Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Long non-coding RNA LOXL1-AS1: a potential biomarker and therapeutic target in human malignant tumors

Long non-coding RNAs (lncRNAs) are transcripts that contain more than 200 nucleotides. Despite their inability to code proteins, multiple studies have identified their important role in human cancer through different mechanisms. LncRNA lysyl oxidase like 1 antisense RNA 1 (LOXL1-AS1), a newly discovered lncRNA located on human chromosome 15q24.1, has recently been shown to be involved in the occurrence and progression of various malignancies, such as colorectal cancer, gastric cancer, hepatocellular carcinoma, prostate cancer, non-small cell lung cancer, ovarian cancer, cervical cancer, breast cancer, glioma, thymic carcinoma, pancreatic carcinoma. LOXL1-AS1 acts as competitive endogenous RNA (ceRNA) and via sponging various miRNAs, including miR-374b-5p, miR-21, miR-423-5p, miR-589-5p, miR-28-5p, miR-324-3p, miR-708-5p, miR-143-3p, miR-18b-5p, miR-761, miR-525-5p, miR-541-3p, miR-let-7a-5p, miR-3128, miR-3614-5p, miR-377-3p and miR-1224-5p to promote tumor cell proliferation, invasion, migration, apoptosis, cell cycle, and epithelial-mesenchymal transformation (EMT). In addition, LOXL1-AS1 is involved in the regulation of P13K/AKT and MAPK signaling pathways. This article reviews the current understanding of the biological function and clinical significance of LOXL1-AS1 in human cancers. These findings suggest that LOXL1-AS1 may be both a reliable biomarker and a potential therapeutic target for cancers.

Implementing and Evaluating a National Integrated Digital Registry and Clinical Decision Support System in Early Intervention in Psychosis Services (Early Psychosis Informatics Into Care): Co-Designed Protocol

BACKGROUND

Early intervention in psychosis (EIP) services are nationally mandated in England to provide multidisciplinary care to people experiencing first-episode psychosis, which disproportionately affects deprived and ethnic minority youth. Quality of service provision varies by region, and people from historically underserved populations have unequal access. In other disease areas, including stroke and dementia, national digital registries coupled with clinical decision support systems (CDSSs) have revolutionized the delivery of equitable, evidence-based interventions to transform patient outcomes and reduce population-level disparities in care. Given psychosis is ranked the third most burdensome mental health condition by the World Health Organization, it is essential that we achieve the same parity of health improvements.

OBJECTIVE

This paper reports the protocol for the program development phase of this study, in which we aimed to co-design and produce an evidence-based, stakeholder-informed framework for the building, implementation, piloting, and evaluation of a national integrated digital registry and CDSS for psychosis, known as EPICare (Early Psychosis Informatics into Care).

METHODS

We conducted 3 concurrent work packages, with reciprocal knowledge exchange between each. In work package 1, using a participatory co-design framework, key stakeholders (clinicians, academics, policy makers, and patient and public contributors) engaged in 4 workshops to review, refine, and identify a core set of essential and desirable measures and features of the EPICare registry and CDSS. Using a modified Delphi approach, we then developed a consensus of data priorities. In work package 2, we collaborated with National Health Service (NHS) informatics teams to identify relevant data currently captured in electronic health records, understand data retrieval methods, and design the software architecture and data model to inform future implementation. In work package 3, observations of stakeholder workshops and individual interviews with representative stakeholders (n=10) were subject to interpretative qualitative analysis, guided by normalization process theory, to identify factors likely to influence the adoption and implementation of EPICare into routine practice.

RESULTS

Stage 1 of the EPICare study took place between December 2021 and September 2022. The next steps include stage 2 building, piloting, implementation, and evaluation of EPICare in 5 demonstrator NHS Trusts serving underserved and diverse populations with substantial need for EIP care in England. If successful, this will be followed by stage 3, in which we will seek NHS adoption of EPICare for rollout to all EIP services in England.

CONCLUSIONS

By establishing a multistakeholder network and engaging them in an iterative co-design process, we have identified essential and desirable elements of the EPICare registry and CDSS; proactively identified and minimized potential challenges and barriers to uptake and implementation; and addressed key questions related to informatics architecture, infrastructure, governance, and integration in diverse NHS Trusts, enabling us to proceed with the building, piloting, implementation, and evaluation of EPICare.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50177.

Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer-The BRAIN Study

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2- breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2- breast cancer.

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings-An Interview Study

BACKGROUND

AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs.

METHODS

Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, 'the framework method', was used to create an analytical framework for data analysis, which was performed by two independent researchers.

RESULTS

Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits.

CONCLUSIONS

Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

Evaluation of clinical decision support systems in oncology: An updated systematic review

With increasing reliance on technology in oncology, the impact of digital clinical decision support (CDS) tools needs to be examined. A systematic review update was conducted and peer-reviewed literature from 2016 to 2022 were included if CDS tools were used for live decision making and comparatively assessed quantitative outcomes. 3369 studies were screened and 19 were included in this updated review. Combined with a previous review of 24 studies, a total of 43 studies were analyzed. Improvements in outcomes were observed in 42 studies, and 34 of these were of statistical significance. Computerized physician order entry and clinical practice guideline systems comprise the greatest number of evaluated CDS tools (13 and 10 respectively), followed by those that utilize patient-reported outcomes (8), clinical pathway systems (8) and prescriber alerts for best-practice advisories (4). Our review indicates that CDS can improve guideline adherence, patient-centered care, and care delivery processes in oncology.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network

OBJECTIVE

Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images.

METHODS

We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning.

RESULTS

MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance.

CONCLUSION

The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.

The effects of cancer clinical decision support systems on patient-reported outcomes: A systematic review

PURPOSE

The implementation of high-quality decision-making support are integral to ensuring the delivery of quality cancer care and subsequently achieving positive patient outcomes. Decision Support Systems (DSS) are increasingly used, however it is not known what the effects are beyond supporting the decision-making process. We aimed to identify and synthesize the available literature regarding the effects of DSS on patient-reported outcomes both during and after cancer treatment.

METHODS

A systematic review was conducted using dual processes to identify empirical literature that reported an evaluation of DSS interventions and patient-reported outcomes. We appraised study quality using the Mixed Methods Appraisal Tool (MMAT). Data were narratively synthesized.

RESULTS

We included 15 studies, categorized as symptom assessment interventions or interactive educational interventions. Findings were mixed regarding the effectiveness of DSS interventions in improving total symptom distress and severity, whereas the majority were effective in reducing mean scores for worst and usual pain. Interventions were not effective in improving other health-related patient-reported outcomes including quality of life, global distress, depression, or self-efficacy and there were mixed effects for reducing decisional conflict. There was moderate to high patient adherence to the interventions and generally high satisfaction and acceptability, yet minimal evidence for the effect of DSS interventions in clinician adherence to intervention recommendations.

CONCLUSIONS

Including patient-reported outcomes in the evaluation of DSS is critical to understand their impact. Inconsistencies in reporting of interventions may, however, be a contributing factor to heterogeneous effects of clinical DSS regarding a broad range of patient-reported outcomes.

Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care

OBJECTIVES

Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU).

METHODS

We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®).

MEASUREMENTS AND MAIN RESULTS

In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)).

CONCLUSIONS

Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia.

Ten Topics to Get Started in Medical Informatics Research

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Clinical Decision Support Systems for Brain Tumour Diagnosis and Prognosis: A Systematic Review

CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings.

Dynamic changes in quality of life, psychological status, and body image in women who underwent a mastectomy as compared with breast reconstruction: an 8-year follow up

BACKGROUND

Surgical decisions and methods of surgery highly influence long term QoL for breast cancer (BC) survivors. This study is aimed towards an exploration of the dynamic changes in quality of life (QoL), anxiety/depression status, and body image (BI) among women with BC who received a mastectomy compared with those receiving breast reconstruction (BR) within an 8-year follow-up period.

METHODS

Women with major BC surgeries were invited to complete the World Health Organization Quality of Life-Brief (WHOQOL-BREF), the European quality of life five dimensions questionnaire (EQ-5D), and a body image scale within 8 years of surgery. Kernel smoothing methods were applied to describe dynamic changes in QoL, anxiety/depression, and BI at different time points. Linear mixed effects models were constructed to identify the interaction between time, different types of surgery, and the determinants of QoL in these patients.

RESULTS

After 1:10 propensity score matching, a total of 741 women who had undergone a BR and mastectomy were included. The BR group exhibited a high WHOQOL QoL score one to five years after surgery with some fluctuations. The mastectomy group had comparatively stable QoL scores on WHOQOL items and were less depressed/anxious. The BR group generally showed fluctuating, higher BI scores two years after surgery, but they exhibited more anxiety/depression during follow up for 8 years. Medical comorbidities, the status of anxiety/depression, and BI were the major factors influencing all domains and items of the WHOQOL BREF among women with BC.

CONCLUSION

The mastectomy group showed a decreased trend toward depression in patients with BC. The BR group showed a significant improvement in QoL in the first 5 years with massive fluctuations. These findings should be considered and discussed in patient participatory decision-making and promotion of QoL for breast cancer survivors.

Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol

INTRODUCTION

Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.

METHODS AND ANALYSIS

This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.

ETHICS AND DISSEMINATION

Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease.

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda-Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.

The implementation of NILS: A web-based artificial neural network decision support tool for noninvasive lymph node staging in breast cancer

Objective

To implement artificial neural network (ANN) algorithms for noninvasive lymph node staging (NILS) to a decision support tool and facilitate the option to omit surgical axillary staging in breast cancer patients with low-risk of nodal metastasis.

Methods

The NILS tool is a further development of an ANN prototype for the prediction of nodal status. Training and internal validation of the original algorithm included 15 clinical and tumor-related variables from a consecutive cohort of 800 breast cancer cases. The updated NILS tool included 10 top-ranked input variables from the original prototype. A workflow with four ANN pathways was additionally developed to allow different combinations of missing preoperative input values. Predictive performances were assessed by area under the receiver operating characteristics curves (AUC) and sensitivity/specificity values at defined cut-points. Clinical utility was presented by estimating possible sentinel lymph node biopsy (SLNB) reduction rates. The principles of user-centered design were applied to develop an interactive web-interface to predict the patient's probability of healthy lymph nodes. A technical validation of the interface was performed using data from 100 test patients selected to cover all combinations of missing histopathological input values.

Results

ANN algorithms for the prediction of nodal status have been implemented into the web-based NILS tool for personalized, noninvasive nodal staging in breast cancer. The estimated probability of healthy lymph nodes using the interface showed a complete concordance with estimations from the reference algorithm except in two cases that had been wrongly included (ineligible for the technical validation). NILS predictive performance to distinguish node-negative from node-positive disease, also with missing values, displayed AUC ranged from 0.718 (95% CI, 0.687-0.748) to 0.735 (95% CI, 0.704-0.764), with good calibration. Sensitivity 90% and specificity 34% were demonstrated. The potential to abstain from axillary surgery was observed in 26% of patients using the NILS tool, acknowledging a false negative rate of 10%, which is clinically accepted for the standard SLNB technique.

Conclusions

The implementation of NILS into a web-interface are expected to provide the health care with decision support and facilitate preoperative identification of patients who could be good candidates to avoid unnecessary surgical axillary staging.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers.

Usability, acceptability, and implementation strategies for the Exercise in Cancer Evaluation and Decision Support (EXCEEDS) algorithm: a Delphi study

INTRODUCTION

Oncology guidelines recommend participation in cancer rehabilitation or exercise services (CR/ES) to optimize survivorship. Yet, connecting the right survivor, with the right CR/ES, at the right time remains a challenge. The Exercise in Cancer Evaluation and Decision Support (EXCEEDS) algorithm was developed to enhance CR/ES clinical decision-making and facilitate access to CR/ES. We used Delphi methodology to evaluate usability, acceptability, and determine pragmatic implementation priorities.

METHODS

Participants completed three online questionnaires including (1) simulated case vignettes, (2) 4-item acceptability questionnaire (0-5 pts), and (3) series of items to rank algorithm implementation priorities (potential users, platforms, strategies). To evaluate usability, we used Chi-squared test to compare frequency of accurate pre-exercise medical clearance and CR/ES triage recommendations for case vignettes when using EXCEEDS vs. without. We calculated mean acceptability and inter-rater agreement overall and in 4 domains. We used the Eisenhower Prioritization Method to evaluate implementation priorities.

RESULTS

Participants (N = 133) mostly represented the fields of rehabilitation (69%), oncology (25%), or exercise science (17%). When using EXCEEDS (vs. without), their recommendations were more likely to be guideline concordant for medical clearance (83.4% vs. 66.5%, X² = 26.61, p < .0001) and CR/ES triage (60.9% vs. 51.1%, X² = 73.79, p < .0001). Mean acceptability was M = 3.90 ± 0.47; inter-rater agreement was high for 3 of 4 domains. Implementation priorities include 1 potential user group, 2 platform types, and 9 implementation strategies.

CONCLUSION

This study demonstrates the EXCEEDS algorithm can be a pragmatic and acceptable clinical decision support tool for CR/ES recommendations. Future research is needed to evaluate algorithm usability and acceptability in real-world clinical pathways.

Decision-making about mastectomy among Chinese women with breast cancer: a mixed-methods study protocol

INTRODUCTION

The prevalence of mastectomy in China is higher than its Western counterparts. Little is known about whether Chinese women with breast cancer have been involved in the decision-making process of mastectomy, the level of decisional conflict, their perceptions of mastectomy and the factors that influence them to undergo a mastectomy. This protocol describes a mixed-methods study that aims to provide an in-depth understanding of decision-making about mastectomy among Chinese women with breast cancer.

METHODS AND ANALYSIS

A three-phase, sequential explanatory mixed-methods design will be adopted. The first phase is a retrospective analysis of medical records to determine the current use of mastectomy. The second phase is a cross-sectional survey to examine women's perceptions of involvement, decisional conflict and the factors influencing them to undergo a mastectomy. The third phase is an individual interview to explore women's decision-making experiences with mastectomy. Quantitative data will be analysed using descriptive statistics, t-test, Fisher's exact test, χ² test, analysis of variance, Pearson's correlation and logistic regression. Qualitative data will be analysed by the inductive content analysis.

ETHICS AND DISSEMINATION

Ethical approvals for this study have been obtained from the human research ethics committees of the University of Newcastle, Australia, Zhongshan Hospital Xiamen University, China, and the First Affiliated Hospital of Xiamen University, China. Written informed consent will be obtained from the participants. Findings of this work will be disseminated at international conferences and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

Not applicable.

Development of a Prognostic Application to Predict Survival for Chinese Women with Breast Cancer (Preprint)

Background

Objective

Methods

Results

Conclusions

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

Computer-Based Decision Tools for Shared Therapeutic Decision-making in Oncology: Systematic Review

BACKGROUND

Therapeutic decision-making in oncology is a complex process because physicians must consider many forms of medical data and protocols. Another challenge for physicians is to clearly communicate their decision-making process to patients to ensure informed consent. Computer-based decision tools have the potential to play a valuable role in supporting this process.

OBJECTIVE

This systematic review aims to investigate the extent to which computer-based decision tools have been successfully adopted in oncology consultations to improve patient-physician joint therapeutic decision-making.

METHODS

This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist and guidelines. A literature search was conducted on February 4, 2021, across the Cochrane Database of Systematic Reviews (from 2005 to January 28, 2021), the Cochrane Central Register of Controlled Trials (December 2020), MEDLINE (from 1946 to February 4, 2021), Embase (from 1947 to February 4, 2021), Web of Science (from 1900 to 2021), Scopus (from 1969 to 2021), and PubMed (from 1991 to 2021). We used a snowball approach to identify additional studies by searching the reference lists of the studies included for full-text review. Additional supplementary searches of relevant journals and gray literature websites were conducted. The reviewers screened the articles eligible for review for quality and inclusion before data extraction.

RESULTS

There are relatively few studies looking at the use of computer-based decision tools in oncology consultations. Of the 4431 unique articles obtained from the searches, only 10 (0.22%) satisfied the selection criteria. From the 10 selected studies, 8 computer-based decision tools were identified. Of the 10 studies, 6 (60%) were conducted in the United States. Communication and information-sharing were improved between physicians and patients. However, physicians did not change their habits to take advantage of computer-assisted decision-making tools or the information they provide. On average, the use of these computer-based decision tools added approximately 5 minutes to the total length of consultations. In addition, some physicians felt that the technology increased patients' anxiety.

CONCLUSIONS

Of the 10 selected studies, 6 (60%) demonstrated positive outcomes, 1 (10%) showed negative results, and 3 (30%) were neutral. Adoption of computer-based decision tools during oncology consultations continues to be low. This review shows that information-sharing and communication between physicians and patients can be improved with the assistance of technology. However, the lack of integration with electronic health records is a barrier. This review provides key requirements for enhancing the chance of success of future computer-based decision tools. However, it does not show the effects of health care policies, regulations, or business administration on physicians' propensity to adopt the technology. Nevertheless, it is important that future research address the influence of these higher-level factors as well.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42021226087; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021226087.

Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography

OBJECTIVE

To assess the similarity and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data.

METHODS

165 consecutive women who underwent FFDM were included. Breasts have been segmented into "dense" and "non-dense" area using the software LIBRA. Segmentation of both FOR PROCESSING and FOR PRESENTATION images have been evaluated by Bland-Altman, Dice index and Cohen's kappa analysis. 74 textural features were computed: 18 features of First Order (FO), 24 features of Gray Level Co-occurrence Matrix (GLCM), 16 features of Gray Level Run Length Matrix (GLRLM) and 16 features of Gray Level Size Zone Matrix (GLSZM). Paired Wilcoxon test, Spearman's rank correlation, intraclass correlation and canonical correlation have been used. Bilateral symmetry and percent density (PD) were also evaluated.

RESULTS

Segmentation from FOR PROCESSING and FOR PRESENTATION gave very different results. Bilateral symmetry was higher when evaluated on features computed using FOR PROCESSING images. All features showed a positive Spearman's correlation coefficient and many FOR-PROCESSING features were moderately or strongly correlated to their corresponding FOR-PRESENTATION counterpart. As regards the correlation analysis between PD and textural features from FOR-PRESENTATION a moderate correlation was obtained only for Gray Level Non Uniformity from GLRLM both on "dense" and "non dense" area; as regards correlation between PD and features from FOR-PROCESSING a moderate correlation was observed only for Maximal Correlation Coefficient from GLCM both on "dense" and "non dense" area.

CONCLUSIONS

Texture features from FOR PROCESSING mammograms seem to be most suitable for assessing breast density.

Physciosporin suppresses mitochondrial respiration, aerobic glycolysis, and tumorigenesis in breast cancer

BACKGROUND

Physciosporin (PHY) is one of the potent anticancer lichen compound. Recently, PHY was shown to suppress colorectal cancer cell proliferation, motility, and tumorigenesis through novel mechanisms of action.

PURPOSE

We investigated the effects of PHY on energy metabolism and tumorigenicity of the human breast cancer (BC) cells MCF-7 (estrogen and progesterone positive BC) and MDA-MB-231 (triple negative BC).

METHODS

The anticancer effect of PHY on cell viability, motility, cancer metabolism and tumorigenicity was evaluated by MTT assay, migration assay, clonogenic assay, anchorage-independent colony formation assay, glycolytic and mitochondrial metabolism analysis, qRT-PCR, flow cytometric analysis, Western blotting, immunohistochemistry in vitro; and by tumorigenicity study with orthotopic breast cancer xenograft model in vivo.

RESULTS

PHY markedly inhibited BC cell viability. Cell-cycle profiling and Annexin V-FITC/PI double staining showed that a toxic dosage of PHY triggered apoptosis in BC cell lines by regulating the B-cell lymphoma-2 (Bcl-2) family proteins and the activity of caspase pathway. At non-toxic concentrations, PHY potently decreased migration, proliferation, and tumorigenesis of BC cells in vitro. Metabolic studies revealed that PHY treatment significantly reduced the bioenergetic profile by decreasing respiration, ATP production, and glycolysis capacity. In addition, PHY significantly altered the levels of mitochondrial (PGC-1α) and glycolysis (GLUT1, HK2 and PKM2) markers, and downregulated transcriptional regulators involved in cancer cell metabolism, including β-catenin, c-Myc, HIF-1α, and NF-κB. An orthotopic implantation mouse model of BC confirmed that PHY treatment suppressed BC growth in vivo and target genes were consistently suppressed in tumor specimens.

CONCLUSION

The findings from our in vitro as well as in vivo studies exhibit that PHY suppresses energy metabolism as well as tumorigenesis in BC. Especially, PHY represents a promising therapeutic effect against hormone-insensitive BC (triple negative) by targeting energy metabolism.

Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

BACKGROUND

The ubiquity of mobile devices has made it possible for clinical decision-support systems (CDSS) to become available to healthcare providers on handheld devices at the point-of-care, including in low- and middle-income countries. The use of CDSS by providers can potentially improve adherence to treatment protocols and patient outcomes. However, the evidence on the effect of the use of CDSS on mobile devices needs to be synthesized. This review was carried out to support a World Health Organization (WHO) guideline that aimed to inform investments on the use of decision-support tools on digital devices to strengthen primary healthcare.

OBJECTIVES

To assess the effects of digital clinical decision-support systems (CDSS) accessible via mobile devices by primary healthcare providers in the context of primary care settings.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, Global Index Medicus, POPLINE, and two trial registries from 1 January 2000 to 9 October 2020. We conducted a grey literature search using mHealthevidence.org and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies.

SELECTION CRITERIA

Study design: we included randomized trials, including full-text studies, conference abstracts, and unpublished data irrespective of publication status or language of publication.  Types of participants: we included studies of all cadres of healthcare providers, including lay health workers and other individuals (administrative, managerial, and supervisory staff) involved in the delivery of primary healthcare services using clinical decision-support tools; and studies of clients or patients receiving care from primary healthcare providers using digital decision-support tools. Types of interventions: we included studies comparing digital CDSS accessible via mobile devices with non-digital CDSS or no intervention, in the context of primary care. CDSS could include clinical protocols, checklists, and other job-aids which supported risk prioritization of patients. Mobile devices included mobile phones of any type (but not analogue landline telephones), as well as tablets, personal digital assistants, and smartphones. We excluded studies where digital CDSS were used on laptops or integrated with electronic medical records or other types of longitudinal tracking of clients.

DATA COLLECTION AND ANALYSIS

A machine learning classifier that gave each record a probability score of being a randomized trial screened all search results. Two review authors screened titles and abstracts of studies with more than 10% probability of being a randomized trial, and one review author screened those with less than 10% probability of being a randomized trial. We followed standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care group. We used the GRADE approach to assess the certainty of the evidence for the most important outcomes.

MAIN RESULTS

Eight randomized trials across varying healthcare contexts in the USA,. India, China, Guatemala, Ghana, and Kenya, met our inclusion criteria. A range of healthcare providers (facility and community-based, formally trained, and lay workers) used digital CDSS. Care was provided for the management of specific conditions such as cardiovascular disease, gastrointestinal risk assessment, and maternal and child health. The certainty of evidence ranged from very low to moderate, and we often downgraded evidence for risk of bias and imprecision. We are uncertain of the effect of this intervention on providers' adherence to recommended practice due to the very low certainty evidence (2 studies, 185 participants). The effect of the intervention on patients' and clients' health behaviours such as smoking and treatment adherence is mixed, with substantial variation across outcomes for similar types of behaviour (2 studies, 2262 participants). The intervention probably makes little or no difference to smoking rates among people at risk of cardiovascular disease but probably increases other types of desired behaviour among patients, such as adherence to treatment. The effect of the intervention on patients'/clients' health status and well-being  is also mixed (5 studies, 69,767 participants). It probably makes little or no difference to some types of health outcomes, but we are uncertain about other health outcomes, including maternal and neonatal deaths, due to very low-certainty evidence. The intervention may slightly improve patient or client acceptability and satisfaction (1 study, 187 participants). We found no studies that reported the time between the presentation of an illness and appropriate management, provider acceptability or satisfaction, resource use, or unintended consequences.

AUTHORS' CONCLUSIONS

We are uncertain about the effectiveness of mobile phone-based decision-support tools on several outcomes, including adherence to recommended practice. None of the studies had a quality of care framework and focused only on specific health areas.   We need well-designed research that takes a systems lens to assess these issues.

Application of Clinical Decision Support System to Assist Breast Cancer Patients with Lifestyle Modifications during the COVID-19 Pandemic: A Randomised Controlled Trial

Clinical decision support systems (CDSS) are data aggregation tools based on computer technology that assist clinicians to promote healthy weight management and prevention of cardiovascular diseases. We carried out a randomised controlled 3-month trial to implement lifestyle modifications in breast cancer (BC) patients by means of CDSS during the COVID-19 pandemic. In total, 55 BC women at stages I-IIIA were enrolled. They were randomly assigned either to Control group, receiving general lifestyle advice (n = 28) or the CDSS group (n = 27), to whom the CDSS provided personalised dietary plans based on the Mediterranean diet (MD) together with physical activity guidelines. Food data, anthropometry, blood markers and quality of life were evaluated. At 3 months, higher adherence to MD was recorded in the CDSS group, accompanied by lower body weight (kg) and body fat mass percentage compared to control (p < 0.001). In the CDSS arm, global health/quality of life was significantly improved at the trial endpoint (p < 0.05). Fasting blood glucose and lipid levels (i.e., cholesterol, LDL, triacylglycerols) of the CDSS arm remained unchanged (p > 0.05) but were elevated in the control arm at 3 months (p < 0.05). In conclusion, CDSS could be a promising tool to assist BC patients with lifestyle modifications during the COVID-19 pandemic.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.

A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.

Emerging role of pioneer transcription factors in targeted ERα positive breast cancer

Transcription factors (TFs) are modular protein groups that preferably bind to DNA sequences and guide genomic expression through transcription. Among these key regulators, “pioneer factors” are an emerging class of TFs that specifically interact with nucleosomal DNA and facilitate accessible genomic binding sites for the additional TFs. There is growing evidence of these specialized modulators in particular malignancies, as highlighted by agents’ clinical efficacy, specifically targeting nuclear hormone receptors. They have been implicated in multiple cancers more recently, with a high proportion inculpating on hormone influential cancers. Moreover, extended crosstalk and cooperation between ERα pioneering factors in estrogen-dependent breast cancer (BC) remain elucidated. This review discusses on the recent advances in our understanding of pioneer TFs in cancer, especially highlighting its potentiality to modulate chromatin condensation to permit ERα recruitment in BC cells. Through the study it was concluded that the highly prospected pioneer TFs in BC, including FOXA1, TLE1, PBX1, and GATA3, possess the potential therapeutic significance and further innovations in the field could yield targeted therapy in cancer treatment.

Preliminary Qualitative and Quantitative Evaluation of DESIREE, a Decision Support Platform for the Management of Primary Breast Cancer Patients

The DESIREE project has developed a platform offering several complementary therapeutic decision support systems (DSSs) to improve care quality for breast cancer patients. A first assessment of the system was carried out in close-to-real tumor boards (TBs). Fourteen TB sessions were organized corresponding to a total of 125 exploitable decisions previously made without the system and re-played with the system after a washout period in three pilot sites. Results show an overestimation of declared compliance with guidelines when not using the system as compared to measured compliance with the recommendations issued from the guideline-based DSS of DESIREE. After using the system, measured compliance rate of decisions with guidelines was significantly improved from 74.4% to 89.6%. Most of the changes in decisions when using the guideline-based DSS were associated with non-compliant decisions that became compliant. Qualitative analysis and interviews showed that despite maturity issues, clinicians found DESIREE DSSs innovative and promising.

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

Simple Summary

Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients.

Abstract

In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.

Molecular Cytogenomic Characterization of the Murine Breast Cancer Cell Lines C-127I, EMT6/P and TA3 Hauschka

BACKGROUND

To test and introduce effective and less toxic breast cancer (BC) treatment strategies, animal models, including murine BC cell lines, are considered as perfect platforms. Strikingly, the knowledge on the genetic background of applied BC cell lines is often sparse though urgently necessary for their targeted and really justified application.

METHODS

In this study, we performed the first molecular cytogenetic characterization for three murine BC cell lines C-127I, EMT6/P and TA3 Hauschka. Besides fluorescence in situ hybridization-banding, array comparative genomic hybridization was also applied. Thus, overall, an in silico translation for the detected imbalances and chromosomal break events in the murine cell lines to the corresponding homologous imbalances in humans could be provided. The latter enabled a comparison of the murine cell line with human BC cytogenomics.

RESULTS

All three BC cell lines showed a rearranged karyotype at different stages of complexity, which can be interpreted carefully as reflectance of more or less advanced tumor stages.

CONCLUSIONS

Accordingly, the C-127I cell line would represent the late stage BC while the cell lines EMT6/P and TA3 Hauschka would be models for the premalignant or early BC stage and an early or benign BC, respectively. With this cytogenomic information provided, these cell lines now can be applied really adequately in future research studies.

Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-positive, HER2-negative Breast Cancer Patients

Determining which patients with early-stage breast cancer should receive chemotherapy is an important clinical issue. Chemotherapy has several adverse side effects, impacting on quality of life, along with significant economic consequences. There are a number of multi-gene prognostic signatures for breast cancer recurrence but there is less evidence that these prognostic signatures are predictive of therapy benefit. Biomarkers that can predict patient response to chemotherapy can help avoid ineffective over-treatment. The aim of this work was to assess if the OncoMasTR prognostic signature can predict pathological complete response (pCR) to neoadjuvant chemotherapy, and to compare its predictive value with other prognostic signatures: EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes. Gene expression datasets from ER-positive, HER2-negative breast cancer patients that had pre-treatment biopsies, received neoadjuvant chemotherapy and an assessment of pCR were obtained from the Gene Expression Omnibus repository. A total of 813 patients with 66 pCR events were included in the analysis. OncoMasTR, EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes numeric risk scores were approximated by applying the gene coefficients to the corresponding mean probe expression values. OncoMasTR, EndoPredict and Oncotype DX prognostic scores were moderately well correlated according to the Pearson's correlation coefficient. Association with pCR was estimated using logistic regression. The odds ratio for a 1 standard deviation increase in risk score, adjusted for cohort, were similar in magnitude for all four signatures. Additionally, the four signatures were significant predictors of pCR. OncoMasTR added significant predictive value to EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes signatures as determined by bivariable and trivariable analysis. In this in silico analysis, OncoMasTR, EndoPredict, Oncotype DX, and Tumor Infiltrating Leukocytes were significantly predictive of pCR to neoadjuvant chemotherapy in ER-positive and HER2-negative breast cancer patients.