RÉSUMÉ
Objective:To explore the clinical value of 18F-fluoromisonidazole (FMISO) PET/CT hypoxia imaging in early response to heavy ion radiotherapy in patients with non-small cell lung cancer(NSCLC). Methods:From April 2018 to January 2021, the 18F-FMISO PET/CT images of 23 NSCLC patients (19 males, 4 females; age (64.9±10.3) years) who received heavy ion radiotherapy in Shanghai Proton and Heavy Ion Center were retrospectively analyzed. The evaluation parameters included tumor volume (TV), tumor to background ratio (TBR) before and after radiotherapy. Patients were divided into hypoxia group and non-hypoxia group with the baseline TBR value≥1.4 as hypoxia threshold. Wilcoxon signed rank test was used to compare the differences of TV and TBR before and after radiotherapy in 2 groups. Results:Of 23 NSCLC patients, 17 were hypoxia and 6 were non-hypoxia. Compared with the baseline, TV after the radiotherapy (59.44(22.86, 99.43) and 33.78(8.68, 54.44) cm 3; z=-3.05, P=0.002) and TBR after the radiotherapy (2.25(2.09, 2.82) and 1.42(1.24, 1.67); z=-3.39, P=0.001) of the hypoxia group were significantly lower, while TV (16.19(6.74, 36.52) and 8.59(4.38, 25.47) cm 3; z=-1.57, P=0.120) and TBR (1.19(1.05, 1.27) and 1.10 (0.97, 1.14); z=-1.89, P=0.060) of the non-hypoxia group decreased with no significant differences. Conclusions:Hypoxic NSCLC tumors are sensitive to heavy ion radiation. Compared with non-hypoxic tumors, hypoxic tumors respond more quickly, and a significant reduction in TV can be observed early after radiotherapy. Heavy ion radiation can significantly improve tumor hypoxia.
RÉSUMÉ
Objective:To investigate the value of nomogram constructed by CT-based radiomics for differentiating benign and malignant thyroid follicular neoplasms.Methods:Totally 200 post-surgery patients with pathologically confirmed thyroid follicular neoplasms in Fudan University Shanghai Cancer Center from January 2016 to December 2018 were retrospectively analyzed. Among the patients, 46 were follicular thyroid carcinoma (FTC) and 154 patients were follicular thyroid adenoma (FTA). The patients were randomly divided into a training set ( n=140) and validation set ( n=60) using a random number table. CT signs and radiomics features of each patient were analyzed within the LIFEx package. A predictive model was developed by the least absolute shrinkage and selection operator regression to build a nomogram based on selected parameters. The predictive effectiveness of differentiating benign and malignant thyroid follicular neoplasms was evaluated by the area under receiver operating characteristic curve (AUC). Calibration plots were formulated to evaluate the reliability and accuracy of the nomogram based on internal (training set) and external (validation set) validity. The clinical value of the nomogram was estimated through the decision curve analysis. Results:The prediction nomogram was built with 4 selected parameters, including grey level zone length matrix (GLZLM)-gray-level zone length matrix_zone length non-uniformity, GLZLM-gray-level zone length matrix_low gray-level zone emphasis, CONVENTIONAL_HUQ3, CONVENTIONAL_HUmean. In training and validation sets, the AUCs for differentiating FTC and FTA were 0.863 (95%CI 0.746-0.932), 0.792 (95%CI 0.658-0.917), accuracy were 87.9% and 75.0%, sensitivity were 67.9% and 66.7%, specificity were 91.1% and 90.5%, respectively. The calibration curves indicated good consistency between actual observation and prediction for differentiating the malignancy. Decision curve analysis demonstrated the nomogram was clinically useful.Conclusions:The CT radiomics mode shows the certain value and great potential to identify benign or malignant thyroid follicular neoplasms and the nomogram can accurately and intuitively predict the malignancy potential in patients with thyroid follicular neoplasms.