Detection of the artery of Adamkiewicz using multidetector row computed tomography in patients with spinal arteriovenous shunt disease

Purpose: To plan a treatment strategy for a spinal arteriovenous shunt (SAVS), identifying the artery of Adamkiewicz (AKA) and its origin is indispensable. However, detecting the AKA is very difficult in patients with an SAVS when using computed tomography angiography (CTA) by the usual method to find the hairpin curved artery because dilated drainage veins nearly always coexist with the hairpin curved AKA. We designed a method to identify the AKA by focusing on the diameter and pathway of the anterior radiculomedullary arteries (RMAs). Material and methods: Seven consecutive patients with an SAVS were surveyed. They underwent contrast-enhanced CTA and conventional angiography from January 2009 to December 2012. Two readers evaluated the CTA images and assumed that the AKA was the artery that ran through the anterior portion of the neural foramen and continued to pass on the ventral side of the spinal cord. Results: Among the seven patients, nine AKAs were detected with conventional angiography. When using our method, seven AKAs and six AKAs were identified on CTA by Reader 1 and Reader 2, respectively. The average sensitivity was 72.3%, and the specificity, accuracy, positive predictive value, and negative predictive value were sufficiently high (i.e. > 85%) for both readers. The kappa value for detecting the AKA was 0.98. Conclusions: Detecting the origin of the AKA with CTA is challenging in patients with an SAVS. However, focusing on the diameter and pathway of the RMAs may allow successful identification

Differentiation of carcinosarcoma from endometrial carcinoma on magnetic resonance imaging using deep learning

Purpose: To verify whether deep learning can be used to differentiate between carcinosarcomas (CSs) and endometrial carcinomas (ECs) using several magnetic resonance imaging (MRI) sequences. Material and methods: This retrospective study included 52 patients with CS and 279 patients with EC. A deep-learning model that uses convolutional neural networks (CNN) was trained with 572 T2-weighted images (T2WI) from 42 patients, 488 apparent diffusion coefficient of water maps from 33 patients, and 539 fat-saturated contrast-enhanced T1-weighted images from 40 patients with CS, as well as 1612 images from 223 patients with EC for each sequence. These were tested with 9-10 images of 9-10 patients with CS and 56 images of 56 patients with EC for each sequence, respectively. Three experienced radiologists independently interpreted these test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for each sequence were compared between the CNN models and the radiologists. Results: The CNN model of each sequence had sensitivity 0.89-0.93, specificity 0.44-0.70, accuracy 0.83-0.89, and AUC 0.80-0.94. It also showed an equivalent or better diagnostic performance than the 3 readers (sensitivity 0.43-0.91, specificity 0.30-0.78, accuracy 0.45-0.88, and AUC 0.49-0.92). The CNN model displayed the highest diagnostic performance on T2WI (sensitivity 0.93, specificity 0.70, accuracy 0.89, and AUC 0.94). Conclusions: Deep learning provided diagnostic performance comparable to or better than experienced radiologists when distinguishing between CS and EC on MRI

Radiation-induced angiosarcoma of the brain

Primary angiosarcoma of the central nervous systemis unusual.We encountered a case of radiation-induced angiosarcoma of the brain. A 65-year-old male was referred to our hospital with drowsiness for the last 6 months. He had undergone radiation therapy for pituitary adenoma 43 years ago. An MRI revealed a right temporal lobe tumour that consisted of a well-demarcated haemorrhagic lesion and an avid contrast enhancement, with significant vasogenic oedema. Surgical resection was performed and a post-operative pathological diagnosis of an angiosarcoma was made. A Thorotrast-associated angiosarcoma has been, hitherto, the only reported case of radiation-induced angiosarcoma of the brain. We present an extremely rare case of primary angiosarcoma of the brain, occurring after external beam radiotherapy

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments

Background: This study aimed to compare deep learning with radiologists’ assessments for diagnosing ovarian carcinoma using MRI. Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. Results: The CNN of each sequence had a sensitivity of 0.77–0.85, specificity of 0.77–0.92, accuracy of 0.81–0.87, and an AUC of 0.83–0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). Conclusion: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI

Hypofractionated Proton Beam Therapy for cT1-2N0M0 Non-small Cell Lung Cancer Patients With Interstitial Lung Disease

Background/Aim: To evaluate the outcomes of proton beam therapy (PBT) for early-stage non-small cell lung cancer (NSCLC) in patients with interstitial lung disease (ILD). Patients and Methods: Between 2002 and 2017, 110 patients receiving hypofractionated PBT for cT1-2N0M0 NSCLC were reviewed.Results: Of the 110 patients, 17 were diagnosed with ILD. The median follow-up period was 37.8 months. No significant difference in the 1-year cumulative rate of grade ≥2 pneumonitis was observed between patients with and those without ILD (17.6% vs. 14.1%, p=0.708). The lung doses were significantly lower in patients with than in those without ILD among patients without grade ≥2 neumonitis. There were no significant differences in overall survival or local recurrence-free rates according to the presence of ILD. Conclusion: PBT appears to be a feasible and effective treatment for cT1-2N0M0 NSCLC inpatients with ILD, but the lung dose should be strictly reduced