The Clinical Importance of Androgen Receptor Status in Response to Neoadjuvant Chemotherapy in Turkish Patients with Local and Locally Advanced Breast Cancer

Wang

et al. 2022

JPM

(1) Background: Neoadjuvant therapy is the main therapeutic strategy for human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients, and the combination of trastuzumab and pertuzumab (HP) has become a routine treatment. How to predict and screen patients who are less likely to respond to neoadjuvant therapy is the focus of research. The androgen receptor (AR) is a biomarker that is widely expressed in all breast cancer subtypes and is probably related to treatment response and prognosis. In this study, we investigated the relationship between AR expression and treatment response in HER2-positive breast cancer patients treated with HP neoadjuvant therapy. (2) Methods: We evaluated early breast cancer patients treated with HP neoadjuvant therapy from Jan. 2019 to Oct. 2020 at Peking University First Hospital Breast Cancer Center. The inclusion criteria were as follows: early HER2-positive breast cancer patients diagnosed by core needle biopsy who underwent both HP neoadjuvant therapy and surgery. We compared the clinical and pathological features between pathological complete response (pCR) and non-pCR patients. (3) Results: We included 44 patients. A total of 90.9% of patients received neoadjuvant therapy of taxanes, carboplatin, trastuzumab and pertuzumab (TCHP), and the total pCR rate was 50%. pCR was negatively related to estrogen receptor (ER) positivity (OR 0.075 [95% confidence interval (CI) 0.008–0.678], p = 0.021) and positively related to high expression levels of AR (OR 33.145 [95% CI 2.803–391.900], p = 0.005). We drew a receiver operating characteristic (ROC) curve to assess the predictive value of AR expression for pCR, and the area under the curve was 0.737 (95% CI 0.585–0.889, p = 0.007). The optimal cutoff of AR for predicting pCR was 85%. (4) Conclusion: AR is a potential marker for the prediction of pCR in HER2-positive breast cancer patients treated with HP neoadjuvant therapy.

Section: Introductionmentioning

confidence: 81%

Androgen Receptor: A New Marker to Predict Pathological Complete Response in HER2-Positive Breast Cancer Patients Treated with Trastuzumab Plus Pertuzumab Neoadjuvant Therapy

Wang

et al. 2022

JPM

“…Previous studies have focused on predicting pCR to NAC using clinical TNM staging, HR status, HER2 status, and Ki-67 expression, yet predictions based solely on clinicopathologic variables have shown limitations [34,35]. Models developed by Li et al [9], based on tumor morphological features, achieved AUCs between 0.69 and 0.81 without further validation.…”

Section: Discussionmentioning

confidence: 99%

Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study

Huang,

Cao,

Chen

et al. 2024

Preprint

Background: The tumor morphological complexity is closely associated with treatment response and prognosis in patients with breast cancer. However, conveniently quantifiable tumor morphological complexity methods are currently lacking. Methods: Women with breast cancer who underwent NAC and pretreatment MRI were retrospectively enrolled at four centers from May 2010 to April 2023. MRI-based fractal analysis was used to calculate fractal dimensions (FDs), quantifying tumor morphological complexity. Features associated with pCR were identified using multivariable logistic regression analysis, upon which a nomogram model was developed, and assessed by the area under the receiver operating characteristic curve (AUC). Cox proportional hazards analysis was used to identify independent prognostic factors for disease-free survival (DFS) and overall survival (OS) and develop nomogram models. Results: A total of 1109 patients (median age, 49 years [IQR, 43-54 years]) were enrolled; 435, 351, and 323 patients were recruited in the training, external validation cohorts 1 and 2, respectively. HR status (odds ratio [OR], 0.234 [0.135, 0.406]; P< 0.001), HER2 status (OR, 3.320 [1.923, 5.729]; P < 0.001), and Global FD (OR, 0.352 [0.261, 0.480]; P < 0.001) were independent predictors of pCR. The nomogram model for predicting pCR achieved AUCs of 0.80 (95% CI: 0.75, 0.86) and 0.74 (95% CI: 0.68, 0.79) in the external validation cohorts. The nomogram model, which integrated global FD and clinicopathological variables can stratify prognosis into low-risk and high-risk groups (log-rank test, DFS: P = 0.04; OS: P < 0.001). Conclusions: Global FD can quantify tumor morphological complexity and the model that combines global FD and clinicopathological variables showed good performance in predicting pCR to NAC and survival in patients with breast cancer.

“… 13 , 20 – 22 However, most studies are only based on traditional radiomics features and do not study the deep learning features of the image, which causes some limitations on the performance of predicting the ALN status. Some studies 23 , 24 also included clinical stage, receptor status, lymphatic vascular infiltration, and other clinicopathological features in the prediction model. However, some specific clinicopathological features, such as lymphatic vascular infiltration, can only be obtained after surgery and has difficulty in effectively guiding clinical decision-making before surgery.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram

Zhao

Technol Cancer Res Treat

et al. 2023

Background: Axillary lymph node (ALN) metastatic load is very important in the diagnosis and treatment of breast cancer (BC). We aimed to construct a model for predicting ALN metastatic load using deep learning radiomics (DLR) techniques based on the preoperative ultrasound and clinicopathologic information of patients with stage T1-2 BC. Methods: Retrospective analysis was performed on 176 patients with pathologically confirmed BC in our hospital from February 2018 to April 2020. ALN metastases were divided into a low-load group (< 3 lymph node metastases) and a high-load group (≥ 3 lymph node metastases) according to pathological results. Pyradiomics and pre-trained ResNet50 were used to extract radiomics and deep learning features, respectively. Independent sample T-test, random forest recursive elimination, and Lasso were used to screen the features to construct the deep learning radiomics signature (DLRS). Based on single/multivariate logistic regression analysis results, a DLR nomogram (DLRN) model was constructed by combining valuable clinical features and DLRS. Results: The DLRS was composed of 3 radiomics features and 14 deep learning features and combined with the maximum diameter of lesions to construct the DLRN. The AUCs of the training and test sets were 0.900 (95% CI: 0.853-0.931) and 0.821 (95% CI: 0.769-0.868), respectively. The calibration curve and Hosmer–Lemeshow test confirmed that the DLRN model has a good consistency. The decision curve also confirmed its good clinical practicality. Conclusion: Ultrasound-based DLRN has an excellent performance in predicting ALN load in patients with BC.