Shimpei Ogawa1,2, Jin-Ichi Hida3, Hideyuki Ike4, Tetsushi Kinugasa5, Mitsuyoshi Ota6, Eiji Shinto7, Michio Itabashi1, Takahiro Okamoto2, Masakazu Yamamoto1, Kenichi Sugihara8, Toshiaki Watanabe9. 1. Department of Surgery, Institute of Gastroenterology, Tokyo Women's Medical University School of Medicine, 8-1, Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. 2. Department of Surgery II, Tokyo Women's Medical University School of Medicine, 8-1, Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. 3. Department of Surgery, Kindai University School of Medicine, 377-2, Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan. hida@surg.med.kindai.ac.jp. 4. Department of Surgery, Saiseikai Yokohama City Nanbu Hospital, 3-2-10, Kounandai, Kounan-ku, Yokohama, Kanagawa, 234-8503, Japan. 5. Department of Surgery, Kurume University School of Medicine, Asahimachi 67 Kurume-city, Fukuoka, 830-0011, Japan. 6. Gastroenterological Center, Yokohama City University Medical Center, 4-57 Urafunecho, Minami-ku, Yokohama, Kanagawa, 232-0024, Japan. 7. Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan. 8. Department of Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan. 9. Division of Surgical Oncology, Department of Surgery, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Abstract
PURPOSE: The goal of the study was to examine prediction of lateral pelvic lymph node (LPLN) metastasis from lower rectal cancer using a logistic model including risk factors for LPLN metastasis and magnetic resonance imaging (MRI) clinical LPLN (cLPLN) status, compared to prediction based on MRI alone. METHODS: The subjects were 272 patients with lower rectal cancer who underwent MRI prior to mesorectal excision combined with LPLN dissection (LPLD) at six institutes. No patients received neoadjuvant therapy. Prediction models for right and left pathological LPLN (pLPLN) metastasis were developed using cLPLN status, histopathological grade, and perirectal lymph node (PRLN) status. For evaluation, data for patients with left LPLD were substituted into the right-side equation and vice versa. RESULTS: Left LPLN metastasis was predicted using the right-side model with accuracy of 86.5%, sensitivity 56.4%, specificity 92.7%, positive predictive value (PPV) 61.1%, and negative predictive value (NPV) 91.2%, while these data using MRI cLPLN status alone were 80.4, 76.9, 81.2, 45.5, and 94.5%, respectively. Similarly, right LPLN metastasis was predicted using the left-side equation with accuracy of 83.8%, sensitivity 57.8%, specificity 90.4%, PPV 60.5%, and NPV 89.4%, and the equivalent data using MRI alone were 78.4, 68.9, 80.8, 47.7, and 91.1%, respectively. The AUCs for the right- and left-side equations were significantly higher than the equivalent AUCs for MRI cLPLN status alone. CONCLUSIONS: A logistic model including risk factors for LPLN metastasis and MRI findings had significantly better performance for prediction of LPLN metastasis compared with a model based on MRI findings alone.
PURPOSE: The goal of the study was to examine prediction of lateral pelvic lymph node (LPLN) metastasis from lower rectal cancer using a logistic model including risk factors for LPLN metastasis and magnetic resonance imaging (MRI) clinical LPLN (cLPLN) status, compared to prediction based on MRI alone. METHODS: The subjects were 272 patients with lower rectal cancer who underwent MRI prior to mesorectal excision combined with LPLN dissection (LPLD) at six institutes. No patients received neoadjuvant therapy. Prediction models for right and left pathological LPLN (pLPLN) metastasis were developed using cLPLN status, histopathological grade, and perirectal lymph node (PRLN) status. For evaluation, data for patients with left LPLD were substituted into the right-side equation and vice versa. RESULTS:Left LPLN metastasis was predicted using the right-side model with accuracy of 86.5%, sensitivity 56.4%, specificity 92.7%, positive predictive value (PPV) 61.1%, and negative predictive value (NPV) 91.2%, while these data using MRI cLPLN status alone were 80.4, 76.9, 81.2, 45.5, and 94.5%, respectively. Similarly, right LPLN metastasis was predicted using the left-side equation with accuracy of 83.8%, sensitivity 57.8%, specificity 90.4%, PPV 60.5%, and NPV 89.4%, and the equivalent data using MRI alone were 78.4, 68.9, 80.8, 47.7, and 91.1%, respectively. The AUCs for the right- and left-side equations were significantly higher than the equivalent AUCs for MRI cLPLN status alone. CONCLUSIONS: A logistic model including risk factors for LPLN metastasis and MRI findings had significantly better performance for prediction of LPLN metastasis compared with a model based on MRI findings alone.
Entities:
Keywords:
Lateral pelvic lymph node (LPLN); Logistic model; Magnetic resonance imaging (MRI); Rectal cancer
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