Correlation of Radiomics with Treatment Response in Liver Metastases

Leila Mostafavi; Fatemeh Homayounieh; Felix Lades; Andrew Primak; Victorine Muse; Gordon J Harris; Mannudeep K Kalra; Subba R Digumarthy

doi:10.1016/j.acra.2023.11.007

Correlation of Radiomics with Treatment Response in Liver Metastases

Acad Radiol. 2023 Dec 11:S1076-6332(23)00625-6. doi: 10.1016/j.acra.2023.11.007. Online ahead of print.

Authors

Leila Mostafavi¹, Fatemeh Homayounieh², Felix Lades³, Andrew Primak⁴, Victorine Muse², Gordon J Harris⁵, Mannudeep K Kalra², Subba R Digumarthy²

Affiliations

¹ Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.); Tumor Imaging Metrics Core (TIMC), Dana-Farber/Harvard Cancer Center, Boston, Massachusetts, USA (L.M., G.J.H.). Electronic address: leila.mostafavi1@gmail.com.
² Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.).
³ Siemens Healthcare GmbH, Forchheim, Germany (F.L.).
⁴ Siemens Healthineers, Malvern, Pennsylvania, USA (A.P.).
⁵ Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.); Tumor Imaging Metrics Core (TIMC), Dana-Farber/Harvard Cancer Center, Boston, Massachusetts, USA (L.M., G.J.H.).

PMID: 38087718
DOI: 10.1016/j.acra.2023.11.007

Abstract

Rationale and objectives: To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response.

Materials and methods: Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatment). Patients were subcategorized into three subgroups based on RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors version 1.1): 66 with SD, 69 with PR, and 68 with PD on follow-up CT. The deidentified baseline and follow-up CT images were exported to the radiomics prototype. The prototype enabled semi-automatic segmentation of the target liver lesions for the extraction of first and high order radiomics. Statistical analyses with logistic regression and random forest classifiers were performed to differentiate SD from PD and PR.

Results: There was no significant difference between the radiomics on the baseline and follow-up CT images of patients with SD (area under the curve (AUC): 0.3). Random forest classifier differentiated patients with PR with an AUC of 0.845. The most relevant feature was the large dependence emphasis's high and low pass wavelet filter (derived gray level dependence matrix features). Random forest classifier differentiated PD with an AUC of 0.731, with the most relevant feature being the surface-to-volume ratio. There was no difference in radiomics among the three groups at baseline; therefore, a response could not be predicted.

Conclusion: Radiomics of liver metastases with semi-automatic segmentation demonstrate differences between SD from PR and PD.

Summary statement: Semiautomatic segmentation and radiomics of metastatic liver disease demonstrate differences in SD from the PR and progressive metastatic on the baseline and follow-up CT. Despite substantial variations in the scanners, acquisition, and reconstruction parameters, radiomics had an AUC of 0.84-0.89 for differentiating stable hepatic metastases from decreasing and increasing metastatic disease.

Keywords: Liver metastases; Oncology imaging; Radiomics; Texture analysis; Treatment response.