M Kim1, H S Kim2, J E Park1, S Y Park3, Y-H Kim4, S J Kim1, J Lee5, M R Lebel6,7. 1. From the Department of Radiology and Research Institute of Radiology (M.K., H.S.K., J.E.P., S.J.K.). 2. From the Department of Radiology and Research Institute of Radiology (M.K., H.S.K., J.E.P., S.J.K.) radhskim@gmail.com. 3. Departments of Clinical Epidemiology and Biostatistics (S.Y.P.). 4. Neurosurgery (Y.-H.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. 5. GE Healthcare (J.L.), Seoul, Korea. 6. GE Healthcare (M.R.L.), Calgary, Alberta, Canada. 7. Department of Radiology (M.R.L.), University of Calgary, Calgary, Alberta, Canada.
Abstract
BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning-based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI. MATERIALS AND METHODS: This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery. RESULTS: The diagnostic performance of 1-mm-slice-thickness MRI + deep learning-based reconstruction (area under the curve, 0.79; 95% CI, 0.69 - 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52-0.70; P < .001). One-millimeter-slice-thickness MRI + deep learning-based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P < .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning-based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P < .001; >270°, 38.8% versus 16.4%, P < .001). CONCLUSIONS: Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning-based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.
BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning-based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI. MATERIALS AND METHODS: This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery. RESULTS: The diagnostic performance of 1-mm-slice-thickness MRI + deep learning-based reconstruction (area under the curve, 0.79; 95% CI, 0.69 - 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52-0.70; P < .001). One-millimeter-slice-thickness MRI + deep learning-based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P < .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning-based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P < .001; >270°, 38.8% versus 16.4%, P < .001). CONCLUSIONS: Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning-based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.
Authors: Christoph P Hofstetter; Michael J Nanaszko; Lynn L Mubita; John Tsiouris; Vijay K Anand; Theodore H Schwartz Journal: Pituitary Date: 2012-09 Impact factor: 4.107
Authors: Minjae Kim; Ho Sung Kim; Hyun Jin Kim; Ji Eun Park; Seo Young Park; Young-Hoon Kim; Sang Joon Kim; Joonsung Lee; Marc R Lebel Journal: Radiology Date: 2020-11-03 Impact factor: 11.105