Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images

Zhang, Kun1; Sun, Kui2; Zhang, Caiyi3; Ren, Kang1,4; Li, Chao1,5; Shen, Lin1; Jing, Di1

2023

10.1007/s00432-022-04446-8

JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY   Impact Factor & Quartile

0171-5216

1432-1335

PurposeWe analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment strategies.MethodsThe research retrospectively collected 238 non-surgical cervical cancer patients treated with radiochemotherapy from 2014 to 2017. These patients were randomly divided into the training set (n = 165) and test set (n = 73). Then, we extract deep features after segmenting the HE-stained image into patches of size 224 x 224. A Lasso-Cox model was constructed with clinical data to predict 5-year OS. C-index evaluated this model performance with 95% CI, calibration curve, and ROC.ResultsBased on multivariate analysis, 2 of 11 clinical characteristics (C-index 0.68) and 2 of 2048 pathomic features (C-index 0.74) and clinical-pathomic model (C-index 0.83) of nomograms predict 5-year survival in the training set, respectively. In test set, compared with the pathomic and clinical characteristics used alone, the clinical-pathomic model had an AUC of 0.750 (95% CI 0.540-0.959), the clinical predictor model had an AUC of 0.729 (95% CI 0.551-0.909), and the pathomic model AUC was 0.703 (95% CI 0.487-0.919). Based on appropriate nomogram scores, we divided patients into high-risk and low-risk groups, and Kaplan-Meier survival probability curves for both groups showed statistical differences.ConclusionWe built a clinical-pathomic model to predict 5-year OS in non-surgical cervical cancer patients, which may be a promising method to improve the precision of personalized therapy.

Cervical cancer Radiochemotherapy Deep learning Overall survival Pathological images

SCI

English

Others

Oncology

Oncology

WOS:000917927700001

SPRINGER

CLINICAL MEDICINE

Web of Science

1.Cent South Univ, Xiangya Hosp, Natl Clin Res Ctr Geriatr Disorders, Dept Oncol, Changsha 410008, Peoples R China
2.Shandong First Med Univ, Shandong Prov Hosp, Dept Radiol, Jing Wu Rd 324, Jinan 250021, Peoples R China
3.Maternal & Child Hlth Hosp Hunan Prov, Dept Obstet & Gynecol, Changsha 410008, Peoples R China
4.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Radiat Oncol, Beijing 100730, Peoples R China
5.Southern Univ Sci & Technol, Shenzhen Peoples Hosp, Dept Radiat Oncol, Affiliated Hosp 1, Shenzhen 518020, Guangdong, Peoples R China

Zhang, Kun,Sun, Kui,Zhang, Caiyi,et al. Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images[J]. JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY,2023.

Zhang, Kun.,Sun, Kui.,Zhang, Caiyi.,Ren, Kang.,Li, Chao.,...&Jing, Di.(2023).Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY.

Zhang, Kun,et al."Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images".JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY (2023).