» Articles » PMID: 34114913

Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA

Overview
Specialties Oncology
Radiology
Date 2021 Jun 11
PMID 34114913
Citations 15
Authors
Affiliations
Soon will be listed here.
Abstract

The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generation sequencing of circulating tumor DNA (ctDNA) encode tumor-specific radiogenomic expression patterns that can be probed to study this problem. Preliminary findings are reported from a radiogenomic analysis of CT imaging, cfDNA, and ctDNA in 24 patients (median age, 64 years; range, 49-74 years) with stage III lung cancer undergoing chemoradiation on a prospective pilot study (NCT00921739) between September 2009 and September 2014. Unsupervised clustering of radiomic signatures resulted in two clusters that were associated with ctDNA mutations ( = .03) and changes in cfDNA concentration after 2 weeks of chemoradiation ( = .02). The radiomic features dissimilarity (hazard ratio [HR] = 0.56; = .05), joint entropy (HR = 0.56; = .04), sum entropy (HR = 0.53; = .02), and normalized inverse difference (HR = 1.77; = .05) were associated with overall survival. These results suggest heterogeneous and low-attenuating disease without a detectable ctDNA mutation was associated with early surges of cfDNA concentration in response to therapy and a generally better prognosis. CT-Quantitative, Radiation Therapy, Lung, Computer Applications-3D, Oncology, Tumor Response, Outcomes Analysis Clinical trial registration no. NCT00921739 © RSNA, 2021.

Citing Articles

Artificial intelligence across oncology specialties: current applications and emerging tools.

Kang J, Lafata K, Kim E, Yao C, Lin F, Rattay T BMJ Oncol. 2025; 3(1):e000134.

PMID: 39886165 PMC: 11203066. DOI: 10.1136/bmjonc-2023-000134.


From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.

Guo Y, Li T, Gong B, Hu Y, Wang S, Yang L Adv Sci (Weinh). 2024; 12(2):e2408069.

PMID: 39535476 PMC: 11727298. DOI: 10.1002/advs.202408069.


Development and validation of a clinical decision tool for preoperative micropapillary and solid pattern lung adenocarcinoma of CT ≤2 cm.

Gao Z, Liu S, Xiao H, Li M, Ren W, Fen Z Int J Surg. 2024; 110(12):7607-7615.

PMID: 38896867 PMC: 11634099. DOI: 10.1097/JS9.0000000000001832.


Association of circulating tumor HPV16DNA levels and quantitative PET parameters in patients with HPV-positive head and neck squamous cell carcinoma.

Tatsumi M, Tanaka H, Takenaka Y, Suzuki M, Fukusumi T, Eguchi H Sci Rep. 2024; 14(1):3278.

PMID: 38332246 PMC: 10853198. DOI: 10.1038/s41598-024-53894-4.


Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.

Tharmaseelan H, Vellala A, Hertel A, Tollens F, Rotkopf L, Rink J Cancer Imaging. 2023; 23(1):95.

PMID: 37798797 PMC: 10557291. DOI: 10.1186/s40644-023-00612-4.


References
1.
Lafata K, Hong J, Geng R, Ackerson B, Liu J, Zhou Z . Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Phys Med Biol. 2018; 64(2):025007. DOI: 10.1088/1361-6560/aaf5a5. View

2.
Siegel R, Miller K, Jemal A . Cancer statistics, 2020. CA Cancer J Clin. 2020; 70(1):7-30. DOI: 10.3322/caac.21590. View

3.
Yu W, Tang C, Hobbs B, Li X, Koay E, Wistuba I . Development and Validation of a Predictive Radiomics Model for Clinical Outcomes in Stage I Non-small Cell Lung Cancer. Int J Radiat Oncol Biol Phys. 2017; 102(4):1090-1097. DOI: 10.1016/j.ijrobp.2017.10.046. View

4.
Newman A, Bratman S, To J, Wynne J, Eclov N, Modlin L . An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014; 20(5):548-54. PMC: 4016134. DOI: 10.1038/nm.3519. View

5.
Chang Y, Lafata K, Wang C, Duan X, Geng R, Yang Z . Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes. Biomed Phys Eng Express. 2021; 6(2):025016. DOI: 10.1088/2057-1976/ab779c. View