Heejin Bae1, Hansang Lee2, Sungwon Kim1, Kyunghwa Han1, Hyungjin Rhee1, Dong-Kyu Kim1, Hyuk Kwon1, Helen Hong3, Joon Seok Lim4. 1. Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. 2. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. 3. Department of Software Convergence, College of Interdisciplinary Studies for Emerging Industries, Seoul Women's University, Seoul, Republic of Korea. 4. Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. JSLIM1@yuhs.ac.
Abstract
OBJECTIVE: To evaluate diagnostic performance of a radiomics model for classifying hepatic cyst, hemangioma, and metastasis in patients with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. METHODS: This retrospective study included 502 CRC patients who underwent contrast-enhanced CT and contrast-enhanced liver MRI between January 2005 and December 2010. Portal-phase CT images of training (n = 386) and validation (n = 116) cohorts were used to develop a radiomics model for differentiating three classes of liver lesions. Among multiple handcrafted features, the feature selection was performed using ReliefF method, and random forest classifiers were used to train the selected features. Diagnostic performance of the developed model was compared with that of four radiologists. A subgroup analysis was conducted based on lesion size. RESULTS: The radiomics model demonstrated significantly lower overall and hemangioma- and metastasis-specific polytomous discrimination index (PDI) (overall, 0.8037; hemangioma-specific, 0.6653; metastasis-specific, 0.8027) than the radiologists (overall, 0.9622-0.9680; hemangioma-specific, 0.9452-0.9630; metastasis-specific, 0.9511-0.9869). For subgroup analysis, the PDI of the radiomics model was different according to the lesion size (< 10 mm, 0.6486; ≥ 10 mm, 0.8264) while that of the radiologists was relatively maintained. For classifying metastasis from benign lesions, the radiomics model showed excellent diagnostic performance, with an accuracy of 84.36% and an AUC of 0.9426. CONCLUSION: Albeit inferior to the radiologists, the radiomics model achieved substantial diagnostic performance when differentiating hepatic lesions from portal-phase CT images of CRC patients. This model was limited particularly to classifying hemangiomas and subcentimeter lesions. KEY POINTS: • Albeit inferior to the radiologists, the radiomics model could differentiate cyst, hemangioma, and metastasis with substantial diagnostic performance using portal-phase CT images of colorectal cancer patients. • The radiomics model demonstrated limitations especially in classifying hemangiomas and subcentimeter liver lesions.
OBJECTIVE: To evaluate diagnostic performance of a radiomics model for classifying hepatic cyst, hemangioma, and metastasis in patients with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. METHODS: This retrospective study included 502 CRCpatients who underwent contrast-enhanced CT and contrast-enhanced liver MRI between January 2005 and December 2010. Portal-phase CT images of training (n = 386) and validation (n = 116) cohorts were used to develop a radiomics model for differentiating three classes of liver lesions. Among multiple handcrafted features, the feature selection was performed using ReliefF method, and random forest classifiers were used to train the selected features. Diagnostic performance of the developed model was compared with that of four radiologists. A subgroup analysis was conducted based on lesion size. RESULTS: The radiomics model demonstrated significantly lower overall and hemangioma- and metastasis-specific polytomous discrimination index (PDI) (overall, 0.8037; hemangioma-specific, 0.6653; metastasis-specific, 0.8027) than the radiologists (overall, 0.9622-0.9680; hemangioma-specific, 0.9452-0.9630; metastasis-specific, 0.9511-0.9869). For subgroup analysis, the PDI of the radiomics model was different according to the lesion size (< 10 mm, 0.6486; ≥ 10 mm, 0.8264) while that of the radiologists was relatively maintained. For classifying metastasis from benign lesions, the radiomics model showed excellent diagnostic performance, with an accuracy of 84.36% and an AUC of 0.9426. CONCLUSION: Albeit inferior to the radiologists, the radiomics model achieved substantial diagnostic performance when differentiating hepatic lesions from portal-phase CT images of CRCpatients. This model was limited particularly to classifying hemangiomas and subcentimeter lesions. KEY POINTS: • Albeit inferior to the radiologists, the radiomics model could differentiate cyst, hemangioma, and metastasis with substantial diagnostic performance using portal-phase CT images of colorectal cancerpatients. • The radiomics model demonstrated limitations especially in classifying hemangiomas and subcentimeter liver lesions.
Authors: E J Limkin; R Sun; L Dercle; E I Zacharaki; C Robert; S Reuzé; A Schernberg; N Paragios; E Deutsch; C Ferté Journal: Ann Oncol Date: 2017-06-01 Impact factor: 32.976
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Authors: Luai R Zarour; Sudarshan Anand; Kevin G Billingsley; William H Bisson; Andrea Cercek; Michael F Clarke; Lisa M Coussens; Charles E Gast; Cristina B Geltzeiler; Lissi Hansen; Katherine A Kelley; Charles D Lopez; Shushan R Rana; Rebecca Ruhl; V Liana Tsikitis; Gina M Vaccaro; Melissa H Wong; Skye C Mayo Journal: Cell Mol Gastroenterol Hepatol Date: 2017-01-20