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.

DEPDC1B enhances malignant phenotypes of multiple myeloma through upregulating CCNB1 and inhibiting p53 signaling pathway.

The identification and investigation of key molecules involved in the pathogenesis of multiple myeloma (MM) hold paramount clinical significance. This study primarily focuses on elucidating the role of DEPDC1B within the context of MM. Our findings robustly affirm the abundant expression of DEPDC1B in MM tissues and cell lines. Notably, DEPDC1B depletion exerted inhibitory effects on MM cell proliferation and migration while concurrently facilitating apoptosis and G2 cell cycle arrest. These outcomes stand in stark contrast to the consequences of DEPDC1B overexpression. Furthermore, we identified CCNB1 as a putative downstream target, characterized by a co-expression pattern with DEPDC1B, mediating DEPDC1B's regulatory influence on MM. Additionally, our results suggest that DEPDC1B knockdown may activate the p53 pathway, thereby impeding MM progression. To corroborate these in vitro findings, we conducted in vivo experiments that further validate the regulatory role of DEPDC1B in MM and its interaction with CCNB1 and the p53 pathway. Collectively, our research underscores DEPDC1B as a potent promoter in the development of MM, representing a promising therapeutic target for MM treatment. This discovery bears significant implications for future investigations in this field.

Intratumoral and Peritumoral Radiomics Based on Preoperative MRI for Evaluation of Programmed Cell Death Ligand-1 Expression in Breast Cancer.

BACKGROUND: Programmed cell death ligand-1 (PD-L1) is a promising target for immune checkpoint blockade therapy in breast cancer. However, the preoperative evaluation of PD-L1 expression in breast cancer is rarely explored. PURPOSE: To determine the ability of radiomics signatures based on preoperative dynamic contrast-enhanced (DCE) MRI to evaluate PD-L1 expression in breast cancer. STUDY TYPE: Retrospective. POPULATION: 196 primary breast cancer patients with preoperative MRI and postoperative pathological evaluation of PD-L1 expression, divided into training (n = 137, 28 PD-L1-positive) and test cohorts (n = 59, 12 PD-L1-positive). FIELD STRENGTH/SEQUENCE: 3.0T; volume imaging for breast assessment DCE sequence. ASSESSMENT: Radiomics features were extracted from the first phase of DCE-MRI by using the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. Three radiomics signatures were constructed based on the intratumoral, peritumoral, and combined intra- and peritumoral regions. The performance of the signatures was assessed using area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy. STATISTICAL TESTS: Univariable and multivariable logistic regression analysis, t-tests, chi-square tests, Fisher exact test or Yates correction, ROC analysis, and one-way analysis of variance. P < 0.05 was considered significant. RESULTS: In the test cohort, the combined radiomics signature (AUC, 0.853) exhibited superior performance compared to the intratumoral (AUC, 0.816; P = 0.528) and peritumoral radiomics signatures (AUC, 0.846; P = 0.905) in PD-L1 status evaluation, although the differences did not reach statistical significance. DATA CONCLUSION: Intratumoral and peritumoral radiomics signatures based on preoperative breast MRI showed some potential accuracy for the non-invasive evaluation of PD-L1 status in breast cancer. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer.

RATIONALE AND OBJECTIVES: Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer. MATERIALS AND METHODS: MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves. RESULTS: Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility. CONCLUSION: The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS.

Lumbar MR-based radiomics nomogram for detecting minimal residual disease in patients with multiple myeloma.

OBJECTIVES: Minimal residual disease (MRD) is a standard for assessing treatment response in multiple myeloma (MM). MRD negativity is considered to be the most powerful predictor of long-term good outcomes. This study aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) of the lumbar spine to detect MRD after MM treatment. METHODS: A total of 130 MM patients (55 MRD negative and 75 MRD positive) who had undergone MRD testing through next-generation flow cytometry were divided into a training set (n = 90) and a test set (n = 40). Radiomics features were extracted from lumbar spinal MRI (T1-weighted images and fat-suppressed T2-weighted images) by means of the minimum redundancy maximum relevance method and the least absolute shrinkage and selection operator algorithm. A radiomics signature model was constructed. A clinical model was established using demographic features. A radiomics nomogram incorporating the radiomics signature and independent clinical factor was developed using multivariate logistic regression analysis. RESULTS: Sixteen features were used to establish the radiomics signature. The radiomics nomogram included the radiomics signature and the independent clinical factor (free light chain ratio) and showed good performance in detecting the MRD status (area under the curve: 0.980 in the training set and 0.903 in the test set). CONCLUSIONS: The lumbar MRI-based radiomics nomogram showed good performance in detecting MRD status in MM patients after treatment, and it is helpful for clinical decision-making. KEY POINTS: • The presence or absence of minimal residual disease status has a strong predictive significance for the prognosis of patients with multiple myeloma. • A radiomics nomogram based on lumbar MRI is a potential and reliable tool for evaluating minimal residual disease status in MM.

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.

Evaluation of Lymphatic Vessel Invasion Determined by D2-40 Using Preoperative MRI-Based Radiomics for Invasive Breast Cancer.

RATIONALE AND OBJECTIVES: Preoperative prediction of LVI status can facilitate personalized therapeutic planning. This study aims to investigate the efficacy of preoperative MRI-based radiomics for predicting lymphatic vessel invasion (LVI) determined by D2-40 in patients with invasive breast cancer. MATERIALS AND METHODS: A total of 203 patients with pathologically confirmed invasive breast cancer, who underwent preoperative breast MRI, were retrospectively enrolled and randomly assigned to the following cohorts: training cohort (n=141) and test cohort (n=62). Then, univariate and multivariate logistic regression were performed to select independent risk factors and build a clinical model. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select predictive features extracted from the early and delay enhancement dynamic contrast-enhanced (DCE)-MRI images, and a radiomics signature was established. Subsequently, a nomogram model was constructed by incorporating the radiomics score and risk factors. Receiver operating characteristic curves were performed to determine the performance of various models. The efficacy of the various models was evaluated using calibration and decision curves. RESULTS: Fourteen radiomics features were selected to construct the radiomics model. The size of the lymph node was identified as an independent risk factor of the clinical model. The nomogram model demonstrated the best calibration and discrimination performance in both the training and test cohorts, with an area under the curve of 0.873 (95% confidence interval [CI]: 0.807-0.923) and 0.902 (95% CI: 0.800-0.963), respectively. The decision curve illustrated that the nomogram model added more net benefits, when compared to the radiomics signature and clinical model. CONCLUSION: The nomogram model based on preoperative DCE-MRI images exhibits satisfactory efficacy for the noninvasive prediction of LVI determined by D2-40 in invasive breast cancer.

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.

Intravoxel incoherent motion imaging combined with diffusion kurtosis imaging to assess the response to radiotherapy in a rabbit VX2 malignant bone tumor model.

PURPOSE: To combine intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) parameters for the evaluation of radiotherapy response in rabbit VX2 malignant bone tumor model. MATERIAL AND METHODS: Forty-seven rabbits with bone tumor were prospectively enrolled and divided into pre-treatment, considerable effect and slight effect group. Treatment response was evaluated using IVIM-DKI. IVIM-based parameters (tissue diffusion [Dt], pseudo-diffusion [Dp], perfusion fraction [fp]), and DKI-based parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were calculated for each animal. Corresponding changes in MRI parameters before and after radiotherapy in each group were studied with one-way ANOVA. Correlations of diffusion parameters of IVIM and DKI model were computed using Pearson's correlation test. A diagnostic model combining different diffusion parameters was established using binary logistic regression, and its ROC curve was used to evaluate its diagnostic performance for determining considerable and slight effect to malignant bone tumor. RESULTS: After radiotherapy, Dt and MD increased, whereas fp and MK decreased (p <  0.05). The differences in Dt, fp, MD, and MK between considerable effect and slight effect groups were statistically significant (p <  0.05). A combination of Dt, fp, and MK had the best diagnostic performance for differentiating considerable effect from slight effect (AUC = 0.913, p <  0.001). CONCLUSIONS: A combination of IVIM- and DKI-based parameters allowed the non-invasive assessment of cellular, vascular, and microstructural changes in malignant bone tumors after radiotherapy, and holds great potential for monitoring the efficacy of tumor radiotherapy.

Spinal MRI-Based Radiomics Analysis to Predict Treatment Response in Multiple Myeloma.

OBJECTIVE: The aim of this study was to explore the clinical utility of spinal magnetic resonance imaging-based radiomics to predict treatment response (TR) in patients with multiple myeloma (MM). METHODS: A total of 123 MM patients (85 in the training cohort and 38 in the test cohort) with complete response (CR) (n = 40) or non-CR (n = 83) were retrospectively enrolled in the study. Key feature selection and data dimension reduction were performed using the least absolute shrinkage and selection operator regression. A nomogram was built by combining radiomic signatures and independent clinical risk factors. The prediction performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Treatment response was assessed by determining the serum and urinary levels of M-proteins, serum-free light chain ratio, and the percentage of bone marrow plasma cells. RESULTS: Thirteen features were selected to build a radiomic signature. The International Staging System (ISS) stage was selected as an independent clinical factor. The radiomic signature and nomogram showed better calibration and higher discriminatory capacity (AUC of 0.929 and 0.917 for the radiomics and nomogram in the training cohort, respectively, and 0.862 and 0.874 for the radiomics and nomogram in the test cohort, respectively) than the clinical model (AUC of 0.661 and 0.674 in the training and test cohort, respectively). Decision curve analysis confirmed the clinical utility of the radiomics model. CONCLUSIONS: Nomograms incorporating a magnetic resonance imaging-based radiomic signature and ISS stage help predict the response to chemotherapy for MM and can be useful in clinical decision-making.

Diffusion kurtosis imaging assessment of the response to radiotherapy in a VX2 bone tumor model: an animal study.

BACKGROUND: Neoadjuvant radiotherapy plays a vital role in the treatment of malignant bone tumors, and non-invasive imaging methods are needed to evaluate the response to treatment. PURPOSE: To assess the value of diffusion kurtosis imaging (DKI) for monitoring early response to radiotherapy in malignant bone tumors. MATERIAL AND METHODS: Treatment response was evaluated in a rabbit VX2 bone tumor model (n = 35) using magnetic resonance imaging (MRI), DKI, and histopathologic examinations. Subjects were divided into three groups: pre-treatment, post-treatment, and control groups. The post-treatment group was subclassified into good response and poor response groups according to the results of histopathologic examination. Apparent diffusion coefficient (ADC) and DKI parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were recorded. The relationship between ADC, DKI parameters, and histopathologic changes after radiotherapy was determined using Pearson's correlation coefficient. The diagnostic performance of these parameters was assessed using receiver operating characteristic analysis. RESULTS: MD in the good response group was higher after treatment than before treatment (P < 0.001) and higher than that in the poor response group (P = 0.009). MD was highly correlated with tumor cell density and apoptosis rate (r = -0.771, P < 0.001 and r = 0.625, P < 0.001, respectively). MD was superior to other parameters for determining the curative effect of radiotherapy, with a sensitivity of 75.0%, specificity of 100.0%, and area under the curve of 0.917 (P < 0.001). CONCLUSION: The correlations between MD, tumor cell density, and apoptosis suggest that MD could be useful for assessing the early response to radiotherapy in rabbit VX2 malignant bone tumors.

Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer Using Preoperative MRI-Based Radiomics.

BACKGROUND: Evaluating tumor-infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored. PURPOSE: To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level. STUDY TYPE: Retrospective. POPULATION: A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results. FIELD STRENGTH/SEQUENCE: 3.0 T; axial T2-weighted imaging (fast spin echo), diffusion-weighted imaging (spin echo-echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo). ASSESSMENT: A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated. STATISTICAL TESTS: The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t-tests and chi-squared tests or Fisher's exact test, Hosmer-Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant. RESULTS: The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9. DATA CONCLUSION: The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

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.

Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers. METHODS: 152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller-Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors. RESULTS: 20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85-0.96, 0.86-0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86-1.00, 0.82-1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64-0.84, 0.46-0.82) in terms of NAC sensitivity and pCR. CONCLUSIONS: The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens. ADVANCES IN KNOWLEDGE: Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.

The Differentiation of Soft Tissue Infiltration and Surrounding Edema in an Animal Model of Malignant Bone Tumor: Evaluation by Dual-Energy CT.

To study the value of dual energy computed tomography in distinguishing soft tissue infiltration from surrounding soft tissue edema in rabbit malignant bone tumor, a malignant bone tumor model was established through implantation of VX2 tumor fragments into the tibiae of rabbits. Tumor adjacent soft tissues were divided into 3 areas according to pathology and computed tomography images. Computed tomography spectral curve slopes and iodine and water concentrations were assessed. Statistical analyses were performed using the Mann-Whitney U test and t test. The spectral curve of the soft tissue infiltration areas has a slope (1.30 ± 0.41) higher than that of the soft tissue edema areas (0.71 ± 0.23; P < .001). The iodine concentration in the soft tissue infiltration areas (8.56 ± 2.15) was higher than that in the in soft tissue edema areas (6.09 ± 1.02; P < .001). Water concentration was similar in the soft tissue infiltration areas (1033.86 ± 10.50) to that of the edema areas (1031.45 ± 12.83; P < .05). Spectral curve analysis and iodine-water concentration are helpful in the differentiation of bone tumor soft tissue infiltration and soft tissue edema.

Evaluating the Scope of Malignant Bone Tumor Using ADC Measurement on ADC Map.

BACKGROUND: It is very important for surgeons to know the accurate borders of malignant bone tumors before they can precisely resect the tumors. The objective of the study is to investigate the usefulness of apparent diffusion coefficient value for estimating the extent of malignant bone tumor. METHODS: VX2 tumor fragments were implanted into the tibiae of 30 rabbits. After 4 weeks, magnetic resonance plain scans were performed and then tumor specimens were cut into sagittal sections and partitioned into histology slices for dot-to-dot comparisons with microscopic findings. The sizes of the tumors measured separately on specimen, conventional magnetic resonance imaging sequences, and diffusion-weighted imaging (by measuring apparent diffusion coefficient value on apparent diffusion coefficient mapping) were compared statistically with each other. RESULTS: The mean tumor sizes measured on specimen and apparent diffusion coefficient mapping (by calculating apparent diffusion coefficient value) were 5.20 ± 0.89 cm and 5.31 ± 0.87 cm, respectively; there was no significant difference between the 2 ( P > .05). The tumor sizes measured on T1WI, T2WI, T2WI with fat suppression were 4.82 ± 0.87 cm, 5.58 ± 0.87 cm, 5.63 ± 0.85 cm, respectively, and these values were significantly different from that measured on specimen (5.20 ± 0.89 cm, P < .05). CONCLUSION: The extent of the VX2 malignant bone tumor can be estimated accurately by measurement of apparent diffusion coefficient value.