机构：[1]The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China.[2]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.[3]University of Electronic Science and Technology of China, Chengdu 610000, China.[4]West China Hospital, Sichuan University, Chengdu 610000, China.四川大学华西医院[5]Affiliated Hospital of Southwest Medical University, Luzhou 646000, China.
This retrospective study aimed to develop and validate deep-learning-based models for grading clear cell renal cell carcinoma (ccRCC) patients. A cohort enrolling 706 patients (n = 706) with pathologically verified ccRCC was used in this study. A temporal split was applied to verify our models: the first 83.9% of the cases (years 2010-2017) for development and the last 16.1% (year 2018-2019) for validation (development cohort: n = 592; validation cohort: n = 114). Here, we demonstrated a deep learning(DL) framework initialized by a self-supervised pre-training method, developed with the addition of mixed loss strategy and sample reweighting to identify patients with high grade for ccRCC. Four types of DL networks were developed separately and further combined with different weights for better prediction. The single DL model achieved up to an area under curve (AUC) of 0.864 in the validation cohort, while the ensembled model yielded the best predictive performance with an AUC of 0.882. These findings confirms that our DL approach performs either favorably or comparably in terms of grade assessment of ccRCC with biopsies whilst enjoying the non-invasive and labor-saving property.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类|2 区医学
小类|2 区肿瘤学
最新[2023]版：
大类|2 区医学
小类|3 区肿瘤学
第一作者：
第一作者机构：[1]The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China.[2]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
推荐引用方式(GB/T 7714)：
Xu Lifeng,Yang Chun,Zhang Feng,et al.Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model.[J].Cancers.2022,14(11):doi:10.3390/cancers14112574.
APA：
Xu Lifeng,Yang Chun,Zhang Feng,Cheng Xuan,Wei Yi...&Song Bin.(2022).Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model..Cancers,14,(11)
MLA：
Xu Lifeng,et al."Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model.".Cancers 14..11(2022)
医学