Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast.

Purpose: Sclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA. Methods: Sixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation. Results: In the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03). Conclusions: Radiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.

Prospective Evaluation of Ultrasound in a Novel Position with MRI Virtual Navigation for MRI-Detected Only Breast Lesions: A Pilot Study of a More Efficient and Economical Method.

The aim of this study was to evaluate the clinical utility of ultrasound (US) with magnetic resonance imaging (MRI) virtual navigation in a novel prone position for MRI-detected incidental breast lesions. Between June 2016 and June 2020, 30 consecutive patients with 33 additional Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5 lesions that were detected on MRI but occult on second-look US were enrolled in the study. All suspicious lesions were located in real-time US using MRI virtual navigation in the prone position and then followed by US-guided biopsy or surgical excision. Pathological results were taken as the standard of reference. The detection rate of US with MRI virtual navigation was calculated. The MRI features and pathological types of these lesions were analyzed. A total of 31 lesions were successfully located with real-time US with MRI virtual navigation and then US-guided biopsy or localization, and the detection rate was 93.9% (31/33). Twenty-seven (87.1%, 27/31) proved to be benign lesions and four (12.9%, 4/31) were malignant lesions at pathology. Of the 33 MRI-detected lesions, 31 (93.9%, 31/33) were non-mass enhancements and two (6.1%, 2/33) were masses. This study showed that real-time US with prone MRI virtual navigation is a novel efficient and economical method to improve the detection and US-guided biopsy rate of breast lesions that are detected solely on MRI.

Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging.

OBJECTIVES: To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs). METHODS: A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis. RESULTS: Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75-0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69-0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02). CONCLUSIONS: A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs. KEY POINTS: • The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer.

Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83-0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72-0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas.

Objective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma. Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature. The model performance was evaluated by ROC analysis, calibration curve and decision curve analysis (DCA). Result: Five optimal features were ultimately selected and contributed to the Radscore construction. Unilocular/multilocular identification was significant difference from semantic features. The Nomogram model showed a better performance in both training cohort (AUC = 0.94, 95%CI 0.86-0.98) and external validation cohort (AUC = 0.92, 95%CI 0.76-0.98). The calibration curve and DCA analysis indicated a better accuracy of the Nomogram model for classification than either Radscore or the loculus alone. Conclusion: The Nomogram model combined radiomic and semantic features could be used as imaging biomarker for classification of serous and mucinous types of ovarian cystadenomas.

Ureteral metastasis from a HER2-enriched breast cancer: a rare case report.

Ureteral metastasis from breast cancer (BC) is very rare, and only a few cases have been reported. We report the first patient with ureteral involvement from human epidermal receptor 2 (HER2) enriched metastatic BC. A 51-year-old woman with HER2-enriched metastatic BC with liver metastasis was diagnosed at her first visit, achieving complete tumor regression by chemotherapy, anti-HER2 treatment, modified mastectomy and radiotherapy. After 1 year, she complained light left flank pain for 1 month, with an elevated cancer antigen 15-3 (CA15-3) level in blood. Computed tomography showed a left proximal ureteral lesion causing ureterectasis and hydronephrosis. A ureteroscope-guided biopsy of the ureteral lesion revealed poorly differentiated carcinoma from metastatic BC. Diagnosing ureter metastasis from BC were established by histopathology and immunohistochemistry. The flank pain and ureteral lesion were absolutely relieved after chemotherapy and anti-HER2 treatment, and CA15-3 level decreased to normal. Regular follow-up examinations every 3 months are performed at our outpatient clinic. With a 20 months follow-up, there has been no further progression up to now. Ureteral metastasis of BC shows nonspecific symptoms, and it is important to recognize this unusual manifestation so that timely appropriate treatment can be initiated in order to better prognosis. Chemotherapy plus anti-HER2 treatment are most effective for hepatic and ureteral metastasis from BC.

Diagnostic Performance Using Automated Breast Ultrasound System for Breast Cancer in Chinese Women Aged 40 Years or Older: A Comparative Study.

The purpose of this study was to investigate the diagnostic performance of the automated breast ultrasound system (ABUS) compared with hand-held ultrasonography (HHUS) and mammography (MG) for breast cancer in women aged 40 y or older. A total of 594 breasts in 385 patients were enrolled in the study. HHUS, ABUS and MG exams were performed for these patients. Follow-up and pathologic findings were used as the reference standard. Based on the reference standard, 519 units were benign or normal and 75 were malignant. The sensitivity, specificity, accuracy and Youden index were 97.33%, 89.79%, 90.74% and 0.87 for HHUS; 90.67%, 92.49%, 92.26% and 0.83 for ABUS; 84.00%, 92.87%, 91.75% and 0.77 for MG, respectively. The specificity of ABUS was significantly superior to that of HHUS (p = 0.024). The area under the receiver operating characteristic curve was 0.936 for HHUS, which was the highest, followed by 0.916 for ABUS and 0.884 for MG. However, the difference was not statistically significant (p > 0.05). In conclusion, the diagnostic performance of ABUS for breast cancer was equivalent to HHUS and MG and potentially can be used as an alternative method for breast cancer diagnosis.

Diagnostic Performance of Automated Breast Ultrasound in Differentiating Benign and Malignant Breast Masses in Asymptomatic Women: A Comparison Study With Handheld Ultrasound.

OBJECTIVES: Our aim was to investigate the diagnostic potential of an automated breast ultrasound (ABUS) system in differentiating benign and malignant breast masses compared with handheld ultrasound (HHUS). METHODS: Women were randomly and proportionally selected from outpatients and underwent both HHUS and ABUS examinations. Masses with final American College of Radiology Breast Imaging Reporting and Data System categories 2 and 3 were considered benign. Masses with final Breast Imaging Reporting and Data System categories 4 and 5 were considered malignant. The diagnosis was confirmed by pathologic results or at least a 1-year follow-up. Automated breast US and HHUS were compared on the basis of their sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Diagnostic consistency and areas under the receiver operating characteristic curves were analyzed. The maximum diameters of masses were compared among HHUS, ABUS, and pathologic results. RESULTS: A total of 599 masses in 398 women were confirmed by pathologic results or at least a 1-year follow-up; 103 of 599 masses were malignant, and 496 were benign. There were no significant differences between ABUS and HHUS in terms of diagnostic accuracy (80.1% versus 80.6%), specificity (77.62% versus 80.24%), positive predictive value (46.12% versus 46.46%), and negative predictive value (97.96% versus 95.67%). There were significant differences in sensitivity (92.23% versus 82.52%; P < .01) and areas under the curve (0.85 versus 0.81; P < .05) between ABUS and HHUS. The correlation of the maximum diameter was slightly higher between ABUS and pathologic results (r = 0.885) than between HHUS and pathologic results (r = 0.855), but the difference was not significant (P > .05). CONCLUSIONS: Automated breast US is better than HHUS in differentiating benign and malignant breast masses, especially with respect to specificity.