Low expression of TOX predicts poor prognosis of patients with breast cancer in the real world: A retrospective study.

Background: TOX is a transcription factor that is implicated in the regulation of T cell exhaustion in tumors. TOX has been proven to have prognostic value in some malignant tumors. We aim to analyze the expression of TOX in breast cancer patients, and the association between TOX and prognostic significance in patients with breast cancer.

The Gustave Roussy Immune score (GRIm score) as a novel prognostic score for early breast cancer patients: A real-world retrospective study.

The aim of this research is to investigate whether the GRIm score serves as a novel prognostic tool for predicting the survival rates among early breast cancer patients undergoing surgical treatment. This retrospective study included 313 cases of breast cancer patients hospitalized in our hospital from January 2015 to November 2015. All enrolled patients received surgery and had no metastasis.

Transcription Factor Forkhead Box Protein 3 (FOXP3) as a Prognostic Indicator for Postoperative Outcomes in Patients with Breast Cancer: Establishment of a Prognostic Nomogram.

Purpose: The current investigation is to assess FOXP3 expression in breast cancer patients and evaluate the predictive significance of FOXP3.

Patients And Methods: A cohort of 313 cases between January 2015 and November 2015 were enrolled this research. Immunohistochemistry (IHC) assay was utilized to detect the expression levels of FOXP3 in primary breast carcinoma specimens.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models.

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS spectra as input features.

The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning.

Background: Limited studies have investigated the predictive value of multiomics signatures (radiomics, deep learning features, pathological features and DLG3) in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). However, no study has explored the relationships among radiomic, pathomic signatures and chemosensitivity. This study aimed to predict pathological complete response (pCR) using multiomics signatures, and to evaluate the predictive utility of radiomic and pathomic signatures for guiding chemotherapy selection.

Integration of nontarget analysis with machine learning modeling for prioritization of odorous volatile organic compounds in surface water.

Assessing the odor risk caused by volatile organic compounds (VOCs) in water has been a big challenge for water quality evaluation due to the abundance of odorants in water and the inherent difficulty in obtaining the corresponding odor sensory attributes. Here, a novel odor risk assessment approach has been established, incorporating nontarget screening for odorous VOC identification and machine learning (ML) modeling for odor threshold prediction. Twenty-nine odorous VOCs were identified using two-dimensional gas chromatography-time of flight mass spectrometry from four surface water sampling sites.

Predicting reactivity dynamics of halogen species and trace organic contaminants using machine learning models.

Reactions of reactive halogen species (Cl, Br, and Cl) with trace organic contaminants (TrOCs) have received much attention in recent years, and their k values are fundamental parameters for understanding their reaction mechanisms. However, k values are usually unknown. In this study, we developed machine learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict k values.

Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy.

Background And Purpose: The Naples Score (NPS) is a novel prognostic indicator that has been used in various cancers, but its potential in breast malignant tumor patients receiving neoadjuvant chemotherapy (NAC) has not been discovered. This study aimed to investigate the relationship between NPS and overall survival (OS) and disease-free survival (DFS) in breast cancer patients.

Methods: A total of 217 breast cancer patients undergoing NAC were incorporated into this retrospectively research.

Development and validation of a nomogram for predicting internal mammary sentinel node metastasis in breast cancer patients.

Objective: Internal mammary nodes are important in breast cancer prognosis, but their diagnosis is often missed in clinical practice, leading to inaccurate staging and treatment. We developed a validated nomogram to predict the presence of internal mammary sentinel nodes (IMSN) metastasis.

Methods: A total of 864 sequential IMSN biopsy procedures from a prospective studies database of 1505 cases were used for model development and validation.

Clinicopathological Characteristics of Breast Cancer Patients with HER-2 Low Expression Receiving Neoadjuvant Therapy.

Introduction: Human epidermal growth factor receptor-2 (HER-2) low expression breast malignant tumors have become a research hotspot in recent years, but it is still unclear whether HER-2 low expression represents a special subtype of breast cancer. However, this molecular type requires more effective treatment regimens in the neoadjuvant therapy stage.

Methods: This study enrolled breast cancer patients who were treated at Harbin Medical University Cancer Hospital with neoadjuvant treatment between October 2011 and May 2019 and was a single-center retrospective study.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning.

Background: Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.

Methods: From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression.

Lower subcutaneous fat index predicts bone metastasis in breast cancer.

Background: Bone metastases affect 50% to 70% of breast cancer (BC) patients and have a high mortality rate. Adipose tissue loss plays a pivotal role in the progression of cancer.

Objective: This study aims to evaluate the prognostic value of adipose tissue for bone metastasis in BC patients.

Predictive value of controlling nutritional status score in postoperative recurrence and metastasis of breast cancer patients with HER2-low expression.

Background: To investigate the predictive value of controlling nutritional status (CONUT) score in Postoperative Recurrence and Metastasis of Breast Cancer Patients with HER2-Low Expression.

Methods: The clinicopathological data of 697 female breast cancer patients who pathology confirmed invasive ductal carcinoma and surgery in Harbin Medical University Tumor Hospital from January 2014 to January 2017 were retrospectively analyzed. The relationship between CONUT score and various clinicopathological factors as well as prognosis was evaluated.

Ultrasound Triggered Tumor Metabolism Suppressor Induces Tumor Starvation for Enhanced Sonodynamic Immunotherapy of Breast Cancer.

Introduction: Sonodynamic therapy (SDT) as an emerging tumor treatment gained wide attention. However, tumor vascular destruction and oxygen depletion in SDT process may lead to further hypoxia. This may lead to enhanced glycolysis, lactate accumulation, and immunosuppression.

The tremendous clinical potential of the microbiota in the treatment of breast cancer: the next frontier.

Although significant advances have been made in the diagnosis and treatment of breast cancer (BC) in recent years, BC remains the most common cancer in women and one of the main causes of death among women worldwide. Currently, more than half of BC patients have no known risk factors, emphasizing the significance of identifying more tumor-related factors. Therefore, we urgently need to find new therapeutic strategies to improve prognosis.

Gold nanoparticle doped Cuhemin nanosheets with a remodeling tumor microenvironment for multiple radiotherapy sensitization.

Effective radiosensitizers are urgently needed due to the serious negative effects that high radiation doses might have. We created an integrated nano-system (Cuhemin-Au) made of Cuhemin nanosheets and Au nanoparticles (Au NPs) for sensitizing radiotherapy to solve this issue. This system can manifest enzyme-like activities to universally suppress the resistance pathways in breast cancer cells for amplifying radiation damage.

Metabolic syndrome is a risk factor for breast cancer patients receiving neoadjuvant chemotherapy: A case-control study.

Purpose: To investigate the impact of metabolic syndrome (MetS) on pathologic complete response (pCR) and clinical outcomes in breast cancer (BC) patients who received neoadjuvant chemotherapy (NAC).

Methods: We analyzed 221 female BC patients at Harbin Medical University Cancer Hospital who received NAC and divided them into MetS and non-MetS groups according to National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria to investigate the association between MetS and clinicopathological characteristics, pathologic response, and long-term survival and to observe the changes in metabolic parameters after NAC.

Results: A total of 53 (24.

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.

Background: It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning.

Prognostic value of a modified systemic inflammation score in breast cancer patients who underwent neoadjuvant chemotherapy.

Background And Purpose: The modified systemic inflammation score (mSIS) system, which is constructed based on the neutrophil to lymphocyte ratio (NLR) and albumin (Alb), has not been applied to evaluate the prognosis of malignant breast cancer patients who underwent neoadjuvant chemotherapy (NAC). The present study aimed to explore the relationship between the mSIS and overall survival (OS), disease-free survival (DFS) and pathological complete response (pCR).

Methods: A total of 305 malignant breast tumor patients who underwent NAC were incorporated into this retrospective analysis.

A multicentre single arm phase 2 trial of neoadjuvant pyrotinib and letrozole plus dalpiciclib for triple-positive breast cancer.

Current therapies for HER2-positive breast cancer have limited efficacy in patients with triple-positive breast cancer (TPBC). We conduct a multi-center single-arm phase 2 trial to test the efficacy and safety of an oral neoadjuvant therapy with pyrotinib, letrozole and dalpiciclib (a CDK4/6 inhibitor) in patients with treatment-naïve, stage II-III TPBC with a Karnofsky score of ≥70 (NCT04486911). The primary endpoint is the proportion of patients with pathological complete response (pCR) in the breast and axilla.

Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework.

Abstract: Background and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM.

Methods: From the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study.

The Pretreatment Systemic Inflammation Response Index as a Useful Prognostic Factor is Better Than Lymphocyte to Monocyte Ratio in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy.

It has been reported that the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR), as well as systemic inflammation response index (SIRI), are closely related with overall survival (OS) in breast cancer patients. However, which one is the optimal indicator is vague. This study incorporates 280 breast cancer patients who received NACT.

Correlation analysis between androgen receptor and the clinicopathological features and prognosis of mammary Paget's disease.

Purpose: Little is known about the prognostic value of androgen receptor (AR) status in mammary Paget's disease (MPD). The purpose of this study was to explore AR status and the distribution of molecular subtypes in MPD as well as the relationship between AR expression and clinicopathological factors and to evaluate its prognostic value.

Methods: We analyzed 170 MPD patients of varying subtypes.