A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurately assessing epidermal growth factor receptor (EGFR) mutation status in head and neck squamous cell carcinoma (HNSCC) patients is crucial for prognosis and treatment selection. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict EGFR mutation status of HNSCC. MATERIALS AND METHODS: A total of 300 HNSCC patients who underwent CECT scans were enrolled in this study. Participants from two hospitals were separated into a training set (n = 200, 56 EGFR-negative and 144 EGFR-positive) from one hospital and an external test set from the other hospital (n = 100, 37 EGFR-negative and 63 EGFR-positive). The least absolute shrinkage and selection operator method was used to select the key features from CECT-based manually extracted radiomics (MER) features and features automatically extracted using a deep learning model (DL, extracted using a GoogLeNet model). The selected independent clinical factors, MER features, and DL features were then combined to construct a DLRN. The DLRN's performance was evaluated using receiver operating characteristics curves. RESULTS: Five MER and six DL features were finally chosen. The DLRN, which includes "gender" and "necrotic areas," along with the selected features, predicted EGFR mutation status of HNSCC (EGFR-negative vs. positive) well in both the training (area under the curve [AUC], 0.901) and test (AUC, 0.875) sets. CONCLUSION: A DLRN using CECT was built to predict EGFR mutation in HNSCC. The model showed high predictive ability and may aid in treatment selection and patient prognosis.

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 Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC. MATERIALS AND METHODS: A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA). RESULTS: Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC. CONCLUSION: A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.

A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin's lymphoma of the palatine tonsil.

OBJECTIVES: Accurate preoperative differentiation between squamous cell carcinoma (SCC) and non-Hodgkin's lymphoma (NHL) in the palatine tonsil is crucial because of their different treatment. This study aimed to construct and validate a contrast-enhanced CT (CECT)-based radiomics nomogram for preoperative differentiation of SCC and NHL in the palatine tonsil. METHODS: This study enrolled 135 patients with a pathological diagnosis of SCC or NHL from two clinical centers, who were divided into training (n = 94; SCC = 50, NHL = 44) and external validation sets (n = 41; SCC = 22, NHL = 19). A radiomics signature was constructed from radiomics features extracted from routine CECT images and a radiomics score (Rad-score) was calculated. A clinical model was established using demographic features and CT findings. The independent clinical factors and Rad-score were combined to construct a radiomics nomogram. Performance of the clinical model, radiomics signature, and nomogram was assessed using receiver operating characteristics analysis and decision curve analysis. RESULTS: Eleven features were finally selected to construct the radiomics signature. The radiomics nomogram incorporating gender, mean CECT value, and radiomics signature showed better predictive value for differentiating SCC from NHL than the clinical model for training (AUC, 0.919 vs. 0.801, p = 0.004) and validation (AUC, 0.876 vs. 0.703, p = 0.029) 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 incorporating gender, mean CECT value, and radiomics signature. This nomogram showed favorable predictive efficacy for differentiating SCC from NHL in the palatine tonsil, and might be useful for clinical decision-making. KEY POINTS: • Differential diagnosis between SCC and NHL in the palatine tonsil is difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, gender, and mean contrast-enhanced CT value facilitates differentiation of SCC from NHL with improved diagnostic efficacy.

A CT-based radiomics signature for preoperative discrimination between high and low expression of programmed death ligand 1 in head and neck squamous cell carcinoma.

PURPOSE: Accurate prediction of the expression level of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) is crucial before immunotherapy. The purpose of this study was to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to discriminate between high and low expression status of PD-L1. METHODS: A total of 179 HNSCC patients who underwent immunohistochemical examination of tumor PD-L1 expression at one of two centers were enrolled in this study and divided into a training set (n = 122; 55 high PD-L1 expression and 67 low PD-L1 expression) and an external validation set (n = 57; 26 high PD-L1 expression and 31 low PD-L1 expression). The least absolute shrinkage and selection operator method was used to select the key features for a CECT-image-based radiomics signature. The performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS: Six features were finally selected to construct the radiomics signature. The performance of the radiomics signature in the discrimination between high and low PD-L1 expression status was good in both the training and validation sets, with areas under the receiver operating characteristics curve of 0.889 and 0.834 for the training and validation sets, respectively. CONCLUSIONS: The constructed CECT-based radiomics signature model showed favorable performance for discriminating between high and low PD-L1 expression status in HNSCC patients. It may be useful for screening out those patients with HNSCC who can best benefit from anti-PD-L1 immunotherapy.

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland.

OBJECTIVE：: Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA. METHODS AND MATERIALS: A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model. RESULTS: The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively. CONCLUSIONS: The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.

Computed tomography, magnetic resonance imaging, and F-deoxyglucose positron emission computed tomography/computed tomography findings of alveolar soft part sarcoma with calcification in the thigh: A case report.

BACKGROUND: Alveolar soft part sarcoma (ASPS) is an extremely rare malignant sarcoma, accounting for less than 1% of all soft-tissue sarcomas. However, limited information is available on multimodal imaging [computed tomography (CT), magnetic resonance imaging (MRI), and positron emission computed tomography/computed tomography (PET/CT)] of ASPS. CASE SUMMARY: This study reports a case of a 35-year-old female patient with ASPS of the left thigh with lung metastasis. The patient presented with a 1-year history of a palpable mass in the lower extremity, which exhibited rapid growth for 3 wk. CT, MRI, and F-deoxyglucose PET/CT examinations were performed. CT showed a slightly hypodense or isodense mass with patchy calcifications. On MRI examination, the mass manifested hyperintensity on T1-weighted, T2-weighted, and diffusion-weighted images with some signal voids. PET/CT images demonstrated an intensely hypermetabolic mass in the left thigh and hypermetabolic nodules in lungs. CONCLUSION: ASPS should be considered as a possible diagnosis when a slow-growing mass is detected in the soft tissue of the extremities, with hyperintensity and numerous signal voids on T1-weighted, T2-weighted, and diffusion-weighted images and intense F-deoxyglucose uptake on PET/CT. ASPS can have calcifications on CT.

Colonic Metastasis From Esophageal Squamous Cell Carcinoma Demonstrated With 18F-FDG PET/CT.

Metastatic involvement of the colon is extremely rare. We report a 63-year-old man with colonic metastasis from esophageal squamous cell carcinoma. Whole-body F-FDG PET/CT showed abnormal tracer uptakes in the sigmoid colon, which was subsequently confirmed as metastatic from esophageal squamous cell carcinoma.

Metastatic Malignant Fibrous Histiocytoma in the Stomach: Imaging With 18F-FDG PET/CT.

Metastatic malignant fibrous histiocytoma (MFH) in the stomach is extremely rare. We report a 73-year-old man with MFH in the left popliteal fossa that metastasized to the stomach. Whole-body F-FDG PET/CT showed abnormal tracer uptakes in the stomach, which was subsequently confirmed as metastatic MFH.

Myofibroma of the Left Supraspinous Muscle on 18F-FDG PET/CT.

Myofibroma of the muscle is extremely rare. This report describes a 46-year-old man with myofibroma of the left supraspinous muscle on 18F-FDG PET/CT and includes MRI and 18F-FDG PET/CT findings. Whole-body 18F-FDG PET/CT showed abnormal tracer uptakes in the left supraspinous muscle, which was subsequently confirmed as myofibroma by histopathology.

Characteristics of elastofibroma dorsi on PET/CT imaging with (18)F-FDG.

PURPOSE: To assess the characteristics of 18F-FDG uptake in elastofibroma dorsi (EFD). METHODS: Seventeen patients with EFD were retrospectively studied. RESULTS: The mean±S.D. of SUV was 2.29±0.60 (range, 1.2-4.3), and the uptake were Grade 0 in 6, Grade 1 in 12, Grade 2 in 7, and Grade 3 in 1. There is no correlation between lesion volume, SUVmax, and computer tomography value. All EFD lesions showed soft tissue density with low or moderate diffused and homogeneous uptake of 18F-FDG. CONCLUSIONS: Mild and moderate uptake of 18F-FDG is frequently observed in EFD, which should be known to avoid making wrong diagnosis.

18F-FDG PET/CT Findings of Metastasis to Spongy Body of Penis From Urothelial Carcinoma of Bladder.

The spongy body of the penis metastasis from other primary sites is a rare clinical entity. It is frequently associated with widespread metastatic disease and poor prognosis clinically. We report a case of a 61-year-old man with a previous history of cystectomy due to infiltrating urothelial carcinoma of the bladder 12 months ago and presented with penile shaft swelling pain and hematuria for 3 months. The restaging F-FDG PET/CT scan demonstrated a hypermetabolic mass at his penile shaft. This lesion was confirmed on phallectomy to be infiltrating urothelial carcinoma metastasis from the known primary bladder tumor.

Localized Airspace Consolidation of Pulmonary Alveolar Proteinosis Mimicking Malignant Lesions in 18F-FDG PET/CT Imaging: One Case Report.

Pulmonary alveolar proteinosis is a rare lung disease caused by diffuse, abnormal intra-alveolar surfactant accumulation. Here, we report a case of autoimmune pulmonary alveolar proteinosis with heterogeneous accumulation of F-FDG in both lungs shown on the combined F-FDG PET/CT.

Feasible Method to enable clear visualization of suspected bladder cancer with 18F-FDG PET/CT.

PURPOSE: To evaluate value of a new method in detecting bladder lesion with (18)F-FDG PET/CT. METHODS: Routine and delayed scans of 14 patients were retrospectively studied. All these bladder lesions were confirmed by pathology. By increasing display threshold of SUVmax, routine images were analyzed for the second time. RESULTS: Of 12 (18)F-FDG-avid cases, 10 cases were confirmed to be primary bladder carcinoma, 2 false-positive cases were inflammation. There were 25% positive cases on routine display and 50% on display with increased SUVmax threshold. CONCLUSION: The method can effectively increase the sensitivity and accuracy in detecting bladder cancer with (18)F-FDG PET/CT.