Radiomics for Diagnosing Lateral Pelvic Lymph Nodes in Rectal Cancer: Artificial Intelligence Enabling Precision Medicin
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EDITORIAL – COLORECTAL CANCER
Radiomics for Diagnosing Lateral Pelvic Lymph Nodes in Rectal Cancer: Artificial Intelligence Enabling Precision Medicine? Tarik Sammour, PhD1, and Sergei Bedrikovetski, BHSc (Hons)2 Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, SA, Australia; 2Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, SA, Australia
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Accurate diagnosis of malignant lymphadenopathy on cross-sectional imaging for rectal cancer remains challenging, with sensitivity and specificity estimated to be approximately 70% to 80% when pathologic assessment is used as the gold standard reference test.1 This often creates a management dilemma because decisions around neoadjuvant treatment and surgical excision with lateral pelvic node dissection (LPLND) hinge on this preoperative nodal assessment. What has always been somewhat unclear is whether the relatively poor diagnostic accuracy of crosssectional imaging for lymphadenopathy is a limitation of the imaging technology, a weakness of human interpretation of the images generated, or a combination of both. In this issue of Annals of Surgical Oncology, Nakanishi et al.2 describe an elegant study using a radiomics-based artificial intelligence prediction model to diagnose abnormal lateral pelvic lymph nodes (LPLN) in a large cohort of rectal cancer patients treated with neoadjuvant chemoradiotherapy (CRTx) and lateral pelvic lymph node dissection. The radiomics method was significantly better at diagnosing true-positive nodes than the traditional parameters of pre-treatment short-axis diameter and size change after neoadjuvant CRTx. As the first published investigation using radiomics to evaluate LPLN disease in rectal cancer, this study represents a significant advance in the field by a group uniquely placed to undertake such research due to their expertise in
Ó Society of Surgical Oncology 2020 First Received: 13 July 2020 Accepted: 22 July 2020 T. Sammour, PhD e-mail: [email protected]
sophisticated machine-learning analytics and a large population of patients who have undergone both CRTx and LPLND. During a 13-year period, 854 patients with rectal cancer underwent total mesorectal excision (TME) in the two Japanese centers involved in this study. Of these patients, 247 also underwent LPLND after CRTx due to the presence of enlarged lateral nodes (axis diameter, C 7 mm) and were eligible for inclusion in the study. Radiomics analysis of contrast-enhanced computed tomography (CT) images was undertaken with the largest lateral node labeled manually. The radiomics score achieved a staggering area-under-the-curve (AUC) value higher than 0.90 in both the primary and validation cohorts for prediction of viable tumor cells via pathologic assessment. On multivariate analysis including relevant clinical variables (age, gender, carcinoembryonic antigen [CEA] level, T stage, tumor height, neoadjuvant protocol) the radiomics score was the only independently predictive factor. Thes
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