Combining contrast-enhanced ultrasound and blood cell analysis to improve diagnostic accuracy of plasma cell mastitis

Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer

BACKGROUND

The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).

METHODS

50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality.

RESULTS

Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype.

CONCLUSION

The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.

MCC950 attenuates plasma cell mastitis in an MDSC-dependent manner

Plasma cell mastitis (PCM) is a sterile inflammatory condition primarily characterized by periductal inflammation and ductal ectasia. Currently, there is a lack of non-invasive or minimally invasive treatment option other than surgical intervention. The NLRP3 inflammasome has been implicated in the pathogenesis and progression of various inflammatory diseases, however, its involvement in PCM has not yet been reported. In this study, we initially observed the pronounced upregulation of NLRP3 in both human and mouse PCM tissue and elucidated the mechanism underlying the attenuation of PCM through inhibition of NLRP3. We established the PCM murine model and collected samples on day 14, when inflammation reached its peak, for subsequent research purposes. MCC950, an NLRP3 inhibitor, was utilized to effectively ameliorate PCM by significantly reducing plasma cell infiltration in mammary tissue, as well as attenuate the expression of pro-inflammatory cytokines including IL-1β, TNF-α, IL-2, and IL-6. Mechanistically, we observed that MCC950 augmented the function of myeloid-derived suppressor cells (MDSCs), which in turn inhibited the infiltration of plasma cells. Furthermore, it was noted that depleting MDSCs greatly compromised the therapeutic efficacy of MCC950. Collectively, our findings suggest that the administration of MCC950 has the potential to impede the progression of PCM by augmenting MDSCs both numerically and functionally, ultimately treating PCM effectively. This study provides valuable insights into the utilization of pharmacological agents for PCM treatment.

The value of Demetics ultrasound-assisted diagnosis system in diagnosis of breast lesions and in assessment Ki-67 status of breast cancer

PURPOSE

This study aims to explore the diagnostic efficiency of the Demetics for breast lesions and assessment of Ki-67 status.

MATERIAL

This retrospective study included 291 patients. Three combined methods (method 1: upgraded BI-RADS when Demetics classified the breast lesion as malignant; method 2: downgraded BI-RADS when Demetics classified the breast lesion as benign; method 3: BI-RADS was upgraded or downgraded according to Demetrics' diagnosis) were used to compare the diagnostic efficiency of two radiologists with different seniority before and after using Demetics. The correlation between the visual heatmap by Demetics and the Ki-67 expression level of breast cancer was explored.

RESULTS

The sensitivity, specificity, and area under curve (AUC) of diagnosis by Demetics, junior radiologist and senior radiologist were 89.5%, 83.1%, 0.863; 76.9%, 82.4%, 0.797 and 81.1%, 89.9%, 0.855, respectively. Method 1 was the best for senior radiologist, which increased AUC from 0.855 to 0.884. For junior radiologist, Method 3 was the best method, improving sensitivity (88.8% vs. 76.9%) and specificity (87.2% vs. 82.4%). Demetics paid more attention to the peripheral area of breast cancer with high expression of Ki-67.

CONCLUSION

Demetics has shown good diagnostic efficiency in the assisted diagnosis of breast lesions and is expected to further distinguish Ki-67 status of breast cancer.

Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators

Background

Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast disease, and abscess formation is a common complication of GLM. The process of abscess formation is accompanied by changes in multiple inflammatory markers. The present study aimed to construct a diagnosis model for the early of GLM abscess formation based on multiple inflammatory parameters.

Methods

Based on the presence or absence of abscess formation on breast magnetic resonance imaging (MRI), 126 patients with GLM were categorised into an abscess group (85 patients) and a non-abscess group (41 patients). Demographic characteristics and the related laboratory results for the 9 inflammatory markers were collected. Logistics univariate analysis and collinearity test were used for selecting independent variables. A regression model to predict abscess formation was constructed using Logistics multivariate analysis.

Results

The univariate and multivariate analysis showed that the N, ESR, IL-4, IL-10 and INF-α were independent diagnostic factors of abscess formation in GLM (P<0. 05). The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.890, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (81.2% and 85.40%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The results of this calibration curve indicated that the model had a good value and stability in predicting abscess formation in GLM. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model.

Conclusion

A predictive model for abscess formation in GLM based on inflammatory markers was constructed in our study, which may provide a new strategy for early diagnosis and treatment of the abscess stage of GLM.

Fluorescence, ultrasonic and photoacoustic imaging for analysis and diagnosis of diseases

Biomedical imaging technology, which allows us to peer deeply within living subjects and visually explore the delivery and distribution of agents in living things, is producing tremendous opportunities for the early diagnosis and precise therapy of diseases. In this feature article, based on reviewing the latest representative examples of progress together with our recent efforts in the bioimaging field, we intend to introduce three typical kinds of non-invasive imaging technologies, i.e., fluorescence, ultrasonic and photoacoustic imaging, in which optical and/or acoustic signals are employed for analyzing various diseases. In particular, fluorescence imaging possesses a series of outstanding advantages, such as high temporal resolution, as well as rapid and sensitive feedback. Hence, in the first section, we will introduce the latest studies on developing novel fluorescence imaging methods for imaging bacterial infections, cancer and lymph node metastasis in a long-term and real-time manner. However, the issues of imaging penetration depth induced by photon scattering and light attenuation of biological tissue limit their widespread in vivo imaging applications. Taking advantage of the excellect penetration depth of acoustic signals, ultrasonic imaging has been widely applied for determining the location, size and shape of organs, identifying normal and abnormal tissues, as well as confirming the edges of lesions in hospitals. Thus, in the second section, we will briefly summarize recent advances in ultrasonic imaging techniques for diagnosing diseases in deep tissues. Nevertheless, the absence of lesion targeting and dependency on a professional technician may lead to the possibility of false-positive diagnosis. By combining the merits of both optical and acoustic signals, newly-developed photoacoustic imaging, simultaneously featuring higher temporal and spatial resolution with good sensitivity, as well as deeper penetration depth, is discussed in the third secretion. In the final part, we further discuss the major challenges and prospects for developing imaging technology for accurate disease diagnosis. We believe that these non-invasive imaging technologies will introduce a new perspective for the precise diagnosis of various diseases in the future.

Physical Therapists Use of Diagnostic Ultrasound Imaging in Clinical Practice: A Review of Case Reports

Objective

Ultrasound diagnostic imaging (USI) is widely utilized in sports medicine, orthopaedics, and rehabilitation. Its use in physical therapy clinical practice is increasing. This review summarizes published patient case reports describing USI in physical therapist practice.

Design

Comprehensive literature review.

Literature Search

PubMed was searched using the keywords "physical therapy" AND "ultrasound" AND "case report" AND "imaging". In addition, citation indexes and specific journals were searched.

Study Selection Criteria

Papers were included if the patient was attending physical therapy, USI was necessary for patient management, the full text was retrievable, and the paper was written in English. Papers were excluded if USI was only used for interventions, such as biofeedback, or if the USI was incidental to physical therapy patient/client management.

Data Synthesis

Categories of data extracted included: 1) Patient presentation; 2) Setting; 3) Clinical indications; 4) Who performed USI; 5) Anatomical region; 6) Methods of USI; 7) Additional imaging; 8) Final diagnosis; and 9) Case outcome.

Results

Of the 172 papers reviewed for inclusion, 42 were evaluated. Most common anatomical regions scanned were the foot and lower leg (23%), thigh and knee (19%), shoulder and shoulder girdle (16%), lumbopelvic region (14%), and elbow/wrist and hand (12%). Fifty-eight percent of the cases were deemed static, while 14% reported using dynamic imaging. The most common indication for USI was a differential diagnosis list that included serious pathologies. Case studies often had more than one indication. Thirty-three cases (77%) resulted in confirmation of a diagnosis, while 29 case reports (67%) documented significant changes in physical therapy intervention strategies due to the USI, and 25 case reports (63%) resulted in referral.

Conclusion

This review of cases provides details on unique ways USI can be used during physical therapy patient care, including aspects that reflect the unique professional framework.

Diagnostic value of radiomics model based on gray-scale and contrast-enhanced ultrasound for inflammatory mass stage periductal mastitis/duct ectasia

Objective

The present study aimed to investigate the clinical application value of the radiomics model based on gray-scale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) images in the differentiation of inflammatory mass stage periductal mastitis/duct ectasia (IMSPDM/DE) and invasive ductal carcinoma (IDC).

Methods

In this retrospective study, 254 patients (IMSPDM/DE: 129; IDC:125) were enrolled between January 2018 and December 2020 as a training cohort to develop the classification models. The radiomics features were extracted from the GSUS and CEUS images. The least absolute shrinkage and selection operator (LASSO) regression model was employed to select the corresponding features. Based on these selected features, logistic regression analysis was used to aid the construction of these three radiomics signatures (GSUS, CEUS and GSCEUS radiomics signature). In addition, 80 patients (IMSPDM/DE:40; IDC:40) were recruited between January 2021 and November 2021 and were used as the validation cohort. The best radiomics signature was selected. Based on the clinical parameters and the radiomics signature, a classification model was built. Finally, the classification model was assessed using nomogram and decision curve analyses.

Results

Three radiomics signatures were able to differentiate IMSPDM/DE from IDC. The GSCEUS radiomics signature outperformed the other two radiomics signatures and the AUC, sensitivity, specificity, and accuracy were estimated to be 0.876, 0.756, 0.804, and 0.798 in the training cohort and 0.796, 0.675, 0.838 and 0.763 in the validation cohort, respectively. The lower patient age (p&lt;0.001), higher neutrophil count (p&lt;0.001), lack of pausimenia (p=0.023) and GSCEUS radiomics features (p&lt;0.001) were independent risk factors of IMSPDM/DE. The classification model that included the clinical factors and the GSCEUS radiomics signature outperformed the GSCEUS radiomics signature alone (the AUC values of the training and validation cohorts were 0.962 and 0.891, respectively). The nomogram was applied to the validation cohort, reaching optimal discrimination, with an AUC value of 0.891, a sensitivity of 0.888, and a specificity of 0.750.

Conclusions

The present study combined the clinical parameters with the GSCEUS radiomics signature and developed a nomogram. This GSCEUS radiomics-based classification model could be used to differentiate IMSPDM/DE from IDC in a non-invasive manner.

Differentiation Between Granulomatous Lobular Mastitis and Breast Cancer Using Quantitative Parameters on Contrast-Enhanced Ultrasound

Objective

To investigate the Contrast-enhanced ultrasound (CEUS) imaging characteristics of granulomatous lobular mastitis (GLM) and the value of differentiating GLM from breast cancer.

Materials and methods

The study included 30 women with GLM (mean age 36.7 ± 5 years [SD]) and 58 women with breast cancer (mean age 48. ± 8 years [SD]) who were scheduled for ultrasound-guided tissue biopsy. All patients were evaluated with conventional US and CEUS prior to the biopsy. In both groups, the parameters of the quantitative and qualitative analysis of the CEUS were recorded and compared. The receiver-operating-characteristics curves (ROC) were created. Sensitivity, specificity, cut-off, and area under the curve (AUC) values were calculated.

Results

TTP values in GLM were statistically higher than in breast cancer (mean, 27.63 ± 7.29 vs. 20.10 ± 6.11), but WIS values were lower (mean, 0.16 ± 0.05 vs. 0.28 ± 0.17). Rich vascularity was discovered in 54.45% of breast cancer patients, but only 30.00% of GLM patients had rich vascularity. The AUC for the ROC test was 0.791 and 0.807, respectively. The optimal cut-off value for TTP was 24.5s, and the WIS cut-off value was 0.185dB/s, yielding 73.33% sensitivity, 84.48% specificity, and 86.21% sensitivity, 70% specificity respectively in the diagnosis of GLM. The lesion scores reduced from 4 to 3 with the addition of CEUS for the patients with GLM. However, the scores did not change for the patients with breast cancer.

Conclusion

CEUS could help distinguish GLM from breast cancer by detecting higher TTP and WIS values, potentially influencing clinical decision-making for additional biopsies.