Multimodality diagnosis of liver tumors: feature analysis with CT, liver-specific and contrast-enhanced MR, and a computer model.
OBJECTIVE
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.


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.


CONCLUSIONS
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."
Seltzer
Steven E
SE
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Getty
David J
DJ
Pickett
Ronald M
RM
Swets
John A
JA
Sica
Gregory
G
Brown
Jeffrey
J
Saini
Sanjay
S
Mattrey
Robert F
RF
Harmon
Ben
B
Francis
Isaac R
IR
Chezmar
Judith
J
Schnall
Mitchell O
MO
Siegelman
Evan S
ES
Ballerini
Rocco
R
Bhat
Sandeep
S
D000328:Adult; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis* / Q000000981:diagnostic imaging; D003198:Computer Simulation; D003287:Contrast Media; D004492:Edetic Acid / Q000031:analogs & derivatives*; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008345:Manganese; D015999:Multivariate Analysis; D015588:Observer Variation; D011732:Pyridoxal Phosphate / Q000031:analogs & derivatives*; D012372:ROC Curve; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed
D003287:Contrast Media; D008345:Manganese; D011732:Pyridoxal Phosphate; D004492:Edetic Acid; C060076:N,N'-bis(pyridoxal-5-phosphate)ethylenediamine-N,N'-diacetic acid