AUTHOR=Jiang Chuncheng , Liu Xin , Qu Qianqian , Jiang Zhonghua , Wang Yunqiang 

TITLE=Prediction of adenocarcinoma and squamous carcinoma based on CT perfusion parameters of brain metastases from lung cancer: a pilot study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1225170

DOI=10.3389/fonc.2023.1225170

ISSN=2234-943X

ABSTRACT=Objectives  Predicting pathological types in patients with adenocarcinoma and squamous carcinoma using CT perfusion imaging parameters based on brain metastasis lesions from lung cancer.
Methods  We retrospectively studied adenocarcinoma and squamous carcinoma patients with brain metastases who got treatment and had their pathologically tested in our hospital from 2019 to 2021. CT perfusion images of the brain were used to segment enhancing tumors and peritumoral edema, as well as to extract CT perfusion parameters. The most relevant perfusion parameters were identified to classify the pathological types. Of the 45 patients in the study cohort (mean age 65.64±10.08 years; M: F=24: 21), 16 were found to have squamous cell carcinoma. 20 patients with brain metastases only, and 25 patients were found to have multiple organ metastases in addition to brain metastases. After admission, all patients were subjected to the CT perfusion imaging examination. Differences in CT perfusion parameters between adenocarcinoma and squamous carcinoma were analyzed. The receiver-operating characteristic (ROC) curves were used to predict the types of pathology of the patients.
Results  Among the perfusion parameters, cerebral blood flow (CBF) and mean transit time (MTT) were significantly different between the two lung cancers (adenocarcinoma vs. squamous cell carcinoma: p<0.001, p=0.012.). Gender and the tumor location were identified as the clinical predictive factors. For the classification of adenocarcinoma and squamous carcinoma, the model combined with CBF and clinical predictive factors gender showed better performance(area-under-the-curve[AUC]: 0.918 0.903, 95% confidence interval [CI]: 0.797-0.979 0.777–0.971). The multiple organ metastases model showed better performance than the brain metastases alone model in subgroup analyses([AUC]: 0.958, 95%[CI]: 0.794 - 0.999).
Conclusions  CT perfusion parameters analysis of brain metastases in patients with primary lung cancer could be used to classify adenocarcinoma and squamous carcinoma.