3차원 재구성기법을 이용한 전산화 단층 촬영에서 작은 폐결절의 부피 측정 소프트웨어 개발 및 정확성 평가

학위논문(석사)--서울대학교 대학원 :의학과 방사선과학전공,2003.Maste

Serial changes of CT findings in patients with chronic hypersensitivity pneumonitis: imaging trajectories and predictors of fibrotic progression and acute exacerbation

Objectives: To evaluate the longitudinal changes of chest CT findings in patients with chronic hypersensitivity pneumonitis (HP) and identify risk factors for fibrotic progression and acute exacerbation (AE). Methods: This retrospective study included patients with chronic HP with follow-up CT. Baseline and serial follow-up CT were evaluated semi-quantitatively. Fibrosis score was defined as the sum of the area with reticulation and honeycombing. The modified CT pattern of Fleischner Society idiopathic pulmonary fibrosis diagnostic guidelines was evaluated. Cox proportional hazards regression was performed to determine significant variables associated with fibrotic progression and AEs. Results: Of 91 patients, mean age was 59.1 years and 61.5% were women. The median follow-up period was 4.9 years. Seventy-nine patients (86.8%) showed fibrotic progression with persistent areas of mosaic attenuation, finally replaced by fibrosis, and 20 (22.0%) developed AE. Baseline fibrosis score and CT pattern of usual interstitial pneumonia (UIP)/probable UIP were independent risk factors for predicting fibrotic progression (hazard ratio [HR] = 1.05, 95% confidence interval [CI] = 1.02?1.09, p < 0.001, for fibrosis score; HR = 2.50, CI = 1.50?4.16, p < 0.001, for CT pattern) and AEs (HR = 1.07, CI = 1.01?1.13, p = 0.019, for fibrosis score; HR = 5.47, CI = 1.23?24.45, p = 0.026, for CT pattern) after adjusting clinical covariables. Conclusion: Fibrotic progression and AE were identified in 86.8% and 22.0% of patients with chronic HP. Fibrosis score and CT pattern of UIP/probable UIP on baseline chest CT may predict fibrotic progression and AE. Key Points: ? Most patients (87%) showed fibrotic progression on long-term follow-up with persistent areas of mosaic attenuation that were finally replaced by fibrosis at a later stage. ? One-fifth of patients (22%) experienced acute exacerbation associated with worse prognosis. ? Fibrosis score (sum of reticulation and honeycombing) and CT pattern of UIP/probable UIP on baseline CT were independent predictors for predicting fibrotic progression and acute exacerbation

Performance of radiomics models for survival prediction in non-small-cell lung cancer: influence of CT slice thickness

Objectives: To investigate whether CT slice thickness influences the performance of radiomics prognostic models in non-small-cell lung cancer (NSCLC) patients. Methods: CT images including 1-, 3-, and 5-mm slice thicknesses acquired from 311 patients who underwent surgical resection for NSCLC between May 2014 and December 2015 were evaluated. Tumor segmentation was performed on CT of each slice thickness and total 94 radiomics features (shape, tumor intensity, and texture) were extracted. The study population was temporally split into development (n = 185) and validation sets (n = 126) for prediction of disease-free survival (DFS). Three radiomics models were built from three different slice thickness datasets (Rad-1, Rad-3, and Rad-5), respectively. Model performance was assessed and compared in three slice thickness datasets and mixed slice thickness dataset using C-indices. Results: In corresponding slice thickness datasets, the C-indices of Rad-1, Rad-3, and Rad-5 for prediction of DFS were 0.68, 0.70, and 0.68 in the development set, and 0.73, 0.73, and 0.76 in the validation set (p = 0.40?0.89 and 0.27?0.90, respectively). Performance of the models was not significantly changed when they were applied to different slice thicknesses data in the validation set (C-index, 0.73?0.76, 0.72?0.73, 0.75?0.76; p = 0.07?0.92). In the mixed slice thickness dataset, performances of the models were similar to or slightly lower than their performances in the corresponding slice thickness datasets (C-index, 0.72?0.75 vs. 0.73?0.76) in the validation set. Conclusions: The performance of radiomics models for predicting DFS in NSCLC patients was not significantly affected by CT slice thickness. Key Points: ? Three radiomics models based on 1-, 3-, and 5-mm CT datasets showed C-indices for predicting disease-free survival of 0.68?0.70 in the development set and 0.73?0.76 in the validation set, without statistical differences (p = 0.27?0.90). ? Application of the radiomics models to different slice thickness datasets showed no significant differences in performance between the values in the prediction of disease-free survival (p = 0.07?0.99). ? Three radiomics models based on 1-, 3-, and 5-mm CT datasets performed well in mixed slice thickness datasets, showing similar or slightly lower performances

Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction

Background: Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose: To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance. Materials and Methods: CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit. Results: A total of 308 patients (mean age 6 standard deviation, 62 years 6 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years 6 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P < .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P < .001 for 5 mm). Conclusion: Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. (C) RSNA, 202

CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma

Purpose: To develop and validate a CT-based radiomic model to simultaneously diagnose anaplastic lymphoma kinase (ALK) rearrangements and epidermal growth factor receptor (EGFR) mutation status of lung adenocarcinoma and to assess whether peritumoural radiomic features add value in the prediction of mutation status. Methods: 503 patients with pathologically proven lung adenocarcinoma containing information on the mutation status were retrospectively included. Intratumoural and peritumoural radiomic features of the primary lesion were extracted from CT. We proposed two-level stepwise binary radiomics-based classification models to diagnose ALK (step1) and EGFR mutation status (step2). The performance of proposed models and added value of peritumoural radiomic features were evaluated by using the areas under receiver operating characteristic curves (AUC) and Obuchowski index in the development and validation sets. Results: Regarding the prediction of ALK rearrangement, the diagnostic performance of the intratumoural radiomic model showed the AUC of 0.77 and 0.68 for the development and validation sets, respectively. As for EGFR mutation, the diagnostic performance of the intratumoural radiomic model showed the AUCs of 0.64 and 0.62 for the development and validation sets, respectively. The radiomics added value to the model based on clinical features (development set [radiomics + clinical model vs. clinical model]: Obuchowski index, 0.76 vs. 0.66, p < 0.001; validation set: 0.69 vs. 0.61, p = 0.075). Adding peritumoural features resulted in no improvement in terms of model performance. Conclusion: The CT radiomics-based model allowed the simultaneous prediction of the presence of ALK and EGFR mutations while adding value to the clinical features