Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study.

BACKGROUND: Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC. METHODS: This study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively. RESULTS: In total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781-0.961) and 0.883 (range: 0.798-0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862-0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837-0.966) and 0.922 (range: 0.872-0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886-0.968). CONCLUSIONS: The ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.

Atypical lipoma of the right piriformis muscle: a case report and review of the literature.

BACKGROUND: Piriformis muscle mass is rare, which is particular for intrapiriformis lipoma. Thus far, only 11 cases of piriformis muscle mass have been reported in the English literature. Herein, we encountered one patient with intrapiriformis lipoma who was initially misdiagnosed. CASE PRESENTATION: The patient is a 50-year-old Chinese man. He complained of osphyalgia, right buttock pain, and radiating pain from the right buttock to the back of the right leg. Both ultrasound and magnetic resonance imaging demonstrated a cyst-like mass in the right piriformis muscle. Ultrasonography-guided aspiration was performed on this patient first, but failed. He was then recommended to undergo mass resection and neurolysis of sciatic nerve. Surprisingly, final histology revealed the diagnosis of intrapiriformis lipoma. The patient exhibited significant relief of symptoms 3 days post-surgery. CONCLUSION: Diagnosis and differential diagnosis of radicular pain are potentially challenging but necessary. Atypical lipoma is prone to be misdiagnosed, especially in rare sites. It is notable for clinicians to be aware of the presence of intrapiriformis lipoma to avoid misdiagnosis and inappropriate treatment.