Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma
Wensong Shi, Yuzhui Hu, Yingli Sun, Guotao Chang, Yulun Yang, He Qian, Zhengpan Wei, Liang Zhao, Ming Li, Huiyu Zheng, Xiangnan Li · 2024
Read Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma by Wensong Shi, Yuzhui Hu, Yingli Sun, Guotao Chang, Yulun Yang, He Qian, Zhengpan Wei, Liang Zhao, Ming Li, Huiyu Zheng, Xiangnan Li (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11651987.txt.
Description
Wensong Shi and co-authors calibrate an AI measure of lung nodules in a 2024 study. How much of a lung nodule is solid predicts how invasive the cancer is, but the standard one-dimensional ratio assumes unreal neatness. Their three-dimensional version lets an AI segment the solid part by brightness threshold. The catch: nobody agreed which threshold to use. Records covered 1,056 patients and 1,179 nodules from two hospitals, all with surgical proof of the true stage.
The study hunts the threshold and cutoff that best sort non-invasive from invasive growths. Getting the number right matters at the bedside: it steers surgeons between watching a nodule and removing a lobe. Two-center data keeps the answer honest.
Catalog record
| CREATOR | Wensong Shi, Yuzhui Hu, Yingli Sun, Guotao Chang, Yulun Yang, He Qian, Zhengpan Wei, Liang Zhao, Ming Li, Huiyu Zheng, Xiangnan Li |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 27 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |