OBJECTIVETo evaluate the diagnostic value of 3.0T high-resolution MRI in mesorectal lymph node metastasis of rectal cancer.

METHODSThe images and postoperative pathological data of patients with pathologically diagnosed rectal cancer who underwent prospective 3.0T two dimensional high-resolution MRI rectal examinations and surgery within two weeks after MRI examination at the First Affiliated Hospital, Sun Yat-sen University from November 2015 to November 2016 were retrospectively collected. Patients who received preoperative neoadjuvant therapy and those who did not undergo operation after MRI examination were excluded. The MRI sequences included high-resolution sagittal, coronal and oblique axial T2 weighted image (T2WI) (repetition time/echo time, 3000-4000 ms/77-87 ms; slice thickness/gap, 3 mm/0 mm; field of view, 18-22 cm). Two abdominal MRI radiologists independently assessed the morphology, margin, signal of all visible mesorectal nodes, measured their minor axes (three times for each radiologist) and gave estimation of the malignancy. The criteria of metastatic nodes on high-resolution MRI T2WI were nodes with irregular shape, ill-defined border and/or heterogeneous signal. The results of MRI diagnosis were compared with postoperative pathology. The sensitivity, specificity, accuracy, positive predictive value(PPV) and negative predictive value(NPV) of mesorectal nodes and nodes with different short-axis diameter ranges were calculated to evaluate the diagnostic efficiency of high-resolution MRI. Kappa statistics was used to evaluate the agreement for per node and for per patient between high-resolution MRI and pathological results. A Kappa value of 0-0.20 indicated poor agreement; 0.21-0.40 fair agreement; 0.41-0.60 moderate agreement; 0.61-0.80 good agreement; and 0.81-1.00 excellent agreement.

RESULTSA total of 81 patients were enrolled in the retrospective cohort study, including 50 males and 31 females with age of (59.3±11.1) years. Histopathology showed 1 case of well differentiated adenocarcinoma, 63 of moderately differentiated adenocarcinoma, 9 of moderately to poorly differentiated adenocarcinoma, 2 of poorly differentiated adenocarcinoma, 3 of mucinous adenocarcinoma and 3 of tubulovillous adenocarcinoma. Histopathological staging showed 2 cases in T1 stage, 20 in T2 stage, 45 in T3 stage and 14 in T4 stage; 34 in N0 stage, 40 in N1 stage and 7 in N2 stage; 76 in M0 stage and 5 in M1 stage. A total of 377 nodes were included in the node-by-node evaluation, of which 168 (44.6%) nodes were metastatic from 58.0% (47/81) patients. The median short-axis diameter was 5.4(2.4-18.6) mm in metastatic nodes, which was significantly larger than 3.8 (2.0-8.7) mm in non-metastatic nodes[Z=10.586, P=0.000]. The sensitivity, specificity, accuracy, PPV and NPV were 74.4% (125/168), 94.7% (198/209), 85.7% (323/377), 91.9% (125/136) and 82.2% (198/241), respectively. The Kappa values between high-resolution MRI and histopathological diagnosis for node-by-node and patient-by-patient were 0.71 and 0.70 respectively, indicating good agreements. Fourteen nodes >10 mm were all metastatic. The results of high-resolution MRI for nodal status were consistent with the results of histopathological diagnosis, and the sensitivity, accuracy and PPV were all 100.0%. Among 124 nodes with short-axis diameter of 5-10 mm, 95 (76.6%) were metastatic, and the sensitivity, specificity, accuracy, PPV and NPV were 78.9% (75/95), 86.2% (25/29), 80.6% (100/124), 94.9% (75/79) and 55.6% (25/45), respectively. The agreement was fair (Kappa value 0.55) between high-resolution MRI and histopathological diagnosis. Among 239 nodes with short-axis diameter ≤5 mm, 59(24.7%) were metastatic, and the sensitivity, specificity, accuracy, PPV and NPV were 61.0% (36/59), 96.1%(173/180), 87.4%(209/239), 83.7%(36/43) and 88.3%(173/196), respectively. The agreement was good (Kappa value 0.63) between high-resolution MRI and histopathological diagnosis.

CONCLUSIONRectal high-resolution MRI has good diagnostic value for estimating metastatic mesorectal nodes by evaluating the morphology, margin and signal of nodes.