Background
Despite the lack of significant differences in long-term outcomes between administration of the same chemotherapy before or after surgery,1 neoadjuvant chemotherapy (NACT) for nonmetastatic breast cancer offers additional advantages. It can downstage tumors, facilitate less invasive surgery, and provide information on treatment response. Pathologic complete response (pCR), defined as the absence of invasive cancer in the surgical specimen after NACT, is strongly prognostic at the patient level. It is used to assess recurrence risk and tailor adjuvant treatment,2–4 leading to a paradigm shift and the wide adoption of NACT.5
Although pCR is a strong prognostic marker, it is not an adequate surrogate marker for long-term outcomes at the trial level,6–11 suggesting that additional factors influence long-term outcomes. It is of clinical interest to identify patients at increased risk of recurrence despite achieving pCR—or those with low risk despite having residual disease—to enable tailored postoperative treatment. For instance, long-term follow-up from the KATHERINE trial revealed that two-thirds of patients allocated to trastuzumab remained relapse-free—patients who are currently potentially overtreated with trastuzumab emtansine, incurring increased toxicity and costs.12 At the same time, meta-analyses have demonstrated that a sizeable minority of patients who achieve pCR eventually experience recurrence and die from metastatic disease,9 despite additional adjuvant chemotherapy not improving outcomes at the group level.13
Given these considerations, the dichotomous endpoint of pCR/residual disease may lead to both over- and undertreatment. Attempts to refine this classification have largely focused on patients with residual disease,14,15 overlooking the fact that even those who achieve pCR are a heterogeneous group in terms of long-term prognosis. In this population-based study, we aim to investigate the factors that affect survival of patients with pCR after NACT.
Methods
Study Design and Objectives
This is a retrospective analysis of a population-based, observational cohort study of prospectively registered patients who received NACT for primary, nonmetastatic breast cancer.16 This study aimed to elucidate factors that influence long-term survival of patients who achieve pCR and patterns of recurrence based on pCR status.
This study was approved by the ethics review committee in Stockholm (2016/1303-31, with subsequent amendments 2018/1049-32, 2021-01147, and 2023–02918-02). The ethical approval granted a waiver for informed consent, as this was a noninterventional study involving data collection and analysis from registries and patient records. The reporting of this study complies with the STROBE guidelines17 and the ESMO Guidance for Reporting Oncology Real-World Evidence (ESMO-GROW) checklist.18
Patient Selection
All patients diagnosed with primary breast cancer between January 1, 2007, and December 31, 2020, in the Stockholm-Gotland region, which comprises 25% of the entire Swedish population, were identified through the National Quality Registry for Breast Cancer (NKBC). NKBC is a nationwide registry that prospectively collects detailed clinical data for all patients diagnosed with in situ or invasive breast cancer in Sweden, with an excellent patient coverage rate of >99%.19,20 Patients who received NACT for stage I–III breast cancer were included. Exclusion criteria included metastatic disease, neoadjuvant endocrine therapy only, contralateral breast cancer diagnosed within 90 days of the primary diagnosis, prior ipsilateral breast cancer, and treatment administered outside the Stockholm-Gotland region.
Electronic medical records were reviewed, and data were extracted regarding patient clinical and pathologic characteristics, NACT, survival, and recurrence details to minimize missing data and misclassification. Date of death was collected from the National Cause of Death Register and cross-verified through medical records. Additionally, the National Patient Register21 was used to confirm the site of metastasis and time of recurrence. Integration of the registers and medical records was facilitated through use of the unique 10-digit Personal Identity Number assigned to all individuals registered in Sweden.
Estrogen receptor (ER) and progesterone receptor (PR) positivity were defined as ≥10% of cancer cells expressing the respective receptor, in accordance with Swedish national guidelines. HER2 status was determined based on the 2018 ASCO/College of American Pathologists (ASCO/CAP) guidelines.22 Subtypes of luminal breast cancer were classified according to the St. Gallen consensus: luminal A-like was defined as PR ≥20% and Ki-67 <15%, whereas luminal B-like was defined as PR <20% or Ki-67 ≥15%.23
Outcomes
In line with neo-STEEP guidelines, pCR was defined as the absence of invasive cancer cells in the resected breast tissue and axilla, regardless of the presence of in situ cancer (ypT0/Tis ypN0). Distant relapse-free survival (DRFS)24 was defined as the time from surgery to distant recurrence or death from any cause, whichever occurred first. This outcome was chosen because it reflects failure of primary treatment and the development of incurable disseminated cancer, without being confounded by unrelated events such as contralateral cancer, while maintaining sufficient power compared with overall survival, considering the prolonged course of metastatic disease. Patients were censored if they were lost to follow-up due to migration or if the event of interest did not occur by the end of the study period (May 2, 2023).
Statistical Analysis
The Wilcoxon rank-sum test was used to compare continuous variables, and the chi-square or Fisher exact test was used for categorical variables. The distribution of first events (either distant recurrence or death) is represented and depicted in a Lexis diagram.25 Kaplan-Meier curves were used to describe DRFS according to subgroups by pCR and baseline subtypes separately. Smoothed hazard rates estimated over time were used to characterize the heterogeneity of patients with pCR or non-pCR.26 Median follow-up for DRFS was calculated using the reverse Kaplan-Meier method. The association of pCR with DRFS was evaluated using multivariable Cox regression analysis, with results presented as adjusted hazard ratios (aHRs), 95% confidence intervals, and adjusted survival curves for covariates based on inverse probability weighting methods accordingly.27 Univariate and multivariable Cox proportional hazards (PH) regression were performed to investigate baseline prognostic factors for DRFS separately in subgroups of patients with and without pCR, with hazard ratio estimates interpreted as a weighted average association measure. To address the PH assumption in subgroup analysis of patients with pCR,28,29 we used the Schoenfeld residuals method30 and applied a nonproportional hazard model (or flexible parametric survival model31) to estimate time-varying associations between nodal status and ER with DRFS, where PH assumptions were not met.
As a sensitivity analysis, we assessed the potential impact of missing covariates in association analysis by first applying multiple imputation32 and then through multivariable models excluding variables with high missingness, such as grade. A 2-tailed P value <.05 was considered statistically significant. Data management and statistical analysis were performed using SAS 9.4 (SAS Institute Inc.) and R version 4.3.1 (R Foundation for Statistical Computing).
Results
Patient Characteristics
Throughout the study period, a total of 2,487 patients diagnosed with primary breast cancer underwent NACT and were subsequently included in the study (Figure 1). At baseline, 43.7% (n=1,088) of patients were classified as luminal (ER-positive/HER2-negative), 32.9% (n=818) as HER2-positive, and 21.8% (n=542) as triple-negative. The distribution of clinical and pathologic characteristics according to pCR status is shown in Table 1, with subtype details provided in Supplementary Tables S1–S3 (available online in the supplementary materials).
Flowchart of patient selection.
Abbreviations: NACT, neoadjuvant chemotherapy; NAT, neoadjuvant treatment; NKBC, National Quality Registry for Breast Cancer; pCR, pathologic complete response.
Citation: Journal of the National Comprehensive Cancer Network 23, 4; 10.6004/jnccn.2024.7093
Patient and Tumor Characteristics at Primary Disease
| Characteristic | Non-pCR n (%) |
pCR n (%) |
P Valuea |
|---|---|---|---|
| Patients, N | 1,826 | 661 | |
| Age | |||
| Median (IQR), y | 51.7 (43.8–61.9) | 50.4 (42.3–60.0) | .003 |
| <55 y | 1,056 (57.8) | 412 (62.3) | .049 |
| ≥55 y | 770 (42.2) | 249 (37.7) | |
| Chemotherapy | <.001 | ||
| Anthracycline and taxane | 1,449 (79.4) | 542 (82.0) | |
| Anthracycline | 89 (4.9) | 14 (2.1) | |
| Taxane | 251 (13.7) | 75 (11.3) | |
| Other | 37 (2.0) | 30 (4.5) | |
| T stage | .045 | ||
| T0–1 | 267 (14.7) | 120 (18.3) | |
| T2 | 1,070 (58.7) | 392 (59.7) | |
| T3 | 408 (22.4) | 122 (18.6) | |
| T4 | 77 (4.2) | 23 (3.5) | |
| Missing | 4 | 4 | |
| Nodal status | .315 | ||
| Negative | 815 (44.8) | 310 (47.2) | |
| Positive | 1,004 (55.2) | 347 (52.8) | |
| Missing | 7 | 4 | |
| Histologic type | <.001 | ||
| Ductal | 1,419 (80.3) | 529 (89.4) | |
| Lobular | 124 (7.0) | 7 (1.2) | |
| Other | 224 (12.7) | 56 (9.5) | |
| Missing | 59 | 69 | |
| Grade | <.001 | ||
| Grade 1 | 48 (2.9) | 2 (0.3) | |
| Grade 2 | 814 (49.6) | 137 (23.8) | |
| Grade 3 | 778 (47.4) | 437 (75.9) | |
| Missing | 186 | 85 | |
| ER | <.001 | ||
| Negative | 501 (27.5) | 391 (59.4) | |
| Positive | 1,324 (72.5) | 267 (40.6) | |
| Missing | 1 | 3 | |
| ER expression level | <.001 | ||
| 0% | 457 (25.2) | 332 (51.2) | |
| 1%–9% | 36 (2.0) | 51 (7.9) | |
| 10%–49% | 82 (4.5) | 60 (9.2) | |
| 50%–100% | 1,237 (68.3) | 206 (31.7) | |
| Missing | 14 | 12 | |
| Progesterone receptor | <.001 | ||
| Negative | 818 (44.9) | 507 (77.1) | |
| Positive | 1,005 (55.1) | 151 (22.9) | |
| Missing | 3 | 3 | |
| HER2 status | <.001 | ||
| HER2 0 | 509 (30.3) | 131 (20.9) | |
| HER2-low | 725 (43.2) | 122 (19.5) | |
| HER2-positive | 445 (26.5) | 373 (59.6) | |
| Missing | 147 | 35 | |
| Clinical subtypes | <.001 | ||
| ER+/HER2− | 994 (55.3) | 94 (14.4) | |
| HER2+ | 445 (24.8) | 373 (57.3) | |
| TNBC | 358 (19.9) | 184 (28.3) | |
| Missing | 29 | 10 | |
| Ki-67 | <.001 | ||
| Median (IQR) | 40 (25–60) | 51 (35–70) | |
| Missing | 43 | 18 | |
| Year of diagnosis | .032 | ||
| 2007–2010 | 282 (15.4) | 88 (13.3) | |
| 2011–2014 | 403 (22.1) | 134 (20.3) | |
| 2015–2018 | 685 (37.5) | 291 (44.0) | |
| 2019–2020 | 456 (25.0) | 148 (22.4) | |
Abbreviations: ER, estrogen receptor; pCR, pathologic complete response; TNBC, triple-negative breast cancer.
Wilcoxon rank-sum test for continuous variables (age, Ki-67); chi-square tests used for others.
Predictors of pCR
In total, 661 (26.6%) patients achieved pCR. Patients with HER2-positive tumors, of whom 96.8% received HER2-targeted therapy, had the highest probability of achieving pCR (45.6%), followed by patients with triple-negative breast cancer (33.9%). Only 8.6% of patients with ER-positive/HER2-negative tumors achieved pCR, including 1.1% of those with luminal A-like tumors and 9.7% with luminal B-like tumors.
Patients who achieved pCR were more likely to be younger and have a lower primary T stage, higher tumor grade, ductal histology, higher Ki-67, HER2-positive tumors, and hormone receptor–negative tumors (Table 1). Within the subset of ER-positive tumors, we observed an inverse association of achieving pCR with increasing levels of ER expression (Supplementary Figure S1). Calendar period of diagnosis was not associated with likelihood of achieving pCR.
Long-Term Survival After NACT
Median follow-up was 5.9 years (IQR, 3.8–9.0). The distribution of events over time is shown in Supplementary Figure S2. Patients with pCR demonstrated significantly improved DRFS compared with those with residual disease (aHR, 0.35; 95% CI, 0.35–0.46; Figure 2, Supplementary Figure S3). The 5-year DRFS rate was 91.0% (95% CI, 88.7–93.3) in patients with pCR, compared with 76.3% (95% CI, 74.3–78.5) in those with residual disease (log-rank P<.0001).
Kaplan-Meier curves for DRFS according to breast cancer subtype and pathologic response to treatment.
Abbreviations: DRFS, distant relapse-free survival; ER, estrogen receptor; pCR, pathologic complete response; TNBC, triple-negative breast cancer.
Citation: Journal of the National Comprehensive Cancer Network 23, 4; 10.6004/jnccn.2024.7093
When plotting hazards over time, both patients with residual disease and, to a lesser extent, those with pCR showed a peak in distant recurrence risk at 1 year postsurgery, followed by a sharp decline in risk. This risk remained elevated for patients who did not achieve pCR for more than a decade postoperatively, regardless of breast cancer subtype (Figure 3, Supplementary Table S4).
Smoothed hazards plots for distant relapse-free survival in (A) the total population and patients with (B) ER-positive/HER2-negative and (C) HER2-positive breast cancer, and (D) TNBC.
Abbreviations: ER, estrogen receptor; pCR, pathologic complete response; TNBC, triple-negative breast cancer.
Citation: Journal of the National Comprehensive Cancer Network 23, 4; 10.6004/jnccn.2024.7093
Factors Affecting Outcome in Patients With pCR and Residual Disease
Among 661 patients with pCR, baseline prognostic factors independently associated with DRFS included age (aHR, 1.04; 95% CI, 1.01–1.06), T3/T4 stage (aHR, 2.02; 95% CI, 1.05–3.87), and HER2 positivity (aHR, 0.34; 95% CI, 0.17–0.68) (Figure 4). Because nodal and ER status violated the PH assumption (P=.024 and P=.002, respectively), a nonproportional hazard model was applied (Supplementary Figure S4). Node positivity was found to be a prognostic factor for distant recurrence within the first year postsurgery (aHR, 2.84; 95% CI, 1.16–6.94), whereas ER positivity was associated with distant recurrence at 10 years (aHR, 4.30; 95% CI, 1.06–17.49) (Figure 4).
Forest plot describing baseline characteristics associated with distant relapse-free survival for patients (A) with and (B) without pCR after neoadjuvant chemotherapy.
Abbreviations: aHR, adjusted hazard ratio; IR, incidence rate; pCR, pathologic complete response.
aIR calculated for the entire first year. Hazard ratio estimated at the 1-year time point.
bIR calculated based on events that occurred in the fourth and fifth years.
cIR calculated based on events that occurred from the sixth to ninth year.
Citation: Journal of the National Comprehensive Cancer Network 23, 4; 10.6004/jnccn.2024.7093
Among patients with residual disease, baseline factors independently associated with DRFS included T3/T4 stage (aHR, 1.42; 95% CI, 1.13–1.78), nodal status (aHR, 1.82; 95% CI, 1.45–2.29), ER positivity (aHR, 0.66; 95% CI, 0.49–0.90), PR positivity (aHR, 0.64; 95% CI, 0.49–0.85), and HER2 positivity (aHR, 0.58; 95% CI, 0.43–0.77) (Figure 4).
Patterns of Relapse According to pCR
Local relapse as first site of recurrence was significantly more common in patients with residual disease compared with those who achieved pCR (5-year cumulative incidence rate, 4.5% vs 1.5%, P<.001; Supplementary Figure S5). In contrast, the 5-year cumulative incidence of central nervous system (CNS) relapse as the first site of metastasis was 5.3% in patients without pCR and 4.1% in those with pCR (P=.21; Supplementary Figure S6).
In total, 474 patients were diagnosed with distant metastasis following NACT (after a median time of 1.4 years for patients with pCR and 2.3 for those with residual disease). The median overall survival from the date of distant recurrence was 15.1 months (95% CI, 13.7–18.1) for patients with residual disease and 13.7 months (95% CI, 8.4–21.2) for those with pCR (log-rank P=.39; inverse probability weighted Cox regression P=.79; Supplementary Figure S7).
Sensitivity Analysis
Given that grade was the greatest contributor to missing data, sensitivity analysis was performed excluding grade for both patients with pCR and those with residual disease (Supplementary Table S5). Additionally, recognizing the association between missing data and worse outcomes in retrospective breast cancer studies,32 we implemented multiple imputation for all missing primary disease data, using 500 imputed datasets (Supplementary Table S6). Our findings remained unchanged in both the sensitivity analysis and the multiple imputation.
Discussion
In this retrospective analysis of a prospectively collected, population-based cohort of 2,487 patients with breast cancer who received NACT, we investigated prognostic factors that influence long-term outcomes despite achieving pCR. We first confirmed known predictors of pCR, as well as its strong prognostic significance across breast cancer subtypes. Among patients who achieved pCR, age, tumor size and HER2 positivity were associated with long-term outcomes. Additionally, ER positivity was associated with late distant recurrence, whereas positive nodal status was associated with distant relapse during the first postoperative year. This may reflect the presence of metastatic disease at the time of diagnosis, which may not have been captured by cross-sectional imaging.
The recognition that patients with residual invasive disease fare significantly worse than those with pCR has led to the development of postneoadjuvant strategies, contributing to an improvement in overall survival.3,12,33 This has also shaped the current clinical trial landscape, where novel agents such as antibody–drug conjugates are being evaluated for this patient population.34 In parallel, newer prognostic schemes such as Residual Cancer Burden and Neo-Bioscore provide more granular classifications of the heterogeneous population with residual disease. However, their clinical utility remains limited, as de-escalation of postneoadjuvant treatment based on these classifiers, beyond pCR/non-pCR status, is not currently recommended. Nevertheless, both previously reported35 and ongoing trials (ClinicalTrials.gov identifier: NCT04675827) incorporate these classifications to identify high-risk patients for therapy escalation.
On the other hand, little focus has been placed on patients who achieve pCR, as they are generally considered to have an excellent prognosis. At the group level, further adjuvant chemotherapy does not impact prognosis.13 Ongoing trials aiming to de-escalate therapy are enrolling unselected patients with pCR following NACT (NCT05812807), despite the fact that a minority will still experience disease relapse.36 These observations suggest that the current “one-size-fits-all” approach to managing patients with pCR is inadequate. Large meta-analyses have demonstrated that only a small percentage of the variation in overall survival among these patients is explained by pCR status.8,9 Our results indicate that, at the population level and across breast cancer subtypes, baseline prognostic factors continue to be important determinants of distant relapse, supporting prior findings in patients with HER2-positive disease who participated in clinical trials.37,38 Therefore, pCR can no longer be considered the “great equalizer” of outcomes. Clinical trials enrolling otherwise high-risk patients who have achieved pCR and randomizing them to postneoadjuvant interventions should be designed to address this issue. These interventions should also include both novel therapeutics, such as vaccines (NCT05232916), and risk stratification using circulating biomarkers in addition to pCR status.39 Moreover, the lack of association between pCR status and the incidence of CNS relapse suggests that current therapies have limited effectiveness due to the pharmacological sanctuary created in the CNS by the intact blood–brain barrier. Current postneoadjuvant therapies do not seem to influence rates of CNS relapse among patients with residual disease,12 further underscoring the unmet clinical need that ongoing trials are attempting to address (NCT04457596, NCT04622319).
The size and population-based design of this study limit selection bias compared with both clinical trials, in which external validity may suffer despite excellent internal validity,40 and institutional retrospective cohort studies. Additionally, the low rates of missing data and misclassification due to cross-referencing with patient charts further improve the interpretability of our results. To mitigate bias, we conducted additional sensitivity analyses and multiple imputations to account for missing data, without affecting our results. However, there are limitations that must be acknowledged. The long study period, during which neoadjuvant treatment evolved both in terms of new medications and indications, may have led to stage migration, as neoadjuvant therapy was increasingly extended to less-advanced tumors in recent years, thus including more low-risk patients with shorter follow-up. Moreover, information on adjuvant therapy was not captured in this study. However, oncology practice in the Stockholm-Gotland region is largely homogeneous, due to annually updated regional guidelines that do not recommend additional adjuvant chemotherapy for patients achieving pCR in routine clinical practice. This homogeneity reduces the impact of this limitation.
Conclusions
Our findings confirm that patients achieving pCR have a better prognosis than those with residual disease. However, age, tumor stage, nodal stage, and HER2 and ER status continue to be associated with long-term outcomes, even after achieving pCR. These results underscore the importance of considering baseline tumor characteristics when exploring postneoadjuvant therapy approaches.
References
- 1.↑
Early Breast Cancer Trialists’ Collaborative Group. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. Lancet Oncol 2018;19:27–39.
- 2.↑
Fisher B, Bryant J, Wolmark N, et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol 1998;16:2672–2685.
- 3.↑
Masuda N, Lee SJ, Ohtani S, et al. Adjuvant capecitabine for breast cancer after preoperative chemotherapy. N Engl J Med 2017;376:2147–2159.
- 4.↑
von Minckwitz G, Huang CS, Mano MS, et al. Trastuzumab emtansine for residual invasive HER2-positive breast cancer. N Engl J Med 2019;380:617–628.
- 5.↑
Loibl S, Andre F, Bachelot T, et al. Early breast cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol 2024;35:159–182.
- 6.↑
Bonnefoi H, Litiere S, Piccart M, et al. Pathological complete response after neoadjuvant chemotherapy is an independent predictive factor irrespective of simplified breast cancer intrinsic subtypes: a landmark and two-step approach analyses from the EORTC 10994/BIG 1-00 phase III trial. Ann Oncol 2014;25:1128–1136.
- 7.↑
von Minckwitz G, Untch M, Blohmer JU, et al. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol 2012;30:1796–1804.
- 8.↑
Conforti F, Pala L, Sala I, et al. Evaluation of pathological complete response as surrogate endpoint in neoadjuvant randomised clinical trials of early stage breast cancer: systematic review and meta-analysis. BMJ 2021;375:e066381.
- 9.↑
Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 2014;384:164–172.
- 10.↑
Squifflet P, Saad ED, Loibl S, et al. Re-evaluation of pathologic complete response as a surrogate for event-free and overall survival in human epidermal growth factor receptor 2-positive, early breast cancer treated with neoadjuvant therapy including anti-human epidermal growth factor receptor 2 therapy. J Clin Oncol 2023;41:2988–2997.
- 11.↑
Villacampa G, Matikas A, Oliveira M, et al. Landscape of neoadjuvant therapy in HER2-positive breast cancer: a systematic review and network meta-analysis. Eur J Cancer 2023;190:112885.
- 12.↑
Loibl S, Mano MS, Untch M, et al. Phase III study of adjuvant ado-trastuzumab emtansine vs trastuzumab for residual invasive HER2-positive early breast cancer after neoadjuvant chemotherapy and HER2-targeted therapy: KATHERINE final IDFS and updated OS analysis. Presented at the 2023 San Antonio Breast Cancer Symposium; December 5–9, 2023; San Antonio, Texas. Abstract GS03-12.
- 13.↑
Spring LM, Fell G, Arfe A, et al. Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis. Clin Cancer Res 2020;26:2838–2848.
- 14.↑
Yau C, Osdoit M, van der Noordaa M, et al. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients. Lancet Oncol 2022;23:149–160.
- 15.↑
Mittendorf EA, Vila J, Tucker SL, et al. The Neo-Bioscore update for staging breast cancer treated with neoadjuvant chemotherapy: incorporation of prognostic biologic factors into staging after treatment. JAMA Oncol 2016;2:929–936.
- 16.↑
Boman C, Liu X, Eriksson Bergman L, et al. A population-based study on trajectories of HER2 status during neoadjuvant chemotherapy for early breast cancer and metastatic progression. Br J Cancer 2024;131:718–728.
- 17.↑
Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med 2007;4:e297.
- 18.↑
Castelo-Branco L, Pellat A, Martins-Branco D, et al. ESMO Guidance for Reporting Oncology Real-World Evidence (GROW). Ann Oncol 2023;34:1097–1112.
- 19.↑
Lofgren L, Eloranta S, Krawiec K, et al. Validation of data quality in the Swedish National Register for Breast Cancer. BMC Public Health 2019;19:495.
- 20.↑
National Breast Cancer Quality Register (NKBC). 2023 Annual Report. Accessed June 1, 2024. Available at: https://statistik.incanet.se/brostcancer/
- 21.↑
National Patient Register. Accessed June 1, 2024. Available at: https://www.socialstyrelsen.se/en/statistics-and-data/registers/national-patient-register/
- 22.↑
Wolff AC, Hammond MEH, Allison KH, et al. Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline focused update. J Clin Oncol 2018;36:2105–2122.
- 23.↑
Goldhirsch A, Wood WC, Coates AS, et al. Strategies for subtypes–dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011;22:1736–1747.
- 24.↑
Litton JK, Regan MM, Pusztai L, et al. Standardized Definitions for Efficacy End Points in neoadjuvant breast cancer clinical trials: NeoSTEEP. J Clin Oncol 2023;41:4433–4442.
- 25.↑
Dahlin S. Exploring the usefulness of Lexis diagrams for quality improvement. BMC Med Inform Decis Mak 2020;20:7.
- 26.↑
Wang JC. Smoothing hazard rates. In: Balakrishnan N, Colton T, Everitt B, et al, eds. Encyclopedia of Biostatistics, 2nd ed. John Wiley & Sons, Inc.; 2005.
- 27.↑
Denz R, Klaaßen-Mielke R, Timmesfeld N. A comparison of different methods to adjust survival curves for confounders. Stat Med 2023;42:1461–1479.
- 28.↑
Sjolander A, Dickman P. Why test for proportional hazards-or any other model assumptions? Am J Epidemiol 2024;193:926–927.
- 29.↑
Jatoi I, Anderson WF, Jeong JH, Redmond CK. Breast cancer adjuvant therapy: time to consider its time-dependent effects. J Clin Oncol 2011;29:2301–2304.
- 30.↑
Grambsch PM, Therneu TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994;81:515–526.
- 31.↑
Royston P, Parmar MK. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 2002;21:2175–2197.
- 32.↑
Plichta JK, Rushing CN, Lewis HC, et al. Implications of missing data on reported breast cancer mortality. Breast Cancer Res Treat 2023;197:177–187.
- 33.↑
Tutt ANJ, Garber JE, Kaufman B, et al. Adjuvant olaparib for patients with BRCA1- or BRCA2-mutated breast cancer. N Engl J Med 2021;384:2394–2405.
- 34.↑
Monteiro MR, Nunes NCC, Junior A, et al. Antibody-drug conjugates in breast cancer: a comprehensive review of how to selectively deliver payloads. Breast Cancer (Dove Med Press) 2024;16:51–70.
- 35.↑
Loibl S, Marme F, Martin M, et al. Palbociclib for residual high-risk invasive HR-positive and HER2-negative early breast cancer-the Penelope-B trial. J Clin Oncol 2021;39:1518–1530.
- 36.↑
Schmid P, Cortés J, Dent R, et al. LBA18 Pembrolizumab or placebo plus chemotherapy followed by pembrolizumab or placebo for early-stage TNBC: updated EFS results from the phase III KEYNOTE-522 study. Ann Oncol 2023;34(Suppl 2):Abstract S1257.
- 37.↑
van Mackelenbergh MT, Loibl S, Untch M, et al. Pathologic complete response and individual patient prognosis after neoadjuvant chemotherapy plus anti-human epidermal growth factor receptor 2 therapy of human epidermal growth factor receptor 2-positive early breast cancer. J Clin Oncol 2023;41:2998–3008.
- 38.↑
Huober J, van Mackelenbergh M, Schneeweiss A, et al. Identifying breast cancer patients at risk of relapse despite pathological complete response after neoadjuvant therapy. NPJ Breast Cancer 2023;9:23.
- 39.↑
Magbanua MJM, Swigart LB, Wu HT, et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann Oncol 2021;32:229–239.
- 40.↑
Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?” Lancet 2005;365:82–93.
