A cross-sectional study on the correlation of image quality, effective dose, and body composition with thyroid, chest, and abdominal computed tomography scans.

Background: The rapid increase in the use of radiodiagnostic examinations in China, especially computed tomography (CT) scans, has led to these examinations being the largest artificial source of per capita effective dose (ED). This study conducted a retrospective analysis of the correlation between image quality, ED, and body composition in 540 cases that underwent thyroid, chest, or abdominal CT scans. The aim of this analysis was to evaluate the correlation between the parameters of CT scans and body composition in common positions of CT examination (thyroid, chest, and abdomen) and ultimately inform potential measures for reducing radiation exposure. Methods: This study included 540 patients admitted to Fudan University Shanghai Cancer Center from January 2015 to December 2019 who underwent both thyroid or chest or abdominal CT scan and body composition examination. Average CT values and standard deviation (SD) values were collected for the homogeneous areas of the thyroid, chest, or abdomen, and the average CT values and SD values of adjacent subcutaneous fat tissue were measured in the same region of interest (ROI). All data were measured three times, and the average was taken to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for each area. The dose-length product (DLP) was recorded, and the ED was calculated with the following: formula ED = k × DLP. Dual-energy X-ray was used to determine body composition and obtain indicators such as percentage of spinal and thigh muscle. Pearson correlation coefficient was used to analyze the correlations between body composition indicators, height, weight, body mass index (BMI), and ED. Results: The correlation coefficients between the SNR of abdominal CT scan and weight, BMI, and body surface area (BSA) were -0.470 (P=0.001), -0.485 (P=0.001), and -0.437 (P=0.002), representing a moderate correlation strength with statistically significant differences. The correlation coefficients between the ED of chest CT scans and weight, BMI, spinal fat percentage, and BSA were 0.488 (P=0.001), 0.473 (P=0.002), 0.422 (P=0.001), and 0.461 (P=0.003), respectively, indicating a moderate correlation strength with statistical differences. There was a weak statistically significant correlation between the SNR, CNR, and ED of the other scans with each physical and body composition index (P=0.023). Conclusions: There were varying degrees of correlation between CT image quality and ED and physical and body composition indices, which may inform novel solutions for reducing radiation exposure.

Association of Clinical Factors and Degree of Early Background Parenchymal Enhancement on Contrast-Enhanced Mammography.

BACKGROUND. Background parenchymal enhancement (BPE) may impact contrast-enhanced mammography (CEM) interpretation, although factors influencing the degree of BPE on CEM are poorly understood. OBJECTIVE. The purpose of our study was to evaluate relationships between clinical factors and the degree of early BPE on CEM. METHODS. This retrospective study included 207 patients (median age, 46 years) who underwent CEM between April 2020 and September 2021. Two radiologists independently assessed the degree of BPE on CEM as minimal, mild, moderate, or marked on the basis of two criteria (criterion 1, using the first of four obtained views; criterion 2, using the first two of four obtained views). The radiologists reached consensus for breast density on CEM. The EMR was reviewed for clinical factors. Radiologists' agreement for degree of BPE was assessed using weighted kappa coefficients. Univariable and multivariable analyses were performed to assess relationships between clinical factors and degree of BPE, treating readers' independent assessments as repeated measurements. RESULTS. Interreader agreement for degree of BPE, expressed as kappa, was 0.80 for both criteria. For both criteria, univariable analyses found degree of BPE to be negatively associated with age (both OR = 0.94), personal history of breast cancer (OR = 0.22-0.30), history of chemotherapy (OR = 0.18-0.21), history of radiation therapy (OR = 0.20-0.21), perimenopausal status (OR = 0.22-0.34), and postmenopausal status (OR = 0.10-0.11) and to be positively associated with dense breasts (OR = 4.13-4.26) and premenopausal status with irregular menstrual cycles (OR = 7.94-14.02). Among premenopausal patients with regular menstrual cycles, degree of BPE was lowest (using postmenopausal patients as reference) for patients in menstrual cycle days 8-14 (OR = 2.56-3.30). In multivariable analysis for both criteria, the only independent predictors of degree of BPE related to menstrual status and time of menstrual cycle (e.g., using premenopausal patients in days 1-7 as reference: OR = 0.21 for both criteria for premenopausal patients in days 8-14 and OR = 0.03-0.04 for postmenopausal patients). CONCLUSION. Clinical factors, including history of breast cancer or breast cancer treatment, breast density, menstrual status, and time of menstrual cycle, are associated with degree of early BPE on CEM. In premenopausal patients, the degree of BPE is lowest on days 8-14 of the menstrual cycle. CLINICAL IMPACT. Given the potential impact of BPE on diagnostic performance, the findings have implications for CEM scheduling and interpretation.

Multiparametric MR Imaging Radiomics Signatures for Assessing the Recurrence Risk of ER+/HER2- Breast Cancer Quantified With 21-Gene Recurrence Score.

BACKGROUND: While the Oncotype DX 21-gene recurrence score (RS) has been recommended for guiding ER+/HER2- breast cancer treatment decisions, it is limited by cost and availability. PURPOSE: To develop a multiparametric MRI-based radiomics model for assessing ER+/HER2- breast cancer patients' 21-gene RS. STUDY TYPE: Retrospective. SUBJECTS: A total of 151 patients with pathologically confirmed ER+/HER2- breast cancers, who underwent preoperative breast MR examinations and 21-gene expression assays, divided into training (n = 106) and validation (n = 45) cohorts. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhancement (DCE) sequence at 1.5 T or 3 T. ASSESSMENT: A total of 1046 radiomics features were extracted from each MRI sequence with a manual lesion segmentation method. After feature dimension reduction by the recursive feature elimination method and dataset balance by the synthetic minority oversampling technique, linear support vector machine classifier models were built to distinguish high RS (RS ≥ 26) from low RS (RS < 26) from T2WI, DWI apparent diffusion coefficient (ADC) maps, DCE and their combination (multiparametric). A model based on clinical characteristics and a fusion model combining clinical characteristics and multiparametric MRI were also built. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis and De Long's test with Bonferroni correction were used. A P value <0.01 was considered statistically significant. RESULTS: The area under the ROC curve (AUC) value of multiparametric radiomics model was 0.92, significantly higher than DCE (0.83), T2WI (0.78), and ADC (0.77) models in the training cohort. The radiomics model also achieved good performance in the validation cohort (AUC = 0.77). The fusion model had significantly higher performance than the clinical model in both the training (AUC = 0.92 and 0.64, respectively) and validation cohorts (AUC = 0.78 and 0.62, respectively). DATA CONCLUSION: The proposed multiparametric MRI-based radiomics models may have potential to help distinguish ER+/HER2- breast cancer patients' recurrence risk. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics.

OBJECTIVE: This study aimed to incorporate clinicopathological, sonographic, and mammographic characteristics to construct and validate a nomogram model for predicting disease-free survival (DFS) in patients with triple-negative breast cancer (TNBC). METHODS: Patients diagnosed with TNBC at our institution between 2011 and 2015 were retrospectively evaluated. A nomogram model was generated based on clinicopathological, sonographic, and mammographic variables that were associated with 1-, 3-, and 5-year DFS determined by multivariate logistic regression analysis in the training set. The nomogram model was validated according to the concordance index (C-index) and calibration curves in the validation set. RESULTS: A total of 636 TNBC patients were enrolled and divided into training cohort (n = 446) and validation cohort (n = 190). Clinical factors including tumor size > 2 cm, axillary dissection, presence of LVI, and sonographic features such as angular/spiculated margins, posterior acoustic shadows, and presence of suspicious lymph nodes on preoperative US showed a tendency towards worse DFS. The multivariate analysis showed that no adjuvant chemotherapy (HR = 6.7, 95% CI: 2.6, 17.5, p < 0.0005), higher axillary tumor burden (HR = 2.7, 95% CI: 1.0, 7.1, p = 0.045), and ≥ 3 malignant features on ultrasound (HR = 2.4, CI: 1.1, 5.0, p = 0.021) were identified as independent prognostic factors associated with poorer DFS outcomes. In the nomogram, the C-index was 0.693 for the training cohort and 0.694 for the validation cohort. The calibration plots also exhibited excellent consistency between the nomogram-predicted and actual survival probabilities in both the training and validation cohorts. CONCLUSIONS: Clinical variables and sonographic features were correlated with the prognosis of TNBCs. The nomogram model based on three variables including no adjuvant chemotherapy, higher axillary tumor load, and more malignant sonographic features showed good predictive performance for poor survival outcomes of TNBC. KEY POINTS: • The absence of adjuvant chemotherapy, heavy axillary tumor load, and malignant-like sonographic features can predict DFS in patients with TNBC. • Mammographic features of TNBC could not predict the survival outcomes of patients with TNBC. • The nomogram integrating clinicopathological and sonographic characteristics is a reliable predictive model for the prognostic outcome of TNBC.

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis.

Objectives: To establish and validate a machine learning-based CT radiomics model to predict metachronous liver metastasis (MLM) in patients with colorectal cancer. Methods: In total, 323 patients were retrospectively recruited from two independent institutions to develop and evaluate the CT radiomics model. Then, 1288 radiomics features were extracted to decode the imaging phenotypes of colorectal cancer on CT images. The optimal radiomics features were selected using a recursive feature elimination selector configured by a support vector machine. To reduce the bias caused by an unbalanced dataset, the synthetic minority oversampling technique was applied to resample the minority samples in the datasets. Then, both radiomics and clinical features were used to train the multilayer perceptron classifier to develop two classification models. Finally, a score-level fusion model was developed to further improve the model performance. Results: The area under the curve (AUC) was 0.78 ± 0.07 for the tumour feature model and 0.79 ± 0.08 for the clinical feature model. The fusion model achieved the best performance, with AUCs of 0.79 ± 0.08 and 0.72 ± 0.07 in the internal and external validation cohorts. Conclusions: Radiomics models based on baseline colorectal contrast-enhanced CT have high potential for MLM prediction. The fusion model combining radiomics and clinical features can provide valuable biomarkers to identify patients with a high risk of colorectal liver metastases.

Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features.

PURPOSE: To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. PATIENTS AND METHODS: Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts. RESULTS: Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort. CONCLUSION: On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.

Comparison of the diagnostic performance of synthesized two-dimensional mammography and full-field digital mammography alone or in combination with digital breast tomosynthesis.

PURPOSE: To investigate whether digital breast tomosynthesis (DBT) and subsequently generated synthesized mammography (SM) images show a better performance than full-field digital mammography (FFDM) images for diagnosing malignant breast lesions. In addition, the radiation doses for SM using different procedures were compared. MATERIALS AND METHODS: This prospective study enrolled 212 women (age ≥ 25 years) with clinically suspicious breast lesions. All participants underwent FFDM and DBT with the same breast compression. Finally, 222 lesions were confirmed by pathological analysis. The mammogram results were evaluated according to the BI-RADS criteria and compared with the pathological results. The diagnostic performances, morphological features and average glandular doses (AGDs) were compared. RESULTS: In total, 141 malignant lesions and 81 benign lesions were confirmed by pathological analysis. The overall AGD showed no significant difference between FFDM and DBT. Compared with 2D imaging, the AUC values of FFDM plus DBT and SM plus DBT were both significantly different overall (P = 0.0002) and remained significantly different in dense breasts (P < 0.0001). In terms of morphologic characteristics, lesions showed similar morphology between FFDM and SM, while the lesion characteristics were discordant from 2D imaging to DBT in 33 lesions in dense breasts. CONCLUSIONS: Compared to FFDM, 2D SM images generated from DBT had significantly improved diagnostic efficacy for detecting malignant breast lesions without increasing radiation doses. This new procedure is useful for characterizing breast lesions, particularly in dense breasts.

Diffusion-weighted MR imaging of salivary glands with gustatory stimulation: comparison before and after radiotherapy.

BACKGROUND: Xerostomia is the most prominent complication in patients with head and neck carcinoma after radiotherapy (RT). Diffusion-weighted magnetic resonance imaging (DWI) with gustatory stimulation may contribute to the evaluation of salivary gland function. PURPOSE: To investigate the value of DWI for quantifying physiological changes of the parotid gland during gustatory stimulation in patients before and after RT. MATERIAL AND METHODS: Magnetic resonance imaging (MRI) was performed in 28 consecutive patients with nasopharyngeal carcinoma before and after RT and clinical xerostomia was also assessed. A DWI sequence was performed once at rest and continually repeated seven times during stimulation with ascorbic acid. Apparent diffusion coefficient (ADC) maps for parotid glands at different time points and the range of increase with stimulation were calculated. Paired two-tailed Student t tests were used to compare the ADC values before and after stimulation, and before and after RT. RESULTS: Before RT, the ADC showed an initial increase (P < 0.001) and then fluctuated during stimulation. After RT, as the clinical xerostomia changed from Grade 0 to Grade 2, the mean ADC at rest increased compared with the pre-RT value (P < 0.001). A similar response to stimulation was observed, but the range of increase between the maximum ADC during stimulation and the baseline value at rest was higher post-RT than pre-RT (P = 0.022). The minimum ADC during stimulation was higher than the baseline value post-RT (P = 0.028), but there was no difference pre-RT (P = 0.603). CONCLUSION: DWI combined with gustatory stimulation seems to display the physiological changes of the parotid gland following RT and may be a potential tool for non-invasively assessing salivary gland function.

Magnetic resonance sialography for investigating major salivary gland duct system after intensity-modulated radiotherapy of nasopharyngeal carcinoma.

BACKGROUND: We investigated the value of magnetic resonance sialography for evaluating xerostomia induced by intensity-modulated radiotherapy for nasopharyngeal carcinoma. METHODS: Fourteen patients with nasopharyngeal carcinoma were treated with intensity-modulated radiotherapy. Salivary function was assessed by magnetic resonance sialography and subjective evaluation criteria pre-treatment, 1 week and 1 year post-radiotherapy. A magnetic resonance sialography categorical scoring system was used to compare the visibility of salivary ducts. RESULTS: The average mean dose was 38.93 Gy to the parotid glands and 59.34 Gy to the submandibular glands. Before radiotherapy, the visibility scores of both the parotid and submandibular ducts increased after secretion stimulation. The scores decreased and the response to stimulation was attenuated 1 week post-radiotherapy. For most of the parotid ducts, the visibility score improved at 1 year post-radiotherapy both at rest and under stimulation, but not for the submandibular ducts. With a median follow-up of 12.3 months, 8/12 patients had grade 1 xerostomia and 4/12 had grade 2 xerostomia. CONCLUSIONS: Magnetic resonance sialography allows non-invasive evaluation of radiation-induced ductal changes in the major salivary glands and enables reliable prediction of radiation-induced xerostomia.

Imaging findings in phyllodes tumors of the breast.

PURPOSE: To study the radiological appearance and pathological features of breast phyllodes tumors (PTs), and to enhance the recognition of the tumor. MATERIALS AND METHODS: Clinical and imaging findings were retrospectively reviewed in 24 women with PTs confirmed by surgical pathology. All of the 24 patients had preoperative MRI and sonography, and 10 had preoperative mammography. RESULTS: The histologic findings were benign, borderline and malignant PTs in 16.7% (4/24), 45.8% (11/24) and 37.5% (9/24) of cases, respectively. The tumor size (p=0.001), irregular shape on sonographic imaging (p=0.039), internal non-enhanced septations (p=0.009), silt-like changes in enhanced images (p=0.006) and signal changes from T2-weighted to enhanced images on MRI (p=0.001) correlated significantly with the histologic grade; the BI-RADS category of the MRI could reflect the PT's histologic grade with a correlation coefficient of 0.440 (p=0.031). If the category BI-RADS ≥4a was considered to be a suspicious malignant lesion, the diagnostic accuracy of mammography, US and MRI would be 70% (7/10), 62.5% (15/24) and 95.8% (23/24), respectively. CONCLUSION: The tumor size and several US and MRI findings can be used to help preoperatively determine the histologic grade of breast PTs. When a patient presents with a progressively enlarging, painless breast mass, MRI should be recommended first.