Axillary Lymph Node Metastasis Research Articles

High Peritumoral and Intratumoral T2 Signal Intensity in HER2-Positive Breast Cancers on Preneoadjuvant Breast MRI: Assessment of Associations With Histopathologic Characteristics.

BACKGROUND. Intratumoral necrosis and peritumoral edema are features of aggressive breast cancer that may present as high T2 signal intensity (T2 SI). Implications of high T2 SI in HER2-positive cancers are unclear. OBJECTIVE. The purpose of this study was to assess associations with histopathologic characteristics of high peritumoral T2 SI and intratumoral T2 SI of HER2-positive breast cancer on MRI performed before initiation of neoadjuvant therapy. METHODS. This retrospective study included 210 patients (age, 24-82 years) with 211 HER2 breast cancers who, from January 1, 2015, to July 30, 2022, underwent breast MRI before receiving neoadjuvant therapy. Two radiologists independently assessed cancers for high peritumoral T2 SI and high intratumoral T2 SI on fat-suppressed T2-weighted imaging and classified patterns of high peritumoral T2 SI (adjacent to tumor vs prepectoral extension). A third radiologist resolved discrepancies. Multivariable logistic regression analyses were performed to identify associations of high peritumoral and intratumoral T2 SI with histopathologic characteristics (associated ductal carcinoma in situ, hormone receptor status, histologic grade, lymphovascular invasion, and axillary lymph node metastasis). RESULTS. Of 211 HER2-positive cancers, 81 (38.4%) had high peritumoral T2 SI, and 95 (45.0%) had high intratumoral T2 SI. A histologic grade of 3 was independently associated with high peritumoral T2 SI (OR = 1.90; p = .04). Otherwise, none of the five assessed histopathologic characteristics were independently associated with high intratumoral T2 SI or high peritumoral T2 SI (p > .05). Cancers with high T2 SI adjacent to the tumor (n = 29) and cancers with high T2 SI with prepectoral extension (n = 52) showed no significant difference in frequency for any of the histopathologic characteristics (p > .05). Sensitivities and specificities for predicting the histopathologic characteristics ranged from 35.6% to 43.7% and from 59.7% to 70.7%, respectively, for high peritumoral T2 SI, and from 37.3% to 49.6% and from 49.3% to 62.7%, respectively, for high intratumoral T2 SI. Interreader agreement was almost perfect for high peritumoral T2 SI (Gwet agreement coefficient [AC] = 0.93), high intratumoral T2 SI (Gwet AC = 0.89), and a pattern of high peritumoral T2 SI (Gwet AC = 0.95). CONCLUSION. The only independent association between histopathologic characteristics and high T2 SI of HER2-positive breast cancer was observed between a histologic grade of 3 and high peritumoral T2 SI. CLINICAL IMPACT. In contrast with previously reported findings in broader breast cancer subtypes, peritumoral and intratumoral T2 SI had overall limited utility as prognostic markers of HER2-positive breast cancer.

Ki-67 Testing in Breast Cancer: Assessing Variability With Scoring Methods and Specimen Types and the Potential Subsequent Impact on Therapy Eligibility.

Abemaciclib was originally FDA approved for patients with ER-positive/HER2-negative breast cancer with Ki-67 expression ≥20%. However, there were no guidelines provided on which specimen to test or which scoring method to use. We performed a comprehensive study evaluating the variation in Ki-67 expression in breast specimens from 50 consecutive patients who could have been eligible for abemaciclib therapy. Three pathologists with breast expertise each performed a blinded review with 3 different manual scoring methods [estimated (EST), unweighted (UNW), and weighted (WT) (WT recommended by the International Ki-67 in Breast Cancer Working Group)]. Quantitative image analysis (QIA) using the HALO platform was also performed. Three different specimen types [core needle biopsy (CNB) (n=63), resection (RES) (n=52), and axillary lymph node metastasis (ALN) (n=50)] were evaluated for each patient. The average Ki-67 for all specimens was 14.68% for EST, 14.46% for UNW, 14.15% for WT, and 11.15% for QIA. For the manual methods, the range between the lowest and highest Ki-67 for each specimen between the 3 pathologists was 8.44 for EST, 5.94 for WT, and 5.93 for UNW. The WT method limited interobserver variability with ICC1=0.959 (EST ICC1=0.922 and UNW=0.949). Using the aforementioned cutoff of Ki-67 ≥20% versus <20% to determine treatment eligibility, the averaged EST method yields 20 of 50 patients (40%) who would have been treatment-eligible, versus 15 (30%) for the UNW, 17 (34%) for the WT, and 12 (24%) for the QIA. There was no statistically significant difference in Ki-67 among the 3 specimen types. The average Ki-67 difference was 4.36 for CNB vs RES, 6.95 for CNB versus ALN, and RES versus ALN (P=0.93, 0.99, and 0.94, respectively). Our study concludes that further refinement in Ki-67 scoring is advisable to reduce clinically significant variation.

26 (PB-7) Poster Spotlight - Core Needle Biopsy outperforms Fine-Needle Aspiration Cytology in diagnosing axillary lymph node metastasis in breast cancer patients

26 (PB-7) Poster Spotlight - Core Needle Biopsy outperforms Fine-Needle Aspiration Cytology in diagnosing axillary lymph node metastasis in breast cancer patients

Risk factors and prognosis of sentinel lymph node metastasis in breast-conserving breast cancer: A retrospective study based on the SEER database.

At present, the risk factors and prognosis of sentinel lymph node metastasis (SLNM) are analyzed based on the study of axillary lymph node metastasis, but whether there is a difference between the two is unclear. Therefore, an accurate and appropriate predictive model needs to be proposed to evaluate patients undergoing sentinel lymph node biopsy (SLNB) for breast cancer. We selected 16983 women with breast cancer from the Surveillance Epidemiology and End Results (SEER) database. They were randomly assigned to two cohorts, one for development (n = 11891) and one for validation (n = 5092). multi-factor logistics regression was used to distinguish risk factors affecting SLNM. The potential prognostic factors were identified using the COX regression analysis. The hazard ratio (HR) and 95% confidence interval (95%CI) were calculated for all results. Multiple Cox models are included in the nomogram, with a critical P value of .05. In order to evaluate the model's performance, Concordance index and receiver operating characteristic curves were used. Six independent risk factors affecting SLNM were screened out from the Logistic regression, including tumor location, number of regional lymph nodes (2-5), ER positive, PR positive, tumor size (T2-3), and histological grade (Grade II-III) are independent risk factors for SLNM in patients (P < .05). Eight prognostic factors were screened out in the multivariate COX regression analysis (P < .05): Age: Age 60 to 79 years, Age ≥ 80 years; Race; Histological grading: Grade II, Grade III; No radiotherapy; Tumor size: T2, T3; ER positive:, sentinel lymph node positive, married. Histological grade, tumor location, T stage, ER status, PR status and the number of SLNB are significantly correlated with axillary SLNM. Age, ethnicity, histological grade, radiotherapy, tumor size, ER status, SLN status, and marital status were independent risk factors for Breast cancer specific survival (BCSS). Moreover, the survival rate of patients with 3 positive SLNs was not significantly different from that with one or two positive SLNs, We concluded that patients with stage N1 breast cancer were exempt from axillary lymph node dissection, which is worthy of further study.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study

Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p<0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p<0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p<0.05). Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Primary fallopian tube cancer followed by primary breast cancer in RAD51C mutation carrier treated with niraparib as first line maintenance therapy: a case report

Given the rarity of RAD51C mutations, the risk and treatment of metachronous breast cancer after the diagnosis of ovarian cancer in RAD51C mutation carriers is not clear, especially for those who have received PARPi treatment. We report the case of a 65-year-old woman diagnosed with stage IIIC high-grade serous primary fallopian tube cancer. The patient had no family history of breast or ovarian cancer. The patient received three cycles of neoadjuvant chemotherapy with paclitaxel and carboplatin and achieved a complete response. After interval debulking surgery, the patient received three cycles of adjuvant chemotherapy. Collection and extraction of saliva DNA for next-generation sequencing identified a RAD51C mutation c.838-2 A > G. The patient received niraparib as front-line maintenance treatment. After 36 months of niraparib treatment, the patient had grade II invasive ductal carcinoma of the left breast that was positive for estrogen receptor (90%) and Ki-67 (30%) and negative for progesterone receptor and human epidermal growth factor receptor 2. Computed tomography revealed the absence of distant metastases. Modified radical mastectomy and axillary lymph node dissection were then performed. The final pathological report of the breast showed a 1.8 cm Bloom-Richardson grade II invasive ductal carcinoma in the left breast with axillary lymph node metastasis (1/21). Finally, the breast cancer was stage IIA, pT1cN1M0. The metachronous breast cancer in this case may be the first report of second primary cancer in fallopian tube cancer patient harboring a RAD51C mutation during niraparib treatment. Further studies are required to determine optimal treatment.

Artificial Intelligence for Breast Ultrasound: AJR Expert Panel Narrative Review.

Breast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, however, include its low specificity and, for automated breast ultrasound screening, the time necessary to review whole-breast ultrasound images. As of this writing, four AI tools that are approved or cleared by the FDA address these limitations. Current tools, which are intended to provide decision support for lesion classification and/or detection, have been shown to increase specificity among non-specialists and to decrease interpretation times. Potential future applications include triage of patients with palpable masses in low-resource settings, preoperative prediction of axillary lymph node metastasis, and preoperative prediction of neoadjuvant chemotherapy response. Challenges in the development and clinical deployment of AI for ultrasound include: the limited availability of curated training datasets compared to mammography; the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability); and the lack of post-implementation evaluation studies. Furthermore, current AI tools for lesion classification were developed based on 2D data, but diagnostic accuracy could potentially be improved if multimodal ultrasound data were used, such as color Doppler, elastography, cine clips, and 3D imaging.

Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.

We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.

Prediction of ≥ 4 axillary lymph node metastasis in cN0 T1-2 breast cancer: A comparative analysis of the per-protocol population of the SINODAR-ONE multicenter randomized clinical trial.

Prediction of ≥ 4 axillary lymph node metastasis in cN0 T1-2 breast cancer: A comparative analysis of the per-protocol population of the SINODAR-ONE multicenter randomized clinical trial.

Ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS) combined with molecular biomarkers in predicting axillary lymph node metastasis of clinical T1-T2 breast cancer.

In the post-American College of Surgeons Oncology Group Z0011 trial era, clinicians are attempting to preoperatively evaluate axillary lymph node (ALN) status using ultrasound. However, the value of preoperative ultrasound examination remains uncertain. The study aimed to investigate the ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS), in combination with molecular biomarkers, to predict the risk of ALN metastasis (ALNM) in clinical T1-T2 breast cancer. A retrospective case-control analysis was conducted on 168 patients with clinical T1-T2 breast cancer at Peking University First Hospital between January 2013 and August 2021. Preoperative ABVS and HHUS examinations were performed. According to the pathology results of the ALN, patients were divided into metastatic and nonmetastatic groups. Logistic regression analyses were used to analyze the ultrasonic characteristics of ABVS and HHUS on clinical T1-T2 breast cancer, and molecular biomarkers were incorporated to predict the risk of ALNM. Of the 168 patients, 88 (52.4%) had ipsilateral ALNM while 80 (47.6%) had no ipsilateral ALNM. The univariate analysis showed that shorter tumor-skin distance (P=0.011), the Adler blood flow grade of II-III (P=0.014), and larger tumor size on ABVS (P<0.001) were associated with ALNM. The multivariate logistic analysis showed that these three risk factors, including the tumor-skin distance [odds ratio (OR) =0.279; P=0.024], the Adler blood flow grade (OR =2.164; P=0.046), and the tumor size on ABVS (OR =1.033; P=0.002), were independent predictive parameters. The tumor-skin distance, tumor size on ABVS, and Adler blood flow grade have diagnostic value for ALNM in clinical T1-T2 breast cancer.

Elucidating Axillary Soft Tissue Involvement in Breast Cancer: Unveiling Metastatic Mechanisms and Therapeutic Perspectives

This perspective focuses on axillary soft tissue (AXT) involvement in breast cancer, revealing diverse pathological entities beyond traditional axillary lymph node metastasis. AXT involvement is linked to increased risks of distant metastasis and locoregional failure, emphasizing its significance in predicting breast cancer outcomes. We posit that AXT involvement could signify a retrograde metastatic event stemming from reactivated circulating tumor cells navigating towards the axillary soft tissue guided by chemokines. Therefore, AXT disease warrants aggressive systemic therapy. Axillary radiation therapy could be a potentially preferable alternative to axillary lymph node dissection. Routine reporting of axillary soft tissue involvement is crucial for accurate treatment planning.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer.

We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions. A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson's coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses. Among all the radiomics models, the "Mass + Margin3mm" model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882-0.930) and 0.922 (95% CI 0.894-0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816-0.889) in primary cohort, 0.870 (95% CI 0.818-0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781-0.884) in primary cohort, 0.911 (95% CI 0.858-0.964) in validation cohort). The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

Discordance of Biomarker Expression Profile between Primary Breast Cancer and Synchronous Axillary Lymph Node Metastasis in Preoperative Core Needle Biopsy

Background: Breast cancer (BC) is a heterogeneous disease made up of clones with different metastatic potential. Intratumoral heterogeneity may cause metastases to show divergent biomarker expression, potentially affecting chemotherapy response. Methods: We investigated the immunohistochemical (IHC) and FISH profile of estrogen receptors (ER), progesterone (PR) receptors, Ki67, and HER2 in a series of BC-matched primary tumors (PTs) and axillary lymph node (ALN) metastases in pre-operative core needle biopsies (CNBs). Phenotypical findings were correlated to morphological features and their clinical implications. Results: Divergent expression between PTs and ALNs was found in 10% of the tumors, often involving multiple biomarkers (12/31, 39%). Most (52%) displayed significant differences in ER and PR staining. HER2 divergences were observed in almost three-quarters of the cases (23/31, 74%), with five (16%) switching from negativity to overexpression/amplification in ALNs. Roughly 90% of disparities reflected significant morphological differences between PTs and ALN metastases. Less than half of the discrepancies (12/31, 39%) modified pre/post-operative treatment options. Conclusions: We observed relevant discrepancies in biomarker expression between PTs and metastatic ALNs in a noteworthy proportion (10%) of preoperative BC CNBs, which were often able to influence therapies. Hence, our data suggest routine preoperative assessment of biomarkers in both PTs and ALNs in cases showing significant morphological differences.

Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer.

Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC.

Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer.

Primary breast cancer is the most common malignant tumor in women worldwide, and axillary lymph node metastasis (ALNM) is an important marker of disease progression in patients with breast cancer. The objective of the present study was to analyze the association between contrast-enhanced ultrasound (CEUS) features and ALNM in primary breast cancer and its predictive value. A total of 120 patients with breast cancer were assigned to the non-metastatic group (n=70) and metastatic group (n=50). The factors influencing ALNM were explored by multivariate logistic regression analysis. The consistency of CEUS, ordinary ultrasonography and pathological examination in the diagnosis of the ALNM of breast cancer was evaluated by consistency testing. The sensitivity, specificity and consistency rate of CEUS features and ordinary ultrasonography were analyzed by receiver operating characteristic curve and four-fold table analyses. High enhancement amplitude, centripetal enhancement sequence, increased maximum cortical thickness, high peak intensity and a larger area under the curve of lymph nodes were more commonly found in the metastatic group than in the non-metastatic group. The lymph node aspect ratio and time to peak were lower in the metastatic group than the non-metastatic group. The time to peak was a protective factor for ALNM in patients with breast cancer. The sensitivity, specificity and coincidence rate with pathological examination of CEUS in the diagnosis of ALNM were 92.00, 90.00 and 90.83%, while these of ordinary ultrasonography were 76.00, 80.00 and 78.33%, respectively. The consistency test indicated that CEUS and pathological examination were consistent in the diagnosis of ALNM in patients with breast cancer, with a κ value of 0.816, indicating a good consistency. The κ value of ordinary ultrasonography and pathological examination was 0.763, also indicating a good consistency. However, these results indicate that CEUS is more valuable than ordinary ultrasonography in the diagnosis of ALNM in cases of breast cancer. In conclusion, the present study indicates that CEUS features were influencing factors associated with ALNM in patients with breast cancer and may serve as an important reference for the preoperative prediction of ALNM in breast cancer.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. Retrospective. 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. 3 TECHNICAL EFFICACY: Stage 2.

Feasibility and accuracy of targeted axillary dissection in breast cancer patients; single center experience.

Axillary complete response (pCR) was observed in approximately half of breast cancer patients who received neoadjuvant chemotherapy (NAC) due to axillary positivity. Preventing axillary morbidity due to unnecessary axillary lymph node dissection (ALND) is extremely important for patients' quality of life. Targeted axillary dissection (TAD) is a technique developed to improve axillary staging and reduce the false negative rate in sentinel lymph node biopsy. Patients with cN1-N2 (clinically node) breast cancer whose axillary lymph node metastasis was confirmed by biopsy and who received NAC marked with a clip were included in the study. Patients who achieved clinical response after systemic treatment underwent sentinel lymph node biopsy (SLNB) with additional methods such as methylene blue guided dissection, skin marking for clip on lymph node (CLN) localization, and wire guided with imaging excision of the CLN. TAD and ALND pathology results were evaluated and analyzed with patient and tumor characteristics. 83 patients who met the eligibility criterias for the study were examined retrospectively. 21 of the patients underwent TAD alone, and 62 patients underwent ALND after TAD. CLN rate was 98.8% in patients underwent only TAD and this rate was increased to 100% in patients underwent ALND after TAD. FNR in SLN, CLN, and TAD were 28.6%, 10.7%, and 3.6%, respectively. TAD is a feasible and reliable surgical approach to detect axillary residual disease with a high success rate.

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis. To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome. Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004). Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.

Reliability of predicting low-burden (≤ 2) positive axillary lymph nodes indicating sentinel lymph node biopsy in primary operable breast cancer — a retrospective comparative study with PET/CT and breast MRI

BackgroundSentinel lymph node biopsy (SLNB) is the standard of care for axillary staging in early breast cancer patients with low-burden axillary metastasis (≤ 2 positive nodes). This study aimed to determine the diagnostic performances of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and breast magnetic resonance imaging in detecting axillary lymph node (ALN) metastases and the reliability to predict ALN burden.MethodsA total of 275 patients with primary operable breast cancer receiving preoperative PET/CT and upfront surgery from January 2001 to December 2022 in a single institution were enrolled. A total of 244 (88.7%) of them also received breast MRI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET/CT and breast MRI were assessed. The predictive values to determine ALN burden were evaluated using radio-histopathological concordance.ResultsPET/CT demonstrated a sensitivity of 53.4%, specificity of 82.1%, PPV of 65.5%, NPV of 73.5%, and accuracy of 70.9% for detecting ALN metastasis, and the corresponding values for MRI were 71.8%, 67.8%, 56%, 80.8%, and 69.2%, respectively. Combining PET/CT and MRI showed a significantly higher PPV than MRI (72.7% vs 56% for MRI alone, p = 0.037) and a significantly higher NPV than PET/CT (84% vs 73.5% for PET/CT alone, p = 0.041). For predicting low-burden axillary metastasis (1–2 positive nodes), the PPVs were 35.9% for PET/CT, 36.7% for MRI, and 55% for combined PET/CT and MRI. Regarding patients with 0–2 positive ALNs in imaging, who were indicated for SLNB, the predictive correctness was 96.1% for combined PET/CT and MRI, 95.7% for MRI alone, and 88.6% for PET/CT alone.ConclusionsPET/CT and breast MRI exhibit high predictive values for identifying low-burden axillary metastasis in patients with operable breast cancer with ≦ 2 positive ALNs on imaging.

2020 Annual Report of National Clinical Database-Breast Cancer Registry: 10-year mortality of elderly breast cancer patients in Japan.

The Japanese Breast Cancer Society initiated the breast cancer registry in 1975, which transitioned to the National Clinical Database-Breast Cancer Registry in 2012. This annual report presents data from 2020 and analyzes the ten-year mortality rates for those aged 65 and older. We analyzed data from 93,784 breast cancer (BC) cases registered in 2020 and assessed 10-year mortality rates for 36,279 elderly patients diagnosed between 2008 and 2012. In 2020, 99.4% of BC cases were females with a median age of 61. Most (65%) were diagnosed at early stages (Stage 0 or I). Breast-conserving surgery rates varied with stages: 58.5% at cStage I, 30.8% at cStage II, and 13.1% at cStage III. Sentinel lymph node biopsy was done in 73.6% of cases, followed by radiotherapy in 70% of those post-conserving surgery and chemotherapy in 21.1% post-surgery. Pathology showed that 63.4% had tumors under 2.0cm, 11.7% had pTis tumors, and 77.3% had no axillary lymph node metastasis. ER positivity was seen in 75.1%, HER2 in 14.3%, and 30% had a Ki67 positivity rate above 30%. Across all stages and subtypes, there was a trend where the 10-year mortality rates increased for individuals older than 65years. In Stage I, many deaths were not directly linked to BC and, for those with HER2-type and triple-negative BC, breast cancer-related deaths increased with age. Within Stage II, patients older than 70years with luminal-type BC often experienced deaths not directly linked to BC, whereas patients below 80years with HER2-type and triple-negative BC, likely had breast cancer-related deaths. In Stage III, breast cancer-related deaths were more common, particularly in HER2 and triple-negative BC. Our prognostic analysis underscores distinct mortality patterns by stage, subtype, and age in elderly BC patients. It highlights the importance of personalized treatment strategies, considering subtype-specific aggressiveness, age-related factors, and comorbidities.