Improved diagnostic performance of plain radiography for cervical ossification of the posterior longitudinal ligament using deep learning.

BACKGROUND: A high false-negative rate has been reported for the diagnosis of ossification of the posterior longitudinal ligament (OPLL) using plain radiography. We investigated whether deep learning (DL) can improve the diagnostic performance of radiologists for cervical OPLL using plain radiographs. MATERIALS AND METHODS: The training set consisted of 915 radiographs from 207 patients diagnosed with OPLL. For the test set, we used 200 lateral cervical radiographs from 100 patients with cervical OPLL and 100 patients without OPLL. An observer performance study was conducted over two reading sessions. In the first session, we compared the diagnostic performance of the DL-model and the six observers. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) at the vertebra and patient level. The sensitivity and specificity of the DL model and average observers were calculated in per-patient analysis. Subgroup analysis was performed according to the morphologic classification of OPLL. In the second session, observers evaluated the radiographs by referring to the results of the DL-model. RESULTS: In the vertebra-level analysis, the DL-model showed an AUC of 0.854, which was higher than the average AUC of observers (0.826), but the difference was not significant (p = 0.292). In the patient-level analysis, the performance of the DL-model had an AUC of 0.851, and the average AUC of observers was 0.841 (p = 0.739). The patient-level sensitivity and specificity were 91% and 69% in the DL model, and 83% and 68% for the average observers, respectively. Both the DL-model and observers showed decreases in overall performance in the segmental and circumscribed types. With knowledge of the results of the DL-model, the average AUC of observers increased to 0.893 (p = 0.001) at the vertebra level and 0.911 (p < 0.001) at the patient level. In the subgroup analysis, the improvement was largest in segmental-type (AUC difference 0.087; p = 0.002). CONCLUSIONS: The DL-based OPLL detection model can significantly improve the diagnostic performance of radiologists on cervical radiographs.

The feasibility of shear wave elastography for diagnosing superficial benign soft tissue masses.

PURPOSE: The purpose of this study was to investigate the feasibility of shear wave ultrasound elastography for differentiating superficial benign soft tissue masses through a comparison of their shear moduli. METHODS: We retrospectively analyzed 48 masses from 46 patients from February 2014 to May 2016. Surgical excision, fine-needle aspiration, and clinical findings were used for the differential diagnosis. The ultrasonographic examinations were conducted by a single musculoskeletal radiologist, and the ultrasonographic findings were reviewed by two other radiologists who were blinded to the final diagnosis. Conventional ultrasonographic features and the median shear modulus were evaluated. We compared the median shear moduli of epidermoid cysts, ganglion cysts, and lipomatous tumors using the Kruskal-Wallis test. Additionally, the Mann-Whitney U test was used to compare two distinct groups. RESULTS: Significant differences were found in the median shear moduli of epidermoid cysts, ganglion cysts, and lipomatous tumors (23.7, 5.8, and 9.2 kPa, respectively; P=0.019). Epidermoid cysts showed a greater median shear modulus than ganglion cysts (P=0.014) and lipomatous tumors (P=0.049). CONCLUSION: Shear wave elastography may contribute to the differential diagnosis of superficial benign soft tissue masses through a direct quantitative analysis.