Lung adenocarcinoma associated with cystic airspaces: Predictive value of CT features in assessing pathologic invasiveness.

OBJECTIVES: Lung adenocarcinoma associated with cystic airspaces (LACA) is a unique entity with limited understanding. Our aim was to evaluate the radiological characteristics of LACA and to study which criteria were predictive of invasiveness. METHODS: A retrospective monocentric analysis of consecutive patients with pathologically confirmed LACA was performed. The diagnosed adenocarcinomas were classified into preinvasive (atypical adenomatous hyperplasia, adenocarcinoma in situ, or minimally invasive adenocarcinoma) and invasive adenocarcinomas. Eight clinical features and twelve CT features were evaluated. Univariable and multivariable analyses were performed to analyse the correlation between invasiveness, and CT and clinical features. The inter-observer agreement was evaluated using κ statistics and intraclass correlation coefficients. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 252 patients with 265 lesions (128 men and 124 women; mean age, 58.0 ± 11.1 years) were enrolled. Multivariable logistic regression indicated that multiple cystic airspaces (OR, 5.599; 95 % CI, 1.865-16.802), irregular shape of cystic airspace (OR, 3.236; 95 % CI, 1.073-9.761), entire tumour size (OR, 1.281; 95 % CI, 1.075-1.526), and attenuation (OR, 1.007; 95 % CI, 1.005-1.010) were independent risk factors for invasive LACA. The AUC of the logistic regression model was 0.964 (95 % CI, 0.944-0.985). CONCLUSION: Multiple cystic airspaces, irregular shape of cystic airspace, entire tumour size, and attenuation were identified as independent risk factors for invasive LACA. The prediction model gives a good predictive performance, providing additional diagnostic information.

Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.

OBJECTIVES: To develop a nomogram based on CT radiomics and clinical features to predict the epidermal growth factor receptor (EGFR) mutations in early-stage lung adenocarcinomas. METHODS: A retrospective analysis of postoperative patients with pathologically confirmed lung adenocarcinoma, which had been tested for EGFR mutations was performed from January 2015 to December 2015. Patients were randomly assigned to training and validation cohorts. A total of 1,078 radiomics features were extracted. least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select clinical and radiomics features, and to establish predictive models. The radiomics score (rad-score) of each patient was calculated. The discrimination of the model was evaluated with area under the curve. RESULTS: 1092 patients (444 men and 648 women; mean age: 59.59±9.6) were enrolled. The radiomics signature consisted of 28 radiomics features and emphysema. The mean validation cohort result of the rad-score for patients with EGFR mutations (0.814±0.988) was significantly higher than those with EGFR wild-type (0.315±1.237; p = 0.001). When combined with clinical features, LASSO regression analysis revealed four radiomics features, emphysema, and three clinical features including sex, age, and histologic subtype as associated with to EGFR mutation status. The nomogram that combined radiomics and clinical features significantly improved the predictive discrimination (AUC: 0.723), which is better than that of the radiomics signature alone (AUC: 0.646). CONCLUSION: A relationship between selected radiomics features and EGFR mutant lung adenocarcinomas is demonstrated. A nomogram, combining radiomics features and clinical features for EGFR prediction in early-stage lung adenocarcinomas, has shown a moderate discriminatory efficiency and high sensitivity, providing additional information for clinicians.

Quality Assurance for Small-Field VMAT SRS and Conventional-Field IMRT Using the Exradin W1 Scintillator.

BACKGROUND: Plastic scintillator detector (PSD) Exradin W1 has shown promising performance in small field dosimetry due to its water equivalence and small sensitive volume. However, few studies reported its capability in measuring fields of conventional sizes. Therefore, the purpose of this study is to assess the performance of W1 in measuring point dose of both conventional IMRT plans and VMAT SRS plans. METHODS: Forty-seven clinical plans (including 29 IMRT plans and 18 VMAT SRS plans with PTV volume less than 8 cm3) from our hospital were included in this study. W1 and Farmer-Type ionization chamber Exradin A19 were used in measuring IMRT plans, and W1 and microchamber Exradin A16 were used in measuring SRS plans. The agreement between the results of different types of detectors and TPS was evaluated. RESULTS: For IMRT plans, the average differences between measurements and TPS in high-dose regions were 0.27% ± 1.66% and 0.90% ± 1.78% (P = 0.056), and were -0.76% ± 1.47% and 0.37% ± 1.34% in low-dose regions (P = 0.000), for W1 and A19, respectively. For VMAT SRS plans, the average differences between measurements and TPS were -0.19% ± 0.96% and -0.59% ± 1.49% for W1 and A16 with no statistical difference (P = 0.231). CONCLUSION: W1 showed comparable performance with application-dedicated detectors in point dose measurements for both conventional IMRT and VMAT SRS techniques. It is a potential one-stop solution for general radiotherapy platforms that deliver both IMRT and SRS plans.