文献类型：期刊文献

英文题名：Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer

作者：Zhang, Yun-Feng[1];Zhou, Chuan[2];Guo, Sheng[1];Wang, Chao[2];Yang, Jin[1];Yang, Zhi-Jun[2];Wang, Rong[2,3];Zhang, Xu[2];Zhou, Feng-Hai[1,2,4]

第一作者：张义福;张彦峰

通信作者：Zhou, FH[1];Zhou, FH[2];Zhou, FH[3]

机构：[1]Gansu Univ Chinese Med, First Clin Med Coll, Lanzhou 730000, Peoples R China;[2]Lanzhou Univ, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[3]Gansu Prov Hosp, Dept Nucl Med, Lanzhou 730000, Peoples R China;[4]Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, First Clin Med Coll, Lanzhou 730000, Peoples R China;[2]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[3]corresponding author), Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China.|[10735]甘肃中医药大学;

年份：2024

卷号：150

期号：2

外文期刊名：JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85184441799);WOS:【SCI-EXPANDED(收录号:WOS:001156373600001)】；

基金：We thank Gansu Provincial Hospital, Lanzhou University and Gansu University of Chinese Medicine for their guidance and advice during the implementation of this project; we thank the onekey AI platform for providing technical support for this study.

语种：英文

外文关键词：Prostate cancer; Bone metastasis; Radiomics; Pathomics; Machine learning; Deep learning

摘要：PurposeBone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer.Methods and materialsOverall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 x 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors.ResultsThe best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit.ConclusionMultimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.