CT radiomics nomogram for prediction of the Ki-67 index in head and neck squamous cell carcinoma.

OBJECTIVES: To construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics nomogram to predict Ki-67 expression level in head and neck squamous cell carcinoma (HNSCC). METHODS: A total of 217 patients with HNSCC who underwent CECT scans and immunohistochemical examination of their Ki-67 index were enrolled in this study. The patients were divided into a training set (n = 140; Ki-67: ≥ 50% [n = 72] and < 50% [n = 68]) and an external test set (n = 77; Ki-67: ≥ 50% [n = 38] and < 50% [n = 39]). The least absolute shrinkage and selection operator method was used to select key features for a CECT-image-based radiomics signature and a radiomics score (Rad-score) was calculated. A clinical model was established using clinical data and CT findings. The independent clinical factors and Rad-score were then combined to construct a radiomics nomogram. The performance characteristics of the Rad-score, clinical model, and nomogram were assessed using ROCs and decision curve analysis. RESULTS: Twenty features were finally selected to construct the Rad-score. The radiomics nomogram incorporating the Rad-score, low histological grade, and lymphatic spread showed higher predictive value for the Ki-67 index (≥ 50% vs. < 50%) than the clinical model on both the training (AUC, 0.919 vs. 0.648, p < 0.001) and test (AUC, 0.832 vs. 0.685, p = 0.030) sets. Decision curve analysis demonstrated that the radiomics nomogram was more clinically useful than the clinical model. CONCLUSIONS: A CECT-based radiomics nomogram was constructed to predict the expression of Ki-67 in HNSCC. This model showed favorable predictive efficacy and might be useful for prognostic evaluation and clinical decision-making in patients with HNSCC. KEY POINTS: • Accurate pre-treatment prediction of Ki-67 index in HNSCC is crucial. • A CECT-based radiomics nomogram showed favorable predictive efficacy in estimation of Ki-67 expression status in HNSCC patients.

A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland.

OBJECTIVES: Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland. METHODS: A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models. RESULTS: Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness. CONCLUSIONS: The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process. KEY POINTS: • Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.

High-resolution MRI assessment of optic nerve sheath diameter in adults: optic nerve sheath variation and a new diagnostic tool for intracranial hypertension.

BACKGROUND: Assessment of optic nerve sheath diameter (ONSD) is a non-invasive measure of intracranial pressure (ICP). However, it is not clear whether healthy individuals exhibit ONSD variation or whether factors other than ICP affect the ONSD. PURPOSE: To investigate whether ONSD was correlated with age, sex, height, weight, eyeball transverse diameter (ETD), or body mass index (BMI), and to develop a new diagnostic model to increase the diagnostic accuracy of intracranial hypertension (IH). MATERIAL AND METHODS: A total of 145 relatively healthy adults and 40 patients with acute IH who underwent high-resolution magnetic resonance imaging (MRI) were enrolled in this study. Linear regression analyses were used to determine the relationship between ONSD and these variables. If correlations were identified, an index ONSDΔ removing variables effects was calculated. ROC analysis was used to assess the IH predictive value of ONSDΔ in terms of sensitivity and specificity. RESULTS: In relatively healthy adults, there was a correlation between ONSD and BMI (P = 0.002), which can be presented as an index ONSDΔ. The ONSDΔ model better predicted IH than the ONSD model (P = 0.035), with a sensitivity of 70.00%, a specificity of 71.72%, and an AUC of 0.755. CONCLUSION: A correlation between ONSD and body mass index (BMI) was found using high-resolution MRI. This result indicates that the effects of BMI should be considered along with the ONSD during ICP monitoring. Meanwhile, the index ONSDΔ was better than the ONSD in predicting IH and could be used to obtain a more precise estimation of ICP.