Predicting pathological highly invasive lung cancer from preoperative [(18)F]FDG PET/CT with multiple machine learning models

PURPOSE: The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer...

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Detalles Bibliográficos
Autores principales: Onozato, Yuki, Iwata, Takekazu, Uematsu, Yasufumi, Shimizu, Daiki, Yamamoto, Takayoshi, Matsui, Yukiko, Ogawa, Kazuyuki, Kuyama, Junpei, Sakairi, Yuichi, Kawakami, Eiryo, Iizasa, Toshihiko, Yoshino, Ichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852187/
https://www.ncbi.nlm.nih.gov/pubmed/36385219
http://dx.doi.org/10.1007/s00259-022-06038-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852187/
https://www.ncbi.nlm.nih.gov/pubmed/36385219
http://dx.doi.org/10.1007/s00259-022-06038-7

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