Neoadjuvant Chemotherapy In Breast Cancer Research Articles

Pretreatment plasma vitamin D and response to neoadjuvant chemotherapy in breast cancer: evidence from pooled analysis of cohort studies

Background: Biological evidence has revealed anti-tumor effect of vitamin D, but whether it could predict the response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients remains inconclusive. The aim was to investigate the association between pretreatment vitamin D level and response to NAC and subsequent survival outcomes in BC patients. Materials and Methods: We systematically searched the Medline, Embase, Cochrane Library and Web of Science databases and clinical trial registries to identify relevant articles from inception to October 8, 2024. Eligible studies investigating the associations between pretreatment plasma vitamin D and response to NAC in BC patients were selected according to the predefined criteria, with the study characteristics extracted by two reviewers. The primary outcome was pathological complete response (pCR), while overall pathological response and event-free survival (EFS) were adopted as secondary outcomes. Summary effect estimates of odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using a random-effects model. Subgroup and sensitivity analyses were performed based on study characteristics and methodological quality. Results: Six retrospective cohort studies involving 1,291 BC patients were included. We observed a significant association between pretreatment vitamin D deficiency and 50% increased odds of non-pCR after NAC (OR=1.50, 95%CI 1.11-2.03, P=0.008) with no heterogeneity (I2=0%). We also identified a significant association of vitamin D with overall pathological response (OR=1.33, 95%CI 1.01-1.75, P=0.046). A similar association with EFS (HR=1.27, 95%CI 0.92-1.75, P=0.139) was also noted although the effect estimate was not statistically significant. Sensitivity analyses based on methodological quality showed consistent findings. Conclusion: Pretreatment vitamin D deficiency is associated with an inferior response to NAC in BC patients. Our meta-analysis advocates further prospective studies with large sample sizes before vitamin D supplementation could be administered to improve NAC response and subsequent prognosis of BC patients.

MR tomographic evaluation of the effectiveness of neoadjuvant chemotherapy for breast cancer based on pharmacokinetic numerical analysis of tumor uptake of paramagnetic contrast in intravenous contrast enhancement

Background. In clinical practice, MRI pathophysiological and pharmacokinetic models with calculations of contrast transport indicators are unacceptably little used.Aim of the study. To propose a pharmacokinetic technique for the quantitative assessment of primary tumors and metastases, the effectiveness of breast cancer chemotherapy (BC), from dynamic contrast enhancement MRI. Material and methods. 18 patients were included who underwent neoadjuvant chemotherapy (NACHT) in four cycles for breast cancer T1-3N0-1M0, followed by radical surgical removal of the tumor. According to the results of a three — year follow-up, patients with a relapse — free course (n = 11) formed group 1, and seven patients with detected metastatic lesions (3 in the liver, 2 in the lungs, 2 in the brain) — group 2. For the initial 120–180 s of MRI with contrast after the injection of a paramagnetic, the simplification is valid for concentrations in tumor and blood and for tranfer coefficient: d{СОПУХОЛЬ(t)} / dt = ККр-Оп * СКРОВЬ (t) , from which it is obvious: ККр-Оп = (СОПУХОЛЬ(Т)) / (∫CКРОВЬ(t) dt), which was used in all calculations of the contrast transfer constant. Gadobutrol contrast is 0.1M/10 kg of body weight, TR = 5.5–6 ms, TE = 2.5 ms. Results. If, after the first cycle of NACHT, the ККр-Оп of the primary breast cancer is < 0.22 ml/min/g of tissue, the probability of subsequent relapse-free course = 0.78. Similarly, a decrease in the KCr-Op Ккр-Оп of the sentinel lymph node after the first cycle of NAHT < 0.08 ml/min/g of tissue with a probability of 0.75 predicts a relapse-free course. Conclusion. The proposed method for calculating the ККр-Оп transfer coefficient allows obtaining additional diagnostic and prognostic information in patients with local and locally advanced forms of breast cancer.

Neoadjuvant chemotherapy in breast cancer: a retrospective pathway assessment in a regional cancer centre.

The optimal care pathway (OCP) for people with breast cancer provides a framework for investigation and management of patients with breast cancer, with delays previously identified regionally. With emphasis on the neoadjuvant pathway, the primary aim of this study was to assess the practicality of implementing the breast cancer OCP timeframes regionally in comparison to nationally referenced standards. A retrospective institutional audit was performed for patients undergoing neoadjuvant therapy for breast cancer. The time from referral to specialist review, completion of investigations, discussion at multidisciplinary team (MDT) meetings, initiation of neoadjuvant chemotherapy (NACT) and surgery were calculated and compared to OCP. Fifty-three patients were included, with 19 patients living rurally (36%). Twenty-four patients (45%) were seen by a specialist surgeon within 2 weeks of referral. Following surgical review, 44 patients (83%) completed investigations within 2 weeks, and 43 patient cases (81%) were discussed at MDT meetings within 2 weeks. Forty-eight patients (91%) were commenced on neoadjuvant treatment within 4 weeks of decision to treat, and 43 patients (81%) underwent surgery within 6 weeks of neoadjuvant treatment completion. Delays from initial referral to NACT were more frequent in rural patients compared to urban (79% vs 94%, P < 0.05). Adherence to OCP timeframes for patients undergoing neoadjuvant therapy in a regional centre was feasible and strategies are needed to bridge gaps identified for rural patients.

Factors Affecting the Complete Response in Breast and Axillary Regions Following Neoadjuvant Chemotherapy for Breast Cancer

Factors Affecting the Complete Response in Breast and Axillary Regions Following Neoadjuvant Chemotherapy for Breast Cancer

Evaluating Biomarker Dynamics in Breast Cancer Post-Neoadjuvant Chemotherapy: A Retrospective Cross-sectional Study

Background: Neoadjuvant chemotherapy (NACT) is integral to breast cancer management, yet its influence on biomarkers, notably androgen receptor (AR), remains underexplored. This study examines post-NACT alterations in receptor status, including ER, PR AR, HER2 and Ki67 index in breast cancer patients. Methods: A cross-sectional study, spanning three years at a tertiary care center, enrolled patients with invasive breast cancer undergoing mastectomy post-NACT. Pre- and post-NACT specimens underwent histological grading and immunohistochemistry for hormone status, HER2 status, and Ki67 index. Discordance between pre- and post-NACT receptor statuses and expression levels of biomarkers was assessed using McNemar’s and Wilcoxon signed rank tests respectively. Results: Among 100 patients, 35 were assessed. Mean age was 43.83 years, with prevalent T1 tumors (34.3%) and N1 nodal involvement (37.1%). Post-NACT, 54.2% showed no histological grade change. Notable alterations included changes in ER (14.2%), PR (14.2%), and HER2 status (8.57%). AR expression showed a significant change following NACT (p=0.03), while ER expression exhibited a trend towards significance (p=0.06). Ki67 index decreased in only 5.7% of cases. Conclusion: This study unveils intricate biomarker dynamics following NACT in breast cancer, with particular emphasis on AR, hitherto not evaluated. Larger investigations are imperative to elucidate clinical implications and tailor treatment strategies for breast cancer patients undergoing NACT.

Residual microcalcifications after neoadjuvant systemic therapy for early breast cancer: Implications for surgical planning and long-term outcomes

Residual microcalcifications on mammograms after neoadjuvant chemotherapy (NACT) pose a challenge in surgical decision-making. This single-centre retrospective review of all patients who had NACT for breast cancer over five years, evaluated the relationship between pathological complete response and residual microcalcifications, controlling for tumour size, nodal stage, grade, and receptor status, as well as the impact of residual microcalcifications on recurrence and survival. There was no significant association between pathological complete response (pCR) and residual microcalcifications (p = 0.763). We computed hazard ratios (HR) for Time to recurrence (TTR) and overall survival (OS) which were both not significant, with HR = 2.599, [0.290, 23.264], p = 0.393 and HR = 1.362 [0.123, 15.062], p = 0.801 respectively. The predictive and prognostic significance of residual microcalcifications remains to be proven. The surgical excision of these lesions should be considered based on individual patient risk.

The use of a clip prior to neoadjuvant chemotherapy for breast cancer with microcalcifications may not always be required.

Neoadjuvant chemotherapy is now a common first line therapy for breast cancer. International guidelines recommend placement of a clip before commencement of therapy to assist with localizing the tumor bed in the event of excellent response-this takes up time and resources. The microcalcifications associated usually persist after chemotherapy and could serve as an alternative marker. We investigated to determine prognostic criteria to avoid the need for a marker clip before neoadjuvant chemotherapy for breast tumors associated with microcalcifications. We performed a 7year single-center bi-site retrospective analytical observational study of 88 women with calcified breast carcinoma treated by neoadjuvant chemotherapy at our bi-site institution between September 2015 and September 2022. This study includied two groups (clip-free tumor localization vs. clip-free tumor non-localization), and investigating quantitative and qualitative predictive factors. The clip-free tumor localization after neoadjuvant chemotherapy was defined by the visibility of residual calcifications on both views of the pre-operative mammogram on the day of or the day prior to surgery. The mean age of the 88 women included in our population was 52.8years (± 12.7years standard deviation). Of the 90 tumors with microcalcifications, 64 carcinomas (71.1%) were localizable with no marker clip after neoadjuvant chemotherapy. The main predictive factors significantly associated with clip-free tumor localization were number of calcifications > 10 (P < 0.0001), grade 2 tumor (P = 0.003) with a probability of locating tumor after neoadjuvant chemotherapy of 97.9%, 95% CI [95.6; 99.0]. More than 10 microcalcifications in a grade 2 breast tumor at the initial diagnosis may obviate the need for a marker clip.

The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort.

Recently, radiomics has emerged as a possible image-derived biomarker, predominantly stemming from retrospective analyses. We aimed to prospectively assess the predictive role of [18F]FDG-PET radiomics in breast cancer (BC). Patients affected by stage I-III BC eligible for neoadjuvant chemotherapy (NAC) staged with [18F]FDG-PET/CT were prospectively enrolled. The pathological response to NAC was assessed on surgical specimens. From each primary breast lesion, we extracted radiomic PET features and their predictive role with respect to pCR was assessed. Uni- and multivariate statistics were used for inference; principal component analysis (PCA) was used for dimensionality reduction. We analysed 93 patients (53 HER2+ and 40 triple-negative (TNBC)). pCR was achieved in 44/93 cases (24/53 HER2+ and 20/40 TNBC). Age, molecular subtype, Ki67 percent, and stage could not predict pCR in multivariate analysis. In univariate analysis, 10 radiomic indices resulted in p < 0.1. We found that 3/22 radiomic principal components were discriminative for pCR. Using a cross-validation approach, radiomic principal components failed to discriminate pCR groups but predicted the stage (mean accuracy = 0.79 ± 0.08). This study shows the potential of PET radiomics for staging purposes in BC; the possible role of radiomics in predicting the pCR response to NAC in BC needs to be further investigated.

Mechanisms of drug resistance to neoadjuvant chemotherapy in breast cancer

Aim. Тo study the molecular genetic characteristics of the tumor microenvironment and the mechanisms of cell death in resistant locally advanced breast cancer.Materials and methods. The study included 48 patients with breast cancer T2–4N0–3M0–1 (mean age 55.6 ± 9.8 years), and 29 patients of comparable age with breast fibroadenoma. According to the design of the study, patients were divided into groups: Group 1 included women with breast cancer resistant to neoadjuvant chemotherapy (n = 23), Group 2 – with breast cancer and a complete response to neoadjuvant chemotherapy (n = 25), control Group – with fibroadenoma (n = 29). The expression of markers CD4+, CD8+, CD20+, CD68+, tumor necrosis factor α (TNF-α), vascular endothelial growth factor A (VEGF A), Ang-2, matrix metalloproteinase 12 (MMP-12), inducible nitric oxide synthase (iNOS), bcl-2, p53, CD95 was assessed using immunohistochemistry.Results. When phenotyping immune cells, the following differences were obtained: in the tumor tissue of patients in Group 1, a significant decrease in the number of cytotoxic CD8+ cells was noted compared to Group 2 (p = 0.001) and control (p = 0.032). In Group 2, a significant increase in the number of CD68+ cells was revealed in relation to Group 1 (p = 0.027). The cytokine profile of the tumor microenvironment in Group 1 is characterized by statistically significant overexpression of TNF-α compared to Group 2 (p &gt;0.001) and the control Group (p = 0.01). With regard to apoptotic factors, noteworthy is the significant decrease in the expression of bcl-2 and p53 in Group 1 compared to Group 2 (p = 0.001 and p = 0.02 accordingly).Conclusion. The presented results can serve as the basis for the creation of diagnostic algorithms that have predictive value regarding the effectiveness of NCT, and also to help identify new targets to justify the use of combined breast cancer treatments in the early stage.

Elastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: A prospective, multicenter, diagnostic study

Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement. Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained. The included patients were randomly divided at a ratio of 8:2 into a training set and an independent test set, with five-fold cross-validation applied to training set. We first identified clinicopathological characteristics and conventional US features significantly associated with the axillary LN response and developed corresponding prediction models. We then constructed deep learning radiomics (DLR) models based on BUS and SWE data. Models performances were compared, and a combination model was developed using significant clinicopathological data and interpreted US features with the SWE-based DLR model. Discrimination, calibration and clinical utility of this model were analyzed using receiver operating characteristic curve, calibration curve and decision curve, respectively. Results: Axillary pathologic complete response (pCR) was achieved in 52.41% of patients. In the test cohort, the clinicopathologic model had an accuracy of 71.30%, while radiologists’ diagnoses ranged from 64.26% to 71.11%, indicating limited to moderate predictive ability for the axillary response to NAC. The SWE-based DLR model, with an accuracy of 80.81%, significantly outperformed the BUS-based DLR model, which scored 59.57%. The combination DLR model boasted an accuracy of 88.70% and a false-negative rate of 8.82%. It demonstrated strong discriminatory ability (AUC, 0.95), precise calibration (p value obtained by Hosmer–Lemeshow goodness-of-fit test, 0.68), and practical clinical utility (probability threshold, 2.5-97.5%). Conclusions: The combination SWE-based DLR model can predict the axillary status after NAC in patients with node-positive breast cancer, and thus, may inform clinical decision-making to help avoid unnecessary axillary LN dissection.

Assessment of clinical and pathological response to neoadjuvant chemotherapy in breast cancer according to molecular subtypes

Background. Breast cancer (BC) is the commonest tumor in women. It is the leading malignancy in world, and representing an emerging oncologic issue in all countries. In Iraq, BC cases reached 7246 in 2021by Iraqi Cancer Registry reports. Objectives. The study conducted for assessment the clinical and pathological responses to neoadjuvant chemotherapy in BC among molecular subtypes and for description the clinico-pathologic features, and patterns of BC according to molecular subtypes. Methods. A prospective study of 60 females with breast cancer histologically confirmed were enrolled. All women received neoadjuvant chemotherapy. Then all patients underwent definitive surgery including MRM+AC or BCS+AC. The patients’ demographic, the pathologic and molecular subtypes date of the primary tumor were recorded. Results. The mostly distributed age group was belong to group 46-55 yrs in 22, 36.7%. The mean age was 49.7±10.8 yrs. The IDC represented the most common histopathological types of BC. The most of cases seen in upper-outer quadrant (UOQ). The results post-chemotherapy as followed: ypTx stage reported in 32 (53.3%), ypT1 in 15 (25.0%), ypT2 in 10 (16.7%) and ypT3 in 3 (5.0%). ypN0 stage in 40 patients (66.7%), ypN1 in 11 (18.3%), ypN2 in 6 (10.0%), and ypN3 in 3 (5.0%). There was a high statistical difference between cT and ypT in pre and post-chemotherapy (P&lt;0.0001), between cN and ypN in pre and post-chemotherapy (P=0.001) and between M and ypM in pre and post-chemotherapy (P=0.002). The mass location (P=0.005) and tumor size (P = 0.011) had significantly impacted on BC responses post NACT. 18(30.0%) had clinical complete response (cCR), 32 women (53.3%) had partial response (cPR), six cases (10.0%) had stable disease, and four cases (6.7%) had progressive disease. The cCR was better seen in T1 (77.8%) than T2 (22.2%), with statistical significant difference (P=0.002). The cCR was better seen in N0 (61.1%) than N1 (38.9%), with a high statistical significant difference (P&lt;0.0001). Conclusions. The commonest age breast cancer is four to five decade. The IDC and mass situated at UOQ are the most common features of BC. Approximately, 30.0% of women have clinical complete response (cCR), 53.3% have partial response (cPR), 10.0% have stable disease, and 6.7% have progressive disease post NACT. The ER positive, PR positive, and HER2neu negative are the mostly frequent subtype recorded in this study. The cCR is better seen in T1 and N0 stages. ER+ and PR+ are more reported after NACT whereas HER2neu- is more post NACT.

On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer

BackgroundPatients with pathologic complete response (pCR) to neoadjuvant chemotherapy for invasive breast cancer (BC) have better outcomes, potentially warranting less extensive surgical and systemic treatments. Early prediction of treatment response could aid in adapting therapies.MethodsOn-treatment biopsies from 297 patients with invasive BC in three randomized, prospective neoadjuvant trials were assessed (GeparQuattro, GeparQuinto, GeparSixto). BC quantity, tumor-infiltrating lymphocytes (TILs), and the proliferation marker Ki-67 were compared to pre-treatment samples. The study investigated the correlation between residual cancer, changes in Ki-67 and TILs, and their impact on pathologic complete response (pCR) and disease-free survival (DFS).ResultsAmong the 297 samples, 138 (46%) were hormone receptor-positive (HR+)/human epidermal growth factor 2-negative (HER2−), 87 (29%) were triple-negative (TNBC), and 72 (24%) were HER2+. Invasive tumor cells were found in 70% of on-treatment biopsies, with varying rates across subtypes (HR+/HER2−: 84%, TNBC: 62%, HER2+: 51%; p < 0.001). Patients with residual tumor on-treatment had an 8% pCR rate post-treatment (HR+/HER2−: 3%, TNBC: 19%, HER2+: 11%), while those without any invasive tumor had a 50% pCR rate (HR+/HER2−: 27%; TNBC: 48%, HER2+: 66%). Sensitivity for predicting residual disease was 0.81, with positive and negative predictive values of 0.92 and 0.50, respectively. Increasing TILs from baseline to on-treatment biopsy (if residual tumor was present) were linked to higher pCR likelihood in the overall cohort (OR 1.034, 95% CI 1.013–1.056 per % increase; p = 0.001) and with a longer DFS in TNBC (HR 0.980, 95% CI 0.963–0.997 per % increase; p = 0.026). Persisting or increased Ki-67 was associated with with lower pCR probability in the overall cohort (OR 0.957, 95% CI 0.928–0.986; p = 0.004) and shorter DFS in TNBC (HR 1.023, 95% CI 1.001–1.047; p = 0.04).ConclusionOn-treatment biopsies can predict patients unlikely to achieve pCR post-therapy. This could facilitate therapy adjustments for TNBC or HER2 + BC. They also might offer insights into therapy resistance mechanisms. Future research should explore whether standardized or expanded sampling enhances the accuracy of on-treatment biopsy procedures.Trial registration GeparQuattro (EudraCT 2005-001546-17), GeparQuinto (EudraCT 2006-005834-19) and GeparSixto (EudraCT 2011-000553-23).

The Immune-Related 27-Gene Signature DetermaIO Predicts Response to Neoadjuvant Atezolizumab plus Chemotherapy in Triple-Negative Breast Cancer.

We assessed the 27-gene RT-qPCR-based DetermaIO assay and the same score calculated from RNA sequencing (RNA-seq) data as predictors of sensitivity to immune checkpoint therapy in the neoTRIPaPDL1 randomized trial that compared neoadjuvant carboplatin/nab-paclitaxel chemotherapy (CT) plus atezolizumab with CT alone in stage II/III triple-negative breast cancer. We also assessed the predictive function of the immuno-oncology (IO) score in expression data of patients treated with pembrolizumab plus paclitaxel (N = 29) or CT alone (N = 56) in the I-SPY2 trial. RNA-seq data were obtained from pretreatment core biopsies from 242 (93.8%) of the 258 patients in the per-protocol-population. The DetermaIO RT-qPCR test, performed in the CAP/CLIA-accredited laboratory of Oncocyte Corp., was available for 220 patients (85.3%). A previously established threshold was used to assign DetermaIO-positive versus DetermaIO-negative status. Publicly available microarray data were used from I-SPY2. IO scores calculated from RNA-seq and RT-qPCR data were highly concordant. In neoTRIPaPDL1, DetermaIO-positive cancers (N = 92, 41.8%) had pathologic complete response (pCR) rates of 69.8% and 46.9% in the CT + atezolizumab and CT arms, respectively. In DetermaIO-negative cases, pCR rates were similar in both arms (44.6% vs. 49.2%; interaction test P = 0.04). PDL1 protein expression and stromal tumor-infiltrating lymphocyte count were not predictive of differential benefit from atezolizumab. In I-SPY2, IO-positive cancers (45.9%) had pCR rates of 85.7% and 16%, with and without immunotherapy, respectively. In IO-negative cancers, pCR rates were 46.7% versus 16.1%. DetermaIO identified patients who benefited from neoadjuvant immunotherapy resulting in improved pCR rate, independently of PDL1.

Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer.

In this study, we aimed to explore if the combination of tumor infiltrating lymphocytes (TILs) and change in tumor load on dynamic contrast-enhanced magnetic resonance imaging leads to better assessment of response to neoadjuvant chemotherapy (NAC) in patients with breast cancer, compared to either alone. In 190 NAC treated patients, MRI scans were performed before and at the end of treatment. The percentage of stromal TILs (%TILs) was assessed in pre-NAC biopsies according to established criteria. Prediction models were developed with linear regression by least absolute shrinkage and selection operator and cross validation (CV), with residual cancer burden as the dependent variable. Discrimination for pathological complete response (pCR) was evaluated using area under the receiver operating characteristic curves (AUC). We used Cox regression analysis for exploring the association between %TILs and recurrence-free survival (RFS). Fifty-one patients reached pCR. In all patients, the %TILs model and change in MRI tumor load model had an estimated CV AUC of 0.69 (95% confidence interval (CI) 0.53-0.78) and 0.69 (95% CI 0.61-0.79), respectively, whereas a model combining the variables resulted in an estimated CV AUC of 0.75 (95% CI 0.66-0.83). In the group with tumors that were ER positive and HER2 negative (ER+/HER2-) and in the group with tumors that were either triple negative or HER2 positive (TN&HER2+) separately, the combined model reached an estimated CV AUC of 0.72 (95% CI 0.60-0.88) and 0.70(95% CI 0.59-0.82), respectively. A significant association was observed between pre-treatment %TILS and RFS (hazard ratio (HR) 0.72 (95% CI 0.53-0.98), for every standard deviation increase in %TILS, p = 0.038). The combination of TILs and MRI is informative of response to NAC in patients with both ER+/HER2- and TN&HER2+ tumors.

Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.

We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895-0.970) in the training set and 0.914 (95%CI: 0.838-0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.

Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.

Dynamic Contrast Enhanced MRI Mapping of Vascular Permeability for Evaluation of Breast Cancer Neoadjuvant Chemotherapy Response Using Image-to-Image Conditional Generative Adversarial Networks.

Dynamic contrast enhanced (DCE) MRI is a non-invasive imaging technique that has become a quantitative standard for assessing tumor microvascular permeability. Through the application of a pharmacokinetic (PK) model to a series of T1-weighed MR images acquired after an injection of a contrast agent, several vascular permeability parameters can be quantitatively estimated. These parameters, including Ktrans, a measure of capillary permeability, have been widely implemented for assessing tumor vascular function as well as tumor therapeutic response. However, conventional PK modeling for translation of DCE MRI to PK vascular permeability parameter maps is complex and time-consuming for dynamic scans with thousands of pixels per image. In recent years, image-to-image conditional generative adversarial network (cGAN) is emerging as a robust approach in computer vision for complex cross-domain translation tasks. Through a sophisticated adversarial training process between two neural networks, image-to-image cGANs learn to effectively translate images from one domain to another, producing images that are indistinguishable from those in the target domain. In the present study, we have developed a novel image-to-image cGAN approach for mapping DCE MRI data to PK vascular permeability parameter maps. The DCE-to-PK cGAN not only generates high-quality parameter maps that closely resemble the ground truth, but also significantly reduces computation time over 1000-fold. The utility of the cGAN approach to map vascular permeability is validated using open-source breast cancer patient DCE MRI data provided by The Cancer Imaging Archive (TCIA). This data collection includes images and pathological analyses of breast cancer patients acquired before and after the first cycle of neoadjuvant chemotherapy (NACT). Importantly, in good agreement with previous studies leveraging this dataset, the percentage change of vascular permeability Ktrans derived from the DCE-to-PK cGAN enables early prediction of responders to NACT.

Peri-Tumoural Lipid Composition and Hypoxia for Early Immune Response to Neoadjuvant Chemotherapy in Breast Cancer.

The deregulation of monounsaturated, polyunsaturated, and saturated fatty acids (MUFAs, PUFAs, SFAs) from de novo synthesis and hypoxia are central metabolic features of breast tumour. Early response markers for neoadjuvant chemotherapy (NACT) are critical for stratified treatment for patients with breast cancer, and restoration of lipid metabolism and normoxia might precede observable structural change. In this study, we hypothesised that peri-tumoural lipid composition and hypoxia might be predictive and early response markers in patients with breast cancer undergoing NACT. Female patients with breast cancer were scanned on a 3T clinical MRI scanner at baseline and Cycle1, with acquisition of lipid composition maps of MUFAs, PUFAs, and SFAs, and hypoxia maps of effective transverse relaxation rate R2*. The percentage change in lipid composition and hypoxia at Cycle1 was calculated with reference to baseline. Tumour-associated macrophages were analysed based on immunostaining of CD163 from biopsy and resection, with the percentage change in the resected tumour calculated across the entire NACT. We found no significant difference in lipid composition and R2* between good and poor responders at baseline and Cycle1; however, the correlation between the percentage change in MUFAs and PUFAs against CD163 suggested the modulation in lipids with altered immune response might support the development of targeted therapies.

Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study

The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer. This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL. 1145 patients (mean age, 50 years±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P<0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways. FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice. This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).

Tremendous and infrequently adenoid cystic carcinoma of the breast without any metastasis for more than 20 years: A case report.

Breast adenoid cystic carcinoma is an extremely rare tumor that is incompletely understood, accounting for less than <0.1% of all breast cancers, with an average diameter of 3 cm, and it is extremely rare to see a large, non-metastatic breast adenoid cystic carcinoma with a diameter of about 30 cm. Since this disease is extremely rare, there are few reports in the literature and limited data on clinical diagnosis and treatment. We present a case of a 71-year-old woman with a large, non-metastatic adenoid cystic carcinoma of the left breast and share our opinion on the diagnosis and treatment of this case. A 71-year-old woman with a 20-year-old left breast mass with local bleeding and rupture for 1 hour presented to our hospital for further diagnosis and treatment. A computed tomography scan showed a large soft tissue mass shadow in the left breast and malignancy was considered. Subsequently, tissue aspiration pathology was performed and the results confirmed adenoid cystic carcinoma of the breast. Intraoperative pathology results of radical mastectomy for left breast cancer diagnosed adenoid cystic carcinoma of the breast and immunohistochemistry results of triple-negative breast cancer. Treatment of adenoid cystic carcinoma of the breast included neoadjuvant chemotherapy for breast cancer, radical mastectomy of the left breast, and postoperative chemotherapy. Initially, neoadjuvant chemotherapy for breast cancer was performed, and the TAC regimen was used to successfully reduce the size of the tumor and gain access to surgical treatment for breast cancer. The patient has recovered well after the surgery, with no wound infection or ulceration, and is now waiting for the patient's physical function to recover for postoperative chemotherapy, with no obvious discomfort. Adenoid cystic carcinoma tumors are usually around 3 cm; such a huge 30 cm adenoid cystic carcinoma of the breast is extremely rare, and it is extremely rare to find a breast malignancy that has not developed regional lymph node and distant metastases for more than 20 years. Clinicians must remain vigilant for early breast malignancies at a high age of incidence and conduct further research for diagnosis to avoid delays.