Delay of Treatment Initiation Does Not Adversely Affect Survival Outcome in Breast Cancer

Tae-Kyung Yoo; Wonshik Han; Hyeong-Gon Moon; Jisun Kim; Jun Woo Lee; Min Kyoon Kim; Eunshin Lee; Jongjin Kim; Dong-Young Noh

doi:10.4143/crt.2015.173

Articles

Page Path: HOME > Cancer Res Treat > Volume 48(3); 2016 > Article

Original Article Delay of Treatment Initiation Does Not Adversely Affect Survival Outcome in Breast Cancer: Tae-Kyung Yoo, MD¹, Wonshik Han, MD, PhD^1,2,, Hyeong-Gon Moon, MD, PhD^1,2, Jisun Kim, MD, PhD^1,a, Jun Woo Lee, MD^1,b, Min Kyoon Kim, MD, PhD^1,c, Eunshin Lee, MD^1,d, Jongjin Kim, MD^1,e, Dong-Young Noh, MD, PhD^1,2; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2016;48(3):962-969.
DOI: https://doi.org/10.4143/crt.2015.173
Published online: October 22, 2015

¹Department of Surgery, Seoul National University College of Medicine, Seoul, Korea

²Laboratory of Breast Cancer Biology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

Correspondence: Wonshik Han, MD, PhD Department of Surgery, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
Tel: 82-2-2072-1958 Fax: 82-2-766-3975 E-mail: hanw@snu.ac.kr

aPresent address: Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
bPresent address: Department of Surgery, Ewha Womans University School of Medicine, Seoul, Korea
cPresent address: Department of Surgery, Kangwon National University, Chuncheon, Korea
dPresent address: Department of Surgery, Bundang Jesaeng Hospital, Seongnam, Korea
ePresent address: Department of Surgery, SMG-SNU Boramae Medical Center, Seoul, Korea

• Received: May 19, 2015 • Accepted: September 30, 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

12,621 Views
209 Download
31 Web of Science
25 Crossref
29 Scopus

prev next

Full Article

Download PDF

Abstract

Purpose
Previous studies examining the relationship between time to treatment and survival outcome in breast cancer have shown inconsistent results. The aim of this study was to analyze the overall impact of delay of treatment initiation on patient survival and to determine whether certain subgroups require more prompt initiation of treatment.
Materials and Methods
This study is a retrospective analysis of stage I-III patients who were treated in a single tertiary institution between 2005 and 2008. Kaplan-Meier survival analysis and Cox proportional hazards regression model were used to evaluate the impact of interval between diagnosis and treatment initiation in breast cancer and various subgroups.
Results
A total of 1,702 patients were included. Factors associated with longer delay of treatment initiation were diagnosis at another hospital, medical comorbidities, and procedures performed before admission for surgery. An interval between diagnosis and treatment initiation as a continuous variable or with a cutoff value of 15, 30, 45, and 60 days had no impact on disease-free survival (DFS). Subgroup analyses for hormone-responsiveness, triple-negative breast cancer, young age, clinical stage, and type of initial treatment showed no significant association between longer delay of treatment initiation and DFS.
Conclusion
Our results show that an interval between diagnosis and treatment initiation of 60 days or shorter does not appear to adversely affect DFS in breast cancer.
Key words: Breast neoplasms, Time-to-treatment, Survival rate

Introduction

While starting treatment for breast cancer without delay is theoretically ideal, there are no established guidelines regarding what in practice constitutes an acceptable interval between the diagnosis of breast cancer and treatment initiation. Many factors may contribute to the delay of treatment initiation and although a number of studies have been conducted to assess what influence this might have on patient survival, their results have been conflicting [1-6].

Regardless of its cause, delay of treatment initiation causes great anxiety to patients and their families. According to a study examining the quality of life across the continuum of breast cancer care, the most anxiety-provoking time for patients is the waiting period for treatment initiation after diagnosis [7]. Most patients fear that their cancer will progress during this time and prolonged delay of treatment initiation can also cause concern to the treating physician. Knowing the potential influence of delay of treatment initiation on patient survival, and distinguishing those patients who require more timely treatment can be clinically valuable.

Through this study, we sought to investigate demographic and clinical pathological factors associated to delay of treatment initiation and to assess the impact of delay of treatment initiation on patient survival and identify which subgroup(s) of patients require more prompt treatment initiation.

Materials and Methods

A retrospective review of patients who underwent surgery for breast cancer at Seoul National University Hospital (SNUH) between July 2005 and June 2008 was performed. Basic clinicopathological data were extracted from SNUH Breast Care Center database, which is a prospectively maintained web-based database, and “event” data were reviewed by the first author using the electronic medical records. Survival data was obtained from the Korean National Statistical Office database. Patients with invasive breast cancer who started their initial treatment at SNUH and for whom either the date of pathological diagnosis or date of referral was known were included. Patients who underwent surgery for in-situ carcinoma, those who underwent palliative operations (including patients diagnosed with distant metastases within 4 months of diagnosis), patients who did not have adjuvant therapy data or those who refused recommended adjuvant treatment were excluded. Patients who received neoadjuvant chemotherapy were excluded as these patients have different clinicopathologic characteristics compared to patients undergoing surgery as initial treatment. Also patients with a treatment delay of 6 months or greater were excluded, presuming that such unusually long intervals would be due to a patient’s, or their family’s refusal of standard treatment, which was not the main concern of this study.

Interval between diagnosis and treatment initiation was defined as time between date of pathological diagnosis and start of treatment. Pathological diagnosis was made by core needle biopsy or fine needle aspiration. Where pathological diagnosis had been made in another institution and therefore date was unknown, date of referral from the other hospital was used instead as there normally is only 2-3 days difference in these two dates. Where both dates were unknown, the patient was excluded from the study.

Patient-level socio-demographic variables included age at diagnosis, marital status, district of residence, comorbidities, hospital of diagnosis, presence of breast cancer-related symptoms at diagnosis, and family history of cancer. District of residence was categorized according to either Seoul-Incheon (Capital) area and its’ satellite cities, or outside the Capital area.

Tumor-specific characteristics included: tumor size, axillary lymph node metastasis status, cancer stage, histologic grade, tumor hormone receptor status, and human epidermal growth factor receptor 2 (HER2) status. Clinical stage was determined by physical examination and referring hospital imaging results which were assessed at the patient’s first visit to SNUH outpatient clinic. Pathological breast cancer staging was defined according to the 7th edition of the American Joint Committee on Cancer.

Clinical characteristics included factors that can delay treatment initiation such as the need for an additional biopsy, preoperative imaging studies performed prior to admission, clinical consultation with other departments due to comorbidities, hospitalization prior to surgery, and immediate breast reconstruction. Imaging studies were categorized according to routine staging work-up (chest computed tomography, bone scan, breast magnetic resonance imaging, or positron emission tomography computed tomography) versus non-routine imaging.

The primary endpoint was disease-free survival (DFS). DFS was calculated from the time of treatment initiation to either the date of breast cancer recurrence or the final outpatient clinic visit. Breast cancer recurrence included locoregional recurrence and distant metastases. Secondary endpoint was overall survival (OS), defined as date of treatment initiation to date of expire or date of last out-patient clinic visit. Analyses were performed to assess the relationship between baseline characteristics with the length of interval between diagnosis and treatment initiation, using chi-square test and t test. In addition the impact of interval length on DFS and OS was evaluated using Kaplan-Meier survival analysis and log-rank test. Multivariate survival analysis adjusting clinicopathologic factors that are known to affect patients’ survival, including age, tumor size, lymph node metastasis, histologic grade, and hormone receptor status, was performed using a Cox proportional hazards regression model. For subgroup analysis, interval between diagnosis and treatment initiation was dichotomized into two groups (0 to 29 and ≥ 30 days).

This study was approved by the Institutional Review Board of SNUH and the committee waived the requirement for informed consent.

Results

A total of 2,256 patients underwent curative surgery for invasive breast cancer at SNUH from July 2005 to June 2008; 554 patients were excluded from the study, including 234 patients who received neoadjuvant chemotherapy and 264 patients whose date of pathological diagnosis or referral date from another hospital was unknown. The mean age of the 1,702 patients who were included in the study was 48.0 years. Their median interval between diagnosis and treatment initiation was 23 days (range, 0 to 134 days); 66.6% of women received initial treatment within 30 days of diagnosis and 1.8% received initial treatment more than 60 days after their diagnosis. The distribution of interval between diagnosis and treatment initiation is shown in Fig. 1.

Various factors were associated with longer interval between diagnosis and treatment initiation. Demographic characteristics significantly associated with longer interval of ≥ 30 days were diagnosis at another hospital (p < 0.001) and medical comorbidities (p=0.015). In addition, interval between diagnosis and treatment initiation was significantly longer for women who underwent imaging studies prior to admission for surgery (p < 0.001), those who required an additional biopsy (p < 0.001), those who required clinical consultation with other departments (p < 0.001), and those who required hospitalization prior to treatment initiation (p < 0.001). Age or immediate reconstructive surgery were not associated with longer interval between diagnosis and treatment initiation (p > 0.05).

Clinical stage and pathological stage did not differ according to interval to treatment initiation. Patients with hormone receptor-positive tumors had longer intervals between diagnosis and treatment initiation (p=0.043). Treatment related factors associated with longer intervals were adjuvant endocrine therapy (p=0.023) and adjuvant chemotherapy (p=0.004) (Table 1).

The median duration of follow up was 5.9 years. 5-Year OS and DFS rate were 95.9% and 91.3%, respectively. An interval of 15, 30 days had no impact on DFS by univariate and multivariate analysis (p=0.079 and p=0.101, respectively) (Table 2, Fig. 2A and B). In addition, a longer interval of 45 or 60 days had no impact on DFS (p=0.431 and p=0.839, respectively) (Fig. 2C and D), and an interval as a continuous variable also had no significant influence (p=0.093). Regarding OS, no significant association between an interval of ≥ 30 days was demonstrated (p=0.952) (Fig. 3).

Subgroup analyses were performed to determine which patients with an interval of 30 days and over might significantly have worse DFS. However, no significant association was found for hormone receptor–positive vs. –negative tumor groups, triple-negative breast cancer, younger (< 40 years) vs. older women, clinical stage T2 or greater vs. stage T1 and clinically lymph node–positive vs. –negative groups.

Discussion

This study showed that a delay of treatment initiation at any cut-off point within 60 days after biopsy confirmation had no impact on DFS and OS in breast cancer. Although with shorter interval, these results are consistent with the recent study by Brazda et al. [1], which showed that delays in time to treatment over 90 days had no effect on OS in breast cancer. Mujar et al. [3] also reported that delays in time to primary treatment over 2 months have no impact on breast cancer survival. However, two population-based cohort studies from Korea reported opposing results and suggested that longer intervals between diagnosis and treatment initiation are related to worse OS in breast cancer [4,6]. Both studies used nation-wide cancer registry data as their source for the cancer diagnosis date, and health insurance data for treatment information. Nation-wide databases such as these tend to be limited in the accuracy and detail of their data. In contrast, in our study we used electronic medical record data derived from a single institution, which would be expected to be more accurate and includes detailed clinicopathologic information and cancer recurrence data. We also excluded patients with an unusually long delay of treatment initiation of more than 6 months. Such patients may have ignored their diagnosis of breast cancer, refused standard treatment, or looked for alternative medical treatments.

Previous studies have reported on the impact of treatment delay on patient survival in various subgroups. Smith et al. [5] found that when younger (< 40 years) patients underwent surgery as their initial treatment, women with a delay in surgical treatment of over 6 weeks had 10% decreased OS compared to women with a delay in surgical treatment of 2 weeks or shorter. McLaughlin et al. [2] reported that late stage breast cancer patients, including metastatic breast cancer, had a worse survival when treatment delay was 60 days or over, whereas Eastman et al. [8] found no relationship between treatment delay and OS in triple negative breast cancer. Regarding our study, subgroup analysis showed that age, clinical stage and hormone receptor status had no impact on DFS.

The mean treatment delay of 23 days in our study was slightly shorter compared to reports from western countries (22-46 days) [1,2,5,8]. This reflects the Korean healthcare system, where fee-for-service reimbursement does not influence treatment delay [4]. Patients can freely choose their medical attendant and hospital, and delay due to referral is relatively short. On the other hand, this interval is longer than results from a Korean nationwide database (14 days) [4] due to the fact that SNUH is a tertiary referral center with most patients being diagnosed at other hospitals (92.3%).

Women with comorbidities had significantly longer treatment delay, as these patients required more clinical work up before beginning treatment. In addition, performing procedures or consultation before treatment initiation was associated with treatment delay. Another factor contributing to longer treatment delay was when the patient was diagnosed at other hospitals. Many patients are referred from secondary or tertiary hospitals to central high-volume hospitals in Korea. This increases the patient’s travel distance and can lead to treatment delay [9-11], and to over-loading [12-14] and provider-related delay in high-volume hospitals. Longer interval in high-volume hospitals was also demonstrated in the report from the Korean Central Cancer Registry [6].

The retrospective analysis is the limitation of this study. The reason for treatment delay could not be accurately evaluated in this retrospective study. In addition, it was impossible to know the time interval between symptom presentation to diagnosis. Patients who received neoadjuvant chemotherapy were excluded, so that patients with more aggressive tumors might have been excluded resulting in a selection bias. After subgrouping, number of patients in each subgroup was sometimes too small to perform survival analyses, which was another limitation.

Conclusion

In conclusion, breast cancer patients who were diagnosed at another hospital or had medical comorbidities were more likely to have a longer interval between diagnosis and treatment initiation. Also, undergoing additional procedures before admission for surgery influenced treatment delay. However treatment delay had no impact on DFS, allowing breast cancer patients to endure the nervous wait until treatment initiation without concern for disease progression.

NOTES

Conflict of interest relevant to this article was not reported.

Acknowledgements

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C3405, HI14C1277, HI13C2148).

Fig. 1.

Distribution of interval between diagnosis and treatment initiation.

Fig. 2.

Kaplan-Meier survival curves of disease-free survival (DFS) by interval between diagnosis and treatment. (A) DFS by interval of ≥ 15 days versus 0-14 days. (B) DFS by interval of ≥ 30 days versus 0-29 days. (C) DFS by interval of ≥ 45 days versus 0-44 days. (D) DFS by interval of ≥ 60 days versus 0-59 days.

Fig. 3.

Kaplan-Meier survival curves of overall survival by interval of ≥ 30 days versus 0-29 days.

Table 1.

Socio-demographic, clinical, and tumor-specific characteristics associated with interval between diagnosis and treatment initiation of ≥ 30 days

Factor	Interval of 0 to 29 days	Interval of > 30 days	p-value^a)
Total (n=1,702)	1,133 (66.6)	569 (33.4)
Age (yr)
≤ 39	180 (15.9)	74 (13.0)	0.336
40-49	478 (42.2)	241 (42.4)
50-59	320 (28.2)	160 (28.1)
60-69	124 (10.9)	78 (13.7)
≥ 70	31 (2.7)	16 (2.8)
Address
Seoul-Incheon area	795 (70.2)	398 (69.9)	0.925
Outside of capital area	338 (29.8)	171 (30.1)
Place of diagnosis
SNUH	494 (43.6)	195 (34.3)	< 0.001
Other hospital	639 (56.4)	374 (65.7)
Education
≤ High school	636 (56.1)	350 (61.5)	0.091
> High school	443 (39.1)	92 (33.7)
Unknown	54 (4.8)	27 (4.7)
Marital status
Married	1,033 (91.2)	516 (90.7)	0.621
Single	60 (5.3)	38 (6.7)
Divorced	9 (0.8)	3 (0.5)
Widowed	7(0.6)	4 (0.7)
Unknown	24 (2.1)	8 (1.4)
Comorbidity
Other than cancer	306 (27.0)	186 (32.7)	0.015
No comorbidities	827 (73.0)	383 (67.3)
Symptom
Yes	761 (67.2)	362 (63.6)	0.155
No	369 (32.6)	207 (36.4)
Unknown	3 (0.3)	0\|
Cancer family history
Breast cancer related	101 (8.9)	43 (7.5)	0.609
Not related to breast cancer	173 (15.3)	85 (14.9)
No cancer family history	859 (75.8)	441 (77.5)
Additional biopsy
Any	36 (3.2)	62 (10.9)	< 0.001
None	1,097 (96.8)	507 (89.1)
Imaging before admission
Any	424 (37.4)	340 (59.8)	< 0.001
None	709 (62.6)	229 (40.2)
Not routine imaging before admission
Any	5 (0.4)	27 (4.7)	< 0.001
None	1,128 (99.6)	542 (95.3)
Hospitalization
Any	2 (0.2)	11 (1.9)	< 0.001
None	1,131 (99.8)	558 (98.1)
Consultation
Any	48 (4.2)	56 (9.8)	< 0.001
None	1,085 (95.8)	513 (90.2)
Immediate reconstruction
Yes	11 (1.0)	6 (1.1)	0.870
No	1,122 (99.0)	563 (98.9)
Clinical stage at diagnosis
Diagnosis by FNA	29 (2.6)	14 (2.5)	0.109
In situ cancer	91 (8.0)	59 (10.4)
T1N0	681 (60.1)	357 (62.7)
≥ T2 or LN(+)	332 (29.3)	139 (24.4)
Pathological stage
T size ≤ 2 cm	625 (55.2)	327 (57.5)	0.366
T size > 2 cm	508 (44.8)	242 (42.5)
No ALN metastasis	727 (64.2)	387 (68.0)	0.115
ALN metastasis	406 (35.8)	182 (32.0)
Histologic grade
Grade 1, 2	527 (50.6)	290 (55.3)	0.075
Grade 3	515 (59.4)	234 (44.7)
Hormone receptor status
Positive	764 (67.4)	411 (72.2)	0.043
Negative	369 (32.6)	158 (27.8)
Ki-67
Low (< 10%)	892 (78.9)	452 (79.7)	0.709
High (≥ 10%)	238 (21.1)	115 (20.3)
Radiotherapy
Yes	761 (67.2)	385 (67.7)	0.837
No	372 (32.8)	184 (32.3)
Chemotherapy
Yes	869 (76.7)	400 (70.3)	0.004
No	264 (23.3)	169 (29.7)
Endocrine therapy
Yes	757 (66.8)	411 (72.2)	0.023
No	376 (33.2)	158 (27.8)

SNUH, Seoul National University Hospital; FNA, fine needle aspiration; LN, lymph node; ALN, axillary lymph node.

^a) p-values are from chi-square test.

Table 2.

Multivariate analysis of factors affecting disease-free survival for interval between diagnosis and treatment initiation 0-14 days versus 15 days, 0-29 days versus ≥ 30 days

Factor	Interval 0-14 days vs. ≥ 15 days		Interval 0-29 days vs. ≥ 30 days
Factor	HR (95% CI)	p-value	HR (95% CI)	p-value
Age (yr)
< 40 vs. ≥ 40	1.395 (0.959-2.031)	0.082	1.381 (0.948-2.011)	0.093
Tumor size (cm)
> 2 vs. ≤ 2	2.176 (1.516-3.124)	< 0.001	2.181 (1.520-3.130)	< 0.001
Axillary lymph node metastasis
Positive vs. negative	2.358 (1.698-3.275)	< 0.001	2.357 (1.698-3.273)	< 0.001
Histologic grade
Grade 3 vs. 1, 2	1.810 (1.212-2.702)	0.004	1.814 (1.215-2.710)	0.004
Hormone receptor
Negative vs. positive	1.798 (1.262-2.562)	0.001	1.786 (1.253-2.546)	0.001
Treatment delay
Shorter vs. longer	1.145 (0.808-1.622)	0.448	1.109 (0.782-1.572)	0.561

HR, hazard ratio obtained by Cox proportional hazard models; CI, confidence interval.

REFERENCES

1. Brazda A, Estroff J, Euhus D, Leitch AM, Huth J, Andrews V, et al. Delays in time to treatment and survival impact in breast cancer. Ann Surg Oncol. 2010;17 Suppl 3:291–6. Article PubMed
2. McLaughlin JM, Anderson RT, Ferketich AK, Seiber EE, Balkrishnan R, Paskett ED. Effect on survival of longer intervals between confirmed diagnosis and treatment initiation among low-income women with breast cancer. J Clin Oncol. 2012;30:4493–500. Article PubMed PMC
3. Mujar M, Dahlui M, Yip CH, Taib NA. Delays in time to primary treatment after a diagnosis of breast cancer: does it impact survival? Prev Med. 2013;56:222–4. Article PubMed
4. Shin DW, Cho J, Kim SY, Guallar E, Hwang SS, Cho B, et al. Delay to curative surgery greater than 12 weeks is associated with increased mortality in patients with colorectal and breast cancer but not lung or thyroid cancer. Ann Surg Oncol. 2013;20:2468–76. Article PubMed
5. Smith EC, Ziogas A, Anton-Culver H. Delay in surgical treatment and survival after breast cancer diagnosis in young women by race/ethnicity. JAMA Surg. 2013;148:516–23. Article PubMed
6. Yun YH, Kim YA, Min YH, Park S, Won YJ, Kim DY, et al. The influence of hospital volume and surgical treatment delay on long-term survival after cancer surgery. Ann Oncol. 2012;23:2731–7. Article PubMed
7. Ganz PA. Quality of life across the continuum of breast cancer care. Breast J. 2000;6:324–30. Article PubMed
8. Eastman A, Tammaro Y, Moldrem A, Andrews V, Huth J, Euhus D, et al. Outcomes of delays in time to treatment in triple negative breast cancer. Ann Surg Oncol. 2013;20:1880–5. Article PubMed
9. Birkmeyer JD, Siewers AE, Marth NJ, Goodman DC. Regionalization of high-risk surgery and implications for patient travel times. JAMA. 2003;290:2703–8. Article PubMed
10. Simunovic M, Theriault ME, Paszat L, Coates A, Whelan T, Holowaty E, et al. Using administrative databases to measure waiting times for patients undergoing major cancer surgery in Ontario, 1993-2000. Can J Surg. 2005;48:137–42. PubMed PMC
11. Stitzenberg KB, Sigurdson ER, Egleston BL, Starkey RB, Meropol NJ. Centralization of cancer surgery: implications for patient access to optimal care. J Clin Oncol. 2009;27:4671–8. Article PubMed PMC
12. Bardell T, Belliveau P, Kong W, Mackillop WJ. Waiting times for cancer surgery in Ontario: 1984-2000. Clin Oncol (R Coll Radiol). 2006;18:401–9. Article PubMed
13. Bilimoria KY, Ko CY, Tomlinson JS, Stewart AK, Talamonti MS, Hynes DL, et al. Wait times for cancer surgery in the United States: trends and predictors of delays. Ann Surg. 2011;253:779–85. Article PubMed
14. Kawakami J, Hopman WM, Smith-Tryon R, Siemens DR. Measurement of surgical wait times in a universal health care system. Can Urol Assoc J. 2008;2:597–603. Article PubMed PMC

Figure & Data

REFERENCES

Citations

Citations to this article as recorded by

Five-year survival prognosis of young, middle-aged, and elderly adult female invasive breast cancer patients by clinical and lifestyle characteristics
Yu-Tung Teng, Yong Alison Wang, Yaa-Hui Dong, Jason J. Liu
Breast Cancer Research and Treatment.2024; 205(3): 619. CrossRef
Prognosis of Patients with Breast Cancer Following Delayed Diagnosis During the COVID-19 Pandemic: A Real-World Cohort Study
Jae Pak Yi, Chang Ik Yoon, Su Hyun Lim, Hoon Choi, Se Jeong Oh, Hyobin Kim, Dae Sun Park, Jong Min Baek, Yong-seok Kim, Ye Won Jeon, Jiyoung Rhu, Young-Joon Kang
Annals of Surgical Oncology.2024;[Epub] CrossRef
Surgical management of breast cancer in Victoria: A state‐wide audit
Nakjun Sung, Arun Muthusamy, Norah Finn, Ella Stuart, Jane Fox, Belinda Yeo
Asia-Pacific Journal of Clinical Oncology.2023; 19(4): 499. CrossRef
Time interval between diagnosis to treatment of breast cancer and the impact of health insurance coverage: a sub analysis of the AMAZONA III Study (GBECAM 0115)
Raíra Mesquita Maschmann, Rafaela Gomes De Jesus, Gustavo Werutsky, Taiane Francieli Rebelatto, Geraldo Queiroz, Sergio Daniel Simon, José Bines, Carlos Henrique Escosteguy Barrios, Daniela Dornelles Rosa
Breast Cancer Research and Treatment.2023; 198(1): 123. CrossRef
Benefits versus drawbacks of delaying surgery due to additional consultations in older patients with breast cancer
Brian L. Egleston, Richard J. Bleicher, Carolyn Y. Fang, Thomas J. Galloway, Slobodan Vucetic
Cancer Reports.2023;[Epub] CrossRef
Time interval between breast cancer diagnosis and surgery is associated with disease outcome
Siji Zhu, Shuai Li, Jiahui Huang, Xiaochun Fei, Kunwei Shen, Xiaosong Chen
Scientific Reports.2023;[Epub] CrossRef
Factors Associated with the Nutritional Status of Women with Non-Metastatic Breast Cancer in a Brazilian High Complexity Oncology Center
Roberto Júnio Gomes Silva, Wesley Rocha Grippa, Luiz Claudio Barreto Silva Neto, Oscar Geovanny Enriquez-Martinez, Júlia Anhoque Cavalcanti Marcarini, Raphael Manhães Pessanha, Fabiano Kenji Haraguchi, Luís Carlos Lopes-Júnior
Nutrients.2023; 15(23): 4961. CrossRef
Factors Associated With Breast Cancer Surgery Delay Within a Coordinated Multihospital Community Health System: When Does Surgical Delay Impact Outcome?
Judy A. Tjoe, Kayla Heslin, Ana C. Perez Moreno, Shanita Thomas, Jessica J.F. Kram
Clinical Breast Cancer.2022; 22(1): e91. CrossRef
Factors Affecting Time to Surgery in Breast Cancer Patients
Anees B. Chagpar, Marissa Howard-McNatt, Akiko Chiba, Edward A. Levine, Jennifer S. Gass, Kristalyn Gallagher, Sharon Lum, Ricardo Martinez, Allric I. Willis, Andrew Fenton, Naveenraj L. Solomon, Magi Senthil, David Edmonson, Jukes P. Namm, Laura Walters,
The American Surgeon™.2022; 88(4): 648. CrossRef
Breast Cancer Care Timeliness Framework: A Quality Framework for Cancer Control
Noor Mastura Mohd Mujar, Maznah Dahlui, Nor Aina Emran, Imisairi Abdul Hadi, Yang Wai Yan, Sarojah Arulanantham, Chan Hooi Chea, Nur Aishah Mohd Taib
JCO Global Oncology.2022;[Epub] CrossRef
Time-to-treatment initiation for cutaneous melanoma reflects disparities in healthcare access in Brazil: a retrospective study
G.D. Pereira Shimada, A. Archanjo da Mota, M. Carvalho de Souza, S.S. Bernardes
Public Health.2022; 210: 1. CrossRef
Association of treatment delay and stage with mortality in breast cancer: a nationwide cohort study in Taiwan
Nai-Chen Shih, Pei-Tseng Kung, Wei-Yin Kuo, Wen-Chen Tsai
Scientific Reports.2022;[Epub] CrossRef
Prognostic factors and outcomes in women with breast cancer in Slovenia in relation to step-wise implementation of organized screening
Sonja Tomšič, Tina Žagar, Ana Mihor, Miran Mlakar, Katarina Lokar, Katja Jarm, Vesna Zadnik, Edward Jay Trapido
PLOS ONE.2022; 17(11): e0278384. CrossRef
Perfis Social e Previdenciário: Influência na Qualidade de Vida dos Pacientes submetidos à Radioterapia
Stella Grigolette Rodrigues , Lilian Chessa Dias, Marielza Regina Ismael Martins
Revista Brasileira de Cancerologia.2022;[Epub] CrossRef
A systematic review and meta-analysis of surgery delays and survival in breast, lung and colon cancers: Implication for surgical triage during the COVID-19 pandemic
Brett A. Johnson, Anthony C. Waddimba, Gerald O. Ogola, James W. Fleshman, John T. Preskitt
The American Journal of Surgery.2021; 222(2): 311. CrossRef
Determinants of delay from cancer diagnosis to treatment initiation in a cohort of brazilian women with breast cancer
Giselle C. Medeiros, Luiz C. S. Thuler, Anke Bergmann
Health & Social Care in the Community.2021; 29(6): 1769. CrossRef
Impact of delayed treatment in women diagnosed with breast cancer: A population‐based study
Peh Joo Ho, Alex R. Cook, Nur Khaliesah Binte Mohamed Ri, Jenny Liu, Jingmei Li, Mikael Hartman
Cancer Medicine.2020; 9(7): 2435. CrossRef
The Waiting Game: How Long Are Breast Cancer Patients Waiting for Definitive Diagnosis?
Alexandra Kovar, Michael Bronsert, Kshama Jaiswal, Colleen Murphy, Dulcy Wolverton, Gretchen Ahrendt, Sarah Tevis
Annals of Surgical Oncology.2020; 27(10): 3641. CrossRef
“First Do No Harm”: Significance of Delays from Diagnosis to Surgery in Patients with Non‐metastatic Breast Cancer
Jennifer Xu, Luke Bromley, Grace Chew, Belinda Yeo
World Journal of Surgery.2020; 44(11): 3812. CrossRef
Comorbidities, age and period of diagnosis influence treatment and outcomes in early breast cancer
Pamela Minicozzi, Liesbet Van Eycken, Florence Molinie, Kaire Innos, Marcela Guevara, Rafael Marcos‐Gragera, Clara Castro, Elisabetta Rapiti, Alexander Katalinic, Ana Torrella, Tina Žagar, Magdalena Bielska‐Lasota, Paolo Giorgi Rossi, Nerea Larrañaga, Joa
International Journal of Cancer.2019; 144(9): 2118. CrossRef
Vulnerabilidade social e câncer de mama: diferenciais no intervalo entre o diagnóstico e o tratamento em mulheres de diferentes perfis sociodemográficos
Ana Lúcia Lobo Vianna Cabral, Luana Giatti, Claudina Casale, Mariângela Leal Cherchiglia
Ciência & Saúde Coletiva.2019; 24(2): 613. CrossRef
Relecture systématique des bilans radiologiques extérieurs de diagnostic du cancer du sein : une valeur ajoutée
Juliette Boudier, Guillaume Oldrini, Philippe Henrot, Julia Salleron, Anne Lesur
Bulletin du Cancer.2019; 106(4): 316. CrossRef
Patient and Care Delays of Breast Cancer in China
Yue-Lin Li, Ya-Chao Qin, Lu-Ying Tang, Yu-Huang Liao, Wei Zhang, Xiao-Ming Xie, Qiang Liu, Ying Lin, Ze-Fang Ren
Cancer Research and Treatment.2019; 51(3): 1098. CrossRef
The influence on survival of delay in the treatment initiation of screening detected non-symptomatic breast cancer
Yan Li, Yidong Zhou, Feng Mao, Jinghong Guan, Yan Lin, Xuejing Wang, Yanna Zhang, Xiaohui Zhang, Songjie Shen, Qiang Sun
Scientific Reports.2019;[Epub] CrossRef
Time from breast cancer diagnosis to therapeutic surgery and breast cancer prognosis: A population‐based cohort study
Louise Eriksson, Jonas Bergh, Keith Humphreys, Fredrik Wärnberg, Sven Törnberg, Kamila Czene
International Journal of Cancer.2018; 143(5): 1093. CrossRef

PubReader
ePub Link

Cite

CITE: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Delay of Treatment Initiation Does Not Adversely Affect Survival Outcome in Breast Cancer

Cancer Res Treat. 2016;48(3):962-969. Published online October 22, 2015

DOI: https://doi.org/10.4143/crt.2015.173

XML Download

Abstract
Introduction
Materials and Methods
Results
Discussion
Conclusion
NOTES
REFERENCES

Delay of Treatment Initiation Does Not Adversely Affect Survival Outcome in Breast Cancer

Fig. 1. Distribution of interval between diagnosis and treatment initiation.

Fig. 2. Kaplan-Meier survival curves of disease-free survival (DFS) by interval between diagnosis and treatment. (A) DFS by interval of ≥ 15 days versus 0-14 days. (B) DFS by interval of ≥ 30 days versus 0-29 days. (C) DFS by interval of ≥ 45 days versus 0-44 days. (D) DFS by interval of ≥ 60 days versus 0-59 days.

Fig. 3. Kaplan-Meier survival curves of overall survival by interval of ≥ 30 days versus 0-29 days.

Fig. 1.

Fig. 2.

Fig. 3.

Delay of Treatment Initiation Does Not Adversely Affect Survival Outcome in Breast Cancer

Factor	Interval of 0 to 29 days	Interval of > 30 days	p-value^a)
Total (n=1,702)	1,133 (66.6)	569 (33.4)
Age (yr)
≤ 39	180 (15.9)	74 (13.0)	0.336
40-49	478 (42.2)	241 (42.4)
50-59	320 (28.2)	160 (28.1)
60-69	124 (10.9)	78 (13.7)
≥ 70	31 (2.7)	16 (2.8)
Address
Seoul-Incheon area	795 (70.2)	398 (69.9)	0.925
Outside of capital area	338 (29.8)	171 (30.1)
Place of diagnosis
SNUH	494 (43.6)	195 (34.3)	< 0.001
Other hospital	639 (56.4)	374 (65.7)
Education
≤ High school	636 (56.1)	350 (61.5)	0.091
> High school	443 (39.1)	92 (33.7)
Unknown	54 (4.8)	27 (4.7)
Marital status
Married	1,033 (91.2)	516 (90.7)	0.621
Single	60 (5.3)	38 (6.7)
Divorced	9 (0.8)	3 (0.5)
Widowed	7(0.6)	4 (0.7)
Unknown	24 (2.1)	8 (1.4)
Comorbidity
Other than cancer	306 (27.0)	186 (32.7)	0.015
No comorbidities	827 (73.0)	383 (67.3)
Symptom
Yes	761 (67.2)	362 (63.6)	0.155
No	369 (32.6)	207 (36.4)
Unknown	3 (0.3)	0\|
Cancer family history
Breast cancer related	101 (8.9)	43 (7.5)	0.609
Not related to breast cancer	173 (15.3)	85 (14.9)
No cancer family history	859 (75.8)	441 (77.5)
Additional biopsy
Any	36 (3.2)	62 (10.9)	< 0.001
None	1,097 (96.8)	507 (89.1)
Imaging before admission
Any	424 (37.4)	340 (59.8)	< 0.001
None	709 (62.6)	229 (40.2)
Not routine imaging before admission
Any	5 (0.4)	27 (4.7)	< 0.001
None	1,128 (99.6)	542 (95.3)
Hospitalization
Any	2 (0.2)	11 (1.9)	< 0.001
None	1,131 (99.8)	558 (98.1)
Consultation
Any	48 (4.2)	56 (9.8)	< 0.001
None	1,085 (95.8)	513 (90.2)
Immediate reconstruction
Yes	11 (1.0)	6 (1.1)	0.870
No	1,122 (99.0)	563 (98.9)
Clinical stage at diagnosis
Diagnosis by FNA	29 (2.6)	14 (2.5)	0.109
In situ cancer	91 (8.0)	59 (10.4)
T1N0	681 (60.1)	357 (62.7)
≥ T2 or LN(+)	332 (29.3)	139 (24.4)
Pathological stage
T size ≤ 2 cm	625 (55.2)	327 (57.5)	0.366
T size > 2 cm	508 (44.8)	242 (42.5)
No ALN metastasis	727 (64.2)	387 (68.0)	0.115
ALN metastasis	406 (35.8)	182 (32.0)
Histologic grade
Grade 1, 2	527 (50.6)	290 (55.3)	0.075
Grade 3	515 (59.4)	234 (44.7)
Hormone receptor status
Positive	764 (67.4)	411 (72.2)	0.043
Negative	369 (32.6)	158 (27.8)
Ki-67
Low (< 10%)	892 (78.9)	452 (79.7)	0.709
High (≥ 10%)	238 (21.1)	115 (20.3)
Radiotherapy
Yes	761 (67.2)	385 (67.7)	0.837
No	372 (32.8)	184 (32.3)
Chemotherapy
Yes	869 (76.7)	400 (70.3)	0.004
No	264 (23.3)	169 (29.7)
Endocrine therapy
Yes	757 (66.8)	411 (72.2)	0.023
No	376 (33.2)	158 (27.8)

Factor	Interval 0-14 days vs. ≥ 15 days		Interval 0-29 days vs. ≥ 30 days
Factor	HR (95% CI)	p-value	HR (95% CI)	p-value
Age (yr)
< 40 vs. ≥ 40	1.395 (0.959-2.031)	0.082	1.381 (0.948-2.011)	0.093
Tumor size (cm)
> 2 vs. ≤ 2	2.176 (1.516-3.124)	< 0.001	2.181 (1.520-3.130)	< 0.001
Axillary lymph node metastasis
Positive vs. negative	2.358 (1.698-3.275)	< 0.001	2.357 (1.698-3.273)	< 0.001
Histologic grade
Grade 3 vs. 1, 2	1.810 (1.212-2.702)	0.004	1.814 (1.215-2.710)	0.004
Hormone receptor
Negative vs. positive	1.798 (1.262-2.562)	0.001	1.786 (1.253-2.546)	0.001
Treatment delay
Shorter vs. longer	1.145 (0.808-1.622)	0.448	1.109 (0.782-1.572)	0.561

Table 1. Socio-demographic, clinical, and tumor-specific characteristics associated with interval between diagnosis and treatment initiation of ≥ 30 days

SNUH, Seoul National University Hospital; FNA, fine needle aspiration; LN, lymph node; ALN, axillary lymph node.

p-values are from chi-square test.

Table 2. Multivariate analysis of factors affecting disease-free survival for interval between diagnosis and treatment initiation 0-14 days versus 15 days, 0-29 days versus ≥ 30 days

HR, hazard ratio obtained by Cox proportional hazard models; CI, confidence interval.