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.
Purpose To investigate and determine the sonographic findings obtained from manually distorted testes to predict testicular atrophy following manual detorsion. Materials and methods Twenty‐two patients who had been diagnosed with testicular torsion and undergone manual detorsion were included. These patients were classified according to the presence or absence of testicular atrophy. The duration of symptoms, presence or absence of hyperperfusion within the entire affected testis, and echogenicity (homogeneous or heterogeneous) within the affected testis were compared using the Mann–Whitney U‐test or Fisher's exact test, as appropriate. Results Testicular atrophy was detected in seven patients. There was a significant difference in the frequency of hyperperfusion within the entire affected testis (with atrophy [present/absent] vs. without atrophy [present/absent] = 0/7 vs. 8/7, P = 0.023) between patients with and without testicular atrophy. No significant differences in the duration of symptoms (with atrophy vs. without atrophy = 7 ± 3.3 h vs. 4.7 ± 3.6 h, P = 0.075) or frequency of echogenicity within the testis (with atrophy [heterogeneous/homogeneous] vs. without atrophy [heterogeneous/homogeneous] = 2/5 vs. 2/13, P = 0.565) were observed between the groups. Conclusions This small cohort study suggests that the presence of hyperperfusion within the entire affected testis immediately after successful manual detorsion is useful in predicting the avoidance of testicular atrophy.
Purpose To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. Results The AUC of the CNNs pertaining to the single and combined image sets were 0.88–0.95 and 0.87–0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88–0.95. Conclusion CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.
We report a case of a 12-year-old boy with an accessory spleen torsion. He presented with left-sided abdominal pain after trauma. A 4 cm oval mass without contrast enhancement was detected on contrast-enhanced computed tomography (CT), and ultrasound (US) showed a 4 cm oval mass below the spleen. The mass mainly consisted of high echoes similar to the spleen; the central part showed irregularly low echoes. Subsequent follow-up daily US examinations showed gradual expansion of the central low echoes with conspicuous hyperechoic dots. Discontinuation of the branch from the splenic artery to the mass was observed, both, on US and CT. These findings led to the diagnosis of a hemorrhagic infarct caused by torsion of the accessory spleen. Laparoscopy showed adherence of the accessory spleen to the omentum and colon by twisting four times around its axis. It was resected and confirmed the diagnosis of a torsioned accessory spleen.
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