Search

Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and prognostic factors in patients with invasive breast cancer. A total of 256 consecutive women who underwent preoperative chest CT and surgery in our institution were included. The Cox proportional hazards model was used to determine the relationship between textural features and DFS. Logistic regression analysis was used to reveal the relationship between textural features and prognostic factors. Of 256 patients, 21 (8.2%) had disease recurrence over a median follow-up of 60 months. For the prediction of shorter DFS, higher histological grade (hazard ratio [HR], 6.12; p < 0.001) and lymphovascular invasion (HR, 2.93; p = 0.029) showed significance, as well as textural features such as lower mean attenuation (HR, 4.71; p = 0.003) and higher entropy (HR, 2.77; p = 0.036). Lower mean attenuation showed a correlation with higher tumor size, and higher entropy showed correlations with higher tumor size and Ki-67. In conclusion, CTTA-derived textural features can be used as a noninvasive imaging biomarker to predict shorter DFS and prognostic factors in patients with invasive breast cancer. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether machine learning–based prediction models using 3-T multiparametric MRI (mpMRI) can predict Ki-67 and histologic grade in stage I–II luminal cancer. Methods: Between 2013 and 2019, consecutive women with luminal cancers who underwent preoperative MRI with diffusion-weighted imaging (DWI) and surgery were included. For prediction models, morphology, kinetic features using computer-aided diagnosis (CAD), and apparent diffusion coefficient (ADC) at DWI were evaluated by two radiologists. Logistic regression analysis was used to identify mpMRI features for predicting Ki-67 and grade. Diagnostic performance was assessed using eight machine learning algorithms incorporating mpMRI features and compared using the DeLong method. Results: Of 300 women, 203 (67.7%) had low Ki-67 and 97 (32.3%) had high Ki-67; 242 (80.7%) had low grade and 58 (19.3%) had high grade. In multivariate analysis, independent predictors for higher Ki-67 were washout component > 13.5% (odds ratio [OR] = 4.16; p < 0.001) and intratumoral high SI on T2-weighted image (OR = 1.89; p = 0.022). Those for higher grade were washout component > 15.5% (OR = 7.22; p < 0.001), rim enhancement (OR = 2.59; p = 0.022), and ADC value < 0.945 × 10-3 mm2/s (OR = 2.47; p = 0.015). Among eight models using these predictors, six models showed the equivalent performance for Ki-67 (area under the receiver operating characteristic curve [AUC]: 0.70) and Naive Bayes classifier showed the highest performance for grade (AUC: 0.79). Conclusions: A prediction model incorporating mpMRI features shows good diagnostic performance for predicting Ki-67 and histologic grade in patients with luminal breast cancers. Key Points: • Among multiparametric MRI features, kinetic feature of washout component >13.5% and intratumoral high signal intensity on T2-weighted image were associated with higher Ki-67. • Washout component >15.5%, rim enhancement, and mean apparent diffusion coefficient value < 0.945 × 10-3mm2/s were associated with higher histologic grade. • Machine learning–based prediction models incorporating multiparametric MRI features showed good diagnostic performance for Ki-67 and histologic grade in luminal breast cancers. [ABSTRACT FROM AUTHOR]

Objective: To compare image quality and diagnostic accuracy of 2D with 3D ultrasonography in solid breast masses. Methods and Material: To rate image quality, two radiologists compared lesion contrast and characterization of 507 solid breast masses in 2D and 3D ultrasonography and then graded the 3D imaging in 3-point scale. To characterize the masses, the same radiologists rated the examination for clarity of margin, posterior acoustic feature, and clustered microcalcifications within a mass. In addition, the masses were assigned BI-RADS categories as proposed by the American College of Radiology, criteria using just ultrasonographic features. In the 202 pathologically confirmed cases, sensitivity, specificity, positive predictive value, negative predictive value, and false negative rate for diagnosis of breast cancer in 2D and 3D ultrasonography were assessed. Image quality and diagnostic accuracy were further evaluated according to the size of the masses. Results: Two observers rated 3D imaging superior to 2D imaging in terms of lesion contrast and characterization of the masses. Especially, superiority of 3D ultrasonography in terms of image quality was increasing in more than 10 mm sized masses. However, diagnostic accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and false negative rate for diagnosis of breast cancer of 3D imaging was not different from 2D imaging. Conclusion: In spite of superior image quality on 3D ultrasonography, it does not provide additional benefits to diagnostic accuracy for diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. Methods: This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan–Meier analysis. Results: Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). Conclusions: Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). Methods: This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. Results: Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (Ve). Conclusions: Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer. Key Points: • Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer. • The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network). • The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (Ve) among perfusion parameters. [ABSTRACT FROM AUTHOR]

Abstract: This study investigated the feasibility of using low-dose multidetector dynamic computed tomography (CT) scan for imaging breast. We measured the radiation dose using a phantom at low- and standard-dose CT. To compare the image quality at low- and standard-dose CT, we evaluated normal breasts in 57 cases. In 44 cases with breast cancer, we assessed the staging and time-enhancement curves of breast cancer. In conclusion, the low-dose multidetector dynamic CT scan is feasible for the evaluation of the breast, with reduced radiation dose and with similar image quality when compared with standard-dose CT scan. In breast cancers, low-dose dynamic CT could be used for the staging of breast cancer before surgery. [Copyright &y& Elsevier]

Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer. [ABSTRACT FROM AUTHOR]

Long-term complications are becoming more important as the survival rate of breast cancer improves. Treatment-related myeloid neoplasm is an important long-term complication in breast cancer survivors as it has a poor prognosis. We evaluated the incidence and risk factors for the development of treatment-related acute myeloid leukaemia (AML)/myelodysplastic syndrome (MDS) in patients treated with early breast cancer. We accessed the national Korean database to identify 153,565 patients diagnosed with breast cancer between January 2007 and October 2016 who underwent surgery for breast cancer. We estimated the cumulative incidence of AML/MDS and analysed the risk factors for developing AML/MDS. Of 153,575 patients, 79,321 received anthracycline-based adjuvant therapy, 14,317 received adjuvant therapy without anthracyclines and 46,657 did not receive adjuvant chemotherapy. Overall, 120 developed AML (105 in the anthracycline group, 9 in the non-anthracycline group and 6 in the control group), and 128 developed MDS (96, 9 and 23 in each group). The 10-year cumulative incidence of AML/MDS was the highest in the anthracycline group (0.221% and 0.199%), followed by the non-anthracycline group (0.122% and 0.163%) and the control group (0.024% and 0.089%). The risk of developing AML/MDS was significantly higher in patients treated with anthracyclines (hazard ratio [HR] 9.531; p < 0.0001 for AML and HR 2.559; p < 0.0001 for MDS) compared to patients in the control group. This study found that anthracycline-based adjuvant therapy significantly increased the risk of AML/MDS in Korean breast cancer patients, with the risk persisting for at least 10 years. While the cumulative incidence was low, the long-term risks of AML/MDS should be taken into account considering the poor outcomes associated with these neoplasms. • Therapy-related myeloid neoplasms are increasing as survival rates for breast cancer improve. • Anthracyclines increased the risk of AML/MDS in breast cancer patients. • Chemotherapy without anthracycline also increased the risk of AML. • Despite the low incidence, therapy-related AML/MDS should be kept in mind. [ABSTRACT FROM AUTHOR]

Currently in diagnostic setting for breast cancer, FFDM and DBT are performed conjunctively. However, performing two imaging modalities may increase radiation exposure by double. Two-dimensional reconstructed images created from DBT with 2DSM, has a potential to replace conventional FFDM in concerning both radiation dosage and image quality. With increasing concerns for individual radiation exposure, studies analyzing radiation dosage in breast imaging modalities are needed. This study compared radiation dosage and image quality between DBT + 2DSM versus FFDM. 374 patients (mean age 52 years) who underwent both DBT and FFDM were retrospectively reviewed. Radiation dosage data were obtained by radiation dosage scoring and monitoring program Radimetrics (Bayer HealthCare, Whippany, NJ). Entrance dose and mean glandular doses in each breast were obtained for both modalities. To compare image quality of DBT + 2DSM and FFDM, a 5-point scoring system for lesion clarity was assessed. The parameters of radiation dosage (entrance dose, mean glandular dose) and image quality (lesion clarity scoring) were compared. For entrance dose, DBT had lower mean dosage (14.8 mGy) compared with FFDM (21.8 mGy, p-value < 0.0001). Mean glandular doses for both breasts were lower in DBT (Left 1.74, Right 2.1) compared with FFDM (Left 2.85, Right 2.74, p-value < 0.0001). Lesion clarity score was higher in DBT with 2DSM (mean score 4.03) compared with FFDM (3.82, p-value < 0.0001). DBT showed lower radiation entrance dose and mean glandular doses to both breasts compared with FFDM. DBT + 2DSM had better image quality than FFDM, suggesting that DBT with 2DSM has potential as an alternative to FFDM. • Digital breast tomosynthesis (DBT) is used adjunctively with conventional mammography. • DBT with 2D reconstructed image benefits in image quality and radiation exposure. • DBT may have potential as an alternative to conventional mammography. [ABSTRACT FROM AUTHOR]

Abstract We aim to investigate the significance of microvascular ultrasonography in breast ductal lesions for distinguishing benign from malignant lesions. Sixty-two ductal lesions were evaluated with gray-scale and three Doppler techniques before biopsy, Superb Microvascular Imaging (SMI), color Doppler (CDI), and power Doppler imaging (PDI). We evaluated number, distribution, and morphology of vessels. The area under the ROC curve of combined use of Doppler imaging was greater than that of gray-scale only and SMI had higher value than CDI and PDI (p < 0.05). Microvascular ultrasonography improves diagnostic performance that distinguishes benign from malignant breast ductal lesions, and SMI is superior to CDI and PDI. Graphical abstract Unlabelled Image Highlights • Breast ductal lesions are caused by various benign and malignant diseases. • Gray-scale ultrasonogrpahy is limited in assessing ductal lesions because secretion can be confused with intraluminal tumor. • We report the usefulness of microvascular ultrasonography to differentiate benign from malignant ductal lesions. • Combined use of Doppler ultrasonography significantly improves diagnostic performance compared to gray-scale only. • Superb Microvascular Imaging is superior to color or power Doppler technique in microvascular evaluation of ductal lesions. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether diffusion-weighted imaging (DWI) aids pre-operative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate additional lesions in breast cancer patients.Methods: DCE-MRI and DWI were performed on 131 lesions, with available histopathological results. The apparent diffusion coefficient (ADC) of each lesion was measured, and the cut-off value for differentiation between malignant and benign lesions was calculated. A protocol combining the ADC cut-off value with DCE-MRI was validated in a cohort of 107 lesions in 77 patients.Results: When an ADC cut-off value of 1.11 × 10-3 mm2/s from the development cohort was applied to the additional lesions in the validation cohort, the specificity increased from 18.9% to 67.6% (P < 0.001), and the diagnostic accuracy increased from 61.7% to 82.2% (P = 0.05), without significant loss of sensitivity (98.6% vs. 90.0%, P = 0.07). The negative predictive values of lesions in the same quadrant had decreased, as had those of lesions ≥1 cm in diameter. The ADC cut-off value in the validation cohort was 1.05 × 10-3 mm2/s.Conclusions: Additional implementation of DWI for breast lesions in pre-operative MRI can help to obviate unnecessary biopsies by increasing specificity. However, to avoid missing cancers, clinicians should closely monitor lesions located in the same quadrant or lesions ≥1 cm.Key Points: • DWI can be used to further differentiate lesions during pre-operative cancer staging. • ADC cut-off values were similar in the development and validation cohorts. • DWI improves both PPV and NPV in cases of multicentric lesions. • DWI improves both PPV and NPV in lesions <1 in diameter. • NPVs are decreased in multifocal lesions and lesions ≥1 cm in diameter. [ABSTRACT FROM AUTHOR]

Shear wave elastography (SWE) was performed independently by two radiologists in 264 solid breast masses. The images were reviewed for color overlay pattern (COP) classification by the two radiologists, double blinded to any information. The interobserver agreement of the COP was almost perfect (κ = 0.908) and high in E max (ICC = 0.89). The AUC value of the COP (0.954) was significantly higher than that of E max (0.915) (p = 0.002) but not significantly different from that of E max combined with COP (0.957) (p = 0.098). The SWE color overlay pattern and E max of breast masses were highly reproducible. The COP had better diagnostic ability than E max , suggesting that COP may be a more reliable parameter for solid breast mass evaluation. [ABSTRACT FROM AUTHOR]

Purpose To compare the diagnostic efficacy of 2DSM and FFDM for evaluation of breast cancer. Materials and methods A retrospective observer performance study was done by 3 breast radiologists. The diagnostic accuracy, image quality, and detailed lesion characterization of each 2DSM and FFDM were evaluated blindly to determine superior modality. Results The diagnostic accuracy was not significantly different between two modalities. The image quality was superior in 2DSM (p-value 0.0362). Conclusions 2DSM showed equivalent diagnostic accuracy and superior image quality to FFDM. Overall characterization of the lesion was better in 2DSM, and 2DSM showed superiority in evaluation of spiculated margins. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study is to evaluate the diagnostic performance of SWE in palpable breast mass and to compare with color overlay pattern in SWE with conventional US and quantitative SWE for assessing palpable breast mass.Methods: SWE and conventional breast US were performed in 133 women with 156 palpable breast lesions (81 benign, 75 malignant) between August 2013 to June 2014. Either pathology or periodic imaging surveillance more than 2 years was a reference standard. Existence of previous image was blinded to performing radiologists. US BI-RADS final assessment, qualitative and quantitative SWE measurements were evaluated. Diagnostic performances of grayscale US, SWE and US combined to SWE were calculated and compared. Correlation between pattern classification and quantitative SWE was evaluated.Results: Both color overlay pattern and quantitative SWE improved the specificity of conventional US, from 81.48% to 96.30% (p=0.0005), without improvement in sensitivity. Color overlay pattern was significantly related to all quantitative SWE parameters and malignancy rate (p<0.0001.). The optimal cutoff of color overlay pattern was between 2 and 3. Emax with optimal cutoff at 45.1 kPa showed the highest Az value, sensitivity, specificity and accuracy among other quantitative SWE parameters (p<0.0001). Echogenic halo on grayscale US showed significant correlation with color overlay pattern and pathology (p<0.0001).Conclusions: In evaluation of palpable breast mass, conventional US combine to SWE improves specificity and reduces the number of biopsies that ultimately yield a benign result. Color overlay pattern classification is more quick and easy and may represent quantitative SWE measurements with similar diagnostic performances. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine. This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients. [ABSTRACT FROM AUTHOR]

Background and purpose. Ultrasound has been used successfully to differentiate benign and malignant breast lesions. The aim of this study was to investigate the correlation between ultrasound and prognostic indicators in breast cancer such as histological type, tumor grade, and biological markers. Materials and methods. Ultrasound findings (shape, margin, orientation, boundary, echo pattern, posterior acoustic feature, and presence of calcifications) of 458 breast cancers were analyzed and correlated with the tumor type, tumor grade, and biological markers by univariate and multivariate logistic regression analyses. The biological markers were estrogen receptor, progesterone receptor, and HER-2/neu. Results. Invasive cancers displayed more frequently an irregular shape, a not parallel orientation, and a hypoechoic or complex echo pattern than carcinoma in situ cases (p<0.05). Poorly differentiated invasive cancers had more frequently not circumscribed margins, an abrupt boundary, and a hypoechoic or complex echo pattern than moderately/well differentiated cancers (p<0.05). Estrogen or progesterone receptor negative cancers more often displayed a hypoechoic or complex echo pattern and HER-2/neu positive cancers had more calcifications (p<0.05). Conclusion. Ultrasound pattern is correlated with tumor type, tumor grade, and biological markers in breast cancers and it may be useful for prediction of prognosis. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 63-year-old woman, who was diagnosed with recurrent solitary fibrous tumor through a magnetic resonance imaging. The patient had received excisional mass biopsy and excision to remove the solitary fibrous tumor (SFT). The patient was recommended for medical follow-up to monitor the diseases.