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Breast-Conserving Surgery

Breast-Conserving Surgery is a surgical procedure that removes the cancerous tumor or lump from the breast while preserving the majority of the breast tissue.
This technique aims to minimize the cosmetic impact and improve the patient's quality of life compared to a mastecomy.
The procedure involves removing the tumor along with a small margin of healthy tissue surrounding it, rather than the entire breast.
Breast-Conserving Surgery is often followed by radiation therapy to ensure complete removal of any remaining cancer cells.
This approach has become the preffered treatment option for many early-stage breast cancer patients, offering effective cancer management with improved aesthetic outcomes.

Most cited protocols related to «Breast-Conserving Surgery»

Fresh frozen breast cancer tissue from every third patient diagnosed and treated between 1991 and 2004 at the Koo Foundation Sun-Yat-Sen Cancer Center (KFSYSCC) were randomly selected for the study. Patients with follow-up periods shorter than three years were excluded, with the exception of those who died of the disease within three years of the initial treatment. In cases of ineligibility, the following sample was selected. The selected tissue samples spanned the major transition periods of adjuvant chemotherapy from CMF (cyclophosphamide, methotrexate and fluorouracil) to CAF (cyclophosphamide, doxorubicin, fluorouracil) and to taxane-based regimens. Four hundred forty seven samples were obtained, but 135 samples were excluded due to insufficient RNA (n = 1), poor RNA quality (n = 116), or unacceptable microarray quality (n = 18). A total of 312 samples were eligible for the study (Cohort 1). Gene expression profiles of an additional 15 lobular breast carcinoma samples, collected between 1999 and 2004 and previously studied, were also included (Cohort 2). All patients were treated by a multidisciplinary team according to the guidelines consistent with the National Comprehensive Cancer Network [18 ]. Following modified radical mastectomy or breast-conserving surgery plus dissection of axillary nodes, patients received radiotherapy, adjuvant chemotherapy, and/or hormonal therapy, if indicated. Neoadjuvant chemotherapy was administered to patients with locally advanced disease. The study was approved by the institutional review board (ID number 20020128A) and ethical approval was obtained from the same board for samples without obtainable informed consent.
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Publication 2011
Axilla Breast-Conserving Surgery Carcinoma, Lobular Chemotherapy, Adjuvant Cyclophosphamide Dissection Doxorubicin Ethics Committees, Research Fluorouracil Freezing Malignant Neoplasm of Breast Malignant Neoplasms Methotrexate Microarray Analysis Modified Radical Mastectomy Neoadjuvant Chemotherapy Patients Radiotherapy taxane Therapeutics Tissues Treatment Protocols
Often it is of interest to estimate (and compare) the cumulative incidences between two or more groups. For example, in the FA data set, it may be of interest to estimate the incidence of HM in the various complementation groups. Likewise, in the breast cancer data, it may be of interest to estimate breast cancer-specific mortality for those with and without a BRCA mutation. This is carried out by first dividing the sample into the subgroups of interest. The cumulative incidences of the event of interest are then calculated for each group separately as outlined above. Table 3Cumulative incidence of haematologic malignancy in Fanconi anaemia patients obtained using the Kaplan–Meier (KM) approach by not adjusting for competing risk events, and estimated by adjusting for competing risk events (CR)
  Overall (%)A (%)C (%)G (%)O (%)
 N (HEM)755 (120)207 (30)78 (19)46 (10)414 (60)
% CensoredKM8486767886
 CR5867445755
10 yearKM6.3515115
 CR5.941175
20 yearKM22.620423219
 CR17.815272114
30 yearKM39.045534534
 CR27.831323123
40 yearKM47.852684544
 CR31.834353127

Column 3 (‘Overall’) shows the cumulative incidence for all the 755 patients. Columns 4, 5, 6 and 7 show the cumulative incidence estimates for patients in complementation groups A, C, G and O (mostly nontyped patients and a small number of patients in uncommon complementation groups). The sample size is denoted N. The number of haematologic malignancy events is denoted by HEM and is given in parentheses in the second row.

provides the cumulative incidences of HM using the Kaplan–Meier approach as well as the competing risks approach, separately for patients in complementation groups FA-A, -C, -G and other patients (O=mostly nontyped patients and a small number of patients in uncommon complementation groups). Likewise, Table 4Breast cancer-specific mortality obtained using the Kaplan–Meier (KM) approach by not adjusting for competing risk events, and estimated by adjusting for competing risk events (CR)
  Mortality due to breast cancer
  Overall (%)BRCA mutation (%)No mutation (%)
 N (BCSS)305 (43)28 (8)276 (35)
% CensoredKM867187
 CR776479
1 yearKM0.33.60.0
 CR0.33.60.0
5 yearKM4.410.93.7
 CR4.310.73.7
10 yearKM14.228.412.8
 CR13.627.012.3
15 yearKM18.637.316.7
 CR17.635.215.9

Column 3 (‘Overall’) shows the cumulative incidence for all the 305 breast cancer patients. Columns 4 and 5 show the cumulative incidence estimates for patients with and without a BRCA mutation. The sample size is denoted N. The number of breast cancer-specific deaths is denoted BCSS and is given in parentheses in the second row. One patient without a BRCA mutation had missing death status and hence was excluded from the analysis.

provides breast cancer-specific mortality for patients with and without a BRCA mutation using the two methods.
The cumulative incidences in the various groups can be compared using nonparametric tests, namely the log-rank test (Kalbfleisch and Prentice 1980 ) when calculating incidences based on the Kaplan–Meier approach or a modified χ2 test (Gray, 1988 ) when calculating incidences in the presence of competing risks. The cumulative incidence estimation methods outlined above are nonparametric, that is, these estimates are not based upon any specific model. Alternative model-based approaches can also be utilized to estimate cumulative incidences of specific events, adjusting for prognostic factors of interest. Under the assumption of noninformative censoring, the Cox proportional hazards model (Cox, 1972 ) can be used. In the presence of competing risk events, a modified Cox proportional hazards model or the competing risk regression approach has been developed by Fine and Gray (1999) . We do not detail these methods here, but refer the reader to the references provided above.
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Publication 2004
Breast-Conserving Surgery Fanconi Anemia Hematologic Neoplasms Malignant Neoplasm of Breast Malignant Neoplasms Mutation Patients Prognostic Factors
The variables of patient demographic, clinicopathological, and treatment variables, were compared using the chi-squared test or Fisher's exact test upon receipt of postoperative RT. To reduce the potential confounding of retrospective studies, a 1:1 match including the variables mentioned above was conducted using propensity score matching (PSM) to create matched cohorts (17 , 18 (link)). BCSS curves were plotted using the Kaplan–Meier method and then compared with the log-rank test. Multivariate Cox proportional hazards models with the backward Wald method were used to assess the independent prognostic indicators related to BCSS. The hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated. Statistical analyses were carried out using SPSS 22.0 (IBM, Armonk, NY, USA). P < 0.05 was considered statistically significant.
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Publication 2019
Breast-Conserving Surgery Patients
The goal of scale revision was to identify a comprehensive, reproducible, and valid set of scales measuring concerns relevant to long-term cancer survivorship, with each scale composed of a set of internally consistent items. To achieve this end, our strategy was 1) to extract scales that were based on the IOC questionnaire items by use of exploratory factor analysis (39 , 40 ); 2) to perform split-sample cross-validation to assess reproducibility of the scales across subsamples (40 ); and 3) to conduct psychometric evaluation to assess the construct and concurrent validity of the proposed scales (41 ).
Exploratory factor analyses were conducted by use of the FACTOR procedure in SAS version 9.1 software (SAS Institute, Inc., Cary, NC). To decrease the dependence of our findings on any particular factor analytic technique, we used three methods of factor extraction (principal components, maximum likelihood, and unweighted least squares) and two methods for selecting the number of factors [the Kaiser-Guttman criterion of retaining factors with eigenvalues greater than 1 (42 , 43 ) and Cattell scree plot technique (44 )] and retained only those items that had factor loadings of greater than 0.50 by all approaches and loaded on factors with a clear interpretation. After factor extraction, we conducted factor rotation, an algorithmic procedure that achieves simplified factor structure by optimizing the grouping of items with common characteristics onto common factors. Because factors were expected to be correlated, we used the oblique promax rotation procedure (45 ).
The reproducibility of factor structure across subsamples was assessed by use of the targeted rotation method of McCrae et al (46 ). This method tests the hypothesis that the factor structure represented in the first sample is replicated in the second sample by extracting the hypothesized number of factors from the second sample, performing a targeted rotation to align the axes in the second factor structure with the axes in the first factor structure (the target), and calculating coefficients of congruence that quantify the fit between the two factor structures. Congruence coefficients compare two sets of factor loadings (item–factor correlations) in terms of both the pattern and magnitude of the loadings and can range from +1 (perfect agreement) to –1 (perfect inverse agreement). The observed congruences are compared with critical values generated by use of Monte Carlo techniques to determine the statistical significance of the fit. We defined a statistically significant congruence as a congruence higher than 95% of congruences obtained by rotating the second factor structure to align with axes in randomly generated target factor structures. For this analysis, we used the SAS Interactive Matrix Language program provided as an appendix in McCrae et al (46 ).
Psychometric evaluation included computation of Cronbach's coefficient alpha statistic for each scale as a measure of internal consistency reliability (47 ). Scales are generally considered reliable if the alpha statistic exceeds 0.70 (48 ). We also computed the coefficient delta or delta statistic, an index of the ability of a scale to discriminate among individuals (49 (link)). The delta statistic can range from 0, corresponding to all respondents giving the same response, to 1, corresponding to a maximally discriminating scale in which responses are uniformly distributed across the range of possible values (49 (link), 50 (link)).
The validity of the scales was evaluated by use of several strategies. Face validity was evaluated by examining item content. Construct validity, including convergent and discriminant validity, was evaluated by examining the Pearson product-moment correlation coefficients (r) among the scale scores and patterns of relationships between the scale scores and the sociodemographic, medical, and treatment characteristics of the sample cross-sectionally. For the latter, scale scores were examined for differences, or lack thereof, across age, years since diagnosis, partnered status, breast-conserving surgery vs mastectomy, chemotherapy status, general health status, number of comorbidities, body mass index, adjuvant hormonal therapy use, and current antidepressant use for depression or anxiety. These analyses used correlation coefficients for continuous variables and analysis of variance (ANOVA) for categorical variables. Concurrent validity was evaluated by forming a priori hypotheses about patterns of association and correlating the scales scores with the CES-D scores and the BCPT symptom scale total and subscale scores. When evaluating the quantitative significance of correlations, we considered an |r| of less than 0.30 to indicate a negligible association, |r| between 0.30 and 0.45 to indicate a moderate association, |r| between 0.45 and 0.60 to indicate a substantial association, and |r| greater than 0.60 to indicate a strong association (51 (link)). In the validity analyses, we used a P value of less than .005 as the critical value for statistical significance to account for the large sample size and multiple comparisons. All P values and tests of statistical significance were two-sided.
We computed scores for both higher-order scales and subscales as the mean of non-missing items that composed the scale. Scores were considered missing if more than 50% of items were missing.
Publication 2008
Antidepressive Agents Anxiety Breast-Conserving Surgery Cancer Survivorship Diagnosis Epistropheus Index, Body Mass Mastectomy Pharmaceutical Adjuvants Pharmacotherapy Psychometrics Specimen Handling Therapeutics
Univariable or multivariable Cox proportional hazards models were used to examine the association between mutations and survival. BCSS was used as the endpoint. Patients with deaths due to other or unknown causes were censored at the date of death, and all other patients were censored at the date of last contact. For the multivariable models, we included as variables: grade size (greater or less than 50 mm), lymph node status (positive or negative) and age (greater or less than 55). This coding scheme was chosen taking into consideration the minimum level of data available across the cohorts in the study. The same scheme was used when performing logistic regression for identifying associations between clinical parameters and mutation presence. To identify associations between events, we used Fisher's exact test for 2 × 2 contingency tables.
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Publication 2016
Breast-Conserving Surgery Mutation Nodes, Lymph Patients

Most recents protocols related to «Breast-Conserving Surgery»

A single-center, retrospective study was conducted. The electronic medical records of patients who underwent mastectomy between July 2015 and June 2020 in Samsung Medical Center were reviewed. Considering the length of the wound, reducing the skin closure time is more of a concern in mastectomy than in breast-conserving surgery. Therefore, most of the cases in which ASS was used for skin closure were mastectomies, and patients who underwent breast-conserving surgery were not included in this study. Also, patients who underwent breast reconstruction immediately following mastectomy were not included.
Publication 2023
Breast Breast-Conserving Surgery Mammaplasty Mastectomy Operative Surgical Procedures Patients Skin Wounds
Categorical variables are expressed as number and percentage. Pearson’s chi-square test was used to compare the results between the different groups. Continuous variables are presented as median with interquartile range.
A Cox proportional hazards model was constructed to estimate the independent effects of the three GA domains on the survival rate during a 6-year follow-up period, after adjustment for age, tumour stage, lymph node (LN) stage, and intrinsic molecular subtype. The primary outcome was OS time, which was defined as the time from the date of cancer diagnosis to the date of death from any cause or the date on which the patient was last known to be alive. The estimated effects of the three GA domains on OS were calculated as hazard ratios (HRs) and 95% confidence intervals (CIs). Adjusted survival curves stratified by the categories of the three GA domains were also generated. To examine the effects of the three GA domains on OS and BCSS, we constructed a Cox proportional hazards model after adjusting for age, tumour stage, LN stage, and intrinsic molecular subtype.
To assess the incremental prognostic value of each GA domain, we estimated Harrell’s concordance statistic (C‐statistic) for different models for OS and BCSS. The C‐statistic is equivalent to the area under the receiver operating characteristic curve, with a value of 0.5 indicating random predictions and a value of 1.0 indicating perfect discrimination between survivors and non-survivors. The first model was a ‘basic’ model that controlled for age, tumour stage, LN stage, and intrinsic molecular subtype. The GA domains were then individually added to the basic model. The final model was a ‘full’ model that included all three GA domains in addition to the covariates in the basic model.
All analyses were conducted using R software version 4.1.3. A two-sided P-value of <0.05 was considered statistically significant.
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Publication 2023
Breast-Conserving Surgery Diagnosis Discrimination, Psychology Malignant Neoplasms Neoplasms Nodes, Lymph Patients Survivors
The study retrospectively included 100 patients who underwent radiotherapy after breast-conserving surgery. The patients were treated using a standard treatment planning process with CT images and at least one set of CBCT images acquired during treatment. The CT images were acquired using the Siemens Medical System scanner with a voxel size of 0.977 × 0.977 × 5 mm3 and a data size of 512 ×512 × 80. The CBCT was acquired using the Varian Edge (Varian Medical Systems, Palo Alto, CA) scanner with a voxel size of 0.977 × 0.977 × 5 mm3. Due to the difference in scanning range and voxel size between CT and CBCT, we first rigidly aligned the CT and resampled the voxel size to match CBCT.
In our study, a DL network generated sCT images from CBCT images. And then, pCT was aligned with CBCT and sCT images, respectively, using the DIR method. Next, the contours on pCT were propagated on CBCT (sCT) images. Physicians first manually outlined pCT and CBCT data contours (target area contours including tumor bed area clinical target volume (CTV) 1, CTV 2, Heart). The final contours propagated on the sCT images have a more similar anatomy to the original CBCT, especially in soft tissues with significant effects. The model was trained, validated, and tested using 52/7/41 patients, corresponding to 4160/560/3280 slices.
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Publication 2023
Breast-Conserving Surgery Heart Patients Physicians Radiotherapy Tissues
Patients’ baseline characteristics were compared according to the receipt of the 21-gene testing using chi-square tests. Multiple logistic regression models were used to determine the predictive factors related to the use of RS testing and chemotherapy. A multicollinearity test was used to assess the data collected. The Kaplan–Meier method was used to assess differences in BCSS and OS and compared by the log-rank test. Multivariable Cox proportional hazards models were performed to determine independent prognostic factors impacting BCSS and OS. A 1:1 propensity score matching (PSM) was used to balance the potential confounders. Statistical analyses were conducted using the SPSS version 25.0. A P value less than 0.05 was considered statistically significant.
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Publication 2023
Breast-Conserving Surgery Genes Patients Pharmacotherapy Prognostic Factors
The following serological tests were used to check the serum samples from the dogs for the presence of antibodies specific to the rough and smooth Brucellae: The RSAT, the 2-ME TAT, rose Bengal test (RBT), and the buffered acidified plate antigen test (BAPAT). DNA of Brucella canis and other smooth Brucellae were identified in buffy coat samples separated from whole blood collected from seropositive dogs by three types of conventional PCRs (AMOS-PCR, Bruce-ladder PCR, and BcSS-PCR).
Brucella canis rough antigen that was used in the RSAT and 2-ME TAT was prepared from heat-killed cells of B. canis reference strain RM666 suspended in a formalized phosphate-buffered saline solution. Brucella canis 2-ME TAT antigen was obtained from the National Veterinary Services Laboratory (NVSL) Ames IA 50010, USA. Both RSAT and 2-ME TAT were carried out in this study to detect antibodies against B. canis, according to the technique adopted by Alton et al. [1 ].
RBT and BAPAT originating from smooth Brucellaabortus antigens were performed in this study to test dog blood samples for the detection of smooth Brucella antibodies according to the techniques described by Alton et al. [1 ] and World Organization for Animal Health (WOAH) [2 ].
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Publication 2023
Animals Antibodies Antigens BLOOD Breast-Conserving Surgery Brucella Brucella canis Canis familiaris Cultured Cells Detection Dogs immunoglobulin B Mineralocorticoid Excess Syndrome, Apparent Phosphates Rose Bengal Saline Solution Serum Tests, Serologic

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More about "Breast-Conserving Surgery"

Breast-Conserving Surgery, also known as lumpectomy, partial mastectomy, or segmental mastectomy, is a surgical procedure that removes the cancerous tumor or lump from the breast while preserving the majority of the breast tissue.
This technique aims to minimize the cosmetic impact and improve the patient's quality of life compared to a mastectomy.
The procedure involves removing the tumor along with a small margin of healthy tissue surrounding it, rather than the entire breast.
Breast-Conserving Surgery is often followed by radiation therapy to ensure complete removal of any remaining cancer cells.
This approach has become the preferred treatment option for many early-stage breast cancer patients, offering effective cancer management with improved aesthetic outcomes.
Researchers and clinicians often utilize statistical software, such as SPSS (Statistical Package for the Social Sciences) version 20 or 22.0, R version 3.6.1, SAS 9.4, or STATA version 12, to analyze data and evaluate the outcomes of Breast-Conserving Surgery.
Additionally, treatment planning systems like the Eclipse system may be used to plan and deliver radiation therapy after the surgery.
The reproducibility and accuracy of Breast-Conserving Surgery research protocols are crucial for advancing this treatment approach.
PubCompare.ai's AI-driven platform can enhance these aspects by helping researchers effortlessly locate and compare protocols from literature, pre-prints, and patents to identify the best options for their research needs.
By leveraging the power of AI-driven analysis, researchers can optimize their Breast-Conserving Surgery research and contribute to the ongoing improvement of this important cancer treatment.