Atypical Enostoses-Series of Ten Cases and Literature Review

Deep learning-based diagnosis of osteoblastic bone metastases and bone islands in computed tomograph images: a multicenter diagnostic study

OBJECTIVE

To develop and validate a deep learning (DL) model based on CT for differentiating bone islands and osteoblastic bone metastases.

MATERIALS AND METHODS

The patients with sclerosing bone lesions (SBLs) were retrospectively included in three hospitals. The images from site 1 were randomly assigned to the training (70%) and intrinsic verification (10%) datasets for developing the two-dimensional (2D) DL model (single-slice input) and "2.5-dimensional" (2.5D) DL model (three-slice input) and to the internal validation dataset (20%) for evaluating the performance of both models. The diagnostic performance was evaluated using the internal validation set from site 1 and additional external validation datasets from site 2 and site 3. And statistically analyze the performance of 2D and 2.5D DL models.

RESULTS

In total, 1918 SBLs in 728 patients in site 1, 122 SBLs in 71 patients in site 2, and 71 SBLs in 47 patients in site 3 were used to develop and test the 2D and 2.5D DL models. The best performance was obtained using the 2.5D DL model, which achieved an AUC of 0.996 (95% confidence interval [CI], 0.995-0.996), 0.958 (95% CI, 0.958-0.960), and 0.952 (95% CI, 0.951-0.953) and accuracies of 0.950, 0.902, and 0.863 for the internal validation set, the external validation set from site 2 and site 3, respectively.

CONCLUSION

A DL model based on a three-slice CT image input (2.5D DL model) can improve the prediction of osteoblastic bone metastases, which can facilitate clinical decision-making.

KEY POINTS

• This study investigated the value of deep learning models in identifying bone islands and osteoblastic bone metastases. • Three-slice CT image input (2.5D DL model) outweighed the 2D model in the classification of sclerosing bone lesions. • The 2.5D deep learning model showed excellent performance using the internal (AUC, 0.996) and two external (AUC, 0.958; AUC, 0.952) validation sets.

Osteoid Osteoma, a Diagnostic Problem: A Series of Atypical and Mimicking Presentations and Review of the Recent Literature

Osteoid osteoma (OO) is a common benign bone tumour, usually affecting young people. Typically, it is localised to the diaphyses or metaphyses of long bones. The classical manifestation includes distinctive night pain, almost always present, responding well to non-steroidal anti-inflammatory drugs, sometimes accompanied by complaints due to physical activity, and a typical picture on additional tests. A characteristic of osteoid osteoma is the presence of a nidus, usually visible on imaging tests. The nidus generally presents as a single, round lytic lesion up to 1 cm in diameter, surrounded by an area of reactive ossification. However, OO is a multifaceted neoplasm, and its diagnosis can cause numerous difficulties. OO can mimic multiple diseases and vice versa, which often leads to a prolonged diagnostic and therapeutic path and associated complications. There are few literature reviews about the differentiation and diagnostic difficulties of osteoid osteoma. Very effective therapies for this tumour are known, such as ablation and resection. Enhanced detection of osteoid osteoma could result in faster diagnosis and less suffering for the patient, avoidance of complications, and reduced costs of incorrect and prolonged treatment.

Osteoblastoma of the maxilla: A case report and review of the literature

Osteoblastoma is a benign tumour with aggressive trait usually seen in young males between 10 and 40 years of age. This condition is quite rare in children. Here, we report one such rare osteoblastoma occurring in the maxilla of a 4-year-old boy and discuss the clinical presentation, radiologic and histologic features, and treatment of this patient, and a brief review.