RATIONALE AND OBJECTIVES: The purpose of this study was to measure and to clarify the diagnostic contributions of image-based features in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. MATERIALS AND METHODS: Six experienced abdominal radiologists each read images from 146 cases (including a contrast material-enhanced computed tomographic [CT] scan and contrast-enhanced and unenhanced magnetic resonance [MR] images) following a checklist-questionnaire requiring them to rate quantitatively each of as many as 131 image features and then reported on each of the two differentiations. The diagnostic value of each feature was assessed, and linear discriminant analysis was used to develop statistical prediction rules (SPRs) for merging feature data into computerized "second opinions." For the two differentiations, accuracy (area under the receiver operating characteristic curve [Az]) was then determined for the radiologists' readings by themselves and for each of three SPRs. RESULTS: Thirty-seven candidate features had diagnostic value for each of the two differentiations (a slightly different feature set for each). Radiologists' performance at both differentiations was excellent (Az = 0.929 [benign vs malignant] and 0.926 [hepatocyte-containing vs non-hepatocyte-containing]). Performance of the SPR that operated on the features from all modalities together was better than that of radiologists (Az = 0.936 [benign vs malignant] and 0.951 [hepatocyte-containing vs non-hepatocyte-containing]), but this difference was of marginal statistical significance (P = .11). Contrast-enhanced MR imaging and contrast-enhanced CT each made significant adjunctive contributions to accuracy compared with unenhanced MR imaging alone. CONCLUSION: Many CT- and MR imaging-based features have diagnostic value in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. Radiologists could also benefit from the fully informed SPR's "second opinions."
RCT Entities:
RATIONALE AND OBJECTIVES: The purpose of this study was to measure and to clarify the diagnostic contributions of image-based features in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. MATERIALS AND METHODS: Six experienced abdominal radiologists each read images from 146 cases (including a contrast material-enhanced computed tomographic [CT] scan and contrast-enhanced and unenhanced magnetic resonance [MR] images) following a checklist-questionnaire requiring them to rate quantitatively each of as many as 131 image features and then reported on each of the two differentiations. The diagnostic value of each feature was assessed, and linear discriminant analysis was used to develop statistical prediction rules (SPRs) for merging feature data into computerized "second opinions." For the two differentiations, accuracy (area under the receiver operating characteristic curve [Az]) was then determined for the radiologists' readings by themselves and for each of three SPRs. RESULTS: Thirty-seven candidate features had diagnostic value for each of the two differentiations (a slightly different feature set for each). Radiologists' performance at both differentiations was excellent (Az = 0.929 [benign vs malignant] and 0.926 [hepatocyte-containing vs non-hepatocyte-containing]). Performance of the SPR that operated on the features from all modalities together was better than that of radiologists (Az = 0.936 [benign vs malignant] and 0.951 [hepatocyte-containing vs non-hepatocyte-containing]), but this difference was of marginal statistical significance (P = .11). Contrast-enhanced MR imaging and contrast-enhanced CT each made significant adjunctive contributions to accuracy compared with unenhanced MR imaging alone. CONCLUSION: Many CT- and MR imaging-based features have diagnostic value in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. Radiologists could also benefit from the fully informed SPR's "second opinions."
Authors: Sandy A Napel; Christopher F Beaulieu; Cesar Rodriguez; Jingyu Cui; Jiajing Xu; Ankit Gupta; Daniel Korenblum; Hayit Greenspan; Yongjun Ma; Daniel L Rubin Journal: Radiology Date: 2010-05-26 Impact factor: 11.105
Authors: Kenneth C Wang; Anthony Jeanmenne; Griffin M Weber; Shrey K Thawait; Shrey Thawait; John A Carrino Journal: J Digit Imaging Date: 2011-06 Impact factor: 4.056
Authors: Jessica C Sieren; Jamie Weydert; Eman Namati; Jacqueline Thiesse; Jered P Sieren; Joseph M Reinhardt; Eric A Hoffman; Geoffrey McLennan Journal: Acad Radiol Date: 2010-02 Impact factor: 3.173