CT-based radiomic machine learning model for predicting response to [¹⁷⁷lu]lu-dota-tate inadvanced gastroenteropancreatic neuroendocrine tumors: a retrospective single-centre study

Capece, Daniela

DOI: 10.5444/esgar2026/se-165
Published: 2026
Container: Not recorded
Publisher: ESGAR
Open access: no

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Cite this work

BibTeX

@article{allodium:10.5444/esgar2026/se-165,
  title = {CT-based radiomic machine learning model for predicting response to [¹⁷⁷lu]lu-dota-tate inadvanced gastroenteropancreatic neuroendocrine tumors: a retrospective single-centre study},
  author = {Capece, Daniela},
  year = {2026},
  doi = {10.5444/esgar2026/se-165},
  url = {https://doi.org/10.5444/esgar2026/se-165}
}

RIS

TY  - JOUR
TI  - CT-based radiomic machine learning model for predicting response to [¹⁷⁷lu]lu-dota-tate inadvanced gastroenteropancreatic neuroendocrine tumors: a retrospective single-centre study
AU  - Capece, Daniela
PY  - 2026
DO  - 10.5444/esgar2026/se-165
UR  - https://doi.org/10.5444/esgar2026/se-165
ER  -

APA

Daniela, C. (2026). CT-based radiomic machine learning model for predicting response to [¹⁷⁷lu]lu-dota-tate inadvanced gastroenteropancreatic neuroendocrine tumors: a retrospective single-centre study. https://doi.org/10.5444/esgar2026/se-165

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datacite · retrieved 2026-09-26T07:54:54.054Z