Impact of COVID-19 Vaccination on Breast Cancer Patients; the Negative Effects of the Pandemic Have Started to Decrease.

doi:10.21203/rs.3.rs-1668035/v1

BACKROUND

The COVID-19 pandemic has caused delays in diagnosis, and treatment of breast cancer patients. We predicted that the effects of the pandemic on breast cancer patients would decrease during the post-vaccination period.

METHODS

The data of 131 female patients who were operated on for breast cancer between March 2019, and March 2022, were analyzed into 3 groups: the control group (CG) between March 2019–2020, the pandemic group (PG) between March 2020–2021, and the vaccine group (VG) between March 2021 - March 2022. Patient admission time (PAT), diagnosis time (DT), tumor size, TNM classification and etc data were analyzed.

RESULTS

Prolonged PAT times in PG compared to CG were observed to be significantly reduced in VG (p = 0.009). In both PG and VG, the DTs were prolonged significantly (p < 0.001 and p < 0.001, respectively). Tumor sizes were found to be larger in PG than in CG and VG (p = 0.003 and p = 0.001, respectively).

CONCLUSION

After COVID-19 vaccination; delays in diagnosis and treatment caused by the pandemic in breast cancer patients have decreased and the increase in tumor size during the pandemic period has regressed.

Hospital Medicine

COVID-19

Pandemic

Vacciation

Breast cancer

patient admission time

diagnosis time

The World Health Organization (WHO) reported cases of pneumonia of unknown etiology in the city of Wuhan, China on December 31, 2019, and the agent was identified as a new coronavirus (2019-nCoV) that has not been detected in humans on January 7, 2020. The disease was later named COVID-19. The World Health Organization defined the COVID-19 outbreak as a global epidemic (pandemic) on March 11, 2020 [1].

The first COVID-19 case in Turkey was observed on March 11, 2020. In the process since the first case was detected, the basic strategy of the Turkish Ministry of Health regarding the epidemic; has been by taking public health measures, reducing the incidence rate, slowing the rise in the epidemic curve, and preventing the intense demand for healthcare services [2]. For this reason, elective surgeries have been stopped in hospitals as of March 2020, and outpatient services have been arranged to serve only patients with a certain number of appointments. Quarantine has been implemented at certain intervals throughout the country [3].

During the pandemic and quarantine process, people started to avoid going to hospitals. A decrease was observed in the number of hospitalizations [4]. Even the number of emergency surgery cases has decreased [5].

Although breast cancer is the most common cancer in women worldwide, there has been a decrease in mortality rates recently due to the improvements achieved in early breast cancer diagnosis with screening programs [6][7].

However, like many patients, breast cancer was also affected by the pandemic and the post-pandemic process. Many national and international scientific committees have published recommendations on cancer management strategies aimed primarily at protecting hospital resources against COVID-19 and reducing the risk of cross-infection. Breast cancer screening programs have been temporarily suspended or slowed down [3]. It is estimated that the slowdown caused by the pandemic will cause a delay in the diagnosis and treatment process of breast cancer.

Vaccination after the COVID-19 pandemic in Turkey was started in January 2021, and 65% of the population received at least 2 doses of vaccine since that date [8]. Firstly, inactivated SARS-CoV-2 vaccine produced by Sinovac Biotech company named CoronoVac, which was approved for emergency use by the World Health Organization in January 2021, was used for vaccination. After April 2021, the Pfizer-BioNTech COVID-19 vaccine, an mRNA vaccine, has also been used. The Ministry of Health announced that in March 2021, normalization will be gradually started [9]. It is predicted that after vaccination, the delay time for treating breast cancer patients will decrease and approach the pre-pandemic period.

Our study examines whether the COVID-19 pandemic causes a delay in the diagnosis and treatment of breast cancer patients and the post-vaccination status of these effects. For this reason, we compared the characteristics of breast cancer patients treated in our clinic before, during the COVID-19 pandemic, and after the vaccination.

This study was conducted in accordance with the Declaration of Helsinki with the approval of the Ankara University Faculty of Medicine Ethics Committee. The collected data were accessed retrospectively from the patient records in the hospital archive and from the online personal health data center of the Ministry of Health named E-NABIZ, with the permission of the patients.

Patient Selection

The data of 254 female patients who were operated on the breast by a single surgical team in a single center between March 11, 2019, and March 11, 2022, were analyzed. 103 patients were excluded from the study because they only had benign lesions. The patients were examined in 3 groups; March 2019 to March 2020 control group (pre-covid)(CG), March 2020 to March 2021 pandemic group (PG), and March 2021 to March 2022 vaccine group (VG). Data from 21 patients who were not vaccinated after March 2021 were excluded from the study.

Variable and outcome definitions

The time elapsed from the first symptom of the patients to the hospital admission and the time from the moment of diagnosis and treatment decision after admission to the hospital was calculated. The number of days from the moment of noticing the first symptom to the medical control was defined as the patient admission time (PAT), and the number of days from the patient's admission to the medical control until the diagnosis and treatment decision was defined as the diagnosis time (DT).

The imaging modalities used in clinical staging were recorded (mammography, breast ultrasound, breast MRI, PET-CT, and others).

Selected surgical methods; were divided into mastectomy and breast-conserving surgery. In the approach to the axilla, sentinel lymph node biopsy (SLNB) was performed in patients who did not show clinical and radiological axillary lymph node involvement. Sentinel lymph node results (benign, metastatic, and micro-metastatic) were reported and analyzed. Apart from these, axillary lymph node dissection (ALND) was performed in patients with axillary involvement or SLNB positive.

Pathological data were included in the study. Tumor diameters were reported in millimeters. AJCC 2018 (8th edition) was used for TNM classification. Tumor grading was evaluated according to the Nottingham Histological Score system. Molecular subtypes luminal A; Estrogen receptor (ER) and Progesterone receptor (PR) positive patients with HER2 gene (HER2+) overexpression positive, Luminal B; Patients with ER, PR, and HER2 all positive, HER2-overexpression; hormone receptor-negative, only HER2 positive patients and Basal-like; reported as triple-negative patients.

Statistical Analysis

Statistical analysis were performed using the IBM Statistical Package for Social Sciences (SPSS) 21 software for Windows. The distributions of the variables were analyzed by histogram and Kolmogorov-Smirnov test. Non-normally distributeand non-categorical parameters; age, PAT, DT, and tumor size were evaluated with the Kruskal-Wallis test. Statistically significant differences were compared between paired groups with post hoc tests. The categorical parameters; application complaints, receiving neoadjuvant therapy, preoperative staging method, type of surgery, sentinel lymph node biopsy and result, axillary lymph node dissection, pathology result, TNM, pathological and molecular subtype results were evaluated with Pearson chi-square test. P value less than 0.05 was considered statistically significant.

The study was conducted on 131 patients, including the control group (n=45)(34.4%), the pandemic group (n=39)(29.8%) and the vaccine group (n=47)(35.9%). It has been observed that the number of patients during the pandemic period decreased by 5% compared to the precovid period but increased again after vaccination. The mean age of the patients was 52.94±11.84 years, and their ages ranged from 25 to 80 years. Age medians did not differ significantly, being 52 (26-78) in the control group, 52 (36-80) in the pandemic group, and 51 (25-72) in the vaccine group; p=0.848. Table 1 summarizes the general characteristics of the continuous data of the study by groups.

In Table 2, the descriptive statistics of the groups according to the continuous variables of the study and the results of the Kruskal-Wallis test are given. Those with significant differences were compared between the groups and adjusted significance values were calculated.

Patient admission delay time varied significantly between groups; H(2) = 29.617, p < 0.001. Pairwise comparisons with adjusted p-values showed that patient admission delay times were significantly higher in the pandemic period than in the pre-pandemic period; H = -44.99, p < 0.001, r = -0.59. Patient admission delay during the vaccination period was significantly reduced compared to the pandemic period; H = 24,352, p = 0.009, r = 0.32. During the vaccination period, patient admission times increased significantly compared with the pre-pandemic period, although not as much as during the pandemic period; H = -20.64, p = 0.026, r = -0.27.

Diagnosis time varied significantly according to the groups. H(2) = 29,070, p < 0.001. Pairwise comparisons with adjusted p-values showed that diagnosis times were significantly higher in both the pandemic period and the vaccination period compared to the pre-vaccination period, respectively, H = -44.527, p < 0.001, r = -0.56 and H = -31.109, p < 0.001, r = -0.41. The mean time to diagnosis during the vaccination period decreased compared with the pandemic period, but it was not statistically significant; H = 11,418, p = 0.492, r = 0.15.

Tumor size varied significantly between groups; H(2) = 13,809, p = 0.001. Pairwise comparisons with adjusted p-values showed that tumor sizes significantly increased during the pandemic period compared to pre-covid and vaccination period, respectively H = -27.075 p = 0.003, r = -0.356, and H = 26.748, p = 0.001, r = 0.351. The mean of tumor sizes in the pre-pandemic period and the vaccination periods are close to each other and are not statistically significant; H = -0.327, p = 1,000, r = -0.004.

Tumor size varied significantly between groups in patients receiving neo-adjuvant chemotherapy; H(2) = 8.910, p = 0.012. Pairwise comparisons with adjusted p-values showed that during the pandemic period, tumor sizes significantly increased in patients receiving neo-adjuvant chemotherapy compared to the control group and vaccine group, respectively H = -11.227 p = 0.017, r = -0.302 and H = 9.909, p = 0.043, r = 0.264. The mean of tumor sizes in the pre-pandemic period and the vaccination periods are close to each other and are not statistically significant; H = -1.318, p = 1,000, r = -0.035.

Tumor size also varied significantly between groups in patients not receiving neo-adjuvant chemotherapy; H(2) = 7.908, p = 0.019. Pairwise comparisons with adjusted p-values showed that tumor sizes significantly decreased in the vaccination period compared to the pandemic period in patients who did not receive neo-adjuvant chemotherapy; H = -18,197 p = 0.033, r = 0. In patients who did not receive neo-adjuvant therapy, tumor sizes increased during the pandemic period compared to the control group, but not statistically significant, H = -17,301, p = 0.051, r = -0.260. There was no significant difference in tumor sizes between the control group and the vaccination group, H = 0.896, p = 1.000, r = 0.013.

The complaints of the patients are shown in Figure 1 and Table 3. The patients were applied with the most common complaint of breast lump in all 3 periods. Other application complaints; mastalgia, skin retraction, nipple discharge, and feeling of fullness, back pain complaints. The complaints of the patients did not differ significantly between the groups. (Lump (p=0.099), mastalgia (p=0.103), skin retraction (p=0.435), nipple discharge (p=0.075), other (p=0485))

In Table 4, the descriptive statistics of the patients in the control, pandemic and vaccination periods according to the categorical variables of the study and the Pearson chi-square test findings are given. The frequency distribution of groups receiving neo-adjuvant therapy (p= 0.988), preoperative staging methods ((mammograpy (p=0.848), breast usg (p=1), breast MRI (p=0.157), PET CT (p=0.494)), type of surgery (p=0.079), sentinel lymph node biopsy (p= 0.505), SLNB result (p=0.651), ALND (P=0.579), pathology result (p=0.169), T distribution of patients with NAC (p=0.150), N distribution of patients with or without NAC (p=0.905) or (p=0.40), M distribution of patients with or without NAC (p=0.391) or (p=0.308), pathological stage patients with or without NAC (p=0.542) or (p=0.188) and molecular subtype (p=0.082) did not differ significantly.

The frequency distribution of the groups differed significantly in the T values of the patients who did not receive neoadjuvant chemotherapy; χ2= 16,066, p=0.041. When the relationship between the meanings was examined, it was understood that the high number of T3 tumors in the pandemic group caused this meaning.

Figure 2 shows the vaccine types of 47 patients in the vaccine group. Accordingly, 4 (8.51%) patients received Sinovac CoronaVac, 10 (21.28%) patients received Pfizer-BioNTech, and the remaining 33 (70.21%) patients received at least one dose of both vaccines.

Because of the COVID-19 pandemic, hospital admissions of patients have been restricted, except for emergencies, in many countries. Studies on this process have shown that even the number of emergency surgery cases has decreased [10]. Likewise, there has been a decrease in breast cancer cases [11]. In our study, a 5% decrease was found in the number of patients during the pandemic period compared to the previous period. In the post-vaccination period, the number of patients reached the pre-pandemic levels and even increased by 1%.

Studies have shown that there are delays in diagnosing breast cancer patients during the pandemic process [12]. Especially at the beginning of the pandemic, the lack of sufficient information about the disease, information pollution, and the fear and anxiety caused by the pandemic decrease the applications of patients to health centers for any reason [13]. There has been a decrease in the access of patients to health services due to reasons such as curfews except for certain permissions given by authorized institutions, and patients who do not have an appointment except in emergencies cannot enter the health centers. Due to similar effects, in our study, we see that patients noticeably delay their application to health institutions compared to the pre-pandemic period, despite noticing their breast-related complaints. In the post-vaccination period, it has been shown that the application times of patients to health institutions have returned to the periods before the pandemic. We think that the re-normalization of health services after vaccination, more information about the COVID-19, and the positive effects of vaccines on the disease have created this effect [14].

Health professionals worldwide have worked devotedly in the most effective way to better fight the epidemic. Because of the devotion of a large part of the personnel power to the fight against the epidemic, delays in diagnosis and treatment have been experienced in breast cancer, as in many diseases. In a systematic review and meta-analysis by Hanna et al.; It has been shown that delay in diagnosis and treatment in breast cancer has a direct effect on survival [15]. Johnson et al.; published a systematic review and meta-analysis that surgical delays may reduce survival in patients with breast, lung, and colon cancer during the COVID-19 pandemic [16]. In our study, there were significant system-related diagnostic delays during the pandemic period compared with the pre-pandemic period. As a result, there were delays in treatment. However, with the decrease of the effects of the pandemic after the vaccination period and the normalization of health services, the patient diagnosis times returned to the periods before the pandemic.

In breast cancer, the relationship between tumor size and patient survival has been shown in many previous studies [17][18]. In our study, an increase was found in the tumor size of patients diagnosed during the pandemic period, regardless of whether they received neoadjuvant chemotherapy. Similarly, the number of patients with T3 tumors during the pandemic period in patients who did not receive neoadjuvant therapy was higher than before the pandemic and during the vaccination period. We think that the delay in patient admission and diagnosis may be effective in the increase in tumor size. Conversely, we believe that the decrease in patient admission times and diagnosis times during the vaccination period compared to the pandemic period has a positive effect on the reduction of tumor size.

Unfortunately, the project was limited in several ways. First, our study was retrospectively conducted with a small number of patients, and second, included patients operated by a single surgical team in a single center. However, as far as we know, our study is the first to include results on breast cancer patients during the post-vaccination pandemic period, and we believe that it may guide other studies in the future.

This study has shown that with the transition to the vaccine after the COVID-19 pandemic, the delay in hospital admission and diagnosis caused by the pandemic and the increase in tumor sizes during the pandemic period has ended. More research is required to better understand the impact of the COVID-19 pandemic on breast cancer patients.

DECLARATION OF INTEREST

All other authors have no conflict of interest to declare.

FUNDING

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

AUTHORS’ CONTRIBUTIONS

Study concepts : Gokhan AVSAR

Study design : Gokhan AVSAR, Sancar BAYAR

Data acquisition : Gokhan AVSAR, Sancar BAYAR, Ali Ekrem UNA

Data analysis and interpretation : Gokhan AVSAR, Ozhan CETINDAG

Statistical analysis : Gokhan AVSAR, Rıza DERYOL

Manuscript preparation : Gokhan AVSAR, Rıza DERYOL, Musluh HAKSEVEN

Manuscript editing : Gokhan AVSAR, Selim TAMAM

Manuscript review : Gokhan AVSAR, Rıza DERYOL, Musluh HAKSEVEN,

Sancar BAYAR, Ali Ekrem UNAL

ACKNOWLEDGEMENTS

I am especially grateful for the contribution of Serkan AKBULUT to the data search.

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Table 1. General characteristics of the study population groups (for quantitative variables)

	Control group (N = 45)(% 34.4)	Pandemi group (N = 39)(% 29.8)	Vaccine Group (N = 47)(% 35.9)	Total (N = 131)
	Median(min-max)	Median(min-max)	Median(min-max)	Mean±SD	Median(min-max)
Age (years)	52 (26-78)	52 (36-80)	51 (25-72)	52.94±11.84	52 (25-80)
PAT (in days)	30 (10-90)	80 (21-120)	45 (10-150)	54.37±33.64	45 (10-150)
DT (in days)	15 (10-30)	21 (12-30)	20 (13-25)	18.33±4.88	18 (10-30)
Tumor size (mm)	20 (1-48)	25 (2-140)	17 (2-10)	24.95±18.87	20 (1-140)

^*Kruskal-Wallis, PAT; patient admission time, DT; diagnosis time, mm; millimeters

Table 2. Comparison of groups (for quantitative variables)

	Groups	N	Mean Rank	Kruskal-Wallis H Chi-square	df	p value
Patient admission time	control group	45	45.20	29.617	2	<0.001
(in days)	pandemic group	39	90.19
	vaccine group	47	65.84
	Total	131
Diagnosis time	control group	45	42.18	29.070	2	<0.001
(in days)	pandemic group	39	84.71
	vaccine group	47	73.29
	Total	131
Tumor size (mm)		131		13.809	2	0.001
Patients with NAC	control group	11	12.27	8.910	2	0.012
	pandemic group	9	23.50
	vaccine group	11	13.59
	Total	31
Patients without NAC	control group	34	45.63	7.908	2	0.019
	pandemic group	30	62.93
	vaccine group	36	44.74
	Total	100

NAC; neo-adjuvant chemotherapy, mm; millimeters, df; degrees of freedom

Table 3. Complaints of patients

Features	Total (N = 131)	Control group (N = 45)	Pandemi group (N = 39)	Vaccine Group (N = 47)	χ²	p value
	n(%)	n(%)	n(%)	n(%)
Patient complaints
Lump	98 (74.8)	32 (71.1)	34 (87.2)	32 (68.1)	4.621	0.099
Mastalgia	19 (14.5)	3 (6.7)	9 (23.1)	7 (14.9)	4.546	0.103
Skin retraction	14 (10.7)	3 (6.7)	6 (15.4)	5 (10.6)	1.664	0.435
Nipple discharge	5 (3.8)	4 (8.9)	1 (2.6)	0 (0.0)	5.185	0.075
Other	6 (4.6)	1 (2.2)	3 (7.7)	2 (4.3)	1.448	0.485

χ²; Pearson chi-square test

Table 4. General characteristics of the study population and comparison of groups (for qualitative variables)

Features	Total (N = 131)	Control group (N = 45)	Pandemic group (N = 39)	Vaccine Group (N = 47)	χ²	p
	n(%)	n(%)	n(%)	n(%)
Neo-adjuvant therapy					0.024	0.988
Yes	31 (23.7)	11 (24.4)	9 (23.1)	11 (23.4)
No	100 (76.3)	34 (75.6)	30 (76.9)	36 (76.6)
Preoperative staging method
Mammography	119 (90.8)	40 (88.9)	36(92.3)	43 (91.5)	0.331	0.848
Breast ultrasound	131 (100)	45 (100)	39 (100)	47 (100)		1
Breast MRI	11 (8.4)	3 (6.7)	6 (15.4)	2 (4.3)	3.699	0.157
PET CT	113 (86.3)	41(91.1)	33 (84.6.2)	39 (83)	1.409	0.494
Others (PET MRI, bone scintigraphy)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
Type of surgery					5.075	0.079
Mastectomy	69 (52.7)	18(40)	25 (64.1)	26 (55.3)
Breast-conserving	62 (47.3)	27(60)	14 (35.9)	21 (44.7)
Sentinel lymph node biopsy					1.368	0.505
No	45 (34.4)	13 (28.9)	16 (41)	16 (34)
Yes	86 (65.6)	32 (71.1)	23 (59)	31 (66)
Sentinel lymph node biopsy results					4.192	0.651
Benign	72 (55)	28 (62.2)	17 (43.6)	27 (57.4)
Metastatic	13 (9.9)	5 (11.1)	5 (12.8)	3 (6.4)
Micro-metastatic	3 (2.3)	1 (2.2)	1 (2.6)	1 (2.1)
Axillary lymph node dissection					2.876	0.579
ALND +	42 (32.1)	11 (24.4)	16 (41)	15 (31.9)
ALND SLNB +	13 (9.9)	5 (11.1)	4 (10.3)	4 (8.5)
Pathology result					11.628	0.169
Invasive ductal carcinoma	107 (81.7)	35 (77.8)	33 (84.7)	39 (83)
Invasive lobular carcinoma	8 (6.1)	3 (6.7)	3 (7.7)	2 (4.3)
DCIS	7 (5.3)	2 (4.4)	1 (2.6)	4 (8.5)
LCIS	2 (1.5)	0 (0.0)	0 (0.0)	2 (4.3)
Mix type (ductal/papiller)	6 (4.6)	5 (11.1)	1 (2.6)	0 (0.0)
Other (mucinous)	1 (0.8)	0 (0.0)	1 (2.6)	0 (0.0)
TNM Classification
Patients with NAC
T					9.446	0.150
T1	10 (32.3)	5 (45.5)	1 (11.1)	4 (36.4)
T2	16 (51.6)	6 (54.5)	4 (44.4)	6 (54.5)
T3	2 (6.5)	0 (0.0)	2 (22..2)	0 (0.0)
T4	3 (9.7)	0 (0.0)	2 (22.2)	1 (9.1)
N					2.153	0.905
N0	8 (25.8)	2 (18.2)	3 (33.3)	3 (27.3)
N1	13 (41.9)	4 (36.4)	4 (44.4)	5 (45.5)
N2	7 (22.6)	4 (36.4)	1 (11.1)	2 (18.2)
N3	3 (9.7)	1 (9.1)	1 (11.1)	1 (9.1)
M					1.879	0.391
M0	30 (96.8)	11 (100)	9 (100)	10 (90.9)
M1	1 (3.2)	0 (0.0)	0 (0.0)	1 (9.1)
Patients without NAC
T					16.066	0.041
T is	6 (6)	2 (5.9)	0 (0.0)	4 (11.1)
T1	51 (51)	20 (58.8)	10 (33.3)	21 (58.3)
T2	40 (40)	12 (35.3)	17 (56.7)	11 (30.6)
T3	2 (2)	0 (0.0)	2 (6.7)	0 (0.0)
T4	1 (1)	0 (0.0)	1 (3.3)	0 (0.0)
N					6.209	0.400
N0	76 (76)	28 (82.4)	22 (73.3)	26 (72.2)
N1	17 (17)	5 (14.7)	5 (16.7)	7 (19.4)
N2	5 (5)	1 (2.9)	3 (10)	1 (2.8)
N3	2 (2)	0 (0.0)	0 (0.0)	2 (5.6)
M					2.357	0.308
M0	99 (99)	34 (100)	29 (96.7)	36 (100)
M1	1 (1)	0 (0.0)	1 (3.3)	0 (0.0)
Pathological stage
Patients with NAC					6.949	0.542
CPR	3 (9.7)	1 (9.1)	1 (11.1)	1 (9.1)
Stage 1	1 (3.2)	1 (9.1)	0 (0.0)	0 (0.0)
Stage 2	14 (45.2)	4 (36.4)	3 (33.3)	7 (63.6)
Stage 3	12 (38.7)	5 (45.5)	5 (55.6)	2 (18.2)
Stage 4	1 (3.2)	0 (0.0)	0 (0.0)	1 (9.1)
Patients without NAC					11.241	0.188
Stage 0	6 (6)	2 (5.9)	0 (0.0)	4 (11.1)
Stage 1	40 (40)	17 (50)	8 (26.7)	15 (41.7)
Stage 2	47 (47)	14 (41.2)	19 (63.3)	14 (38.9)
Stage 3	6 (6)	1 (2.9)	2 (6.7)	3 (8.3)
Stage 4	1 (1)	0 (0.0)	1 (100)	0 (0.0)
Molecular subtype					11.205	0.082
Luminal A	75 (61.5)	26 (60.5)	27 (71.1)	22 (53.7)
Luminal B	29 (23.8)	12 (27.9)	3 (7.9)	14 (34.1)
HER2-overexpression	8 (6.6)	3 (7.0)	2 (5.3)	3 (7.3)
Basal-like	10 (8.2)	2 (4.7)	6 (15.8)	2 (4.9)

USG; ultrasonography, MRI; magnetic resonance imaging , PET; positron emission tomography, CT; computed tomography. ALND; axillary lymph nodes dissection, ALND SNLB +; axillary lymph nodes found positive after sentinel lymph node at frozen sections. DCIS; ductal carcinoma in situ, LCIS; lobular carcinoma in situ, T: tumor, N; lymph nodes, M; metastasis, NAC; neo-adjuvant chemotherapy, CPR; complete pathologic response. χ²; Pearson chi-square test, Bold p values indicate statistical significance.

Impact of COVID-19 Vaccination on Breast Cancer Patients; the Negative Effects of the Pandemic Have Started to Decrease.

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Abstract

BACKROUND

METHODS

RESULTS

CONCLUSION

Figures

Introduction

Materials And Methods

Patient Selection

Variable and outcome definitions

Statistical Analysis

Results

Discussion

Conclusion

Declarations

References

Tables

Status:

Version 1