A deep learning-based method for the detection and segmentation of breast masses in ultrasound images.

Objective.Automated detection and segmentation of breast masses in ultrasound images are critical for breast cancer diagnosis, but remain challenging due to limited image quality and complex breast tissues. This study aims to develop a deep learning-based method that enables accurate breast mass detection and segmentation in ultrasound images.Approach.A novel convolutional neural network-based framework that combines the You Only Look Once (YOLO) v5 network and the Global-Local (GOLO) strategy was developed. First, YOLOv5 was applied to locate the mass regions of interest (ROIs). Second, a Global Local-Connected Multi-Scale Selection (GOLO-CMSS) network was developed to segment the masses. The GOLO-CMSS operated on both the entire images globally and mass ROIs locally, and then integrated the two branches for a final segmentation output. Particularly, in global branch, CMSS applied Multi-Scale Selection (MSS) modules to automatically adjust the receptive fields, and Multi-Input (MLI) modules to enable fusion of shallow and deep features at different resolutions. The USTC dataset containing 28 477 breast ultrasound images was collected for training and test. The proposed method was also tested on three public datasets, UDIAT, BUSI and TUH. The segmentation performance of GOLO-CMSS was compared with other networks and three experienced radiologists.Main results.YOLOv5 outperformed other detection models with average precisions of 99.41%, 95.15%, 93.69% and 96.42% on the USTC, UDIAT, BUSI and TUH datasets, respectively. The proposed GOLO-CMSS showed superior segmentation performance over other state-of-the-art networks, with Dice similarity coefficients (DSCs) of 93.19%, 88.56%, 87.58% and 90.37% on the USTC, UDIAT, BUSI and TUH datasets, respectively. The mean DSC between GOLO-CMSS and each radiologist was significantly better than that between radiologists (p< 0.001).Significance.Our proposed method can accurately detect and segment breast masses with a decent performance comparable to radiologists, highlighting its great potential for clinical implementation in breast ultrasound examination.

AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

Repurposing GnRH-A as a Near-Infrared Fluorescent Probe for Diagnosis and Surgical Navigation of Breast Cancer Tumors and Metastases.

Breast cancer, globally the most common cancer in women, presents significant challenges in treatment. Breast-conserving surgery (BCS), a less traumatic and painful alternative to radical mastectomy, not only preserves the breast's appearance but also supports postsurgical functional recovery. However, accurately identifying tumors, precisely delineating margins, and thoroughly removing metastases remain complex surgical challenges, exacerbated by the limitations of current imaging techniques, including poor tumor uptake and low signal contrast. Addressing these challenges, our study developed a series of GnRHR-targeted probes (YQGN-n) for fluorescence imaging and surgical navigation of breast cancer through a drug repositioning strategy. Notably, YQGN-7, with its high cellular affinity (Kd of 217.8 nM), demonstrates exceptional selectivity and specificity for breast cancer tumors, surpassing traditional imaging agents like ICG in tumor uptake and pharmacokinetic properties. Furthermore, YQGN-7's effectiveness in surgical navigation, both for primary breast tumors and metastases, highlights its potential as a revolutionary tool in BCS.

Examining the Association Between the COVID-19 Pandemic and the Rate of Diagnostic Tests for Breast, Cervical, and Colorectal Cancer in Manitoba, Canada.

Background: Strategies to minimize the impact of the COVID-19 pandemic led to a reduction in diagnostic testing. It is important to assess the magnitude and duration of this impact to plan ongoing care and avoid long-lasting impacts of the pandemic. Objective: We examined the association between the COVID-19 pandemic and the rate of diagnostic tests for breast, cervical, and colorectal cancer in Manitoba, Canada. Design and Participants: A population-based, cross-sectional study design with an interrupted time series analysis was used that included diagnostic tests from January 1, 2015 until August 31, 2022. Setting: Manitoba, Canada. Main Outcomes: Outcomes included mammogram, breast ultrasound, colposcopy, and colonoscopy rates per 100,000. Cumulative and percent cumulative differences between the fitted and counterfactual number of tests were estimated. Mean, median, and 90th percentile number of days from referral to colonoscopy date by referral type (elective, semiurgent, urgent) were determined. Results: In April 2020, following the declaration of the COVID-19 public health emergency, bilateral mammograms decreased by 77%, unilateral mammograms by 70%, breast ultrasounds by 53%, colposcopies by 63%, and colonoscopies by 75%. In Winnipeg (the largest urban center in the province), elective and semiurgent colonoscopies decreased by 76% and 39%, respectively. There was no decrease in urgent colonoscopies. As of August 2022, there were an estimated 7270 (10.7%) fewer bilateral mammograms, 2722 (14.8%) fewer breast ultrasounds, 836 (3.3%) fewer colposcopies, and 11 600 (13.8%) fewer colonoscopies than expected in the absence of COVID-19. As of December 2022, in Winnipeg, there were an estimated 6030 (23.9%) fewer elective colonoscopies, 313 (2.6%) fewer semiurgent colonoscopies, and 438 (27.3%) more urgent colonoscopies. Conclusions: In Manitoba, the COVID-19 pandemic was associated with sizable decreases in diagnostic tests for breast, colorectal, and cervical cancer. Two and a half years later, there remained large cumulative deficits in bilateral mammograms, breast ultrasounds, and colonoscopies.

A Retrospective Analysis of Diagnostic Breast Imaging Outcomes in Young Women at a Tertiary Care Center.

(1) Purpose: The purpose of this study was to describe the outcomes of diagnostic breast imaging and the incidence of delayed breast cancer diagnosis in the study population. (2) Methods: We collected the outcome data from diagnostic mammograms and/or breast ultrasounds (USs) performed on women between the ages of 30 and 50 with symptomatic breast clinical presentations between 2018 and 2019. (3) Results: Out of 171 eligible patients, 10 patients (5.8%) had BIRADS 0, 90 patients (52.6%) had benign findings (BIRADS 1 and 2), 41 (24.0%) patients had probable benign findings requiring short-term follow-up (BIRADS 3), while 30 (17.5%) patients had findings suspicious of malignancy (BIRADS 4 and 5). In the BIRADS 3 group, 92.7% had recommended follow-up, while in BIRADS 4 and 5, only 83.3% underwent recommended biopsy at a mean time of 1.7 weeks (range 0-22 wks) from their follow-up scan. Ten (6%) patients were diagnosed with breast cancer, all of whom had BIRADS 4 or 5, with a mean time of breast cancer diagnosis from initial diagnostic imaging of 2.2 weeks (range 1-22 wks). No patients had delayed breast cancer diagnosis in our cohort. (4) Conclusions: We conclude that diagnostic mammograms and breast US are appropriate investigations for clinical breast concerns in women aged 30-50 years.

ER-Targeted PET for Initial Staging and Suspected Recurrence in ER-Positive Breast Cancer.

Importance: There are insufficient data comparing 16α-18F-fluoro-17ß-estradiol (FES) positron emission tomography (PET) computed tomography (CT) with standard-of-care imaging (SOC) for staging locally advanced breast cancer (LABC) or evaluating suspected recurrence. Objective: To determine the detection rate of FES PET/CT and SOC for distant metastases in patients with estrogen receptor (ER)-positive LABC and recurrences in patients with ER-positive BC and suspected recurrence. Design, Setting, and Participants: This diagnostic study was conducted as a single-center phase 2 trial, from January 2021 to September 2023. The study design provided 80% power to find a 20% detection rate difference. Participants included patients with ER-positive LABC (cohort 1) or suspected recurrence (cohort 2). Data were analyzed from September 2023 to February 2024. Exposure: Participants underwent both SOC imaging and experimental FES PET/CT. When there were suspicious lesions on imaging, 1 was biopsied for histopathological reference standard to confirm presence (true positive) or absence (false positive) of malignant neoplasm. Main Outcomes and Measures: The outcome of interest was the detection rate of FES PET CT vs SOC for distant metastases and recurrences. Results: A total of 124 patients were accrued, with 62 in cohort 1 (median [IQR] age, 52 [32-84] years) and 62 in cohort 2 (median [IQR] age, 66 [30-93] years). In cohort 1, of 14 true-positive findings, SOC imaging detected 12 and FES detected 11 (P > .99). In cohort 2, of 23 true-positive findings, SOC detected 16 and FES detected 18 (P = .77). In 30 patients with lobular histology, of 11 true-positive findings, SOC detected 5 and FES detected 9 (P = .29). There were 6 false-positive findings on SOC and 1 false-positive finding on FES PET/CT (P = .13). Conclusions and Relevance: In this diagnostic study with pathological findings as the reference standard, no difference was found between FES PET/CT and current SOC imaging for detecting distant metastases in patients with ER-positive LABC or recurrences in patients with ER-positive tumors and suspected recurrence. FES PET/CT could be considered for both clinical indications, which are not part of current Appropriate Use Criteria for FES PET. The findings regarding FES PET/CT in patients with lobular tumors, and for lower false positives than current SOC imaging, warrant further investigation.

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.

A prospective study of HER3 expression pre and post neoadjuvant therapy of different breast cancer subtypes: implications for HER3 imaging therapy guidance.

PURPOSE: HER3, a member of the EGFR receptor family, plays a central role in driving oncogenic cell proliferation in breast cancer. Novel HER3 therapeutics are showing promising results while recently developed HER3 PET imaging modalities aid in predicting and assessing early treatment response. However, baseline HER3 expression, as well as changes in expression while on neoadjuvant therapy, have not been well-characterized. We conducted a prospective clinical study, pre- and post-neoadjuvant/systemic therapy, in patients with newly diagnosed breast cancer to determine HER3 expression, and to identify possible resistance mechanisms maintained through the HER3 receptor. EXPERIMENTAL DESIGN: The study was conducted between May 25, 2018 and October 12, 2019. Thirty-four patients with newly diagnosed breast cancer of any subtype (ER ± , PR ± , HER2 ±) were enrolled in the study. Two core biopsy specimens were obtained from each patient at the time of diagnosis. Four patients underwent a second research biopsy following initiation of neoadjuvant/systemic therapy or systemic therapy which we define as neoadjuvant therapy. Molecular characterization of HER3 and downstream signaling nodes of the PI3K/AKT and MAPK pathways pre- and post-initiation of therapy was performed. Transcriptional validation of finings was performed in an external dataset (GSE122630). RESULTS: Variable baseline HER3 expression was found in newly diagnosed breast cancer and correlated positively with pAKT across subtypes (r = 0.45). In patients receiving neoadjuvant/systemic therapy, changes in HER3 expression were variable. In a hormone receptor-positive (ER +/PR +/HER2-) patient, there was a statistically significant increase in HER3 expression post neoadjuvant therapy, while there was no significant change in HER3 expression in a ER +/PR +/HER2+ patient. However, both of these patients showed increased downstream signaling in the PI3K/AKT pathway. One subject with ER +/PR -/HER2- breast cancer and another subject with ER +/PR +/HER2 + breast cancer showed decreased HER3 expression. Transcriptomic findings, revealed an immune suppressive environment in patients with decreased HER3 expression post therapy. CONCLUSION: This study demonstrates variable HER3 expression across breast cancer subtypes. HER3 expression can be assessed early, post-neoadjuvant therapy, providing valuable insight into cancer biology and potentially serving as a prognostic biomarker. Clinical translation of neoadjuvant therapy assessment can be achieved using HER3 PET imaging, offering real-time information on tumor biology and guiding personalized treatment for breast cancer patients.

Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer.

BACKGROUND: The aim of this study is to assess the efficacy of a multiparametric ultrasound imaging omics model in predicting the risk of postoperative recurrence and molecular typing of breast cancer. METHODS: A retrospective analysis was conducted on 534 female patients diagnosed with breast cancer through preoperative ultrasonography and pathology, from January 2018 to June 2023 at the Affiliated Cancer Hospital of Xinjiang Medical University. Univariate analysis and multifactorial logistic regression modeling were used to identify independent risk factors associated with clinical characteristics. The PyRadiomics package was used to delineate the region of interest in selected ultrasound images and extract radiomic features. Subsequently, radiomic scores were established through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM) methods. The predictive performance of the model was assessed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated. Evaluation of diagnostic efficacy and clinical practicability was conducted through calibration curves and decision curves. RESULTS: In the training set, the AUC values for the postoperative recurrence risk prediction model were 0.9489, and for the validation set, they were 0.8491. Regarding the molecular typing prediction model, the AUC values in the training set and validation set were 0.93 and 0.92 for the HER-2 overexpression phenotype, 0.94 and 0.74 for the TNBC phenotype, 1.00 and 0.97 for the luminal A phenotype, and 1.00 and 0.89 for the luminal B phenotype, respectively. Based on a comprehensive analysis of calibration and decision curves, it was established that the model exhibits strong predictive performance and clinical practicability. CONCLUSION: The use of multiparametric ultrasound imaging omics proves to be of significant value in predicting both the risk of postoperative recurrence and molecular typing in breast cancer. This non-invasive approach offers crucial guidance for the diagnosis and treatment of the condition.

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Targeted axillary dissection reduces residual nodal disease in clinically node- positive breast cancer after neoadjuvant chemotherapy.

BACKGROUND: Any advantage of performing targeted axillary dissection (TAD) compared to sentinel lymph node (SLN) biopsy (SLNB) is under debate in clinically node-positive (cN+) patients diagnosed with breast cancer. Our objective was to assess the feasibility of the removal of the clipped node (RCN) with TAD or without imaging-guided localisation by SLNB to reduce the residual axillary disease in completion axillary lymph node dissection (cALND) in cN+ breast cancer. METHODS: A combined analysis of two prospective cohorts, including 253 patients who underwent SLNB with/without TAD and with/without ALND following NAC, was performed. Finally, 222 patients (cT1-3N1/ycN0M0) with a clipped lymph node that was radiologically visible were analyzed. RESULTS: Overall, the clipped node was successfully identified in 246 patients (97.2%) by imaging. Of 222 patients, the clipped lymph nodes were non-SLNs in 44 patients (19.8%). Of patients in cohort B (n=129) with TAD, the clipped node was successfully removed by preoperative image-guided localisation, or the clipped lymph node was removed as the SLN as detected on preoperative SPECT-CT. Among patients with ypSLN(+) (n=109), no significant difference was found in non-SLN positivity at cALND between patients with TAD and RCN (41.7% vs. 46.9%, p=0.581). In the subgroup with TAD with axillary lymph node dissection (ALND; n=60), however, patients with a lymph node (LN) ratio (LNR) less than 50% and one metastatic LN in the TAD specimen were found to have significantly decreased non-SLN positivity compared to others (27.6% vs. 54.8%, p=0.032, and 22.2% vs. 50%, p=0.046). CONCLUSIONS: TAD by imaging-guided localisation is feasible with excellent identification rates of the clipped node. This approach has also been found to reduce the additional non-SLN positivity rate to encourage omitting ALND in patients with a low metastatic burden undergoing TAD.

Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.

Background: Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers. Methods: This retrospective study enrolled 281 patients undergoing NAT from the Duke-Breast-Cancer-MRI dataset. Radiomic features were extracted from pre- and post-contrast first-phase T1WI images. The Synthetic Minority Oversampling Technique (SMOTE) was applied, then the dataset was randomly divided into training and validation groups (7:3). The radiomics model was built using selected optimal features. Support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were classifiers. Receiver operating characteristic curves were used to assess predictive performance. Results: LightGBM performed best in predicting pCR [area under the curve (AUC): 0.823, 95% confidence interval (CI) [0.743-0.902], accuracy 74.0%, sensitivity 85.0%, specificity 67.2%]. During subgroup analysis, RF was most effective in pCR prediction in luminal breast cancers (AUC: 0.914, 95% CI [0.847-0.981], accuracy 87.0%, sensitivity 85.2%, specificity 88.1%). In triple-negative breast cancers, LightGBM performed best (AUC: 0.836, 95% CI [0.708-0.965], accuracy 78.6%, sensitivity 68.2%, specificity 90.0%). Conclusion: The LightGBM-based radiomics model performed best in predicting pCR in patients with breast cancer. RF and LightGBM showed promising results for luminal and triple-negative breast cancers, respectively.

Evaluating the impact of delayed-phase imaging in Contrast-Enhanced Mammography on breast cancer staging: A comparative study of abbreviated versus complete protocol.

PURPOSE: Contrast-enhanced mammography (CEM) is an innovative imaging tool for breast cancer detection, involving intravenous injection of a contrast medium and the assessment of lesion enhancement in two phases: early and delayed. The aim of the study was to analyze the topographic concordance of lesions detected in the early- versus delayed phase acquisitions. MATERIALS AND METHODS: Approved by the Ethics Committee (No. 118/20), this prospective study included 100 women with histopathological confirmed breast neoplasia (B6) at the Radiodiagnostics Department of the Maggiore della Carità Hospital of Novara, Italy from May 1, 2021, to October 17, 2022. Participants underwent CEM examinations using a complete protocol, encompassing both early- and delayed image acquisitions. Three experienced radiologists blindly analyzed the CEM images for contrast enhancement to determine the topographic concordance of the identified lesions. Two readers assessed the complete study (protocol A), while one reader assessed the protocol without the delayed phase (protocol B). The average glandular dose (AGD) of the entire procedure was also evaluated. RESULTS: The analysis demonstrated high concordance among the three readers in the topographical identification of lesions within individual quadrants of both breasts, with a Cohen's κ > 0.75, except for the lower inner quadrant of the right breast and the retro-areolar region of the left breast. The mean whole AGD was 29.2 mGy. The mean AGD due to CEM amounted to 73% of the whole AGD (21.2 mGy). The AGD attributable to the delayed phase of CEM contributed to 36% of the whole AGD (10.5 mGy). CONCLUSIONS: As we found no significant discrepancy between the readings of the two protocols, we conclude that delayed-phase image acquisition in CEM does not provide essential diagnostic benefits for effective disease management. Instead, it contributes to unnecessary radiation exposure.

An investigation of tomosynthesis on the diagnostic efficacy of spot compression mammography.

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM.

Predicting pathological complete response after neoadjuvant chemotherapy in breast cancer by clinicopathological indicators and ultrasound parameters using a nomogram.

The study explored the impact of pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) and changes in tumor blood supply on pathological complete response (pCR) in breast cancer (BC) patients following neoadjuvant chemotherapy (NACT). Additionally, a nomogram for predicting pCR was established and validated. The study included BC patients undergoing NACT at Yongchuan Hospital of Chongqing Medical University from January 2019 to October 2023. We analyzed the correlation between pCR and clinicopathological factors, as well as tumor ultrasound features, using chi-square or Fisher's exact test. We developed and validated a nomogram predicting pCR based on regression analysis results. The study included 176 BC patients. Logistic regression analysis identified AAPR [odds ratio (OR) 2.616, 95% confidence interval (CI) 1.140-5.998, P = 0.023], changes in tumor blood supply after two NACT cycles (OR 2.247, 95%CI 1.071-4.716, P = 0.032), tumor histological grade (OR 3.843, 95%CI 1.286-10.659, P = 0.010), and HER2 status (OR 2.776, 95%CI 1.057-7.240, P = 0.038) as independent predictors of pCR after NACT. The nomogram, based on AAPR, changes in tumor blood supply after two NACT cycles, tumor histological grade, and HER2 status, demonstrated a good predictive capability.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study.

BACKGROUND: Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS: This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS: The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS: Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

Practice of Mammography in a Private Nigerian Cancer Foundation: Our Experience.

BACKGROUND: Despite the proven effectiveness of mammography in screening and early breast cancer detection, there is still a huge disparity in both access to breast care and the quality of services provided in Nigeria. Non-governmental organizations (NGOs) have attempted to bridge this gap through awareness campaigns and subsidized breast imaging services. OBJECTIVES: To document the mammographic findings of adult females in a private NGO and assess the benefits of mammography practice in our locality. MATERIAL AND METHODS: This was a retrospective evaluation of mammographic examinations carried out over a two-year period (January 2020- December 2021) in a private cancer foundation in Abuja, Nor t h Ce nt r al Nigeria. Demographic details, clinical and mammographic features were analyzed with a statistical level of significance set at p≤0.05. RESULT: The age range of 565 women evaluated in this study was 31-84 years with the majority (55.7%) of them in the 40-49 year range. More than half (52.7%) of the women had had at least one previous mammogram. Screening was the predominant indication for mammograms in 361 women (63.9%) while 204(36.1%) were symptomatic. Breast pain (59.6%) and breast lump (26.3%) were the most common clinical indications. The predominant breast density pattern was the American College of Radiologists Breast Imaging and Reporting Data System (ACR BIRADS) type B (Scattered fibroglandular densities) in 241 women (42.7%). Mammogram was normal in 206 women (34.7%) while 52 (8.8%) had intraparenchymal findings. The final assessment showed that most of the mammograms were BIRADS category 1(69.6%) and 2(13.8%) signifying normal and benign findings. Body mass index, parity, age at first pregnancy, menopausal status, and breast density had significant relationships with the final BIRADS category. CONCLUSION: Mammography is an invaluable part of breast care in our locality. Evaluation of mammographic services in our private NGO showed a predominance of screening mammography while a majority of the women with symptomatic breast diseases had normal and benign findings.

CONTEXTE: Malgré l'efficacité avérée de la mammographie dans le dépistage et la détection précoce du cancer du sein, il existe encore une énorme disparité tant dans l'accès aux soins du sein que dans la qualité des services fournis au Nigeria. Les organisations non gouvernementales (ONG) ont tenté de combler cette lacune grâce à des campagnes de sensibilisation et à des services d'imagerie mammaire subventionnés. OBJECTIFS: Documenter les résultats mammographiques des femmes adultes dans une ONG privée et évaluer les avantages de la pratique de la mammographie dans notre localité. MATÉRIEL ET MÉTHODES: Il s'agissait d'une évaluation rétrospective des examens mammographiques réalisés sur une période de deux ans (janvier 2020 - décembre 2021) dans une fondation de lutte contre le cancer privée à Abuja, au Nigeria. Les détails démographiques, les caractéristiques cliniques et mammographiques ont été analysés avec un niveau de signification statistique fixé à p ≤ 0,05. RÉSULTAT: La tranche d'âge des 565 femmes évaluées dans cette étude était de 31 à 84 ans, la majorité (55,7 %) d'entre elles se situant dans la tranche d'âge de 40 à 49 ans. Plus de la moitié (52,7 %) des femmes avaient déjà subi au moins une mammographie précédente. Le dépistage était l'indication prédominante pour les mammographies chez 361 femmes (63,9 %), tandis que 204 (36,1 %) étaient symptomatiques. Les douleurs mammaires (59,6 %) et les masses mammaires (26,3 %) étaient les indications cliniques les plus courantes. Le motif de densité mammaire prédominant était de type B du système de notation et de rapport d'imagerie mammaire du Collège Américain des Radiologues (ACR BIRADS) chez 241 femmes (42,7 %). La mammographie était normale chez 206 femmes ( 34, 7 %) , t andi s que 52 ( 8, 8 %) présent ai ent des anomal i es intraparenchymateuses. L'évaluation finale a montré que la plupart des mammographies étaient classées BIRADS catégorie 1 (69,6 %) et 2 (13,8 %), ce qui signifie des résultats normaux et bénins. L'indice de masse corporelle, la parité, l'âge à la première grossesse, le statut ménopausique et la densité mammaire avaient des relations significatives avec la catégorie BIRADS finale. CONCLUSION: La mammographie est un élément inestimable des soins du sein dans notre localité. L'évaluation des services mammographiques dans notre ONG privée a montré une prédominance de la mammographie de dépistage, tandis que la majorité des femmes atteintes de maladies mammaires symptomatiques présentaient des résultats normaux et bénins. MOTS-CLÉS: Mammographie, Femmes, Nigeria, Soins du sein, Imagerie mammaire, Organisation non gouvernementale.