BACKGROUND: Increased level of stromal tumor-infiltrating lymphocytes (sTILs) are associated with therapeutic outcomes and prognosis in triple-negative breast cancer (TNBC). This study aimed to investigate the associations of clinicopathologic and sonographic features with sTILs level in TNBC.
METHODS: This study included invasive TNBC patients with postoperative evaluation of sTILs after surgical resection. Tumor shape, margin, orientation, echo pattern, posterior features, calcification, and vascularity were retrospectively evaluated. The patients were categorized into high-sTILs (≥ 20%) and low-sTILs (< 20%) level groups. Chi-square or Fisher\'s exact tests were used to assess the association of clinicopathologic and sonographic features with sTILs level.
RESULTS: The 171 patients (mean ± SD age, 54.7 ± 10.3 years [range, 22‒87 years]) included 58.5% (100/171) with low-sTILs level and 41.5% (71/171) with high-sTILs level. The TNBC tumors with high-sTILs level were more likely to be no special type invasive carcinoma (p = 0.008), higher histologic grade (p = 0.029), higher Ki-67 proliferation rate (all p < 0.05), and lower frequency of associated DCIS component (p = 0.026). In addition, the TNBC tumors with high-sTILs level were more likely to be an oval or round shape (p = 0.001), parallel orientation (p = 0.011), circumscribed or micro-lobulated margins (p < 0.001), complex cystic and solid echo patterns (p = 0.001), posterior enhancement (p = 0.002), and less likely to have a heterogeneous pattern (p = 0.001) and no posterior features (p = 0.002).
CONCLUSIONS: This preliminary study showed that preoperative sonographic characteristics could be helpful in distinguishing high-sTILs from low-sTILs in TNBC patients.

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.
CONCLUSIONS: 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.

OBJECTIVE: To evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models.
METHODS: This retrospective study included 262 patients with breast cancer-183 in the training cohort (144 LVI-negative and 39 LVI-positive cases) and 79 in the validation cohort (59 LVI-negative and 20 LVI-positive cases). Radiomics features were extracted from the intra- and peritumoral breast regions using multimodal MRI to generate gross tumor volume (GTV)_radiomics and gross tumor volume plus peritumoral volume (GPTV)_radiomics. Subsequently, DL models (GTV_DL and GPTV_DL) were constructed based on the GTV and GPTV to determine the LVI status. Finally, the most effective radiomics and DL models were integrated with imaging findings to establish a hybrid model, which was converted into a nomogram to quantify the LVI risk.
RESULTS: The diagnostic efficiency of GPTV_DL was superior to that of GTV_DL (areas under the curve [AUCs], 0.771 and 0.720, respectively). Similarly, GPTV_radiomics outperformed GTV_radiomics (AUC, 0.685 and 0.636, respectively). Univariate and multivariate logistic regression analyses revealed an association between imaging findings, such as MRI-axillary lymph nodes and peritumoral edema (AUC, 0.665). The hybrid model achieved the highest accuracy by combining GPTV_DL, GPTV_radiomics, and imaging findings (AUC, 0.872).
CONCLUSIONS: The diagnostic efficiency of the GPTV-derived radiomics and DL models surpassed that of the GTV-derived models. Furthermore, the hybrid model, which incorporated GPTV_DL, GPTV_radiomics, and imaging findings, demonstrated the effective determination of LVI status prior to surgery in patients with breast cancer.

OBJECTIVE: Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC.
METHODS: In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05.
RESULTS: GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set.
CONCLUSIONS: The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.

BACKGROUND: In up to 72 % of HER2+ invasive breast cancer (IBC), a ductal carcinoma in situ (DCIS) component is present. The presence of DCIS is associated with increased positive surgical margins after breast-conserving surgery (BCS). The aim of this study was to assess surgical margins, recurrence and survival in a nationwide cohort of HER2+ IBC with versus without a DCIS component, treated with neoadjuvant systemic therapy (NST) and BCS.
METHODS: Women diagnosed with HER2+ IBC treated with NST and BCS, between 2010 and 2019, were selected from the Netherlands Cancer Registry and linked to the Dutch Nationwide Pathology Databank. Kaplan-Meier and Cox regression analyses were performed to determine locoregional recurrence rate (LRR) and overall survival (OS) and associated clinicopathological variables. Surgical outcomes and prognosis were compared between IBC only and IBC+DCIS.
RESULTS: A total of 3056 patients were included: 1832 with IBC and 1224 with IBC+DCIS. Patients with IBC+DCIS had significantly more often positive surgical margins compared to IBC (12.8 % versus 4.9 %, p < 0.001). Five-year LRR was significantly higher in patients with IBC+DCIS compared to IBC (6.8 % versus 3.6 %, p < 0.001), but the presence of DCIS itself was not significantly associated with LRR after adjusting for confounders in multivariable analysis. Five-year OS did not differ between IBC+DCIS and IBC (94.9 % versus 95.7 %, p = 0.293).
CONCLUSIONS: The presence of DCIS is associated with higher rates of positive surgical margins, but not with LRR and lower OS when adjusted for confounders. Further research is necessary to adequately select IBC+DCIS patients for BCS after NST.

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

OBJECTIVE: Invasive lobular carcinoma (ILC) is the second most common histological subtype of invasive breast cancer, following the no special type (NST) invasive carcinoma. It has historically been assumed that ILC occurs bilaterally in 20-29 % of cases, which has influenced the inclusion of MRI in the standard workup of ILC according to European guidelines. However, challenging this long-held belief regarding the bilateral occurrence of ILC opens up the possibility of revising the guidelines and using MRI only for more specific indications. This study aims to evaluate whether the previously reported high percentage of bilaterality still holds true and to question the added value of MRI in the standard workup of ILC.
METHODS: A retrospective cohort study was conducted following approval from the institutional review board (EC 21/18/249) at Antwerp University Hospital (UZA). The cohort comprised female patients of all ages who had been diagnosed with either ILC or NST invasive carcinoma and had sought consultation at the UZA breast clinic. A comprehensive database was established to collect information on patient characteristics, imaging, and pathology.
RESULTS: A total of 271 patients with ILC were included in the study, with incidence dates ranging from 01/01/2007 to 01/01/2023. Among these patients, a synchronous bilateral ILC lesion was observed in 1.85 % (5/271) of cases. This proportion is significantly lower than the reported percentage of patients with a bilateral lesion in the literature population, which stands at 4.95 %. The reference group consisted of 809 patients with NST invasive carcinoma, with incidence dates ranging from 01/01/2017 to 01/01/2023. In the control group, a synchronous bilateral NST lesion was observed in 3.96 % (32/809) of cases. There is no significant difference in the bilaterality rates between the group of ILC patients and the group of NST patients. Furthermore, MRI did not detect any histopathologically confirmed contralateral ILC lesion that had not already been detected by mammography or ultrasound.
CONCLUSIONS: The study results indicate a lower occurrence of bilateral ILC than previously assumed. Additionally, the incidence of synchronous bilateral lesions in ILC patients is not higher compared to patients with NST invasive carcinoma. Performing an MRI does not provide additional value in detecting bilateral carcinomas in ILC. Consequently, it is recommended that the current European guidelines be reassessed, and the indications for undergoing an MRI should be adjusted accordingly.

The number of mitotic cells is an important indicator of grading invasive breast cancer. It is very challenging for pathologists to identify and count mitotic cells in pathological sections with naked eyes under the microscope. Therefore, many computational models for the automatic identification of mitotic cells based on machine learning, especially deep learning, have been proposed. However, converging to the local optimal solution is one of the main problems in model training. In this paper, we proposed a novel multilevel iterative training strategy to address the problem. To evaluate the proposed training strategy, we constructed the mitotic cell classification model with ResNet50 and trained the model with different training strategies. The results showed that the models trained with the proposed training strategy performed better than those trained with the conventional strategy in the independent test set, illustrating the effectiveness of the new training strategy. Furthermore, after training with our proposed strategy, the ResNet50 model with Adam optimizer has achieved 89.26% F1 score on the public MITOSI14 dataset, which is higher than that of the state-of-the-art methods reported in the literature.

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OBJECTIVE: We aimed to update the trend of hypofractionated whole-breast irradiation (HF-WBI) use over time in the US and examine factors associated with lack of HF-WBI adoption for patients with early-stage invasive breast cancer (IBC) or ductal carcinoma in situ (DCIS) undergoing a lumpectomy.
METHODS: Among patients who underwent a lumpectomy, we identified 928,034 patients with early-stage IBC and 330,964 patients with DCIS in the 2004 to 2020 National Cancer Database. We defined HF-WBI as 2.5-3.33 Gy/fraction to the breast and conventionally fractionated WBI as 1.8-2.0 Gy/fraction. We evaluated the trend of HF-WBI utilization using a generalized linear model with the log link and binomial distribution. Factors associated with HF-WBI utilization were assessed using multivariable logistic regression in patients diagnosed between 2018 and 2020.
RESULTS: Among patients with IBC, HF-WBI use has significantly increased from 0.7% in 2004 to 63.9% in 2020. Similarly, HF-WBI usage among patients with DCIS has also increased significantly from 0.4% in 2004 to 56.6% in 2020. Black patients with IBC were less likely than White patients to receive HF-WBI (adjusted odds ratio [AOR] 0.81; 95% CI, 0.77-0.85). Community cancer programs were less likely to administer HF-WBI to patients with IBC (AOR, 0.80; 95% CI, 0.77-0.84) and to those with DCIS (AOR, 0.87; 95% CI, 0.79-0.96) than academic/research programs. Younger age, positive nodes, larger tumor size, low volume programs, and facility location were also associated with lack of HF-WBI adoption in both patient cohorts.
CONCLUSIONS: HF-WBI utilization among postlumpectomy patients has significantly increased from 2004 to 2020 and can finally be considered standard of care in the US. We found substantial disparities in adoption within patient and facility subgroups. Reducing disparities in HF-WBI adoption has the potential to further alleviate health care costs while improving patients\' quality of life.