著者

Daiki ABURAKAWA Masayuki KANAMORI Toshiaki AKASHI Shiho SATO Ryuta SAITO Teiji TOMINAGA

出版者

The Japan Neurosurgical Society

雑誌

NMC Case Report Journal (ISSN:21884226)

巻号頁・発行日

vol.8, no.1, pp.535-543, 2021 (Released:2021-09-29)

参考文献数

17

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Corpus callosum swelling has been reported to occur after ventriculoperitoneal shunting for long-standing hydrocephalus. This report presents a case of corpus callosum swelling after intraventricular tumor resection. A 34-year-old woman presented with a headache that worsened over 1 month. Magnetic resonance (MR) images revealed a mass lesion in the left lateral ventricle and obstructive hydrocephalus. She underwent subtotal resection with a transcallosal approach. After tumor resection, she had long-lasting status epilepticus followed by consciousness disturbance. T2-weighted MR images obtained 8 hr after the operation showed a hyperintense area in the corpus callosum. The patient then presented with bilateral dilated pupils 14 hr after the operation due to acute hydrocephalus and tension pneumocephalus. An emergent re-craniotomy was performed and a ventricular drain was placed. The patient recovered consciousness 3 days after the operation. However, she experienced progressive corpus callosum swelling 25 days after the operation, which improved since then. Approximately 4 months after the operation, she returned to her usual workplace with no neurocognitive functional decline. Two years later, she was doing well with no radiological abnormal findings except corpus callosum thinning. Thus, corpus callosum swelling can develop not only after shunting for chronic hydrocephalus but also after intraventricular tumor resection. It occurred relatively acutely and there was no decline in intelligence after long-term follow-up. This case suggests that corpus callosum swelling after intraventricular tumor resection is a rare but noteworthy complication that can improve without intervention.

著者

Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki Aoki

出版者

Japanese Society for Magnetic Resonance in Medicine

雑誌

Magnetic Resonance in Medical Sciences (ISSN:13473182)

巻号頁・発行日

pp.mp.2021-0068, (Released:2021-12-10)

参考文献数

32

被引用文献数

5

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Purpose: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy.Methods: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge–Weber syndrome (SWS) cases.Results: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were −0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and −2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03).Conclusion: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.