MDCT Evaluation of Costal Bone Lesions: Comparison of Axial, Multiplanar, and 3D Volume-Rendered Images: A Retrospective Study

The Diagnostic Performance of Multi-Detector Computed Tomography (MDCT) in Depiction of Acute Spondylodiscitis in an Emergency Department

Background: The diagnosis of acute spondylodiscitis can be very difficult because clinical onset symptoms are highly variable. The reference examination is MRI, but very often the first diagnostic investigation performed is CT, given its high availability in the acute setting. CT allows rapid evaluation of other alternative diagnoses (e.g., fractures), but scarce literature is available to evaluate the accuracy of CT, and in particular of multi-detector computed tomography (MDCT), in the diagnosis of suspected spondylodiscitis. The aim of our study was to establish MDCT accuracy and how this diagnostic method could help doctors in the depiction of acute spondylodiscitis in an emergency situation by comparing the diagnostic performance of MDCT with MRI, which is the gold standard. Methods: We searched our radiological archive for all MRI examinations of patients who had been studied for a suspicion of acute spondylodiscitis in the period between January 2017 and January 2021 (n = 162). We included only patients who had undergone MDCT examination prior to MRI examination (n = 25). The overall diagnostic value of MDCT was estimated, using MRI as the gold standard. In particular, the aim of our study was to clarify the effectiveness of CT in radiological cases that require immediate intervention (stage of complications). Therefore, the radiologist, faced with a negative CT finding, can suggest an elective (not urgent) MRI with relative serenity and without therapeutic delays. Results: MDCT allowed identification of the presence of acute spondylodiscitis in 13 of 25 patients. Specificity and positive predictive value were 100% for MDCT, while sensitivity and negative predictive value were 68% and 50%, respectively, achieving an overall accuracy of 76%. In addition, MDCT allowed the identification of paravertebral abscesses (92%), fairly pathognomonic lesions of spondylodiscitis pathology. Conclusions: The MDCT allows identification of the presence of acute spondylodiscitis in the Emergency Department (ED) with a satisfactory accuracy. In the case of a positive CT examination, this allows therapy to be started immediately and reduces complications. However, we suggest performing an elective MRI examination in negative cases in which pathological findings are hard to diagnose with CT alone.

Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network

OBJECTIVE

To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists.

MATERIALS AND METHODS

A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I).

RESULTS

The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time.

CONCLUSIONS

Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.

Evaluation of Rib Fractures on a Single-in-plane Image Reformation of the Rib Cage in CT Examinations

RATIONALE AND OBJECTIVES

This study aimed to evaluate the diagnostic performance of using a reformatted single-in-plane image reformation of the rib cage for the detection of rib fractures in computed tomography (CT) examinations, employing different levels of radiological experience.

MATERIALS AND METHODS

We retrospectively evaluated 10 consecutive patients with and 10 patients without rib fractures, whose CT scans were reformatted to a single-in-plane image reformation of the rib cage. Eight readers (two radiologists, two residents in radiology, and four interns) independently evaluated the images for the presence of rib fractures using a reformatted single-in-plane image and a multi-planar image reformation. The time limit was 30 seconds for each read. A consensus of two radiologist readings was considered as the reference standard. Diagnostic performance (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) was assessed and evaluated per rib and per location (anterior, lateral, posterior). To determine the time limit, we prospectively analyzed the average time it took radiologists to assess the rib cage, in a bone window setting, in 50 routine CT examinations. McNemar test was used to compare the diagnostic performances.

RESULTS

Single image reformation was successful in all 20 patients. The sensitivity, specificity, PPV, and NPV for the detection of rib fractures using the conventional multi-planar read were 77.5%, 99.2%, 89.9%, and 98.0% for radiologists; 46.3%, 99.7%, 92.5%, and 95.3% for residents; and 29.4%, 99.4%, 82.5%, and 93.9% for interns, respectively. Sensitivity, PPV, and NPV increased across all three groups of experience, using the reformatted single-in-plane image of the rib cage (radiologists: 85.0%, 98.6%, and 98.7%; residents: 80.0%, 92.8%, and 98.2%; interns: 66.9%, 89.9%, and 97.1%), whereas specificity did not change significantly (99.9%, 99.4%, and 99.3%). The diagnostic performance of the interns and residents was significantly better when evaluating the single-in-plane image reformations (P < .01). The diagnostic performance of the radiologists was better when evaluating the single-in-plane image reformations; however, there was no significant difference (statistical power: 0.32).

CONCLUSIONS

The diagnostic performance for the detection of rib fractures, using CT images that have been reformatted to a single-in-plane image, improves for readers from different educational levels when the evaluation time is restricted to 30 seconds or less.