Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

doi:10.35711/aimi.v5.i1.93993

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 5, Issue 1

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (2190)

All Articles published online

The chart showing PDF series, HTML series, Figures (1-6) series, Tables (1-4) series.

Item

Count

PDF

HTML

1009

Figures (1-6)

243

Tables (1-4)

234

Sum=1531

Featured Article

The chart showing Browse series, Download series.

Item

Count

Browse

113

Download

296

Sum=409

Publishing Process of This Article

Item

Count

Browse

Download

148

Sum=199

Sep 28, 2024 (publication date) through Mar 22, 2025

Times Cited of This Article

Times Cited (0)

Journal Information of This Article

Publication Name

Artificial Intelligence in Medical Imaging

ISSN

2644-3260

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

Artif Intell Med Imaging. Sep 28, 2024; 5(1): 93993
Published online Sep 28, 2024. doi: 10.35711/aimi.v5.i1.93993

Full Size Figure Download Figure

Figure 2 Demonstration of radiomics model construction. A: Image segmentation; B: Features extraction; C: Features selection; D: Model construction.

Citation: Zhao ZC, Liu JX, Sun LL. Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study. Artif Intell Med Imaging 2024; 5(1): 93993
URL: https://www.wjgnet.com/2644-3260/full/v5/i1/93993.htm
DOI: https://dx.doi.org/10.35711/aimi.v5.i1.93993