Aims To develop and verify pathological models using pathological features basing on HE images to predict survival invasive endocervical adenocarcinoma (ECA) postoperatively.
Methods There are 289 ECA patients were classified into training and validation cohort. A histological signature was produced in 191 patients and verified in the validation groups. Histological models combining the histological features were built, proving the incremental value of our model to the traditional staging system for individualised prognosis estimation.
Results Our model included five chosen histological characteristics and was significantly related to overall survival (OS). Our model had AUC of 0.862 and 0.955, 0.891 and 0.801 in prognosticating 3-year and 5 year OS in the training and validation cohort, respectively. In training cohorts, our model had better performance for evaluation of OS (C-index: 0.832; 95% CI 0.751 to 0.913) than International Federation of Gynecology and Obstetrics (FIGO) staging system (C-index: 0.648; 95% CI 0.542 to 0.753) and treatment (C-index: 0.687; 95% CI 0.605 to 0.769), with advanced efficiency of the classification of survival outcomes. Furthermore, in both cohorts, a risk stratification system was built that was able to precisely stratify stage I and II ECA patients into high-risk and low-risk subpopulation with significantly different prognosis.
Conclusions A nomogram with five histological signatures had better performance in OS prediction compared with traditional staging systems in ECAs, which might enable a step forward to precision medicine.
- Uterine Cervical Neoplasms
- Pathology, Surgical
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
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Handling editor Mona El-Bahrawy.
R-ZL, XY and S-WZ contributed equally.
Contributors Li-Li Liu is responsible for the overall content. Conception and design: Li-Li Liu. Performing experiments: Xia Yang and Shi-Wen Zhang. Drafting of the article: Rong-Zhen Luo and Xia Yang. Acquisition and interpretation of data, review, editing and approval of the manuscript: all authors. Rong-Zhen Luo, Xia Yang and Shi-Wen Zhang were contributed equally in this work.
Funding The National Natural Science Foundation of China (No. 82072853). The Natural Science Foundation of Guangdong province (No. 2021A1515010688). The authenticity of this article has been validated by uploading the key raw data onto the Research Data Deposit public platform (www.researchdata.org.cn) with the approval RDD number RDDB2021001679.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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