» Articles » PMID: 35692757

Association of CT-Based Delta Radiomics Biomarker With Progression-Free Survival in Patients With Colorectal Liver Metastases Undergo Chemotherapy

Overview
Journal Front Oncol
Specialty Oncology
Date 2022 Jun 13
PMID 35692757
Authors
Affiliations
Soon will be listed here.
Abstract

Methods: This retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020. We divided the lesions into training cohort and testing cohort with a ratio of 7:3. Two - dimensional region of interest (ROI) was obtained by manually delineating the largest layers of each metastasis lesion. The expanded ROI (3 mm and 5 mm) were also included in the study to characterize microenvironment around tumor. For each of the ROI, 1,316 radiomics features were extracted from delineated plain scan, arterial, and venous phase CT images before and after neoadjuvant chemotherapy. Delta radiomics features were constructed by subtracting the radiomics features after treatment from the radiomics features before treatment. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were applied in the training cohort to select the valuable features. Based on clinical characteristics and radiomics features, 7 Cox proportional-hazards model were constructed to predict the PFS of patients. C-index value and Kaplan Meier (KM) analysis were used to evaluate the efficacy of predicting PFS of these models. Moreover, the prediction performance of one-year PFS was also evaluated by area under the curve (AUC).

Results: Compared with the PreRad (Radiomics form pre-treatment CT images; C-index [95% confidence interval (CI)] in testing cohort: 0.614(0.552-0.675) and PostRad models (Radiomics form post-treatment CT images; 0.642(0.578-0.707), the delta model has better PFS prediction performance (Delta radiomics; 0.688(0.627-0.749). By incorporating clinical characteristics, CombDeltaRad obtains the best performance in both training cohort [C-index (95% CI): 0.802(0.772-0.832)] and the testing cohort (0.744(0.686-0.803). For 1-year PFS prediction, CombDeltaRad model obtained the best performance with AUC (95% CI) of 0.871(0.828-0.914) and 0.745 (0.651-0.838) in training cohort and testing cohort, respectively.

Conclusion: CT radiomics features have the potential to predict PFS in patients with colorectal cancer and liver metastasis who undergo neoadjuvant chemotherapy. By combining pre-treatment radiomics features, post-treatment radiomics features, and clinical characteristics better prediction results can be achieved.

Citing Articles

Delta radiomics: an updated systematic review.

Nardone V, Reginelli A, Rubini D, Gagliardi F, Del Tufo S, Belfiore M Radiol Med. 2024; 129(8):1197-1214.

PMID: 39017760 PMC: 11322237. DOI: 10.1007/s11547-024-01853-4.


Adjuvant chemotherapy or no adjuvant chemotherapy? A prediction model for the risk stratification of recurrence or metastasis of nasopharyngeal carcinoma combining MRI radiomics with clinical factors.

Wu Q, Chang Y, Yang C, Liu H, Chen F, Dong H PLoS One. 2023; 18(9):e0287031.

PMID: 37751422 PMC: 10522047. DOI: 10.1371/journal.pone.0287031.


Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.

Granata V, Fusco R, Setola S, Galdiero R, Maggialetti N, Patrone R Infect Agent Cancer. 2023; 18(1):18.

PMID: 36927442 PMC: 10018963. DOI: 10.1186/s13027-023-00495-x.

References
1.
Muratore A, Zorzi D, Bouzari H, Amisano M, Massucco P, Sperti E . Asymptomatic colorectal cancer with un-resectable liver metastases: immediate colorectal resection or up-front systemic chemotherapy?. Ann Surg Oncol. 2006; 14(2):766-70. DOI: 10.1245/s10434-006-9146-1. View

2.
Alberts S, Horvath W, Sternfeld W, Goldberg R, Mahoney M, Dakhil S . Oxaliplatin, fluorouracil, and leucovorin for patients with unresectable liver-only metastases from colorectal cancer: a North Central Cancer Treatment Group phase II study. J Clin Oncol. 2005; 23(36):9243-9. DOI: 10.1200/JCO.2005.07.740. View

3.
Haga A, Takahashi W, Aoki S, Nawa K, Yamashita H, Abe O . Standardization of imaging features for radiomics analysis. J Med Invest. 2019; 66(1.2):35-37. DOI: 10.2152/jmi.66.35. View

4.
Fong Y, Fortner J, Sun R, Brennan M, Blumgart L . Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann Surg. 1999; 230(3):309-18; discussion 318-21. PMC: 1420876. DOI: 10.1097/00000658-199909000-00004. View

5.
Beckers R, Lambregts D, Schnerr R, Maas M, Rao S, Kessels A . Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study. Eur J Radiol. 2017; 92:64-71. DOI: 10.1016/j.ejrad.2017.04.019. View