Predicting Invasiveness in Lepidic Pattern Adenocarcinoma of Lung: Analysis of Visual Semantic and Radiomic Features
Lung biopsy results showing noninvasive LPA underestimate invasiveness. Although statistically non-significant, some semantic features showed potential for predicting invasiveness, with septal stretching absent in all noninvasive cases, and solid consistency present in a significant portion of invasive cases.
Authors
- Fereidoun G. Abtin (ORCID: https://orcid.org/0000-0003-2927-0883)
- William D. Wallace (ORCID: https://orcid.org/0000-0002-9850-9723)
- Robert D. Suh (ORCID: https://orcid.org/0000-0001-7872-9967)
- Igor Barjaktarević (ORCID: https://orcid.org/0000-0002-8096-0858)
- Seyed Mohammad Hossein Tabatabaei (ORCID: https://orcid.org/0000-0002-7386-6695)
- Bianca E. Villegas
- Scott J. Genshaft (ORCID: https://orcid.org/0000-0002-0999-3016)
- Sean Johnson (ORCID: https://orcid.org/0000-0002-7885-9216)
- Grace Hyun J. Kim (ORCID: https://orcid.org/0000-0003-1225-3489)
- Matthew S. Brown (ORCID: https://orcid.org/0000-0001-6805-8151)
Institutions
- University of Southern California (US)
- Harvard University (US)
- University of California, Los Angeles (US)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- Medical Sciences
- Published
- 2024-10-18
- DOI
- https://doi.org/10.3390/medsci12040057
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00