Time-varying effect and long-term survival analysis in breast cancer patients treated with neoadjuvant chemotherapy

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Background

Patients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor.

Methods

To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale.

Results

A simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78.

Conclusion

Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017

PURPOSE

Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period.

METHODS

Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks.

RESULTS

Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival.

CONCLUSION

These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Associations between urban exposome and recurrence risk among survivors of acute myocardial infarction in Beijing, China

Few previous studies have investigated the impacts of coexposure to multiple urban environmental factors on the prognosis of acute myocardial infarction (AMI) events. This study aimed to evaluate the associations between the urban exposome and AMI recurrence. We used data from 88,509 AMI patients from a large cohort obtained from the Beijing Cardiovascular Disease Surveillance System between 2013 and 2019. Twenty-six types of urban exposures were assessed within 300-m, 500-m, and 1000-m buffers of patients' home addresses in the baseline and cumulative average levels. We used the Cox proportional hazard model along with the Elastic Net (ENET) algorithm to estimate the hazard ratios (HRs) of recurrent AMI per interquartile range increase in each selected urban exposure. The increased risk of AMI recurrence was significantly associated with lower urban function diversity in the 500-m buffer, longer distance to subway stations and higher PM2.5 for both baseline and cumulative average exposure. The cumulative averages of two urban factors, including the distance to parks, and the density of fruit and vegetable shops in the 1000-m buffer, were also identified as significant factors affecting the risk of AMI recurrence. These findings can help improve the urban design for promoting human cardiovascular health.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Contrast-enhanced ultrasonography for early prediction of response of neoadjuvant chemotherapy in breast cancer

Neoadjuvant chemotherapy (NAC) is widely accepted as a primary treatment for inoperable or locally advanced breast cancer before definitive surgery. However, not all advanced breast cancers are sensitive to NAC. Contrast-enhanced ultrasonography (CEUS) has been considered to assess tumor response to NAC as it can effectively reflect the condition of blood perfusion and lesion size. Therefore, this study aimed to evaluate the diagnostic performance of CEUS to predict early response in different regions of interest in breast tumors under NAC treatment. This prospective study included 82 patients with advanced breast cancer. Parameters of TIC (time-intensive curve) between baseline and after the first cycle of NAC were calculated for the rate of relative change (Δ), including Δpeak, ΔTTP (time to peak), ΔRBV (regional blood volume), ΔRBF (regional blood flow) and ΔMTT (mean transit time). The responders and non-responders were distinguished by the Miller-Payne Grading (MPG) system and parameters from different regions of tumors were compared in these two groups. For ROI 1(the greatest enhancement area in the central region of the tumor), there were significant differences in Δpeak1, ΔRBV1 and ΔRBF1 between responders and non-responders. For ROI 2 (the greatest enhancement area on edge of the tumor), there were significant differences in Δpeak2 and ΔRBF2 between the groups. The Δpeak1 and ΔRBF2 showed good prediction (AUC 0.798-0.820, p ≤ 0.02) after the first cycle of NAC. When the cut-off value was 0.115, the ΔRBF2 had the highest diagnostic accuracy and the maximum NPV. Quantitative TIC parameters could be effectively used to evaluate early response to NAC in advanced breast cancer.

Quality of life in patients with breast cancer with neoadjuvant chemotherapy: a systematic review

OBJECTIVES

The aims of this systematic review were to assess the impact of neoadjuvant chemotherapy (NAC) on breast cancer (BC) patients' quality of life (QOL), to compare the different regimens of NAC on BC patients' QOL, to compare NAC versus adjuvant chemotherapy on BC patients' QOL and to identify predictors of QOL on patients with BC receiving NAC.

DESIGN

The design used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

DATA SOURCES

Cinahl, Embase, Pubmed, Scopus, Web of Science, Cochrane library and PsycINFO were searched through 27 December 2021.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

The inclusion criteria were included: patients with BC receiving NAC, outcome measures related to QOL and published in English. The exclusion criteria were included: duplicates or overlapping participants, not original research, data or full text not available and qualitative study.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers used standardised methods to search, screen and code included studies. The risk of bias in individual studies was evaluated with Cochrane collaboration's tool for assessing risk bias, Newcastle Ottawa Score or Joanna Briggs Institute Critical Appraisal tool. This systematic review performs narrative synthesis based on several different themes.

RESULTS

The initial search resulted in 2994 studies; 12 of these studies fulfilled inclusion criteria. There was no significant difference in the QOL of BC before and after NAC, but patients experienced adverse reactions and depression during chemotherapy. Different regimens of NAC have different effects on patients' QOL. Patients with NAC had more severe physical discomfort than those with adjuvant chemotherapy. However, BC patients' QOL can be improved by intervening on social or family support, and these predictors, including chronotype, QOL before NAC and depression.

CONCLUSIONS

More original research is needed in future to understand the profile and predictors of QOL in patients with BC on NAC, which will help clinicians and patients make decisions and deal with NAC-related issues.

Prediction of pathological complete response to neoadjuvant chemotherapy in patients with breast cancer using a combination of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging

This study aimed to evaluate the value of dynamic contrast-enhanced ultrasound (CEUS) combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting pathological complete response (pCR) in patients with breast cancer receiving neoadjuvant chemotherapy (NAC). Fifty-seven female patients with breast cancer (mean age, 50.46 years; range, 32-66 years) scheduled for NAC were recruited. CEUS and DCE-MRI were performed before and after NAC. Imaging features and their changes were compared with postoperative pathological results. After the clinical differences were balanced using propensity score matching, univariate and multiple logistic regression analyses were used to derive the characteristics independently associated with pCR. Receiver operating characteristic curve analysis was performed to assess diagnostic performance. After six to eight cycles of NAC, 24 (42.1%) patients achieved pCR, while 33 (57.9%) did not. Multivariate analysis showed that enhancement order on CEUS and DCE-MRI before NAC, reduction in diameter and enhancement shape on CEUS, maximum diameter on DCE-MRI, and the type of progressive dynamic contrast enhancement after NAC were independently associated with pCR after NAC. The area under the receiver operating characteristic curve for CEUS+DCE-MRI was 0.911 (95% confidence interval, 0.826-0.997), and the specificity and positive predictive values were 87.0% and 87.5%. CEUS and DCE-MRI have the potential for assessing the pathological response to NAC in patients with breast cancer; their combination showed the best diagnostic performance. CEUS+DCE-MRI has proved beneficial for comprehensive assessment and personalizing treatment strategies for patients with breast cancer.

A real-world US study of recurrence risks using combined clinicopathological features in HR-positive, HER2-negative early breast cancer

Aim: To assess invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS) in hormone receptor-positive, HER2-negative early breast cancer with combined clinicopathological criteria from monarchE, a phase III study of abemaciclib. Methods: US electronic health records were used to compare outcomes between high-risk (≥4 lymph nodes, or 1-3 lymph nodes and grade 3, tumor ≥5 cm, or Ki-67 ≥20%) versus nonhigh-risk groups using Kaplan-Meier methods and Cox regression models. Results: The high-risk group (n = 557) was at higher risk for IDFS and DRFS events than the nonhigh-risk group (n = 3471). IDFS events (hazard ratio: 3.07; 95% CI: 2.45-3.83) and DRFS events (hazard ratio: 3.15; 95% CI: 2.49-3.97) were significantly higher for the high-risk group. Conclusion: Risk of recurrence was three-times greater in the high-risk group, highlighting the need for better therapies.

Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.

Development and application of a dynamic prediction model for esophageal cancer

Background

Current prediction models of esophageal cancer (EC) are limited to predicting at a specific time point, and ignore changes in hazard ratios of predictive variables, known as time-varying effects. Our study aimed to investigate variables with time-varying effects in EC and to develop a prediction model that can update the 5-year predicted dynamic overall survival (DOS) probability during the follow-up period.

Methods

Firstly, the clinicopathological information and survival data of 4,541 patients with EC was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2007 and 2011 for modeling. Secondly, the time-varying effect of variables was assessed and the dynamic prediction model was developed based on the proportional baselines landmark supermodel.

Results

Here, we found that age at diagnosis, sex, location of primary tumor, histological type, chemotherapy, surgery, and T stage showed significant time-varying effects on overall survival. Thirdly, the prediction model was validated by an internal SEER validation cohort and a Chinese patient cohort, respectively, and achieved promising results as follows: area under the curve (AUC) =0.733 (internal validation) and 0.864 (external validation). The heuristic shrinkage factor was 0.995. Finally, several clear cases were selected as examples for model application to map the patient’s 5-year DOS curves and to respectively demonstrate the impact of different variables’ time-varying effect on survival.

Conclusions

Overall, our results suggest that the existence of time-varying effect highlights the importance of updating the predicted survival probability during the follow-up period. Moreover, this prediction model can be used to assist doctors in making more-individualized treatment decisions based on a dynamic assessment of patient prognosis.

Dynamic prediction and analysis based on restricted mean survival time in survival analysis with nonproportional hazards

BACKGROUND AND OBJECTIVE

In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only calculates the mean survival time of patients within a period of time after the start of follow-up and may not accurately portray the change in a patient's life expectancy over time.

METHODS

The life expectancy can be adjusted for the time the patient has already survived and defined as the conditional restricted mean survival time (cRMST). A dynamic RMST model based on the cRMST can be established by incorporating time-dependent covariates and covariates with time-varying effects. We analyzed data from a study of primary biliary cirrhosis (PBC) to illustrate the use of the dynamic RMST model, and a simulation study was designed to test the advantages of the proposed approach. The predictive performance was evaluated using the C-index and the prediction error.

RESULTS

Considering both the example results and the simulation results, the proposed dynamic RMST model, which can explore the dynamic effects of prognostic factors on survival time, has better predictive performance than the RMST model. Three PBC patient examples were used to illustrate how the predicted cRMST changed at different prediction times during follow-up.

CONCLUSIONS

The use of the dynamic RMST model based on the cRMST allows for the optimization of evidence-based decision-making by updating personalized dynamic life expectancy for patients.

Time-varying prognostic effects of primary tumor sidedness and grade after curative liver resection for colorectal liver metastases

BACKGROUND

The veracity of the proportional hazards (PH) requirement is rarely scrutinized in most areas of cancer research, although fulfilment of this assumption underpins widely-used Cox survival models. We sought to critically appraise the existence of prognostic factors with time-dependent effects and to characterize their impact on survival among CLM patients.

METHODS

Consecutive patients who underwent liver resection with curative intent for CLM at the Singapore General Hospital were identified from a prospectively-maintained database. We evaluated PH of 55 candidate variables, and parameters which departed significantly from proportionality were included in Cox models that incorporated an interaction term to account for time-dependent effects. As sensitivity analyses, we fitted Weibull mixture 'cure' models to handle long plateaus in the tails of survival curves, and also analyzed the restricted mean survival time.

RESULTS

318 consecutive patients who underwent curative liver resection for CLM between Jan 2000 and Nov 2016 were included in this analysis. Hazard ratios for tumor grade (poorly-versus well- and moderately-differentiated) were found to decrease from 3.135 (95% CI: 1.637-6.003) at 12 months to 2.048 (95% CI: 1.038-4.042) after 24 months, and ceased to be significant at 26 months. Compared to left-sided tumors, a right-sided tumor location was found to portend worse prognosis for the first 10 months after resection but subsequently confer a survival benefit due to a crossing of survival curves. Corroborating this observation, long-term cure fractions were estimated to be 25.5% (95% CI: 17.4%-33.6%) and 34.2% (95% CI: 17.4%-50.9%) among patients with left-sided and right-sided primary disease respectively.

CONCLUSION

Primary tumor sidedness and grade appear to exert time-varying prognostic effects in CLM patients undergoing curative liver resection.

Clinical and pathological response to neoadjuvant chemotherapy with different chemotherapy regimens predicts the outcome of locally advanced breast cancer

Background

This retrospective analysis was designed to research whether clinical response partial response (PR)/complete response (CR) and pathological response (PCR) to neoadjuvant chemotherapy can translate into prognosis benefit pathological response in patients with locally advanced breast cancer and whether different chemotherapy regimens will influence the outcomes.

Methods

One hundred and thirty-five patients with breast cancer patients who received neoadjuvant chemotherapy were included in the retrospective analysis. Patients were followed up strictly. Overall survival (OS) was evaluated by the Kaplan-Meier analysis. The comparison of the clinical and pathological characteristics and recurrence was performed using the carried out by chi-squared and Fisher's exact tests. Univariate and multivariate analyses were performed by the Cox regression analysis.

Results

Clinical response was strongly correlated with lymph nodes status (P=0.032). The OS comparison of pathological response between the pCR group and non-pCR groups did not exhibit statistically significant differences (P=0.400). A similar non-significant response result was observed in the comparison of clinical response between the PR/CR and SD/PD groups group (P=0.108). Univariate and multivariate analyses did not support clinical response (P=0.156 P=0.095 respectively) or pathological response (P=0.600 P=0.144 respectively) as the predictors of prognosis. There were no significant differences in either the comparison of the clinical response group it seems no statistically significance (P=0.496) or the comparison of the pathological response group (P=0.460). OS analyses across different neoadjuvant chemotherapy regimens demonstrated no significant differences (P=0.307). In the PR/CR and PD/SD comparison of every single regimen, there were no significant differences. However, for patients with PR/CR patients from the comparison of five regimens, namely, TAC, FAC, AC-T, AT and TCBP demonstrated a significant difference (P=0.022). In the group of patients with luminal A breast cancer, the result of the Fisher's exact test approached significant (P=0.059).

Conclusions

Neither PR/CR nor pCR can translate into long-term outcome benefit. PR/CR and PCR are not independent predictors in patients with advanced breast cancer. Patients who received a taxane + anthracycline regimen exhibited a higher recurrence rate than any other regimens, especially those patients with luminal A breast cancer.

Effects of neoadjuvant chemotherapy on respiratory function in patients with breast cancer

Objective

To evaluate changes in chest X-rays, pulmonary function tests (PFTs) and quality of life in female breast cancer patients who had been treated with four cycles of neoadjuvant chemotherapy consisting of a regimen of cyclophosphamide, epirubicin and 5-fluorouracil (CEF regimen), and to determine the correlation between pulmonary function parameters and declined quality of life.

Methods

Twenty-nine eligible female patients diagnosed with breast cancer at the first visit who were 20−60 years old, were classified as the American Society of Anesthesiologists (ASA) I−II and patients whose body mass index (BMI) <30 kg/m² were recruited and subjected to chest X-ray examinations, PFTs and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30 (EORTC QLQ-C30) questionnaire before and after receiving 4 cycles of the CEF regimen.

Results

In this study, chest X-rays showed no abnormal changes after chemotherapy, but significant decreases in carbon monoxide diffusing capacity (DLCO) and percentage of the DLCO predicted value (DLCO%) (P<0.001). A significant increase in maximal ventilatory volume (MVV) (P=0.004) was observed, and most patients experienced dyspnea (P=0.031) and fatigue (P<0.001). However, there was no significant correlation between the changes in these PFTs parameters and the results of the EORTC QLQ-C30 (P>0.05).

Conclusions

Neoadjuvant chemotherapy can reduce lung diffusion function and quality of life in females with breast cancer.

Dynamic prediction and prognostic analysis of patients with cervical cancer: a landmarking analysis approach

PURPOSE

Providing up-to-date information on patient prognosis is important in determining the optimal treatment strategies. The currently available prediction models, such as the Cox model, are limited to making predictions from baseline and do not consider the time-varying effects of covariates.

METHODS

A total of 1501 cervical cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database were included. We introduced three landmark dynamic prediction models (models 1-3) that explore the dynamic effects of prognostic factors to obtain 5-year dynamic survival rate predictions at different prediction times. The performances of these models were evaluated by Harrell's C-index and the Brier score using cross-validation.

RESULTS

Some variables did not meet the proportional hazards assumption, indicating that the constant hazard ratios were unreliable. Model 3, which showed the best performance for prediction, was selected as the final model. Significant time-varying effects were observed for age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and histological subtypes. Three patients were as examples used to illustrate how the predicted probabilities change at different prediction times during follow-up.

CONCLUSIONS

Model 3 can effectively incorporate covariates with time-varying effects and update the probability of surviving an additional 5 years at different prediction times. The use of the landmark approach may provide evidence for clinical decision making by updating personalized information for patients.

Neoadjuvant Chemoradiotherapy and Tumor Recurrence in Patients with Early T-Stage Cancer of the Lower Rectum

BACKGROUND

The role neoadjuvant chemoradiotherapy (nCRT) plays in oncological outcomes in early T-stage rectal cancer is uncertain. The present work aims to clarify prognostic outcomes by estimating the effect of nCRT on tumor recurrence prior to major surgery compared with major surgery alone.

PATIENTS AND METHODS

Prospectively collected data were retrospectively analyzed for patients diagnosed with localized rectal adenocarcinoma ≤ 8 cm from the anal verge, with final histopathology ≤ T2 (≤ ypT2/≤ pT2), regardless of magnetic resonance imaging staging, between 1990 and 2017. As the effect of nCRT on recurrence varied over time, thereby violating the Cox proportional hazards assumption, the effect of nCRT on recurrence hazards was estimated using a time-varying multivariate Cox model over two separate time intervals (≤ 1 year and > 1 year postsurgery) by nCRT.

RESULTS

Long-course nCRT was associated with a 5.6-fold increase in the hazard of recurrence ≤ 1 year postsurgery [hazard ratio (HR) 5.6; 95% confidence interval (CI) 1.2-24.9; P = 0.02], but there was no increase in recurrence hazards > 1 year (HR 0.84; 95% CI 0.4-2.0; P = 0.70). In subgroup analysis restricted to ≤ mrT2/≤ ypT2 and ≤ pT2 tumors (omitting > mrT2 tumors), the effect of nCRT on recurrence no longer varied over time, indicating that tumor heterogeneity was responsible for the observed increased recurrence hazards ≤ 1 year postsurgery; That is, > mrT2 tumors that were downstaged to ≤ ypT2 after nCRT were responsible for the time-varying effects of nCRT and increased recurrence hazards ≤ 1 year postsurgery. Subsequently, no difference was found in prognostic outcomes either with or without nCRT before surgery in the homogeneous population of ≤ mrT2/≤ ypT2 and ≤ pT2 tumors.

CONCLUSIONS

No evidence was found to indicate that nCRT prior to surgery reduces tumor recurrence in early T-stage lower rectal cancer compared with surgery alone.

Patterns of relapse after neoadjuvant chemotherapy in breast cancer: implications for surveillance in clinical practice

PURPOSE

This study aimed to identify patterns of relapse after neoadjuvant chemotherapy (NAC) for breast cancer to refine follow-up recommendations.

METHODS

Retrospective analysis on 523 breast cancer patients treated with NAC at two public hospitals in Singapore between 2000 and 2014.

RESULTS

Majority of patients (71.9%) had locally advanced disease. Median follow-up was 55 months. 5-year recurrence rate was significantly higher in triple negative breast cancer (TNBC) than non-TNBC subtypes (38.4% vs. 29.5%; p = 0.042); 85% of recurrences involved distant sites. Among TNBC and HR (hormone receptor)-/HER2+ subtypes, 97.0% and 95.0% of relapses occurred within 3 years from diagnosis respectively while 10.6% of relapses among HR+ subgroup occurred beyond 5 years. Recurrence risk in high-grade tumours decreased with time. Stage III at diagnosis (hazard ratio = 2.94; p < 0.001), grade 3 tumours (hazard ratio = 2.87; p = 0.018), not achieving pathologic complete response (pCR) (hazard ratio = 8.77; p = 0.003) and not receiving adjuvant radiotherapy (hazard ratio = 3.19; p < 0.001) were independent predictors of inferior recurrence-free survival. Serum CA 15-3 was raised in 49% of patients upon relapse; it correlated with inferior post-relapse survival (median 11 months vs. 22 months; p = 0.019).

CONCLUSIONS

While more intensive follow-up during the first 3 years may be required for patients who do not achieve pCR, especially those with TNBC and HR-/HER2+ tumours, the benefit from blood tests such as CA 15-3 appears limited, and the benefit from intensification of surveillance remains to be addressed in prospective studies on high-risk patients.

Evaluation of the response of breast cancer patients to neoadjuvant chemotherapy by combined contrast-enhanced ultrasonography and ultrasound elastography

The purpose of the present study was to investigate whether contrast-enhanced ultrasonography (CEUS) in combination with ultrasound elastography (UE) is able to accurately predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 65 breast cancer patients who received NAC at the First Affiliated Hospital of Zhejiang University (Hangzhou, China) between February 2016 and August 2017 and were recruited for the present study. Prior to and after NAC, examination by CEUS, UE or their combination was performed. Pathological results were obtained at the end of each chemotherapy cycle, based on which 41 cases were assigned to the response group and 24 to the non-response group. Kappa values were 0.710, 0.434 and 0.836 for CEUS, UE and CEUS+UE, respectively. The area under the receiver operating characteristic curves for CEUS, UE and CEUS+UE for determining the response to NAC was 0.864 [95% confidence interval (CI), 0.765–0.964], 0.715 (95% CI, 0.579–0.850) and 0.910 (95% CI, 0.826–0.993), respectively. It was identified that the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of CEUS+UE were higher than those of CEUS and US individually. The prediction accuracy was 89.2, 90.8 and 100% for CEUS, UE and their combination, respectively. CEUS and UE have their own advantages in evaluating the clinical efficacy of NAC in breast cancer, and a higher accuracy was achieved when the two techniques were applied in combination. Therefore, a combination of CEUS and UE may be a preferred method for the clinical assessment of the efficacy of NAC in breast cancer patients.

Impact of time to local recurrence on the occurrence of metastasis in breast cancer patients treated with neoadjuvant chemotherapy: A random forest survival approach

Background

We studied the relationship between time to ipsilateral breast tumor recurrence (IBTR) and distant metastasis-free survival (DMFS) in patients with breast cancer treated by neoadjuvant chemotherapy (NAC).

Methods

Between 2002 and 2012, 1199 patients with primary breast cancer were treated with NAC. Clinical, radiological and pathological data were retrieved from medical records. Multivariate analysis was performed with the random survival forest (RSF) method, to evaluate the relationship between time to local recurrence and DMFS.

Results

Time to IBTR, local recurrence and molecular subtype were the factors most strongly associated with DMFS. In the total population, DMFS increased linearly with recurrence time, up to 50 months. For recurrences after 50 months, DMFS was similar for all times to recurrence. Considering molecular subtypes separately, the threshold was similar for the TNBC subtype (50 months), but appeared to occur later for the luminal and HER2-positive subtypes (75 months).

Conclusion

A threshold of 50 months seems to differentiate between early and late recurrences and could be used to guide the medical management of local breast tumour recurrences.

Time-varying effects of prognostic factors associated with long-term survival in breast cancer

The impact of some prognostic factors on breast cancer survival has been shown to vary with time since diagnosis. However, this phenomenon has not been evaluated in Asians. In the present study, 4886 patients were recruited from the Shanghai Breast Cancer Survival Study, a longitudinal study of patients diagnosed during 2002-2006, with a median follow-up time of 11.2 years. Cox model incorporating time-by-covariate interactions was used to describe the time-varying effects of prognostic factors related to overall survival and disease-free survival. Age ≥65 years showed a progressively negative effect on breast cancer prognosis over time, whereas tumour size >2 cm had a lasting and constant impact. Age significantly modified the effects of the tumour grade, nodal status and oestrogen receptor (ER) status on breast cancer survival. The detrimental effect of poorly differentiated tumours was time limited and more obvious in patients aged 45-54 years. Having ≥4 positive lymph nodes had a persistent and negative impact on prognosis, although it attenuated in later years; the phenomenon was more prominent in the 55-64-year age group. ER-positive status was protective in the first 3 years after diagnosis but was related to a higher risk of recurrence in later years; the time-point when ER-positive status turned into a risk factor was earlier in younger patients. These results suggest that older age, positive lymph node status, larger tumour size and ER-positive status are responsible for late death or recurrence in Asian breast cancer survivors. Extended endocrine therapy should be given earlier in younger ER-positive patients.

Reporting and methodological quality of survival analysis in articles published in Chinese oncology journals

Survival analysis methods have gained widespread use in the filed of oncology. For achievement of reliable results, the methodological process and report quality is crucial. This review provides the first examination of methodological characteristics and reporting quality of survival analysis in articles published in leading Chinese oncology journals.To examine methodological and reporting quality of survival analysis, to identify some common deficiencies, to desirable precautions in the analysis, and relate advice for authors, readers, and editors.A total of 242 survival analysis articles were included to be evaluated from 1492 articles published in 4 leading Chinese oncology journals in 2013. Articles were evaluated according to 16 established items for proper use and reporting of survival analysis.The application rates of Kaplan-Meier, life table, log-rank test, Breslow test, and Cox proportional hazards model (Cox model) were 91.74%, 3.72%, 78.51%, 0.41%, and 46.28%, respectively, no article used the parametric method for survival analysis. Multivariate Cox model was conducted in 112 articles (46.28%). Follow-up rates were mentioned in 155 articles (64.05%), of which 4 articles were under 80% and the lowest was 75.25%, 55 articles were100%. The report rates of all types of survival endpoint were lower than 10%. Eleven of 100 articles which reported a loss to follow-up had stated how to treat it in the analysis. One hundred thirty articles (53.72%) did not perform multivariate analysis. One hundred thirty-nine articles (57.44%) did not define the survival time. Violations and omissions of methodological guidelines included no mention of pertinent checks for proportional hazard assumption; no report of testing for interactions and collinearity between independent variables; no report of calculation method of sample size. Thirty-six articles (32.74%) reported the methods of independent variable selection. The above defects could make potentially inaccurate, misleading of the reported results, or difficult to interpret.There are gaps in the conduct and reporting of survival analysis in studies published in Chinese oncology journals, severe deficiencies were noted. More endorsement by journals of the report guideline for survival analysis may improve articles quality, and the dissemination of reliable evidence to oncology clinicians. We recommend authors, readers, reviewers, and editors to consider survival analysis more carefully and cooperate more closely with statisticians and epidemiologists.

Marriage is a dependent risk factor for mortality of colon adenocarcinoma without a time-varying effect

BACKGROUND

It has been well recognized that the effects of many prognostic factors could change during long-term follow-up. Although marriage has been proven to be a significant prognostic factor for the survival of colon cancer, whether the effect of marriage is constant with time remain unknown. This study analyzed the impact of marital status on the mortality of colon cancer patients with an extended Cox model that allowed for time-varying effects.

METHODS

We identified 71,955 patients who underwent colectomy between 2004 and 2009 to treat colon adenocarcinoma from the Surveilance, Epidemiology and End Results Database. The multivariate extended Cox model was used to evaluate the effect of marital status on all-cause mortality, while the Fine-Gray competing risks model was used for colon cancer-specific mortality, with death from other causes as the competing risk.

RESULTS

The unmarried patients carried a 1.37-fold increased risk of all-cause mortality compared with the married patients (95%CI: 1.33-1.40; p<0.001), and the hazard ratio remained constant over time. Being unmarried was at a higher risk of death from colon adenocarcinoma as well as death from other causes. Four variables including tumor site, tumor grade, sex and TNM stage were proved to have time-varying effects on survival.

CONCLUSIONS

Marriage is a dependent prognosis factor for survival of surgically treated colon adenocarcinoma patients. Psychological interventions are suggested to improve receipt of treatment among unmarried patients, as their poor survival may be due to the inefficient treatment.

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.