Improved web-based calculators for predicting breast carcinoma outcomes

Tata Memorial Centre Evidence Based Management of Breast cancer

ABSTRACT

The incidence of breast cancer is increasing rapidly in urban India due to the changing lifestyle and exposure to risk factors. Diagnosis at an advanced stage and in younger women are the most concerning issues of breast cancer in India. Lack of awareness and social taboos related to cancer diagnosis make women feel hesitant to seek timely medical advice. As almost half of women develop breast cancer at an age younger than 50 years, breast cancer diagnosis poses a huge financial burden on the household and impacts the entire family. Moreover, inaccessibility, unaffordability, and high out-of-pocket expenditure make this situation grimmer. Women find it difficult to get quality cancer care closer to their homes and end up traveling long distances for seeking treatment. Significant differences in the cancer epidemiology compared to the west make the adoption of western breast cancer management guidelines challenging for Indian women. In this article, we intend to provide a comprehensive review of the management of breast cancer from diagnosis to treatment for both early and advanced stages from the perspective of low-middle-income countries. Starting with a brief introduction to epidemiology and guidelines for diagnostic modalities (imaging and pathology), treatment has been discussed for early breast cancer (EBC), locally advanced, and MBC. In-depth information on loco-regional and systemic therapy has been provided focusing on standard treatment protocols as well as scenarios where treatment can be de-escalated or escalated.

An easy, fast, and accurate way for implementing the standards of care for the management of patients with endometrial carcinoma into daily clinical practice: the ESGO mobile app

The European Society of Gynaecological Oncology (ESGO), the European Society for Radiotherapy and Oncology (ESTRO), and the European Society of Pathology (ESP) jointly published comprehensive evidence-based guidelines on all relevant issues of diagnosis and treatment in endometrial carcinoma in a multidisciplinary setting. In order to improve their implementation, a free downloadable easy-to-use mobile app was developed.Two interactive decision tools were created for (1) helping users to identify the recommended surgical steps, especially in terms of nodal staging approach based on the pre-operatively assumed risk group (tool #1), and (2) to facilitate prognostic risk group allocation and adjuvant treatment decision-making after primary surgery integrating both clinicopathological and molecular markers (if known) (tool #2). Algorithms and readable guidelines were also incorporated into the mobile app on all relevant issues of diagnosis and treatment. The scientific content presented in the app will be updated and modified in the future based on new evidence and user feedback.This article presents the decision tools and two practical examples of using these calculators to illustrate that the ESGO mobile app (available without the necessity of an internet connection) can provide fast and accurate responses to complex clinical questions that require the evaluation of numerous parameters.

Survival outcomes of breast cancer patients with recurrence after surgery according to period and subtype

PURPOSE

To analyze and compare the survival rates of recurrent breast cancer patients in Korea between two periods (period I: 2000-2007; period II: 2008-2013) and to identify the factors associated with outcomes and changes over time in the duration of survival after recurrence.

METHODS

We retrospectively analyzed 2,407 patients who had recurrent breast cancer with treated between January 2000 and December 2013 and divided them into two periods according to the year of recurrence. We reviewed the age at diagnosis, clinical manifestations, pathology report, surgical methods, types of adjuvant treatment, type of recurrence, and follow-up period.

RESULTS

The median follow-up was 30.6 months (range, 0-223.4) from the time of relapse, and the median survival time was 42.3 months. Survival after recurrence (SAR) significantly improved from 38.0 months in period I to 49.7 months in period II (p < 0.001). In the analysis performed according to the hormone receptor and HER2 status subtypes, all subtypes except the triple-negative subtype showed higher SAR in period II than period I. Age at diagnosis, tumor stage, and treatment after recurrence were significantly correlated with survival outcomes.

CONCLUSION

The survival outcomes of Korean patients with breast cancer after the first recurrence have improved in Korea. Such improvements may be attributed to advances in treatment.

A Comparison between the Online Prognostic Tool PREDICT and myBeST for Women with Breast Cancer in Malaysia

The PREDICT breast cancer is a well-known online calculator to estimate survival probability. We developed a new prognostic model, myBeST, due to the PREDICT tool’s limitations when applied to our patients. This study aims to compare the performance of the two models for women with breast cancer in Malaysia. A total of 532 stage I to III patient records who underwent surgical treatment were analysed. They were diagnosed between 2012 and 2016 in seven centres. We obtained baseline predictors and survival outcomes by reviewing patients’ medical records. We compare PREDICT and myBeST tools’ discriminant performance using receiver-operating characteristic (ROC) analysis. The five-year observed survival was 80.3% (95% CI: 77.0, 83.7). For this cohort, the median five-year survival probabilities estimated by PREDICT and myBeST were 85.8% and 82.6%, respectively. The area under the ROC curve for five-year survival by myBeST was 0.78 (95% CI: 0.73, 0.82) and for PREDICT was 0.75 (95% CI: 0.70, 0.80). Both tools show good performance, with myBeST marginally outperforms PREDICT discriminant performance. Thus, the new prognostic model is perhaps more suitable for women with breast cancer in Malaysia.

myBeST-A Web-Based Survival Prognostic Tool for Women with Breast Cancer in Malaysia: Development Process and Preliminary Validation Study

Women with breast cancer are keen to know their predicted survival. We developed a new prognostic model for women with breast cancer in Malaysia. Using the model, this study aimed to design the user interface and develop the contents of a web-based prognostic tool for the care provider to convey survival estimates. We employed an iterative website development process which includes: (1) an initial development stage informed by reviewing existing tools and deliberation among breast surgeons and epidemiologists, (2) content validation and feedback by medical specialists, and (3) face validation and end-user feedback among medical officers. Several iterative prototypes were produced and improved based on the feedback. The experts (n = 8) highly agreed on the website content and predictors for survival with content validity indices ≥ 0.88. Users (n = 20) scored face validity indices of more than 0.90. They expressed favourable responses. The tool, named Malaysian Breast cancer Survival prognostic Tool (myBeST), is accessible online. The tool estimates an individualised five-year survival prediction probability. Accompanying contents were included to explain the tool's aim, target user, and development process. The tool could act as an additional tool to provide evidence-based and personalised breast cancer outcomes.

The majority of 922 prediction models supporting breast cancer decision-making are at high risk of bias

OBJECTIVES

To systematically review the currently available prediction models that may support treatment decision-making in breast cancer.

STUDY DESIGN AND SETTING

Literature was systematically searched to identify studies reporting on development of prediction models aiming to support breast cancer treatment decision-making, published between January 2010 and December 2020. Quality and risk of bias were assessed using the Prediction model Risk Of Bias (ROB) Assessment Tool (PROBAST).

RESULTS

After screening 20,460 studies, 534 studies were included, reporting on 922 models. The 922 models predicted: mortality (n = 417 45%), recurrence (n = 217, 24%), lymph node involvement (n = 141, 15%), adverse events (n = 58, 6%), treatment response (n = 56, 6%), or other outcomes (n = 33, 4%). In total, 285 models (31%) lacked a complete description of the final model and could not be applied to new patients. Most models (n = 878, 95%) were considered to contain high ROB.

CONCLUSION

A substantial overlap in predictor variables and outcomes between the models was observed. Most models were not reported according to established reporting guidelines or showed methodological flaws during the development and/or validation of the model. Further development of prediction models with thorough quality and validity assessment is an essential first step for future clinical application.

Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia

Prediction of survival probabilities based on models developed by other countries has shown inconsistent findings among Malaysian patients. This study aimed to develop predictive models for survival among women with breast cancer in Malaysia. A retrospective cohort study was conducted involving patients who were diagnosed between 2012 and 2016 in seven breast cancer centres, where their survival status was followed until 31 December 2021. A total of 13 predictors were selected to model five-year survival probabilities by applying Cox proportional hazards (PH), artificial neural networks (ANN), and decision tree (DT) classification analysis. The random-split dataset strategy was used to develop and measure the models' performance. Among 1006 patients, the majority were Malay, with ductal carcinoma, hormone-sensitive, HER2-negative, at T2-, N1-stage, without metastasis, received surgery and chemotherapy. The estimated five-year survival rate was 60.5% (95% CI: 57.6, 63.6). For Cox PH, the c-index was 0.82 for model derivation and 0.81 for validation. The model was well-calibrated. The Cox PH model outperformed the DT and ANN models in most performance indices, with the Cox PH model having the highest accuracy of 0.841. The accuracies of the DT and ANN models were 0.811 and 0.821, respectively. The Cox PH model is more useful for survival prediction in this study's setting.

A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Aims

Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2− breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts.

Methods

Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41–5.4), and poor (PPG  > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient.

Results

Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278–0.346)]/PREDICT [κ = 0.398 (0.35–0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB.

Conclusion

Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.

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

Background

Objective

Methods

Results

Conclusions

Prognostic difficulties of men with breast cancer

Most adequately powered studies confirm a worse prognosis for males versus matched females with breast cancer. There is in-stage migration for stage I cancers with a different ratio of tumor/normal breast tissue in males. Younger men have a better prognosis, largely the result of increased morbidity in the elderly, exacerbated by smoking, low socioeconomic differences, and ethnic disparity. BRCA2 carriers with MBC have a worse outcome than noncarriers as do men with amplification of EMSY. Men with tumors having a high cytosol level of plasminogen activator inhibitor 1 (PAI-1) may have more invasive cancers leading to earlier spread and hence a worse outcome. PREDICT+ is a useful prognostic model for MBC and multigene testing enables more specific systemic therapies to be used.

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

Several prognosis prediction models have been developed for breast cancer (BC) patients with curative surgery, but there is still an unmet need to precisely determine BC prognosis for individual BC patients in real time. This is a retrospectively collected data analysis from adjuvant BC registry at Samsung Medical Center between January 2000 and December 2016. The initial data set contained 325 clinical data elements: baseline characteristics with demographics, clinical and pathologic information, and follow-up clinical information including laboratory and imaging data during surveillance. Weibull Time To Event Recurrent Neural Network (WTTE-RNN) by Martinsson was implemented for machine learning. We searched for the optimal window size as time-stamped inputs. To develop the prediction model, data from 13,117 patients were split into training (60%), validation (20%), and test (20%) sets. The median follow-up duration was 4.7 years and the median number of visits was 8.4. We identified 32 features related to BC recurrence and considered them in further analyses. Performance at a point of statistics was calculated using Harrell's C-index and area under the curve (AUC) at each 2-, 5-, and 7-year points. After 200 training epochs with a batch size of 100, the C-index reached 0.92 for the training data set and 0.89 for the validation and test data sets. The AUC values were 0.90 at 2-year point, 0.91 at 5-year point, and 0.91 at 7-year point. The deep learning-based final model outperformed three other machine learning-based models. In terms of pathologic characteristics, the median absolute error (MAE) and weighted mean absolute error (wMAE) showed great results of as little as 3.5%. This BC prognosis model to determine the probability of BC recurrence in real time was developed using information from the time of BC diagnosis and the follow-up period in RNN machine learning model.

Individualized Prediction of Breast Cancer Survival Using Flexible Parametric Survival Modeling: Analysis of a Hospital-Based National Clinical Cancer Registry

Simple Summary

Prognostication of breast cancer patients is essential for risk communication and clinical decision-making. Many clinical tools for the survival prediction of breast cancer patients have been developed over the years. However, most of them were developed from Western countries. Studies have shown that these tools did not perform well in other ethnicities, such as Asian populations, including Thai. This study developed a new prediction model for survival predictions using modern statistical methods that allow a more accurate estimation of the baseline survival. The model was entitled the Individualized Prediction of Breast cancer Survival or the IPBS model. It contains twelve routinely available predictors that oncologists usually evaluate in daily practice. The survival information provided by the model was proven to be acceptably accurate and might be useful for physicians and patients, especially in Thailand or other Asian countries, to arrive at the most appropriate management plan.

Abstract

Prognostic models for breast cancer developed from Western countries performed less accurately in the Asian population. We aimed to develop a survival prediction model for overall survival (OS) and disease-free survival (DFS) for Thai patients with breast cancer. We conducted a prognostic model research using a multicenter hospital-based cancer clinical registry from the Network of National Cancer Institutes of Thailand. All women diagnosed with breast cancer who underwent surgery between 1 January 2010 and 31 December 2011 were included in the analysis. A flexible parametric survival model was used for developing the prognostic model for OS and DFS prediction. During the study period, 2021 patients were included. Of these, 1386 patients with 590 events were available for a complete-case analysis. The newly derived individualized prediction of breast cancer survival or the IPBS model consists of twelve routinely available predictors. The C-statistics from the OS and the DFS model were 0.72 and 0.70, respectively. The model showed good calibration for the prediction of five-year OS and DFS. The IPBS model provides good performance for the prediction of OS and PFS for breast cancer patients. A further external validation study is required before clinical implementation.

Cost-effectiveness analysis of radiotherapy techniques for whole breast irradiation

Background

The current standard of care (SOC) for whole breast radiotherapy (WBRT) in the US is conventional tangential photon fields. Advanced WBRT techniques may provide similar tumor control and better normal tissue sparing, but it is controversial whether the medical benefits of an advanced technology are significant enough to justify its higher cost.

Objective

To analyze the cost-effectiveness of six advanced WBRT techniques compared with SOC.

Methods

We developed a Markov model to simulate health states for one cohort of women (65-year-old) with early-stage breast cancer over 15 years after WBRT. The cost effectiveness analyses of field-in-field (FIF), hybrid intensity modulated radiotherapy (IMRT), full IMRT, standard volumetric modulated arc therapy (STD-VMAT), multiple arc VMAT (MA-VMAT), non-coplanar VMAT (NC-VMAT) compared with SOC were performed with both tumor control and radiogenic side effects considered. Transition probabilities and utilities for each health state were obtained from literature. Costs incurred by payers were adopted from literature and Medicare data. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER) were calculated. One-way sensitivity analyses and probabilistic sensitivity analyses (PSA) were performed to evaluate the impact of uncertainties on the final results.

Results

FIF has the lowest ICER value of 1,511 $/QALY. The one-way analyses show that the cost-effectiveness of advanced WBRT techniques is most sensitive to the probability of developing contralateral breast cancer. PSAs show that SOC is more cost effective than almost all advanced WBRT techniques at a willingness-to-pay (WTP) threshold of 50,000 $/QALY, while FIF, hybrid IMRT and MA-VMAT are more cost-effective than SOC with a probability of 59.2%, 72.3% and 72.6% at a WTP threshold of 100,000 $/QALY, respectively.

Conclusions

FIF might be the most cost-effective option for WBRT patients at a WTP threshold of 50,000 $/QALY, while hybrid IMRT and MA-VMAT might be the most cost-effective options at a WTP threshold of 100,000 $/QALY.

Challenges in Adjuvant Therapy for Premenopausal Women Diagnosed With Luminal Breast Cancers

More than 90% of women with newly diagnosed breast cancer present with stage I to III disease and, with optimal multidisciplinary therapy, are likely to survive their disease. Of these patients, 70% are hormone receptor-positive and candidates for adjuvant endocrine therapy. The adoption of cumulatively better adjuvant treatments contributed to improved outcomes in patients with hormone receptor-positive, early-stage breast cancer. Premenopausal women with hormone receptor-positive breast cancer often present with complex disease and have inferior survival outcomes compared with their postmenopausal counterparts. Risk stratification strategies, including classic clinicopathologic features and newer gene expression assays, can assist in treatment decisions, including adjuvant chemotherapy use and type or duration of endocrine therapy. Gene expression assays may help identify patients who can safely forgo chemotherapy, although to a lesser extent among premenopausal patients, in whom they may play a role only in node-negative disease. Patients at lower risk of recurrence can be adequately treated with tamoxifen alone, whereas higher-risk patients benefit from ovarian function suppression with tamoxifen or an aromatase inhibitor. The role of adding newer therapies such as CDK4/6 inhibitors to adjuvant endocrine therapy is not yet clear. Breast cancer treatments are associated with several side effects, with major impact on patients' quality of life and treatment adherence, particularly in premenopausal women for whom these side effects may be more prominent as the result of the abrupt decrease in estrogen concentrations. Personalized management of treatment side effects, addressing patients' concerns, and health promotion should be an integral part of the care of premenopausal women diagnosed with luminal breast cancers.

Mammographic density changes following BC treatment

BACKGROUND

Mammographic density (MD) reduction is associated with lower risk of breast cancer (BC) recurrence and may be used as a marker of treatment outcome; however, trends in MD following BC therapies and the factors associated with such trends are poorly understood. The aim of this study was to investigate MD changes following BC treatment and the factors associated with these changes.

METHODS

A total of 226 BC-affected patients who received BC treatments were examined. MD was assessed by the Laboratory for individualized Radiodensity Assessment (LIBRA) software. A Wilcoxon ranked signed test was used to investigate the differences in MD before and after treatment and median independent test to assess the associated factors.

RESULTS

Significant differences in MD between baseline and follow-up mammograms were observed for all MD measures: percent density (p ≤ 0.005), dense area (p ≤ 0.004), and nondense area (p ≤ 0.02). After adjustment, these differences were more pronounced among younger at BC diagnosis (p ≤ 0.001), premenopausal (p ≤ 0.003), and obese women (p ≤ 0.05). Changes in MD were evident regardless of the treatment regimen. MD reduction was observed among patients with high baseline MD (p < 0.001), younger at BC diagnosis (p ≤ 0.04), premenopausal (p < 0.001), and normal body mass index (p = 0.04). Patients who experienced an increase in nondense area had high percent density at baseline (p ≤ 0.001).

CONCLUSION

Two different MD changes were observed over time: MD increase and decrease. Baseline MD, menopausal status, age at BC diagnosis, and body mass index influenced these changes.

Modeling breast cancer survival and metastasis rates from moderate-sized clinical data

Predicting time-dependent survival probability of a breast cancer patient using information such as primary tumor size, grade, node spread status, and patient age at the time of surgery can be of immense help in managing life expectations and strategizing postoperative treatment. However, for moderate-sized clinical datasets the application of standard Kaplan-Meier theory to determine survival probability as a function of multiple cofactors can become challenging when continuous variables like tumor diameter and survival time are segmented into a large number of narrow intervals, a problem commonly termed the curse of dimensionality. We circumvent this problem by modeling the patient-to-patient distribution of primary tumor diameter with a realistic, right-skewed function, and then matching the diameter-marginalized survival with the mean Kaplan-Meier survival for the data. We apply this procedure on a recent clinical data from 1875 breast cancer patients and develop parameters that can be readily used to estimate post-surgery survival for an arbitrary time length. Finally, we show that the observed fraction of node-positive patients can be quantitatively explained within a simple tumor growth and metastasis framework. Employing two different tumor growth models from the literature (i.e., Gompertz and logistic growth models), we utilize the observed fraction-node-positive data to determine metastasis rates from the surface of a primary tumor and its patient-to-patient distribution.

Management of hormone receptor-positive, HER2-negative early breast cancer

The majority of breast cancers are diagnosed at an early stage and are hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative. Significant advances have been made in the management of early stage HR-positive, HER2-negative breast cancer, resulting in improved survival outcomes. In this review, we discuss important factors to consider in the management of this disease. In particular, we discuss the role of adjuvant endocrine therapy, specific endocrine therapy agents, the duration of adjuvant endocrine therapy, treatment-related side effects, and the role of genomic assays and other biomarkers when considering treatment recommendations for individuals with HR-positive, HER2-negative early breast cancer. Finally, we address emerging data to individualize therapeutic decision-making and provide future considerations.

A Comparison Between the Online Prediction Models CancerMath and PREDICT as Prognostic Tools in Thai Breast Cancer Patients

Background and Purpose

Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients.

Patients and Methods

A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis.

Results

For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p<0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p<0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p<0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65-0.82) and 0.75 (95% CI; 0.68-0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71-0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71-0.84).

Conclusion

CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.

Life Expectancy after Curative-intent Treatment of Breast Cancer: Impact on Long-term Follow-up Care

Long-term survival rates after treatment for breast cancer are directly influenced by early deaths resulting from disease. For longer-term breast cancer survivors, survival rates appear deceptively low. We hypothesize that the actual survival curve for long-term survivors approaches the overall survival of the general population. The Surveillance, Epidemiology, and End Results database (1988 to 2002) was used to identify patients with nonmetastatic breast cancer who underwent definitive surgical treatment. The survival of the general population was constructed by using national life tables with an age-matched population. Comparisons of survivals were made for 3-, 5-, and 7-year breast cancer survivor cohorts. Of 237,180 patients, 92.4 per cent survived three years, 82.1 per cent five years, and 58.1 per cent seven years. Stage I patients have equivalent or better survivals compared with the age-matched general population in all three cohorts. Stage II patients reached equivalent conditional survival between eight and nine years after diagnosis regardless of cohort. Stage III patients required achieving nine to 10 years after diagnosis to achieve equivalent survival probability, except in 7-year survivors, in whom 10 to 11 years was required. In all stages, once equivalence was reached, survival exceeded the general population over the remaining years. Initial cancer stage influences overall survival for many years after diagnosis. Patients with Stage I cancer return to the general population risk as early as three years after diagnosis, whereas higher stages can require up to nine years to achieve parity with a more generalized population. These findings should be factored into general health screening issues for long-term breast cancer survivors.

Evaluation of growth production factors as predictors of the incidence and severity of white striping and woody breast in broiler chickens

White striping (WS) and woody breast (WB) have been previously associated with older and heavier birds. However, there is limited information supporting the association between these 2 muscle conditions and growth parameters. The objectives of this study were 1) to investigate the relationship between WS and WB using different growth production factors and 2) to propose a predictive model that uses growth production factors to investigate the incidence and severity of WS and WB. A combined database of 4,332 broilers pooled from 7 research experiments conducted from 2016 to 2017 at Texas A&M University was used in this study. Parameters such as sex, age (4 wk, 6 wk, and 8 wk), strain (standard A vs. high-breast-yield [B and C]), live weight categories (500 g increments), and breast weight categories (250 g increments) were included in the model. Results showed that WS was 12% more likely to be present in non-WB fillets. The association between WS and WB suggests a moderate relationship between the ranks of both outcome variables (ρ = 0.57, P < 0.0001). Variables such as age, live weight, and sex were not as important as breast weight and strain in the severity prediction of WS and WB. Butterfly fillets above 750 g and with high-breast-yielding strains were more likely associated with higher severity of WS and WB scores. No post hoc variable selection was performed. Both models show good discrimination. The WS model produced an uncorrected area under the curve (AUC) of 0.739, with a bootstrap corrected estimate of 0.736. The WB model produced an uncorrected AUC of 0.753 and a bootstrap corrected estimate of 0.752. Therefore, the growth production factors analyzed in this study indicated that there is a moderate relationship between WS and WB myopathies and were jointly predictive of the severity of WS and WB. Potentially other factors not included in this study may play a major role in the relationship of these 2 myopathies. More research should be done to investigate this possibility.

Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice

We developed prognostic models for breast cancer-specific survival (BCSS) that consider anatomic stage and other important determinants of prognosis and survival in breast cancer, such as age, grade, and receptor-based subtypes with the intention to demonstrate that these factors, conditional on stage, improve prediction of BCSS. A total of 20,928 patients with stage I–III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1990 and 2016, who received surgery as an initial treatment were identified to generate prognostic models by Fine-Gray competing risk regression model. Model predictive accuracy was assessed using Harrell’s C-index. The Aalen–Johansen estimator and a selected Fine–Gray model were used to estimate the 5-year and 10-year BCSS probabilities. The performance of the selected model was evaluated by assessing discrimination and prediction calibration in an external validation dataset of 29,727 patients from the National Comprehensive Cancer Network (NCCN). The inclusion of age, grade, and receptor-based subtype in addition to stage significantly improved the model predictive accuracy (C-index: 0.774 (95% CI 0.755–0.794) vs. 0.692 for stage alone, p < 0.0001). Young age (<40), higher grade, and TNBC subtype were significantly associated with worse BCSS. The selected model showed good discriminative ability but poor calibration when applied to the validation data. After recalibration, the predictions showed good calibration in the training and validation data. More refined BCSS prediction is possible through a model that has been externally validated and includes clinical and biological factors.

Breast Cancer Characteristics and Survival among Users versus Nonusers of Raloxifene

Raloxifene reduces breast cancer incidence. However, it is unclear whether it also reduces mortality from breast cancer. For raloxifene to reduce incidence but not mortality, breast cancer survival would have to be worse for raloxifene users than nonusers. Surveillance Epidemiology and End Results-Medicare was used to identify women with invasive breast cancer diagnosed from 2007 to 2015 at ages 65-89 who had prior Medicare Part D (prescription drug) enrollment; breast cancer characteristics and survival were assessed among raloxifene regular users (≥180 days in past year) versus nonusers. Logistic regression was used to assess cancer characteristics. Two methods utilizing proportional hazards models were employed to assess breast cancer-specific survival. In method 1, survival was assessed adjusting for demographics, mammography use, and chronic conditions in the subset with Medicare fee-for-service enrollment. In method 2, predicted survival as a function of breast cancer characteristics was modeled in nonusers and the model applied to users to predict survival. A total of 116,317 raloxifene nonusers and 1,223 regular users were identified. Users were significantly more likely to have hormone receptor (HR)-negative cancers, but less likely to have T2+, N1+, and metastatic disease. There were 10,869 and 101 breast cancer-related deaths in nonusers and regular users, respectively. The HR (users vs. nonusers) for breast cancer-specific survival in method 1 was 0.94 (95% confidence interval, 0.73-1.22). In method 2, predicted survival was higher in users than nonusers (8-year survival 84.9% vs. 83.4%). Breast cancer-specific survival in raloxifene users was not worse than in nonusers, providing indirect evidence that raloxifene reduces breast cancer-related mortality.

The use of a predictive statistical model to make a virtual control arm for a clinical trial

Background

Randomized clinical trials compare participants receiving an experimental intervention to participants receiving standard of care (SOC). If one could predict the outcome for participants receiving SOC, a trial could be designed where all participants received the experimental intervention, with the observed outcome of the experimental group compared to the prediction for those individuals.

Methods

We used the CancerMath calculator to predict outcomes for participants in two large clinical trials of adjuvant chemotherapy for breast cancer: NSABPB15 and CALGB9344. NSABPB15 was the training set, and we used the modified algorithm to predict outcomes for two groups from CALGB9344: one which received standard of care (SOC) chemotherapy and one which received paclitaxel in addition. We made a prediction for each individual CALGB9344 participant, assuming each received only SOC.

Results

The predicted outcome for the group which received only SOC matched what was observed in the CALGB9344 trial. In contrast, the predicted outcome for the group also receiving paclitaxel was significantly worse than what was observed for this group. This matches the conclusion of CALGB9344 that adding paclitaxel to SOC improves survival.

Conclusion

This project proves that a statistical model can predict the outcome of clinical trial participants treated with SOC. In some circumstances, a predictive model could be used instead of a control arm, allowing all participants to receive experimental treatment. Predictive models for cancer and other diseases could be constructed using the vast amount of outcomes data available to the federal government, and made available for public use.

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

Background

Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform (https://www.evidencio.com) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation.

Methods

Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation.

Results

Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods.

Conclusions

This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0761-5) contains supplementary material, which is available to authorized users.

Development and validation of nomograms predicting survival in Chinese patients with triple negative breast cancer

Background

Triple negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Nomograms predicting outcomes of TNBC are needed for risk management.

Methods

Nomograms were based on an analysis of 296 non-metastatic TNBC patients treated at Sun Yat-sen Memorial Hospital from 2002 to 2014. The end points were disease-free survival (DFS) and overall survival (OS). Predictive accuracy and discriminative ability were evaluated by concordance index (C-index), area under the curve (AUC) and calibration curve, and compared with the American Joint Committee on Cancer (AJCC) staging system, PREDICT and CancerMath. Models were subjected to bootstrap internal validation and external validation using independent cohorts of 191 patients from the second Xiangya Hospital and Peking University Shenzhen Hospital between 2007 and 2012.

Results

On multivariable analysis of training cohort, independent prognostic factors were stromal tumor-infiltrating lymphocytes (TILs), tumor size, node status, and Ki67 index, which were then selected into the nomograms. The calibration curves for probability of DFS and OS showed optimal agreement between nomogram prediction and actual observation. The C-index of nomograms was significantly higher than that of the seventh and eighth AJCC staging system for predicting DFS (training: 0.743 vs 0.666 (P = 0.003) and 0.664 (P = 0.024); validation: 0.784 vs 0.632 (P = 0.02) and 0.607 (P = 0.002)) and OS (training: 0.791 vs 0.683 (P = 0.004) and 0.677 (P < 0.001); validation: 0.783 vs 0.656 (P = 0.006) and 0.606 (P = 0.001)). Our nomograms had larger AUCs compared with PREDICT and CancerMath. In addition, the nomograms showed good performance in stratifying different risk groups of patients both in the training and validation cohorts.

Conclusion

We have developed novel and practical nomograms that can provide individual prediction of DFS and OS for TNBC based on stromal TILs, tumor size, node status, and Ki67 index. Our nomograms may help clinicians in risk consulting and selection of long term survivors.

Electronic supplementary material

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Communicating prognosis to women with early breast cancer - overview of prediction tools and the development and pilot testing of a decision aid

Background

Shared decision-making in oncology requires information on individual prognosis. This comprises cancer prognosis as well as competing risks of dying due to age and comorbidities. Decision aids usually do not provide such information on competing risks. We conducted an overview on clinical prediction tools for early breast cancer and developed and pilot-tested a decision aid (DA) addressing individual prognosis using additional chemotherapy in early, hormone receptor-positive breast cancer as an example.

Methods

Systematic literature search on clinical prediction tools for the effects of drug treatment on survival of breast cancer. The DA was developed following criteria for evidence-based patient information and International Patient Decision Aids Standards. We included data on the influence of age and comorbidities on overall prognosis. The DA was pilot-tested in focus groups. Comprehension was additionally evaluated through an online survey with women in breast cancer self-help groups.

Results

We identified three prediction tools: Adjuvant!Online, PREDICT and CancerMath. All tools consider age and tumor characteristics. Adjuvant!Online considers comorbidities, CancerMath displays age-dependent non-cancer mortality. Harm due to therapy is not reported.

Twenty women participated in focus groups piloting the DA until data saturation was achieved. A total of 102 women consented to participate in the online survey, of which 86 completed the survey. The rate of correct responses was 90.5% and ranged between 84 and 95% for individual questions.

Conclusions

None of the clinical prediction tools fulfilled the requirements to provide women with all the necessary information for informed decision-making. Information on individual prognosis was well understood and can be included in patient decision aids.

Electronic supplementary material

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Prognostic models for breast cancer: a systematic review

BACKGROUND

Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.

METHODS

We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.

RESULTS

From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.

CONCLUSIONS

Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

Considering the biology of late recurrences in selecting patients for extended endocrine therapy in breast cancer

Extended endocrine therapy can reduce recurrences occurring more than 5 years after diagnosis (late recurrences) in estrogen receptor (ER)-positive breast cancer. Given the side effects of endocrine therapy, optimal patient selection for extended treatment is crucial. Enhanced understanding of late recurrence biology could optimize patient selection in this setting. We therefore summarized the current knowledge of late recurrence biology, clinical trials on extended endocrine therapy, and tools for predicting late recurrence and benefit from treatment extension. Extending 5 years of tamoxifen therapy with 5 years of tamoxifen or an aromatase inhibitor (AI) reduces late recurrence risk by 2-5%, but results of extending AI-based therapy are inconsistent. Although several clinicopathological parameters and multigene assays are prognostic for late recurrence, selection tools predicting benefit from extended endocrine therapy are sparse. Therefore, we additionally performed a pooled analysis using 2231 mRNA profiles of patients with ER-positive/human epidermal growth factor receptor 2-negative breast cancer. Gene Set Enrichment Analysis was applied on genes ranked according to their association with early and late recurrence risk. Higher expression of estrogen-responsive genes was associated with a high recurrence risk beyond 5 years after diagnosis when patients had received no systemic therapy. Although 5 years of endocrine therapy reduced this risk, this effect disappeared after treatment cessation. This suggests that late recurrences of tumors with high expression of estrogen-responsive genes are likely ER-driven. Long-term intervention in this pathway by means of extended endocrine therapy might reduce late recurrences in patients with tumors showing high expression of estrogen-responsive genes.

Development and validation of a new predictive model for breast cancer survival in New Zealand and comparison to the Nottingham prognostic index

Background

The only available predictive models for the outcome of breast cancer patients in New Zealand (NZ) are based on data in other countries. We aimed to develop and validate a predictive model using NZ data for this population, and compare its performance to a widely used overseas model, the Nottingham Prognostic Index (NPI).

Methods

We developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based regional breast cancer registry in NZ (Auckland, 9182 patients), and assessed its performance in this data set (internal validation) and in an independent NZ population-based series of 2625 patients in Waikato (external validation). The data included all women with primary invasive breast cancer diagnosed from 1 June 2000 to 30 June 2014, with follow up to death or Dec 31, 2014. We used multivariate Cox proportional hazards regression to assess predictors and to calculate predicted 10-year breast cancer mortality, and therefore survival, probability for each patient. We assessed observed survival by the Kaplan Meier method. We assessed discrimination by the C statistic, and calibration by comparing predicted and observed survival rates for patients in 10 groups ordered by predicted 10-year survival. We compared this NZ model with the Nottingham Prognostic Index (NPI) in this validation data set.

Results

Discrimination was good: C statistics were 0.84 for internal validity and 0.83 for an independent external validity. For calibration, for both internal and external validity the predicted 10-year survival probabilities in all groups of patients, ordered by predicted survival, were within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZ model showed good discrimination even within the prognostic groups defined by the NPI.

Conclusions

These results for the New Zealand model show good internal and external validity, transportability, and potential clinical value of the model, and its clear superiority over the NPI. Further research is needed to assess other potential predictors, to assess the model’s performance in specific subgroups of patients, and to compare it to other models, which have been developed in other countries and have not yet been tested in NZ.

Electronic supplementary material

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Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

Background

In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence).

Methods

An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model.

Results

For recurrence < 5 years, age (OR 0.96, 95% CI = 0.95–0.97), number of pregnancies (OR 0.94, 95% CI = 0.89–0.99), family history of other cancers (OR 0.73, 95% CI = 0.60–0.89), hormone therapy (OR 0.76, 95% CI = 0.61–0.96), dissected lymph nodes (OR 0.98, 95% CI = 0.97–0.99), right-sided BC (OR 0.87, 95% CI = 0.77–0.99), diabetes (OR 0.77, 95% CI = 0.60–0.98), history of breast operations (OR 0.38, 95% CI = 0.17–0.88), smoking (OR 5.72, 95% CI = 2.11–15.55), history of breast disease (OR 3.32, 95% CI = 1.92–5.76), in situ component (OR 1.58, 95% CI = 1.35–1.84), tumor necrosis (OR 1.87, 95% CI = 1.57–2.22), sentinel lymph node biopsy (SLNB) (OR 2.90, 95% CI = 2.05–4.11) and SLNB+axillary node dissection (OR 3.50, 95% CI = 2.26–5.42), grade 3 (OR 1.79, 95% CI = 1.46–2.21), stage 2 (OR 2.71, 95% CI = 2.18–3.35), stages 3 and 4 (OR 5.01, 95% CI = 3.52–7.13), and mastectomy+radiotherapy (OR 2.97, 95% CI = 2.39–3.68) were predictors of recurrence < 5 years. Moreover, relative to mastectomy without radiotherapy (as reference for comparison), quadrantectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence < 5 years. (OR 17.58, 95% CI = 6.70–46.10 vs. OR: 2.50, 95% CI = 2–3.12).

Accuracy, sensitivity, and specificity of the model were 82%, 75.6%, and 74.9%, respectively.

For recurrence > 5 years, stage 2 cancer (OR 1.67, 95% CI = 1.31–2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI = 1.81–3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence > 5 years (OR 7.62, 95% CI = 1.52–38.15 vs. OR 1.75, 95% CI = 1.32–2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively.

Conclusion

For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (> 80%).

Electronic supplementary material

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Survival analysis according to period and analysis of the factors influencing changes in survival in patients with recurrent breast cancer: a large-scale, single-center study

BACKGROUND

We performed this study to analyze changing survival patterns regarding recurrent breast cancer in Korea during the last 16 years (1993-2008). We also sought to determine factors possibly influencing outcomes and changes over time in the duration of survival after recurrence.

METHODS

We retrospectively analyzed 9671 patients with breast cancer treated between January 1993 and December 2008, comparing the periods 1993-2002 and 2003-2008.We retrospectively reviewed the collected database including the age at diagnosis, clinical manifestations, pathology report, surgical methods, types of adjuvant treatment modalities, type of recurrence, and follow-up period.

RESULTS

There were 1944 cases (20.1%) of recurrence. Median age at the first recurrence was 49.5 years (range 21.8-92.9). Median follow-up was 28.8 months (range 0-228.0) from the time of relapse. Median survival time was 35.0 months. Survival after recurrence (SAR) significantly improved in 2003-2008 compared to that in 1993-2002. Median survival time increased from 27.6 months in the period I to 42.3 months in period II (p = 0.001). Independent prognostic factors after the first recurrence by multivariate analysis were age at diagnosis, tumor size, nodal status, tumor grade, subtype, anti-hormonal therapy, time at diagnosis, and disease-free interval.

CONCLUSIONS

Outcomes of breast cancer have been improving recently, and survival time after the first recurrence of breast cancer has steadily increased in recent decades. We confirmed that advances in treatments have contributed to this improvement in survival after the first recurrence.

Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath

Purpose: To compare PREDICT and CancerMath, two widely used prognostic models for invasive breast cancer, taking into account their clinical utility. Furthermore, it is unclear whether these models could be improved.Experimental Design: A dataset of 5,729 women was used for model development. A Bayesian variable selection algorithm was implemented to stochastically search for important interaction terms among the predictors. The derived models were then compared in three independent datasets (n = 5,534). We examined calibration, discrimination, and performed decision curve analysis.Results: CancerMath demonstrated worse calibration performance compared with PREDICT in estrogen receptor (ER)-positive and ER-negative tumors. The decline in discrimination performance was -4.27% (-6.39 to -2.03) and -3.21% (-5.9 to -0.48) for ER-positive and ER-negative tumors, respectively. Our new models matched the performance of PREDICT in terms of calibration and discrimination, but offered no improvement. Decision curve analysis showed predictions for all models were clinically useful for treatment decisions made at risk thresholds between 5% and 55% for ER-positive tumors and at thresholds of 15% to 60% for ER-negative tumors. Within these threshold ranges, CancerMath provided the lowest clinical utility among all the models.Conclusions: Survival probabilities from PREDICT offer both improved accuracy and discrimination over CancerMath. Using PREDICT to make treatment decisions offers greater clinical utility than CancerMath over a range of risk thresholds. Our new models performed as well as PREDICT, but no better, suggesting that, in this setting, including further interaction terms offers no predictive benefit. Clin Cancer Res; 24(9); 2110-5. ©2018 AACR.

Lymphovascular invasion after neoadjuvant chemotherapy is strongly associated with poor prognosis in breast carcinoma

PURPOSE

Few studies evaluated the prognostic value of the presence of lymphovascular invasion (LVI) after neoadjuvant chemotherapy (NAC) for breast cancer (BC).

METHODS

The association between LVI and survival was evaluated in a cohort of BC patients treated by NAC between 2002 and 2011. Five post-NAC prognostic scores (ypAJCC, RCB, CPS, CPS + EG and Neo-Bioscore) were evaluated and compared with or without the addition of LVI.

RESULTS

Out of 1033 tumors, LVI was present on surgical specimens in 29.2% and absent in 70.8% of the cases. Post-NAC LVI was associated with impaired disease-free survival (DFS) (HR 2.54; 95% CI 1.96-3.31; P < 0.001), and the magnitude of this effect depended on BC subtype (P_interaction = 0.003), (luminal BC: HR 1.83; P = 0.003; triple negative BC: HR 3.73; P < 0.001; HER2-positive BC: HR 6.21; P < 0.001). Post-NAC LVI was an independent predictor of local relapse, distant metastasis, and overall survival; and increased the accuracy of all five post-NAC prognostic scoring systems.

CONCLUSIONS

Post-NAC LVI is a strong independent prognostic factor that: (i) should be systematically reported in pathology reports; (ii) should be used as stratification factor after NAC to propose inclusion in second-line trials or adjuvant treatment; (iii) should be included in post-NAC scoring systems.

Personalized Prognostic Prediction Models for Breast Cancer Recurrence and Survival Incorporating Multidimensional Data

Background

In this study, we developed integrative, personalized prognostic models for breast cancer recurrence and overall survival (OS) that consider receptor subtypes, epidemiological data, quality of life (QoL), and treatment.

Methods

A total of 15 314 women with stage I to III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1997 and 2012 were used to generate prognostic models by Cox regression analysis in a two-stage study. Model performance was assessed by calculating the area under the curve (AUC) and calibration analysis and compared with Nottingham Prognostic Index (NPI) and PREDICT.

Results

Host characteristics were assessed for 10 809 women as the discovery population (median follow-up = 6.09 years, 1144 recurrence and 1627 deaths) and 4505 women as the validation population (median follow-up = 7.95 years, 684 recurrence and 1095 deaths). In addition to the known clinical/pathological variables, the model for recurrence included alcohol consumption while the model for OS included smoking status and physical component summary score. The AUCs for recurrence and OS were 0.813 and 0.810 in the discovery and 0.807 and 0.803 in the validation, respectively, compared with AUCs of 0.761 and 0.753 in discovery and 0.777 and 0.751 in validation for NPI. Our model further showed better calibration compared with PREDICT. We also developed race-specific and receptor subtype-specific models with comparable AUCs. Racial disparity was evident in the distributions of many risk factors and clinical presentation of the disease.

Conclusions

Our integrative prognostic models for breast cancer exhibit high discriminatory accuracy and excellent calibration and are the first to incorporate receptor subtype and epidemiological and QoL data.

An analysis from the Quality Outcomes Database, Part 1. Disability, quality of life, and pain outcomes following lumbar spine surgery: predicting likely individual patient outcomes for shared decision-making

OBJECTIVE Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifically, clinical registry data are progressively informing health care decision-making. In this analysis, the authors used data from a national prospective outcomes registry (the Quality Outcomes Database) to develop a predictive model for 12-month postoperative pain, disability, and quality of life (QOL) in patients undergoing elective lumbar spine surgery. METHODS Included in this analysis were 7618 patients who had completed 12 months of follow-up. The authors prospectively assessed baseline and 12-month patient-reported outcomes (PROs) via telephone interviews. The PROs assessed were those ascertained using the Oswestry Disability Index (ODI), EQ-5D, and numeric rating scale (NRS) for back pain (BP) and leg pain (LP). Variables analyzed for the predictive model included age, gender, body mass index, race, education level, history of prior surgery, smoking status, comorbid conditions, American Society of Anesthesiologists (ASA) score, symptom duration, indication for surgery, number of levels surgically treated, history of fusion surgery, surgical approach, receipt of workers' compensation, liability insurance, insurance status, and ambulatory ability. To create a predictive model, each 12-month PRO was treated as an ordinal dependent variable and a separate proportional-odds ordinal logistic regression model was fitted for each PRO. RESULTS There was a significant improvement in all PROs (p < 0.0001) at 12 months following lumbar spine surgery. The most important predictors of overall disability, QOL, and pain outcomes following lumbar spine surgery were employment status, baseline NRS-BP scores, psychological distress, baseline ODI scores, level of education, workers' compensation status, symptom duration, race, baseline NRS-LP scores, ASA score, age, predominant symptom, smoking status, and insurance status. The prediction discrimination of the 4 separate novel predictive models was good, with a c-index of 0.69 for ODI, 0.69 for EQ-5D, 0.67 for NRS-BP, and 0.64 for NRS-LP (i.e., good concordance between predicted outcomes and observed outcomes). CONCLUSIONS This study found that preoperative patient-specific factors derived from a prospective national outcomes registry significantly influence PRO measures of treatment effectiveness at 12 months after lumbar surgery. Novel predictive models constructed with these data hold the potential to improve surgical effectiveness and the overall value of spine surgery by optimizing patient selection and identifying important modifiable factors before a surgery even takes place. Furthermore, these models can advance patient-focused care when used as shared decision-making tools during preoperative patient counseling.

Conditional disease-free survival among patients with breast cancer

Conditional disease-free survival (CDFS) reflects changes over time. Because traditional disease-free survival (DFS) is estimated from the date of diagnosis, it is limited in the ability to predict risk of recurrence in patients who have been disease free. In this study, we determined CDFS of breast cancer patients and estimated the prognostic factors for DFS.We retrospectively reviewed clinical data of 7587 consecutive patients who underwent curative surgery for breast cancer between January 2004 and December 2013 at Samsung Medical Center. Univariate and multivariate analyses were performed to identify risk factors for DFS, which was computed using the Kaplan-Meier method. CDFS rates were based on cumulative DFS estimates.Median follow-up duration was 20.59 months. Three-year DFS was 93.46% at baseline. Three-year CDFS survival estimates for patients who had been disease free for 1, 2, 3, 4, and 5 years after treatment were calculated as 92.84%, 92.37%, 93.03%, 89.41%, and 79.64%, respectively. Three-year CDFS increased continuously each year after 1 year of DFS in hormone receptor (HR)-negative patients but decreased each year in HR-positive patients.In HR-positive patients who are disease free after 3 years, continuous care including surveillance and metastases workup should be considered, although this is not recommended in the current guidelines. On the other hand, the social costs may be reduced in HR-negative patients by extending the surveillance interval. Further studies are needed to identify indicators of DFS prognosis in breast cancer patients.

Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia

BACKGROUND

CancerMath is a set of web-based prognostic tools which predict nodal status and survival up to 15 years after diagnosis of breast cancer. This study validated its performance in a Southeast Asian setting.

METHODS

Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).

RESULTS

The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).

CONCLUSIONS

The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.

Internet tools to enhance breast cancer care

Internet tools have become a great aid in the daily practice of physicians who treat breast cancer patients. In cancer care there are frequent and important intersections where major decisions need to be made; these include (1) whether or not to give chemotherapy; (2) how much toxicity to expect, and (3) the life expectancy of the patient, considering non-breast cancer comorbidities. These decisions can be made more accurately using calculators based on data sets of thousands of patients as opposed to physician intuition. Such tools also help patients and caregivers in optimal decision making, as they estimate the absolute benefits and risks of treatment. In this perspective we describe selected internet sites that are useful across several domains of care, including the potential benefits of different adjuvant regimens for early breast cancer, prognosis after neoadjuvant therapy, prognosis for ductal carcinoma in situ, and toxicity and life expectancy estimates. We review the variables required to use the tools, the results obtained, the methods of validation, and the advantages and disadvantages of each tool.

The importance of molecular markers for diagnosis and selection of targeted treatments in patients with cancer

The past 30 years have seen the introduction of a number of cancer therapies with the aim of restricting the growth and spread of primary and metastatic tumours. A shared commonality among these therapies is their targeting of various aspects of the cancer hallmarks, that is traits that are essential to successful tumour propagation and dissemination. The evolution of molecular-scale technology has been central to the identification of new cancer targets, and it is not a coincidence that improved therapies have emerged at the same time as gene expression arrays and DNA sequencing have enhanced our understanding of cancer genetics. Modern tumour pathology is now viewed at the molecular level ranging from IHC biomarkers, to gene signature classifiers and gene mutations, all of which provide crucial information about which patients will respond to targeted therapy regimens. In this review, we briefly discuss the general types of targeted therapies used in a clinical setting and provide a short background on immunohistochemical, gene expression and DNA sequencing technologies, before focusing on three tumour types: breast, lung and colorectal cancers. For each of these cancer types, we provide a background to the disease along with an overview of the current standard therapies and then focus on the relevant targeted therapies and the pathways they inhibit. Finally, we highlight several strategies that are pivotal to the successful development of targeted anti-cancer drugs.

Prediction of lymph node involvement in patients with breast tumors measuring 3-5 cm in a middle-income setting: the role of CancerMath

BACKGROUND

In settings with limited resources, sentinel lymph node biopsy (SNB) is only offered to breast cancer patients with small tumors and a low a priori risk of axillary metastases.

OBJECTIVE

We investigated whether CancerMath, a free online prediction tool for axillary lymph node involvement, is able to identify women at low risk of axillary lymph node metastases in Malaysian women with 3-5 cm tumors, with the aim to offer SNB in a targeted, cost-effective way.

METHODS

Women with non-metastatic breast cancers, measuring 3-5 cm were identified within the University Malaya Medical Centre (UMMC) breast cancer registry. We compared CancerMath-predicted probabilities of lymph node involvement between women with versus without lymph node metastases. The discriminative performance of CancerMath was tested using receiver operating characteristic (ROC) analysis.

RESULTS

Out of 1,017 patients, 520 (51 %) had axillary involvement. Tumors of women with axillary involvement were more often estrogen-receptor positive, progesterone-receptor positive, and human epidermal growth factor receptor (HER)-2 positive. The mean CancerMath score was higher in women with axillary involvement than in those without (53.5 vs. 51.3, p = 0.001). In terms of discrimination, CancerMath performed poorly, with an area under the ROC curve of 0.553 (95 % confidence interval CI 0.518-0.588). Attempts to optimize the CancerMath model by adding ethnicity and HER2 to the model did not improve discriminatory performance.

CONCLUSION

For Malaysian women with tumors measuring 3-5 cm, CancerMath is unable to accurately predict lymph node involvement and is therefore not helpful in the identification of women at low risk of node-positive disease who could benefit from SNB.

Development of individual survival estimating program for cancer patients' management

Objectives

The goal of this report is to present an individual patient's survival estimation curve using the each institution's survival data after Cox proportional hazard analysis.

Methods

The program was developed in three parts: input of basic data from Cox proportional hazard analysis, input of individual patient's covariates, and presentation of individual patient's survival curve. In the first part, the average survival rates with each survival time were entered as the means of covariates using the results of Cox proportional hazard analysis. In the second part, the individual patient's values of each covariate were entered for the calculation of survival estimation. In the third part, the survival curve was displayed according to the input data.

Results

The data of 2,652 breast cancer patients were analyzed. Cox regression analysis was conducted using the covariates of age, tumor size, N stage, and M stage. The individual patient's survival curve was presented using the basic data and covariate factors. In the breast cancer patients, the program presented survival curves according to each patient's age, tumor size, N stage, and M stage. The data of 251 thyroid cancer patients were analyzed by a similar method.

Conclusions

We developed a program to present individual survival curves of cancer patients. This program will be useful for clinicians to assist their decision-making and discussion with patients.

The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

Are we able to predict survival in ER-positive HER2-negative breast cancer? A comparison of web-based models

Background:

Several prognostic models have been proposed and demonstrated to be predictive of survival outcomes in breast cancer. In the present article, we assessed whether three of these models are comparable at an individual level.

Methods:

We used a large data set (n=965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath.net and PREDICT, and we assessed concordance of these models in 10-year survival prediction.

Results:

Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0.74 (95% CI 0.71–0.77) for the Cancermath.net model and 0.72 (95% CI 0.69–0.75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the subgroup, with a predicted survival higher than 90% with a median score dispersion at 0.08 (range 0.06–0.10). Dispersion, however, reached 30% for the subgroups with a predicted survival between 10 and 50%.

Conclusion:

This study revealed that the three web-based predictors equally perform well at the population level, but exhibit a high degree of discordance in the intermediate and poor prognosis groups.

Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study

Background

In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia.

Materials and Methods

We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic).

Results

We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66).

Conclusion

The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

Prognostic factors for survivals from first relapse in breast cancer patients: analysis of deceased patients

PURPOSE

This study was performed to evaluate prognostic factors for survival from first relapse (SFFR) in stage I-III breast cancer patients.

MATERIALS AND METHODS

From June 1994 to June 2008, 3,835 patients were treated with surgery plus postoperative radiotherapy and adjuvant chemotherapy for stage I-III breast cancer at Samsung Medical Center. Among them, a total of 224 patients died by June 2009, and 175 deaths were of breast cancer. Retrospective review was performed on medical records of 165 patients who met the inclusion criteria of this study. Univariate and multivariate analysis were done on survivals according to variables, such as age, stage, hormone status of tumor, disease-free interval (DFI), sites of first failure, number of organs involved by recurrent disease (NOR), application of salvage treatments, and existence of brain or liver metastasis (visceral metastasis).

RESULTS

Patients' median overall survival time was 38 months (range, 8 to 123 months). Median SFFR was 17 months (range, 5 to 87 months). Ninety percent of deaths occurred within 40 months after first recurrence. The patients with SFFR ≤1 year had tendency of triple-negativity, shorter DFI (≤2 years), larger NOR (>3), visceral metastasis for first relapse than the patients with SFFR >1 year. In multivariate analysis, longer DFI (>2 vs. ≤2 years), absence of visceral metastasis, and application of salvage treatments were statistically significant prognosticators for longer SFFR.

CONCLUSION

The DFI, application of salvage treatments, and visceral metastasis were significant prognostic factors for SFFR in breast cancer patients.

Predicting and communicating the risk of recurrence and death in women with early-stage breast cancer: a systematic review of risk prediction models

BACKGROUND

It is a challenge for oncologists to distinguish patients with breast cancer who can forego adjuvant systemic treatment without negatively affecting survival from those who cannot. Risk prediction models (RPMs) have been developed for this purpose. Oncologists seem to have embraced RPMs (particularly Adjuvant!) in clinical practice and often use them to communicate prognosis to patients. We performed a systematic review of published RPMs and provide an overview of the prognosticators incorporated and reported clinical validity. Subsequently, we selected the RPMs that are currently used in the clinic for a more in-depth assessment of clinical validity. Finally, we assessed lay comprehensibility of the reports generated by RPMs.

METHODS

Pubmed, EMBASE, and Web of Science were searched. Two reviewers independently selected relevant articles and extracted data. Agreement on article selection and data extraction was achieved in consensus meetings.

RESULTS

We identified RPMs based on clinical prognosticators (N = 6) and biomolecular features (N = 14). Generally predictions from RPMs seem to be accurate, except for patients ≤ 50 years or ≥ 75 years at diagnosis, in addition to Asian populations. RPM reports contain much medical jargon or technical details, which are seldom explained in lay terms.

CONCLUSION

The accuracy of RPMs' prognostic estimates is suboptimal in some patient subgroups. This urgently needs to be addressed. In their current format, RPM reports are not conducive to patient comprehension. Communicating survival probabilities using RPM might seem straightforward, but it is fraught with difficulties. If not done properly, it can backfire and confuse patients. Evidence to guide best communication practice is needed.