Individualized and generalized models for predicting observer performance on liver metastasis detection using CT.

Purpose: Radiologists exhibit wide inter-reader variability in diagnostic performance. This work aimed to compare different feature sets to predict if a radiologist could detect a specific liver metastasis in contrast-enhanced computed tomography (CT) images and to evaluate possible improvements in individualizing models to specific radiologists. Approach: Abdominal CT images from 102 patients, including 124 liver metastases in 51 patients were reconstructed at five different kernels/doses using projection domain noise insertion to yield 510 image sets. Ten abdominal radiologists marked suspected metastases in all image sets. Potentially salient features predicting metastasis detection were identified in three ways: (i) logistic regression based on human annotations (semantic), (ii) random forests based on radiologic features (radiomic), and (iii) inductive derivation using convolutional neural networks (CNN). For all three approaches, generalized models were trained using metastases that were detected by at least two radiologists. Conversely, individualized models were trained using each radiologist's markings to predict reader-specific metastases detection. Results: In fivefold cross-validation, both individualized and generalized CNN models achieved higher area under the receiver operating characteristic curves (AUCs) than semantic and radiomic models in predicting reader-specific metastases detection ability ( p < 0.001 ). The individualized CNN with an AUC of mean (SD) 0.85(0.04) outperformed the generalized one [ AUC = 0.78 ( 0.06 ) , p = 0.004 ]. The individualized semantic [ AUC = 0.70 ( 0.05 ) ] and radiomic models [ AUC = 0.68 ( 0.06 ) ] outperformed the respective generalized versions [semantic AUC = 0.66 ( 0.03 ) , p = 0.009 ; radiomic AUC = 0.64 ( 0.06 ) , p = 0.03 ]. Conclusions: Individualized models slightly outperformed generalized models for all three feature sets. Inductive CNNs were better at predicting metastases detection than semantic or radiomic features. Generalized models have implementation advantages when individualized data are unavailable.

Computer-Aided Nodule Assessment and Risk Yield (CANARY) may facilitate non-invasive prediction of EGFR mutation status in lung adenocarcinomas.

Computer-Aided Nodule Assessment and Risk Yield (CANARY) is quantitative imaging analysis software that predicts the histopathological classification and post-treatment disease-free survival of patients with adenocarcinoma of the lung. CANARY characterizes nodules by the distribution of nine color-coded texture-based exemplars. We hypothesize that quantitative computed tomography (CT) analysis of the tumor and tumor-free surrounding lung facilitates non-invasive identification of clinically-relevant mutations in lung adenocarcinoma. Comprehensive analysis of targetable mutations (50-gene-panel) and CANARY analysis of the preoperative (≤3 months) high resolution CT (HRCT) was performed for 118 pulmonary nodules of the adenocarcinoma spectrum surgically resected between 2006-2010. Logistic regression with stepwise variable selection was used to determine predictors of mutations. We identified 140 mutations in 106 of 118 nodules. TP53 (n = 48), KRAS (n = 47) and EGFR (n = 15) were the most prevalent. The combination of Y (Yellow) and G (Green) exemplars, fibrosis within the surrounding lung and smoking status were the best discriminators for an EGFR mutation (AUC 0.77 and 0.87, respectively). None of the EGFR mutants expressing TP53 (n = 5) had a good prognosis based on CANARY features. No quantitative features were significantly associated with KRAS mutations. Our exploratory analysis indicates that quantitative CT analysis of a nodule and surrounding lung may noninvasively predict the presence of EGFR mutations in pulmonary nodules of the adenocarcinoma spectrum.

Comparison of Methods for Analyzing Human Adipose Tissue Macrophage Content.

OBJECTIVE: The relationship between inflammation, obesity, and adverse metabolic conditions is associated with adipose tissue macrophages (ATM). This study compared the measurements of human ATM using flow cytometry, immunohistochemistry (IHC), and real-time polymerase chain reaction (RT-PCR) of ATM markers. METHODS: A new software program (AMCounter) was evaluated to help measure ATM using IHC, and this was compared to flow cytometry and RT-PCR. RESULTS: IHC had good intraindividual reproducibility for total (CD68), proinflammatory (CD14), and anti-inflammatory (CD206) ATM. The AMCounter improved interreader agreement and was more time efficient. Flow cytometry had acceptable intraindividual reproducibility for the percentage of CD68+ cells that were CD14+ or CD206+ , but not for ATMs per gram of tissue. ATMs per gram of tissue was much greater using IHC than flow cytometry. The flow cytometry and IHC measures of ATM from the same biopsies were not correlated. There were statistically significant correlations between RT-PCR CD68 and IHC CD68, CD14, and CD206 ATMs per 100 adipocytes. Also of interest were statistically significant correlations between RT-PCR CD68 and IHC CD68, CD14, and adipose flow cytometry measures of CD68+ , CD68+ /CD14+ , and CD68+ /CD206+ ATMs per gram of tissue. CONCLUSIONS: The AMCounter software helps provide reproducible and efficient measures of IHC ATMs. Flow cytometry, IHC, and RT-PCR measures of adipose inflammation provide somewhat different information.