratory-gated   Imaging. 2010;37(4):691-698.
               CT scans with disease recurrence in lung cancer patients      doi: 10.1007/s00259-009-1291-x
               treated with SBRT. PLoS One. 2017;12(1):e0169172.
                                                               18.  Wu J, Aguilera T, Shultz D,  et al. Early-stage non-small
               doi: 10.1371/journal.pone.0169172                  cell lung cancer: Quantitative imaging characteristics of
            10.  Kakino R, Nakamura M, Mitsuyoshi T, et al. Application and   (18)F fluorodeoxyglucose PET/CT allow prediction of
               limitation of radiomics approach to prognostic prediction   distant metastasis. Radiology. 2016;281(1):270-278.
               for lung stereotactic body radiotherapy using breath-     doi: 10.1148/radiol.2016151829
               hold CT images with random survival forest: A  multi-
               institutional study. Med Phys. 2020;47(9):4634-4643.  19.  Pyka T, Bundschuh RA, Andratschke N,  et al. Textural
                                                                  features in pre-treatment [F18]-FDG-PET/CT are correlated
               doi: 10.1002/mp.14380                              with risk of local recurrence and disease-specific survival in
            11.  Sawayanagi S, Yamashita H, Nozawa Y, et al. Establishment   early stage NSCLC patients receiving primary stereotactic

            Volume 1 Issue 4 (2024)                         9                                doi: 10.36922/aih.3541
   10   11   12   13   14   15   16   17   18   19   20