A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer.
OBJECTIVE
The aim of this study was to demonstrate the principle of supporting radiologists by using a computer algorithm to quantitatively analyse MRI morphological features used by radiologists to predict the presence or absence of metastatic disease in local lymph nodes in rectal cancer.


METHODS
A computer algorithm was developed to extract and quantify the following morphological features from MR images: chemical shift artefact; relative mean signal intensity; signal heterogeneity; and nodal size (volume or maximum diameter). Computed predictions on nodal involvement were generated using quantified features in isolation or in combinations. Accuracies of the predictions were assessed against a set of 43 lymph nodes, determined by radiologists as benign (20 nodes) or malignant (23 nodes).


RESULTS
Predictions using combinations of quantified features were more accurate than predictions using individual features (0.67-0.86 vs 0.58-0.77, respectively). The algorithm was more accurate when three-dimensional images were used (0.58-0.86) than when only middle image slices (two-dimensional) were used (0.47-0.72). Maximum node diameter was more accurate than node volume in representing the nodal size feature; combinations including maximum node diameter gave accuracies up to 0.91.


CONCLUSIONS
We have developed a computer algorithm that can support radiologists by quantitatively analysing morphological features of lymph nodes on MRI in the context of rectal cancer nodal staging. We have shown that this algorithm can combine these quantitative indices to generate computed predictions of nodal status which closely match radiological assessment. This study provides support for the feasibility of computer-assisted reading in nodal staging, but requires further refinement and validation with larger data sets.
Department of Radiology, Churchill Hospital, Oxford, UK. donald.tse@gmail.com
D M L
DM
Tse
N
N
Joshi
E M
EM
Anderson
M
M
Brady
F V
FV
Gleeson
D000328:Adult; D000368:Aged; D000465:Algorithms; D002277:Carcinoma / Q000473:pathology* / Q000556:secondary*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008198:Lymph Nodes; D008207:Lymphatic Metastasis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012004:Rectal Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity