Solitary myofibroma of the lumbar vertebra in young adult: A case report with 4-year follow-up of postoperative CT or MRI

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis

BACKGROUND

Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals.

METHODS

This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy.

RESULTS

Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations.

CONCLUSIONS

Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.

Myofibroma/myofibromatosis: MDCT and MR imaging findings in 24 patients with radiological-pathological correlation

Background

The aim of this study was to characterize the radiological features of myofibroma on multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) and correlate the imaging findings with pathologic features.

Methods

The radiological findings of 24 patients with 29 myofibromas were retrospectively reviewed. All images were evaluated with emphasis on density, signal intensity, hypointense area, and enhancement, correlating these with pathologic findings.

Results

On plain MDCT scan, 4(26.7%) tumors were homogeneous isodensity, 4(26.7%) tumors were heterogeneous hyperdensity, and 7(46.7%) tumors were heterogeneous hypodensity. On contrast-enhanced MDCT scan, all tumors (9/9) showed heterogeneous enhancement with moderate in 3(33.3%) and marked in 6(66.7%) tumors, and their enhancements were higher compared to adjacent skeletal muscle (P = 0.0001). On MRI, heterogeneous slight hyperintensity, homogeneous slight hyperintensity, and heterogeneous hypointensity on T1-weighted imaging (T1WI) were observed in 14(82.3%), 1(5.9%) and 2(11.8%) tumors, respectively. On T2-weighted imaging (T2WI) and fat-suppressed (FS) T2WI, all tumors demonstrated heterogeneous hyperintensity. All tumors showed heterogeneous marked enhancement on FS contrast-enhanced T1WI. On T1WI, T2WI, FS T2WI, and FS contrast-enhanced T1WI, irregular strip or/and patchy hypointensities were found in 16(94.1%), 12(100%), 17(100%) and 17(100%) tumors, respectively, and pseudocapsule was seen in 5(29.4%) tumors. The hypointensities and pseudocapsule on MRI were exactly corresponding to pathological interlacing collagen fibers and fibrosis. The age of the recurrent group was lower than that of the non-recurrent group (P = 0.001) and the tumors without pseudocapsule were more likely to recur than those with pseudocapsule (P = 0.034).

Conclusion

Myofibromas are characterized by heterogeneous density or signal intensity, with moderate or marked enhancement. The hypointensities and pseudocapsule on MRI may be helpful in diagnosis, and the absence of pseudocapsule and younger age may be risk factors for tumor recurrence.