Impact of Multi-parameter Images Obtained from Dual-energy CT on Radiomics to Predict Pathological Grading of Bladder Urothelial Carcinoma

Overview

Journal Abdom Radiol (NY)

Publisher Springer

Specialties Gastroenterology
Radiology

Date 2024 Aug 12

PMID 39134869

Authors

Wei Wei

Shigeng Wang

Mengting Hu

Xiaoyu Tong

Yong Fan

Jingyi Zhang

Qiye Cheng

Deshuo Dong

Lei Liu

Affiliations

Soon will be listed here.

Abstract

Objective: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC).

Materials And Methods: A retrospective analysis of preoperative DECT examination was conducted on 112 patients diagnosed with BUC. This cohort included 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. DECT can provide material decomposition images of venous phase Iodine maps and Water maps based on the differences in attenuation of substances, as well as VMIs at 40 to 140 keV (interval 10 keV). A total of 13 image sets were obtained, and radiomics features were extracted and analyzed from each set to achieve preoperative prediction of BUC. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. Receiver operating curves (ROC) were plotted to evaluate the performance of 13 models obtained from each image set.

Results: Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100 keV VMIs included the Dependence Variance of the GLDM feature set. There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models. In the testing set, the AUCs of the models established through 40 keV to 140 keV (interval of 10 keV) image sets were 0.895, 0.874, 0.855, 0.889, 0.841, 0.868, 0.852, 0.847, 0.889, 0.887 and 0.863 respectively. The AUCs for the models established using the Iodine maps and Water maps image sets were 0.873 and 0.852, respectively.

Conclusion: Despite the differences in the selected radiomic features from DECT multi-parameter images, the performance of radiomics models in predicting the pathological grading of BUC was not affected by the variations in the types of images used for model training.

References

Wang Y, Wu J, Luo W, Zhang H, Shi G, Shen Y . ALPK2 acts as tumor promotor in development of bladder cancer through targeting DEPDC1A. Cell Death Dis. 2021; 12(7):661. PMC: 8249393. DOI: 10.1038/s41419-021-03947-7. View

Wang H, Cai Q, Huang Y, Li M, Wen Z, Lin Y . Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer. Radiology. 2022; 305(1):127-134. DOI: 10.1148/radiol.211804. View

Zhang G, Xu L, Zhao L, Mao L, Li X, Jin Z . CT-based radiomics to predict the pathological grade of bladder cancer. Eur Radiol. 2020; 30(12):6749-6756. DOI: 10.1007/s00330-020-06893-8. View

Humphrey P, Moch H, Cubilla A, Ulbright T, Reuter V . The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part B: Prostate and Bladder Tumours. Eur Urol. 2016; 70(1):106-119. DOI: 10.1016/j.eururo.2016.02.028. View

Ferro M, Di Lorenzo G, Buonerba C, Lucarelli G, Russo G, Cantiello F . Predictors of Residual T1 High Grade on Re-Transurethral Resection in a Large Multi-Institutional Cohort of Patients with Primary T1 High-Grade/Grade 3 Bladder Cancer. J Cancer. 2018; 9(22):4250-4254. PMC: 6277616. DOI: 10.7150/jca.26129. View

Ping Z, Zhan X, Chen T, Zheng Y, Jiang M, Li Y . Survival Outcome of Partial Cystectomy versus Transurethral Bladder Tumor Resection in T1 High-Grade Bladder Cancer Patients: A Propensity Score Matching Study. J Oncol. 2022; 2022:3016725. PMC: 9626196. DOI: 10.1155/2022/3016725. View

Cai Q, Wen Z, Huang Y, Li M, Ouyang L, Ling J . Investigation of Synthetic Magnetic Resonance Imaging Applied in the Evaluation of the Tumor Grade of Bladder Cancer. J Magn Reson Imaging. 2021; 54(6):1989-1997. DOI: 10.1002/jmri.27770. View

Wang H, Hu D, Yao H, Chen M, Li S, Chen H . Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol. 2019; 29(11):6182-6190. DOI: 10.1007/s00330-019-06222-8. View

Panebianco V, Narumi Y, Barchetti G, Montironi R, Catto J . Should We Perform Multiparametric Magnetic Resonance Imaging of the Bladder Before Transurethral Resection of Bladder? Time to Reconsider the Rules. Eur Urol. 2019; 76(1):57-58. DOI: 10.1016/j.eururo.2019.03.046. View

10.

Heinrich A, Schenkl S, Buckreus D, Guttler F, Teichgraber U . CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction. Eur Radiol. 2021; 32(1):424-431. PMC: 8660750. DOI: 10.1007/s00330-021-08206-z. View

11.

Bicci E, Mastrorosato M, Danti G, Lattavo L, Bertelli E, Cozzi D . Dual-Energy CT applications in urinary tract cancers: an update. Tumori. 2022; 109(2):148-156. DOI: 10.1177/03008916221088883. View

12.

Kozikowski M, Suarez-Ibarrola R, Osiecki R, Bilski K, Gratzke C, Shariat S . Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis. Eur Urol Focus. 2021; 8(3):728-738. DOI: 10.1016/j.euf.2021.05.005. View

13.

Han D, Yu Y, Yu N, Dang S, Wu H, Jialiang R . Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT. Br J Radiol. 2020; 93(1114):20200131. PMC: 7548376. DOI: 10.1259/bjr.20200131. View

14.

Choe J, Do K, Lee J, Lee S, Lee J, Seo J . Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer. Eur Radiol. 2018; 29(2):915-923. DOI: 10.1007/s00330-018-5639-0. View

15.

Cao Y, Feng J, Wang C, Yang F, Wang X, Xu J . LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images. Radiol Med. 2023; 129(2):229-238. DOI: 10.1007/s11547-023-01747-x. View

16.

Wang Z, He Y, Wang N, Zhang T, Wu H, Jiang X . Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images. Medicine (Baltimore). 2020; 99(18):e20093. PMC: 7440185. DOI: 10.1097/MD.0000000000020093. View

17.

Chen Y, Zhong J, Wang L, Shi X, Lu W, Li J . Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT. Eur Radiol. 2022; 32(8):5480-5490. PMC: 9279234. DOI: 10.1007/s00330-022-08628-3. View

18.

Li J, Dong D, Fang M, Wang R, Tian J, Li H . Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol. 2020; 30(4):2324-2333. DOI: 10.1007/s00330-019-06621-x. View

19.

Nakagawa M, Naiki T, Naiki-Ito A, Ozawa Y, Shimohira M, Ohnishi M . Usefulness of advanced monoenergetic reconstruction technique in dual-energy computed tomography for detecting bladder cancer. Jpn J Radiol. 2021; 40(2):177-183. PMC: 8803668. DOI: 10.1007/s11604-021-01195-5. View

20.

Schreck J, Laukamp K, Niehoff J, Michael A, Boriesosdick J, Woltjen M . Metal artifact reduction in patients with total hip replacements: evaluation of clinical photon counting CT using virtual monoenergetic images. Eur Radiol. 2023; 33(12):9286-9295. PMC: 10667386. DOI: 10.1007/s00330-023-09879-4. View