The Diagnostic Value of a Nomogram Based on Clinical Imaging and MRIBased Radiomic Features in Triple-Negative Breast Cancer

OBJECTIVE

This study aimed to determine the utility of a radiomic nomogram combined with clinical imaging and radiomic features based on MRI for the diagnosis of triple-negative breast cancer.

METHODS

Multi-parametric MRI images of 136 breast cancer patients were retrospectively analyzed, 95 cases were stratified into the training cohort, and 41 cases were selected for the test group. According to the pathological molecular typing, the patients were divided into 23 cases of triple-negative breast cancer and 113 cases of non-triple-negative breast cancer. ITK software was used to manually delineate the lesion volume region of interest (VOI), and the Pyradiomics package was used to extract radiomic features for screening and model building. The platform was then used to analyze the clinical and imaging risk factors of breast cancer to build a characteristic model separately. Finally, a radiomic nomogram was constructed by integrating the radiomic and independent clinical image features. The diagnostic performance of the model was assessed using ROC curves.

RESULTS

Univariate and multivariate analyses showed that the menstrual cycle, glandular density, and skin thickening were risk factors for clinical imaging characteristics of triple-negative breast cancer. The Area Under the Curve (AUC) was 0.839 and 0.826 for univariate and multivariate analysis, respectively. After screening, 11 radiomic features participated in the calculation of the radiomic score, and its AUC in the test set was 0.803. Combining it further with clinical models, the AUC improved to 0.899.

CONCLUSION

The radiomic nomogram developed in this study has great value in the diagnosis of triple-negative breast cancer.

Combining Molecular Subtypes with Multivariable Clinical Models Has the Potential to Improve Prediction of Treatment Outcomes in Prostate Cancer at Diagnosis

Clinical management of prostate cancer is challenging because of its highly variable natural history and so there is a need for improved predictors of outcome in non-metastatic men at the time of diagnosis. In this study we calculated the model score from the leading clinical multivariable model, PREDICT prostate, and the poor prognosis DESNT molecular subtype, in a combined expression and clinical dataset that were taken from malignant tissue at prostatectomy (n = 359). Both PREDICT score (p < 0.0001, IQR HR = 1.59) and DESNT score (p < 0.0001, IQR HR = 2.08) were significant predictors for time to biochemical recurrence. A joint model combining the continuous PREDICT and DESNT score (p < 0.0001, IQR HR = 1.53 and 1.79, respectively) produced a significantly improved predictor than either model alone (p < 0.001). An increased probability of mortality after diagnosis, as estimated by PREDICT, was characterised by upregulation of cell-cycle related pathways and the downregulation of metabolism and cholesterol biosynthesis. The DESNT molecular subtype has distinct biological characteristics to those associated with the PREDICT model. We conclude that the inclusion of biological information alongside current clinical prognostic tools has the potential to improve the ability to choose the optimal treatment pathway for a patient.

Machine Learning, Clinical Notes and Knowledge Graphs for Early Prediction of Acute Kidney Injury in the Intensive Care

Acute kidney injury (AKI) is an abrupt decrease of kidney function which is common in the intensive care. Many AKI prediction models have been proposed, but an analysis of what is the added value of clinical notes and medical terminologies has not yet been conducted. We developed and internally validated a model to predict AKI that includes not only clinical variables, but also clinical notes and medical terminologies. Our results were overall good (AUROC > 0.80). The best model used only clinical variables (AUROC 0.899).

Neuroethics: what the study of brain disorders can tell about moral behavior

The growing interest in the study of morality has led to the birth of a new discipline in the field of moral philosophy called Neuroethics, a multidisciplinary approach that aims to combine philosophy and neuroscience. In this editorial, we explored the relevance of clinical models affected by neurological/psychiatric disorders to learn more about mechanisms sub-serving ethical behaviour at neural and cognitive level.

openEHR Based Contextual Problem List

The problem list is a key facet of the digital patient record that has historically been difficult to curate. This paper presents an implementation of a contextual problem list using openEHR. It describes the modelling approach, key model elements, and how these are assembled to underpin a Problem Oriented Medical Record. Finally, it discusses issues associated with how problem lists may be used.

Interpersonal Emotion Regulation: From Research to Group Therapy

The concept of interpersonal emotion regulation (IER) refers to a variety of processes in which emotion regulation occurs as part of live social interactions and includes, among others, also those interpersonal interactions in which individuals turn to others to be helped or to help the others in managing emotions. Although IER may be a concept of interest in group therapy, specific theoretical insights in this field appear to be missed. In this article, we firstly provide a review of IER definitions, of classifications of IER strategies, and of IER clinical conceptualizations. Afterwards, we discuss the relevance of considering IER for group therapy, both in terms of non-specific group therapeutic factors and of group therapy techniques promoting adaptive emotion regulation, underlining the potentially relevant role of IER behaviors as intrinsically involved in group experience.

The role of aldosterone inhibitors in cardiac ischemia-reperfusion injury

Myocardial ischaemia-reperfusion (I/R) injury is a well-known term for exacerbation of cellular destruction and dysfunction after the restoration of blood flow to a previously ischaemic heart. A vast number of studies that have demonstrated that the role of mineralocorticoids in cardiovascular diseases is based on the use of pharmacological mineralocorticoid receptor (MR) antagonists. This review paper aimed to summarize current knowledge on the effects of MR antagonists on myocardial I/R injury as well as postinfarction remodeling. Animal models, predominantly the Langendorff technique and left anterior descending coronary artery occlusion, have confirmed the potency of MR antagonists as preconditioning and postconditioning agents in limiting infarct size and postinfarction remodeling. Several preclinical studies in rodents have established and proved possible mechanisms of cardioprotection by MR antagonists, such as reduction of oxidative stress, reduction of inflammation, and apoptosis, therefore limiting the infarct zone. However, the results of some clinical trials are inconsistent, since they reported no benefit of MR antagonists in acute myocardial infarction. Due to this, further studies and the results of ongoing clinical trials regarding MR antagonist administration in patients with acute myocardial infarction are being awaited with great interest.

Defining the Contextual Problem List

The Problem Oriented Medical Record (POMR) is considered a key charting method to support clinical care. Although not uniformly represented amongst digital health systems, this paper presents a clinical model to represent multiple clinical perspectives from a single problem list. The contextual problem list model is defined according to primary diagnosis, comorbidities and problems arising from the primary condition. It is represented within the patient record as a single composition according to the prescribed context. The model pattern could help alleviate the traditional criticisms of paper and digitally based problem records.

Application of the Calgary Family Assessment and Intervention Models: Reflections on the Reciprocity Between the Personal and the Professional

Much has been written about the global implementation of the Calgary Family Assessment and Intervention Models (CFAM/CFIM) and the application of these practice models in various clinical settings. The purpose of this article is to provide a brief update on the background of CFAM/CFIM, and the current applications of the models as evidenced in the English-language literature. Little has been written about the use of CFAM/CFIM in a personal context, however. As originators of the models, we offer our own narratives and reflections about the reciprocity between the personal and professional applications of our models and the ways that our personal experiences have extended our understanding about the utility of the models for clinical practice with families.

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry

BACKGROUND

The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known.

METHODS

Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the OR per adjusted SD for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement.

RESULTS

The ORs for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding ORs were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score.

CONCLUSIONS

By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%.

IMPACT

Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

A review of clinical models for the evaluation of human TB vaccines

While much progress has been made in the fight against the scourge of tuberculosis (TB), we are still some way from reaching the ambitious targets of eliminating it as a global public health problem by the mid twenty-first century. A new and effective vaccine that protects against pulmonary TB disease will be an essential element of any control strategy. Over a dozen vaccines are currently in development, but recent efficacy trial data from one of the most advanced candidates have been disappointing. Limitations of current preclinical animal models exist, together with a lack of a complete understanding of host immunity to TB or robust correlates of disease risk and protection. Therefore, in the context of such obstacles, we discuss the lessons identified from recent efficacy trials, current concepts of biomarkers and correlates of protection, the potential of innovative clinical models such as human challenge and conducting trials in high-incidence settings to evaluate TB vaccines in humans, and the use of systems vaccinology and novel technologies including transcriptomics and metabolomics, that may facilitate their utility.

Using Clinical Risk Models for Lung Nodule Classification

Evaluation and diagnosis of indeterminate pulmonary nodules is a significant and increasing burden on our health care system. The advent of lung cancer screening with low-dose computed tomography only exacerbates this problem, and more surgeons will be evaluating smaller and screening discovered nodules. Multiple calculators exist that can help the clinician diagnose lung cancer at the bedside. The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) model helps to determine who needs lung cancer screening, and the McWilliams and Mayo models help to guide the primary care clinician or pulmonologist with diagnosis by estimating the probability of cancer in patients with indeterminate pulmonary nodules. The Thoracic Research Evaluation And Treatment (TREAT) model assists surgeons to determine who needs a surgical biopsy among patients referred for suspicious lesions. Additional work is needed to develop decision support tools that will facilitate the use of these models in clinical practice, to complement the clinician's judgment and enhance shared decision making with the patient at the bedside.

2. Clinical models applicable to disasters

Processes used for research of disasters are similar to those used for the assessment, determination of needs, planning, selection of interventions and their implementation, and evaluation of patient outcomes in the clinical setting. A patient suffering a stroke and another patient with an acute inflammation of the gallbladder serve as examples. Patients travel along the same longitudinal progression as do disasters and clinical assessments are conducted using the body's organ systems.

Breast cancer chemoprevention: beyond tamoxifen

A large number of new potential chemoprevention agents are available that target molecular abnormalities found in estrogen receptor (ER)-negative and/or ER-positive precancerous breast tissue and have side effect profiles that differ from tamoxifen. Classes of agents currently undergoing evaluation in clinical prevention trials or those for which testing is planned in the near future include new selective ER modulators, aromatase inactivators/inhibitors, gonadotrophin-releasing hormone agonists, monoterpenes, isoflavones, retinoids, rexinoids, vitamin D derivatives, and inhibitors of tyrosine kinase, cyclooxygenase-2, and polyamine synthesis. New clinical testing models will use morphological and molecular biomarkers to select candidates at highest short-term risk, to predict the response to a particular class of agent, and to assess the response in phase II prevention trials. If validated, morphological and molecular markers could eventually replace cancer incidence as an indicator of efficacy in future phase III trials.