RÉSUMÉ
Objective:To investigate the value of an MRI and digital pathology images based omics nomogram for the prediction of recurrence risk in soft tissue sarcoma (STS).Methods:This was a retrospective cohort study. From January 2016 to March 2021, 192 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled, among which 112 patients in the Laoshan campus were enrolled as training set, and 80 patients in the Shinan campus were enrolled as validation set. The patients were divided into recurrence group ( n=87) and no recurrence group ( n=105) during follow-up. The clinical and MRI features of patients were collected. The radiomics features based on fat saturated T 2WI images and pathomics features based on digital pathology images of the lesions were extracted respectively. The clinical model, radiomics model, pathomics model, radiomics-pathomics combined model, and omics nomogram which combined the optimal prediction model and the clinical model were established by multivariate Cox regression analysis. The concordance index (C index) and time-dependent area under the receiver operating characteristic curve (t-AUC) were used to evaluate the performance of each model in predicting STS postoperative recurrence. The DeLong test was used for comparison of t-AUC between every two models. The X-tile software was used to determine the cut-off value of the omics nomogram, then the patients were divided into low risk ( n=106), medium risk ( n=64), and high risk ( n=22) groups. Three groups′ cumulative recurrence-free survival (RFS) rates were calculated and compared by the Kaplan-Meier survival curve and log-rank test. Results:The performance of the radiomics-pathomics combined model was superior to the radiomics model and pathomics model, with C index of 0.727 (95% CI 0.632-0.823) and medium t-AUC value of 0.737 (95% CI0.584-0.891) in the validation set. The omics nomogram was established by combining the clinical model and the radiomics-pathomics combined model, with C index of 0.763 (95% CI 0.685-0.842) and medium t-AUC value of 0.783 (95% CI0.639-0.927) in the validation set. The t-AUC value of omics nomogram was significantly higher than that of clinical model, TNM model, radiomics model, and pathomics model in the validation set ( Z=3.33, 2.18, 2.08, 2.72, P=0.001, 0.029, 0.037, 0.007). There was no statistical difference in t-AUC between the omics nomogram and radiomics-pathomics combined model ( Z=0.70, P=0.487). In the validation set, the 1-year RFS rates of STS patients in the low, medium, and high recurrence risk groups were 92.0% (95% CI 81.5%-100%), 55.9% (95% CI 40.8%-76.6%), and 37.5% (95% CI 15.3%-91.7%). In the training and validation sets, there were statistically significant in cumulative RFS rates among the low, medium, and high groups of STS patients (training set χ2=73.90, P<0.001; validation set χ2=18.70, P<0.001). Conclusion:The omics nomogram based on MRI and digital pathology images has favorable performance for the prediction of STS recurrence risk.
RÉSUMÉ
Objective:To investigate the value of 18F-FDG PET/CT imaging signs and metabolic parameters in predicting tumor spread through air spaces (STAS) of stage Ⅰ lung adenocarcinoma. Methods:From January 2019 to December 2021, clinical, imaging and metabolic parameters of 381 patients (126 males, 255 females, age (61.2±9.2) years) with stage Ⅰ lung adenocarcinoma were retrospectively analyzed in the Affiliated Hospital of Qingdao University. According to the postoperative pathological results, patients were divided into STAS positive group and STAS negative group. According to the operation time, patients were divided into training set ( n=254) and verification set ( n=127). χ2 test or Mann-Whitney U test was used to compare the differences of different parameters between patients with STAS positive and negative, and binary logistic regression analysis was used to select the predictors of STAS status. The prediction model was established, and ROC curve was used to evaluate the predictive efficacy. Results:There were 49(19.3%, 49/254) patients with STAS positive and 205(80.7%, 205/254) patients with STAS negative in the training set, while those were 35(27.6%, 35/127) and 92(72.4%, 92/127) in the verification set. In the training set, the differences of age ( z=-2.30, P=0.021), type of lesions ( χ2=6.81, P=0.009), spiculation ( χ2=12.64, P<0.001), bronchus truncation ( χ2=6.98, P=0.008), ground glass ribbon sign ( χ2=26.93, P<0.001) and SUV max ( z=-4.62, P<0.001) between the two groups were statistically significant. Multivariate logistic regression analysis showed that age (odds ratio ( OR)=1.048, 95% CI: 1.004-1.094, P=0.032), ground glass ribbon sign ( OR=3.857, 95% CI: 1.693-8.788, P=0.001) and SUV max ( OR=1.133, 95% CI: 1.001-1.282, P=0.049) were independent predictors of STAS status in stage Ⅰ lung adenocarcinoma patients. The logistic regression model was P=1/(1+ e - x), x=-5.292+ 0.480×age (year)+ 1.493×ground glass ribbon sign+ 0.170×SUV max. The AUCs of the model in the training set and verification set were 0.770 and 0.801, with the sensitivity of 81.6%(40/49) and 82.9%(29/35), and the specificity of 69.8%(143/205) and 65.2%(60/92), respectively. Conclusion:Age, ground glass ribbon sign and SUV max have good predictive effects on the occurrence of STAS in stage Ⅰ lung adenocarcinoma.