Prognostic Algorithms Research Articles

Does the Pathologic Fracture Predict Severe Paralysis in Patients with Metastatic Epidural Spinal Cord Compression (MESCC)?-A Retrospective, Single-Center Cohort Analysis.

Currently, there is uncertainty about the predictive factors for metastatic epidural spinal cord compression (MESCC) and consecutive symptomatology in tumor patients. Prognostic algorithms for identifying patients at risk for paralysis are missing. The influence of the pathologic fracture on the patient's symptoms is widely discussed in the literature and we hypothesize that pathologic fractures contribute to spinal cord compression and are therefore predictive of severe paralysis. We tested this hypothesis in 136 patients who underwent surgery for spinal metastases. The most common primary cancers were prostate (24.3%, n = 33), breast (11.0%, n = 15), lung (10.3%, n = 14), and cancer of unknown primary (10.3%, n = 14). MESCC primarily affected the thoracic (77.2%, n = 105), followed by the lumbar (13.2%, n = 18) and cervical (9.6%, n = 13) spine. Pathologic fractures occurred in 63.2% (n = 86) of patients, mainly in osteolytic metastases. On the American spinal injury association (ASIA) impairment scale (AIS), 63.2% (n = 86) of patients exhibited AIS grade D and 36.8% (n = 50) AIS grade C-A preoperatively. The presence of a pathologic fracture alone did not predict severe paralysis (AIS C-A, p = 0.583). However, the duration of sensorimotor impairments, patient age, spinal instability neoplastic score (SINS), and the epidural spinal cord compression (ESCC) grade together predicted severe paralysis (p = 0.006) as did the ESCC grade 3 alone (p = 0.028). This is in contrast to previous studies that stated no correlation between the degree of spinal cord compression and the severity of neurologic impairments. Furthermore, the high percentage of pathologic fractures found in this study is above previously reported incidences. The risk factors identified can help to predict the development of paralysis and assist in the improvement of follow-up algorithms and the timing of therapeutic interventions.

Split-Second Cryptocurrency Forecast Using Prognostic Deep Learning Algorithms: Data Curation by Deephaven

Split-Second Cryptocurrency Forecast Using Prognostic Deep Learning Algorithms: Data Curation by Deephaven

New prognostic model in patients with advanced urothelial carcinoma treated with second-line immune checkpoint inhibitors

BackgroundBellmunt Risk Score, based on Eastern Cooperative Oncology Group (ECOG) performance status (PS), hemoglobin levels and presence of liver metastases, is the most established prognostic algorithm for patients with advanced...

Data-driven system prognostics and diagnostics for SMT component placement machines

Prognostic-based maintenance analyses past events and predicts the future state of a machine based on the understanding of the degradation function of the machine’s components. Diagnostics-based maintenance tests equipment according to a fixed routine for a machine’s proper functioning and reliability. Current Surface-mount Technology (SMT) machines are not equipped with self-prognostic and diagnostic functions. In this paper, a system prognostic and diagnostic method is proposed, implemented in software, for estimating a machine’s health condition and faulty components of a SMT component placement machine outfitted with machine logs that consist of take-up count, miss count and time information. At each execution period the method processes features extracted from the machine logs to obtain a set of parity parameters, which are further used to analyse the machine. The prognostic algorithm computes the health status indicator of the component placement machine. The computed final status indicator is compared to a threshold value to check the system’s health condition. The diagnostic algorithm predicts and identifies the faulty pick-up nozzles and faulty input trays. The proposed algorithms minimise the effects of faulty components on production lines and assist to produce optimal maintenance decisions and reliability functions for equipment.

Influence of Iron Deficiency on Clinical and Haemodynamic Parameters in Pulmonary Arterial Hypertension Cohorts.

Iron deficiency (Fedef) has been shown to be common in patients with group 1 or pulmonary arterial hypertension (PAH). Several studies have shown a negative impact of Fedef on clinical and haemodynamic parameters of the disease, but data from individual studies have not been strong enough to lead to incorporation of the finding of Fedef into prognostic or therapeutic algorithms. The goal of this meta-analysis was to combine data from available studies to better define any associations between Fedef and established variables of prognostic importance in PAH. A literature search identified nine studies with extractable data relevant to the study questions. The impact of Fedef upon the following parameters was evaluated: 6-minute walk distance (6MWD), WHO-functional class, N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, echocardiography, and findings from right heart catheterisation (RHC). Pooled results were reported as mean difference or risk difference with 95% confidence intervals utilising a random effects modeling approach. Fedef in the PAH population was common (47% of cases) and was associated with cardiovascular dysfunction (lower tricuspid annular plane systolic excursion [TAPSE], elevated NT-proBNP, and lower mixed venous oxygen saturation) and with reduction in functional capacity (lower 6MWD and higher functional class). This meta-analysis strengthens the relationships between Fedef and several markers of poor outcome in PAH. Fedef in patients with PAH warrants further scrutiny and merits consideration as a cause of clinical deterioration. Even though causation and longitudinal relationships between Fedef and PAH could not be identified, effect of Fedef on factors that affect disease prognosis is noteworthy and worthy of more focussed studies.

An online randomised controlled trial of prognosticating imminent death in advanced cancer patients: Clinicians give greater weight to advice from a prognostic algorithm than from another clinician with a different profession.

A second opinion or a prognostic algorithm may increase prognostic accuracy. This study assessed the level to which clinicians integrate advice perceived to be coming from another clinician or a prognostic algorithm into their prognostic estimates, and how participant characteristics and nature of advice received affect this. An online double-blind randomised controlled trial was conducted. Palliative doctors, nurses and other types of healthcare professionals were randomised into study arms differing by perceived source of advice (algorithm or another clinician). In fact, the advice was the same in both arms (emanating from the PiPS-B14 prognostic model). Each participant reviewed five patient summaries. For each summary, participants: (1) provided an initial probability estimate of two-week survival (0% 'certain death'-100% 'certain survival'); (2) received advice (another estimate); (3) provided a final estimate. Weight of Advice (WOA) was calculated for each summary (0 '100% advice discounting' - 1 '0% discounting') and multilevel linear regression analyses were conducted. NCT04568629. A total of 283 clinicians were included in the analysis. Clinicians integrated advice from the algorithm more than advice from another clinician (WOA difference=-0.12 [95% CI -0.18, -0.07], p< 0.001). There was no interaction between study arm and participant profession, years of palliative care or overall experience. Advice of intermediate strength (75%) was given a lower WOA (0.31) than advice received at either the 50% (WOA 0.40) or 90% level (WOA 0.43). The overall interaction between strength of advice and study arm on WOA was significant (p< 0.001). Clinicians adjusted their prognostic estimates more when advice was perceived to come from a prognostic algorithm than from another clinician. Research is needed to understand how clinicians make prognostic decisions and how algorithms are used in clinical practice.

Prognostic Factors across Poorly Differentiated Neuroendocrine Neoplasms: A Pooled Analysis

Introduction: Poorly differentiated neuroendocrine carcinomas (NECs) are characterized by aggressive clinical course and poor prognosis. No reliable prognostic markers have been validated to date; thus, the definition of a specific NEC prognostic algorithm represents a clinical need. This study aimed to analyze a large NEC case series to validate the specific prognostic factors identified in previous studies on gastro-entero-pancreatic and lung NECs and to assess if further prognostic parameters can be isolated. Methods: A pooled analysis of four NEC retrospective studies was performed to evaluate the prognostic role of Ki-67 cut-off, the overall survival (OS) according to primary cancer site, and further prognostic parameters using multivariable Cox proportional hazards model and machine learning random survival forest (RSF). Results: 422 NECs were analyzed. The most represented tumor site was the colorectum (n = 156, 37%), followed by the lungs (n = 111, 26%), gastroesophageal site (n = 83, 20%; 66 gastric, 79%) and pancreas (n = 42, 10%). The Ki-67 index was the most relevant predictor, followed by morphology (pure or mixed/combined NECs), stage, and site. The predicted RSF response for survival at 1, 2, or 3 years showed decreasing survival with increasing Ki-67, pure NEC morphology, stage III–IV, and colorectal NEC disease. Patients with Ki-67 <55% and mixed/combined morphology had better survival than those with pure morphology. Morphology pure or mixed/combined became irrelevant in NEC survival when Ki-67 was ≥55%. The prognosis of metastatic patients who did not receive any treatment tended to be worse compared to that of the treated group. The prognostic impact of Rb1 immunolabeling appears to be limited when multiple risk factors are simultaneously assessed. Conclusion: The most effective parameters to predict OS for NEC patients could be Ki-67, pure or mixed/combined morphology, stage, and site.

Real-life challenges using personalized prognostic scoring systems in acute myeloid leukemia.

Personalized medicine is a challenge for patients with acute myeloid leukemia (AML). The identification of several genetic mutations in several AML trials led to the creation of a personalized prognostic scoring algorithm known as the Knowledge Bank (KB). In this study, we assessed the prognostic value of this algorithm on a cohort of 167 real life AML patients. We compared KB predicted outcomes to real-life outcomes. For patients younger than 60-year-old, OS was similar in favorable and intermediate ELN risk category. However, KB algorithm failed to predict OS for younger patients in the adverse ELN risk category and for patients older than 60 years old in the favorable ELN risk category. These discrepancies may be explained by the emergence of several new therapeutic options as well as the improvement of allogeneic stem cell transplantation (aHSCT) outcomes and supportive cares. Personalized medicine is a major challenge and predictions models are powerful tools to predict patient's outcome. However, the addition of new therapeutic options in the field of AML requires a prospective validation of these scoring systems to include recent therapeutic innovations.

Use of High-Plex Data Reveals Novel Insights into the Tumour Microenvironment of Clear Cell Renal Cell Carcinoma.

Although immune checkpoint inhibitors (ICIs) have significantly improved the oncological outcomes, about one-third of patients affected by clear cell renal cell carcinoma (ccRCC) still experience recurrence. Current prognostic algorithms, such as the Leibovich score (LS), rely on morphological features manually assessed by pathologists and are therefore subject to bias. Moreover, these tools do not consider the heterogeneous molecular milieu present in the Tumour Microenvironment (TME), which may have prognostic value. We systematically developed a semi-automated method to investigate 62 markers and their combinations in 150 primary ccRCCs using Multiplex Immunofluorescence (mIF), NanoString GeoMx® Digital Spatial Profiling (DSP) and Artificial Intelligence (AI)-assisted image analysis in order to find novel prognostic signatures and investigate their spatial relationship. We found that coexpression of cancer stem cell (CSC) and epithelial-to-mesenchymal transition (EMT) markers such as OCT4 and ZEB1 are indicative of poor outcome. OCT4 and the immune markers CD8, CD34, and CD163 significantly stratified patients at intermediate LS. Furthermore, augmenting the LS with OCT4 and CD34 improved patient stratification by outcome. Our results support the hypothesis that combining molecular markers has prognostic value and can be integrated with morphological features to improve risk stratification and personalised therapy. To conclude, GeoMx® DSP and AI image analysis are complementary tools providing high multiplexing capability required to investigate the TME of ccRCC, while reducing observer bias.

A preliminary study of tracking B-cell kinetics in patients with lung transplantation by monitoring kappa-deleting recombination excision circles.

This study aims to evaluate humoral immune system response by measuring copy numbers of kappa-deleting recombination excision circles (KREC) gene segment from B lymphocytes in patients with lung transplantation. Between September 2015 and November 2016, a total of 11 patients (8 males, 3 females; mean age: 45.4±12.0 years; range, 23 to 59 years) who underwent lung transplantation with different primary indications were included. The copy numbers of KREC gene segment were quantified using real-time polymerase chain reaction method in peripheral blood samples collected pre- and post-transplantation. The samples of the patients were compared with the KREC l evels i n deoxyribonucleic acid extracted from blood samples of healthy children. There was no significant change in KREC levels between pre- and post-operation (p=0.594 and p=0.657), although the median values indicated that the highest increase in the KREC levels (7x105- 12x105; 85-170) was on Day 7 of transplantation. There was a positive correlation between the KREC levels (mL in blood) and lymphocytes at 24 h after transplantation (p=0.043) and between KREC copies per 106 of blood and age on Day 7. Our preliminary results suggest that KREC l evels a s an indicator of B lymphocyte production are elevated after lung transplantation. A prognostic algorithm by tracking B cell kinetics after post-transplantation for long-term follow-up can be developed following the confirmation of these preliminary results with more patient samples.

Assessment of endogenous intoxication based on the study of medium and low molecular weight substances in patients with myocardial infarction against a background of chronic obstructive pulmonary disease

Introduction. Frequent comorbidity of chronic obstructive pulmonary disease and myocardial infarction is due to the commonality of a number of etiopathogenetic links and the development of endogenous intoxication syndrome. The convincing markers of endogenous intoxication syndrome are medium and low molecular weight molecules including medium and low molecular weight substances and oligopeptides.Aim. To study the levels of medium and low molecular weight substances in patients with myocardial infarction against the background of chronic obstructive pulmonary disease.Materials and methods. 225 patients with myocardial infarction were examined. In 195 patients the infarction developed against the background of COPD, in 130 patients – without COPD. The comparison group consisted of 110 somatically healthy individuals. Substances of medium and low molecular weight (MLMWS) and oligopeptides (OP) were determined by direct spectrometry (according to M.Y. Malakhova, 1995) in plasma, erythrocytes and urine. Endogenous intoxication indices and intoxication coefficient were calculated on the basis of these indices. Statistical processing of the data was performed using SPSS 26.0 software package.Results. In blood plasma and erythrocytes, the levels of average molecules in both studied groups were statistically significantly higher compared to controls. The highest levels were detected in comorbid patients. In the group of patients with myocardial infarction without chronic obstructive pulmonary disease 60% of the examined patients had phase I endogenous intoxication. Phase III intoxication prevailed among comorbid patients with myocardial infarction against chronic obstructive pulmonary disease – 62,6%.Conclusions. Molecules of average mass have proven to be informative indices of endogenous intoxication syndrome in patients with myocardial infarction accompanied by chronic obstructive pulmonary disease. This opens the prospect of using these indices in the development of assessment scales and the creation of prognostic algorithms in patients with cardiorespiratory comorbidity.

Performance of a multi-biomarker panel for prediction of cardiovascular event in patients with chronic kidney disease

BackgroundPatients with chronic kidney disease (CKD) undergoing coronary catheterization are at increased risk of cardiovascular events (CVE). Measuring biomarkers before the procedure may guide clinicians in identifying patients at higher risk of future cardiovascular events. MethodsIn this sub-study the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA), 927 patients underwent coronary catheterization and were followed up for two years. Using machine learning algorithm and targeted proteomics from samples of patients with CKD, 4 biomarkers (kidney injury molecule-1, N-terminal pro B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) were integrated into a prognostic algorithm to predict CVE. Results from the panel are expressed in a graded fashion (CVE higher risk and lower risk) using a data-driven cutoff optimized for balanced sensitivity and specificity. ResultsDuring the 2-year follow-up, 74 CVE were ascertained. 51 (rate: 51/378 = 13.5%) events occurred in stage 1–2 CKD and 23 (rate: 23/68 = 33.8%) events occurred in stage 3–5 CKD. The C-statistic for predicting 2-years cardiovascular events in all 446 patients was 0.77 (0.72, 0.82). The model was well-calibrated (Hosmer-Lemeshow test p-value >0.40). Considering patients at CVE lower-risk within each CKD staging group as a reference, the hazard ratio (95% confidence interval) of cardiovascular events was 2.82 (1.53, 5.22) for CKD stage 1–2/CVE higher-risk, and 8.32 (1.12, 61.76) for CKD stage 3–5/CVE higher-risk. ConclusionMeasuring biomarker panel prior to coronary catheterization may be useful to individualize CVE risk assessment among patients with CKD.

Developing and validating a prognostic model for mortality in patients with heart failure

Abstract Background Heart Failure (HF) constitutes a major public health problem which remains highly prevalent in the UK and globally. Primary care plays a role in the long-term management of patients with heart failure but there are no clinically useful prognostic models to guide primary care professionals in determining those at increased risk of mortality, who could benefit from appropriate advanced care planning or escalation of treatment. Several predictive models exist but due to several limitations, clinical utility of these in the UK are limited. Purpose This study developed and validated a prognostic algorithm, the Nottingham Heart Failure Model, to predict the one-year risk of all-cause mortality in patients with HF. Methods This was a retrospective open cohort study using electronic primary care records from the UK Clinical Practice Research Datalink (CPRD) linked with secondary care records from Hospital Episode Statistics (HES) and death registration data from the Office for National Statistics (ONS). Adults with a diagnosis of heart failure were identified and multivariable Cox regression models were used to identify the risk factors that were independently associated with mortality. A prognostic model was developed to predict the one-year risk of mortality in patients with heart failure, and this was validated by bootstrapping 200 samples of the derivation data. The cohort was then split into even quartiles based on predicted risk scores from the model. These risk groups ranged from one to four with one indicating the quartile of scores with the lowest risk. Kaplan Meier curves were then produced for each group. The Royston D-statistic was then used to assess the discrimination of these quartiles by quantifying the observed separation between these groups. Results A total of 109,577 patients with diagnoses of heart failure were followed up from 1 January 2000 to 31 December. There were 16,437 deaths during the period of follow up. We identified 29 factors which were significantly associated with mortality risk and a prognostic model which incorporated these factors, was developed. The resulting prognostic model obtained a model validation score (C-statistic) of 0.79. The agreement between the observed and predicted proportion of events showed excellent apparent calibration. The D statistic of 1.65 suggests that the model provides a reasonably high amount of prognostic separation. Conclusion(s) Several patient characteristics including comorbidities, treatments, and biomarkers are significantly associated with heart failure mortality. This new model has the potential to accurately predict heart failure mortality within one year and will enable primary care professionals to better risk stratify their patients with heart failure. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The Aziz Foundation

SIOG2022-0014 - Developing a prognostic algorithm to assist in primary treatment decision making of older women with early breast cancer

SIOG2022-0014 - Developing a prognostic algorithm to assist in primary treatment decision making of older women with early breast cancer

Prognosis of LED lumen degradation using Bayesian optimized neural network approach

Light Emitting Diodes (LEDs) are among the most widely used electronic devices for everyday lighting applications due to their durability over incandescent lamps. However, there are no standardized approaches to predict the reliability of LEDs as they are manufactured and tested as per the user requirements and applications. This dramatically limits developing generic prognostic algorithms pertaining to predicting the remaining useful life (RUL) of LEDs. In this study, we propose a Bayesian optimized neural network approach to predict the lumen degradation trends of LEDs. The proposed method does not require an accurate physical model representing the LED degradation behavior and does not require a large amount of degradation data. We have used a particle filter algorithm to train a simple two-layer feedforward neural network model and use the trained model to predict the lumen degradation of LEDs. Also, the weight decay issues commonly encountered in particle filter algorithm are addressed using three different resampling strategies and particle roughening method. To evaluate the effectiveness of the proposed approach, Root Mean Squared Error (RMSE) and Relative Accuracy (RA) were used as the prognostic metrics.

Overall survival in colon cancer patients with different levels of circulating tumor cells and feasibility of enhancing its prognostic value

Development of personalized approaches to diagnosis, treatment and prognosis of colon cancer (CC) still remains challenging. Levels of circulating tumor (CTC) and cancer stem cells (CSC) are promising non-invasive prognostic factors. Our aim was to assess the overall survival (OS) of patients with stage II–IV CC with different levels of CTCs as well as to enhance their prognostic value by additionally determining the level of CD44+ CSCs. Material and methods. The study included 299 patients with stage II–IV CC. All patients underwent surgery followed by adjuvant chemotherapy (FOLFOX). patients with stage IV CC with resectable liver metastases underwent simultaneous resection of the primary tumor and liver metastases, followed by FOLFOX chemotherapy. the proportion of CTCs was evaluated before surgery using Veridex CellSearch™, and the level of CD44+ CSCs was determined in the tissue of the removed tumor by the IHC method. OS was studied in patients with different CTC levels, cumulative OS was calculated by Kaplan–Meier`s method. prognostic algorithm was designed by logistic regression analysis and cox proportional hazards model. Results. OS was found to be lower in patients with higher CTC levels divided into ranges: 0, 1–3, 4–9, ≥10 (χ2=11.59, p=0.009); thus enabling us to use it for prognosis. its prognostic value is enhanced by estimation of CD44+ CSC in tumor. Statistically significant conjugation between CTC and CD44+ ranges was found. an increase in CTC level by 1 range resulted in the increase in the risk of fatal outcome by 1.58 times (р=0.002); the additive increase in CD44+ expression ≥10 % resulted in the increase in the risk of fatal outcome by 7.2 times (p&lt;0.001). For individual risk assessment, a model for calculating the prognostic coefficient K with high diagnostic sensitivity and specificity was developed, and its mathematical expression was proposed. the value of K≥0.411 indicates a high risk of adverse outcome. Conclusion. The prognostic algorithm for the risk of unfavorable outcome of patients with CC, based on the assessment of CTC and CD44+ CSC levels, was developed.

Clinical Impact of Monoclonal Antibodies in the Treatment of High-Risk Patients with SARS-CoV-2 Breakthrough Infections: The ORCHESTRA Prospective Cohort Study.

The clinical impact of anti-spike monoclonal antibodies (mAb) in Coronavirus Disease 2019 (COVID-19) breakthrough infections is unclear. We present the results of an observational prospective cohort study assessing and comparing COVID-19 progression in high-risk outpatients receiving mAb according to primary or breakthrough infection. Clinical, serological and virological predictors associated with 28-day COVID-19-related hospitalization were identified using multivariate logistic regression and summarized with odds ratio (aOR) and 95% confidence interval (CI). A total of 847 COVID-19 outpatients were included: 414 with primary and 433 with breakthrough infection. Hospitalization was observed in 42/414 (10.1%) patients with primary and 8/433 (1.8%) patients with breakthrough infection (p < 0.001). aOR for hospitalization was significantly lower for breakthrough infection (aOR 0.12, 95%CI: 0.05–0.27, p < 0.001) and higher for immunocompromised status (aOR:2.35, 95%CI:1.08–5.08, p = 0.003), advanced age (aOR:1.06, 95%CI: 1.03–1.08, p < 0.001), and male gender (aOR:1.97, 95%CI: 1.04–3.73, p = 0.037). Among the breakthrough infection group, the median SARS-CoV-2 anti-spike IgGs was lower (p < 0.001) in immunocompromised and elderly patients >75 years compared with that in the immunocompetent patients. Our findings suggest that, among mAb patients, those with breakthrough infection have significantly lower hospitalization risk compared with patients with primary infection. Prognostic algorithms combining clinical and immune-virological characteristics are needed to ensure appropriate and up-to-date clinical protocols targeting high-risk categories.

Self-Cognizant Prognostics for the Design and Implementation of Mission-Critical Telemedicine Systems under the Influence of Heavy Rainfall

This article discusses the design and implementation strategy for highly reliable communication systems that support autonomous ambulances on an unmanned aerial vehicle platform. Statistics show that more accidents occur during heavy rainfall, but radio links that operate in excess of 10 GHz are particularly prone to rain-induced attenuation and depolarization. Maximizing quality of service to provide reliable wireless links for telemedicine systems is therefore an important issue to be thoroughly addressed in optimizing system reliability. A prognostics and network health management framework for automated adjustment of the link and system margins is proposed, based on statistical results of point rainfall attenuation obtained from long-term measurement and scattering with a case study of a 39 GHz signal propagating through rain. The results are applied to a self-cognizant prognostics algorithm for smart autonomous ambulances that support critical operations across difficult terrain. Near forward scattering of 58° and near backward scattering of 175°, as well as perpendicular scattering, were studied. This provides important insights into implementing self-cognizant prognostics system resource management such that 5G-based, as well as moving toward 6G, telemedicine systems can be optimized for reliability given the appropriate system fade margin derived from the measurement results.

Prognostic Gene Expression-Based Signature in Clear-Cell Renal Cell Carcinoma.

Simple SummaryIn this study, we identified molecular markers for disease progression from ccRCC tissue samples. Using the selected biomarkers and clinical data from the TCGA cohort, we developed a gene expression-based signature which enhances the prognostic prediction of clinicopathological variables and could help to provide personalized disease management.The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and non-progressive ccRCCs using Illumina Hi-seq 4000. Differentially expressed genes (DEG) were intersected with the RNA-sequencing data from the TCGA. The overlapping genes were used for further analysis. A total of 132 genes were found to be prognosis-related genes. LASSO regression enabled the development of the best prognostic six-gene panel. Cox regression analyses were performed to identify independent clinical prognostic parameters to construct a combined nomogram which includes the expression of CERCAM, MIA2, HS6ST2, ONECUT2, SOX12, TMEM132A, pT stage, tumor size and ISUP grade. A risk score generated using this model effectively stratified patients at higher risk of disease progression (HR 10.79; p < 0.001) and cancer-specific death (HR 19.27; p < 0.001). It correlated with the clinicopathological variables, enabling us to discriminate a subset of patients at higher risk of progression within the Stage, Size, Grade and Necrosis score (SSIGN) risk groups, pT and ISUP grade. In summary, a gene expression-based prognostic signature was successfully developed providing a more precise assessment of the individual risk of progression.

Are We Overtreating Patients With T1a HER2+ Breast Cancer? An Analysis of the National Cancer Database

IntroductionThe potential benefit of systemic therapy in patients with T1a HER2+ cancers is not well understood, and no consensus guidelines exist. We sought to investigate practice patterns of chemotherapy use in this population. MethodsFrom the National Cancer Database (2013-2018), we identified female patients with HER2+ cancers staged as cT1aN0 or pT1aN0 and stratified by receipt of chemotherapy. Using univariate and multivariable analyses we assessed the clinicopathologic features associated with the receipt of chemotherapy. We also compared rates of overall survival (OS). ResultsOf 5176 women with cT1aN0 HER2+ cancers, 88 (2%) received neoadjuvant chemotherapy. Younger age and hormone-receptor (HR) negative tumors were factors independently associated with receipt of neoadjuvant chemotherapy (all P < .001). Of 11,688 women with pT1aN0 HER2+ cancers, 5,588 (48%) received adjuvant chemotherapy. Rates of use increased over the analysis period from 39% in 2013 to 53% in 2018 (P < .001). Factors independently associated with receipt of adjuvant chemotherapy included younger age, having a poorly differentiated tumor, exhibiting lymphovascular invasion, undergoing adjuvant radiation (all P < .001). There were no differences in OS when comparing those who did and did not receive chemotherapy in either group. ConclusionsThe use of chemotherapy in patients with HER2+ T1a cancers is increasing over time and is, as expected, more common among patients with unfavorable clinicopathologic features. Since no prognostic algorithm currently exists, more prospective data is needed to understand which of these patients may derive benefit from systemic therapy and which may safely avoid the morbidity of chemotherapy.