Relationship Between Formulaic Breast Volume And Risk Of Breast Cancer Based On Linear Measurement

Abstract Background: Whether breast volume is a risk factor for breast cancer has been controversial. This study aimed to evaluate whether or not the significant association between breast volume and risk of breast cancer based on linear measurement by applying Propensity Score Matching (PSM) was present. Methods: The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Calculate breast volume using formula based on the linear measurement of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer by risk factors adjusted for potential confounders. Results: There was a significant difference in breast volume between two groups before Propensity Score Matching(P=0.014) : P=0.009, OR=1.002, 95% CI: 1.000~1.003). Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volume than in the control group（P=0.009, OR=1.002， 95%CI：1.000～1.003). However, there was no significant statistical difference between two groups in independent sample Mann-Whitney U test (P=0.438) and in conditional logistic regression (P=0.446). Conclusions: After PSM for the potential confounders factors, the breast volume of cases did not differ from that of controls. The risk of breast cancer may not related to breast volume in Chinese women.

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Relationship between formulaic breast volume and risk of breast cancer based on linear measurement

10.21203/rs.3.rs-23397/v3 ◽

2020 ◽

Author(s):

xiaoxia li ◽

Chunlan Zhou ◽

Yanni Wu ◽

Xiaohong Chen

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Propensity Score ◽

Propensity Score Matching ◽

Cox Regression ◽

Conditional Logistic Regression ◽

Control Group ◽

Breast Volume ◽

Linear Measurements ◽

Significant Difference

Abstract Background: Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements,was present by applying propensity score matching (PSM).Methods: The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders. Results: There was a significant difference in breast volume between the two groups before propensity score matching(P = 0.014) . Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group（P = 0.009, OR = 1.002， 95%CI：1.000~1.003). However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446). Conclusions: After PSM for potential confounding factors, the breast volume of cases did not differ from that of controls. The risk of breast cancer may not be related to breast volume in Chinese women.

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Relationship between formulaic breast volume and risk of breast cancer based on linear measurements

10.21203/rs.3.rs-23397/v2 ◽

2020 ◽

Author(s):

xiaoxia li ◽

Chunlan Zhou ◽

Yanni Wu ◽

Xiaohong Chen

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Propensity Score ◽

Propensity Score Matching ◽

Cox Regression ◽

Conditional Logistic Regression ◽

Control Group ◽

Breast Volume ◽

Linear Measurements ◽

Significant Difference

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Relationship between formulaic breast volume and risk of breast cancer based on linear measurements

BMC Cancer ◽

10.1186/s12885-020-07499-5 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Xiaoxia Li ◽

Chunlan Zhou ◽

Yanni Wu ◽

Xiaohong Chen

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Propensity Score ◽

Propensity Score Matching ◽

Conditional Logistic Regression ◽

Control Group ◽

Case Group ◽

Breast Volume ◽

Linear Measurements ◽

Significant Difference

Abstract Background Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements, was present by applying propensity score matching (PSM). Methods The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders. Results There was a significant difference in breast volume between the two groups before propensity score matching (P = 0.014). Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group(P = 0.009, OR = 1.002, 95%CI:1.000 ~ 1.003). However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446). Conclusions After PSM for potential confounding factors, there is no significant difference in breast volume estimated by BREAST-V formula between the case group and the control group. The risk of breast cancer may not be related to breast volume in Chinese women.

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Relationship between formulaic breast volume and risk of breast cancer based on linear measurement

10.21203/rs.3.rs-23397/v4 ◽

2020 ◽

Author(s):

Xiaoxia Li ◽

Chunlan Zhou ◽

Yanni Wu ◽

Xiaohong Chen

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Propensity Score ◽

Propensity Score Matching ◽

Conditional Logistic Regression ◽

Control Group ◽

Case Group ◽

Breast Volume ◽

Linear Measurements ◽

Significant Difference

Abstract Background: Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements, was present by applying propensity score matching (PSM).Methods: The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders.Results: There was a significant difference in breast volume between the two groups before propensity score matching(P = 0.014) . Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group（P = 0.009, OR = 1.002， 95%CI：1.000~1.003). However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446).Conclusions: After PSM for potential confounding factors, there is no significant difference in breast volume estimated by BREAST-V formula between the case group and the control group. The risk of breast cancer may not be related to breast volume in Chinese women.

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Relationship between formulaic breast volume and risk of breast cancer based on linear measurements

10.21203/rs.3.rs-23397/v6 ◽

2020 ◽

Author(s):

Xiaoxia Li ◽

Chunlan Zhou ◽

Yanni Wu ◽

Xiaohong Chen

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Propensity Score ◽

Propensity Score Matching ◽

Conditional Logistic Regression ◽

Control Group ◽

Case Group ◽

Breast Volume ◽

Linear Measurements ◽

Significant Difference

Abstract Background: Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements,was present by applying propensity score matching (PSM).Methods: The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders.Results: There was a significant difference in breast volume between the two groups before propensity score matching(P = 0.014) . Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group（P = 0.009, OR = 1.002， 95%CI：1.000~1.003). However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446).Conclusions: After PSM for potential confounding factors, there is no significant difference in breast volume estimated by BREAST-V formula between the case group and the control group. The risk of breast cancer may not be related to breast volume in Chinese women.

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Relationship between formulaic breast volume and risk of breast cancer based on linear measurements

10.21203/rs.3.rs-23397/v5 ◽

2020 ◽

Author(s):

xiaoxia li ◽

Chunlan Zhou ◽

Yanni Wu ◽

Xiaohong Chen

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Propensity Score ◽

Propensity Score Matching ◽

Conditional Logistic Regression ◽

Control Group ◽

Case Group ◽

Breast Volume ◽

Linear Measurements ◽

Significant Difference

Abstract Background: Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements,was present by applying propensity score matching (PSM).Methods: The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders. Results: There was a significant difference in breast volume between the two groups before propensity score matching(P = 0.014) . Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group（P = 0.009, OR = 1.002， 95%CI：1.000~1.003). However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446). Conclusions: After PSM for potential confounding factors, there is no significant difference in breast volume estimated by BREAST-V formula between the case group and the control group. The risk of breast cancer may not be related to breast volume in Chinese women.

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Efficacy Evaluation of Thymosin Alpha 1 in Non-severe Patients With COVID-19: A Retrospective Cohort Study Based on Propensity Score Matching

Frontiers in Medicine ◽

10.3389/fmed.2021.664776 ◽

2021 ◽

Vol 8 ◽

Author(s):

ChenLu Huang ◽

Ling Fei ◽

Wei Xu ◽

WeiXia Li ◽

XuDong Xie ◽

...

Keyword(s):

Propensity Score ◽

Hospital Stay ◽

Propensity Score Matching ◽

Standard Therapy ◽

Control Group ◽

Thymosin Α1 ◽

Significant Difference ◽

Thymosin Alpha 1 ◽

And Control ◽

Related Mortality

Objective: Thymosin alpha 1 (Thymosin-α1) is a potential treatment for patients with COVID-19. We aimed to determine the effect of Thymosin-α1 in non-severe patients with COVID-19.Methods: We retrospectively enrolled 1,388 non-severe patients with COVID-19. The primary and secondary clinical outcomes were evaluated with comparisons between patients treated with or without Thymosin-α1 therapy.Results: Among 1,388 enrolled patients, 232 patients (16.7%) received both Thymosin-α1 therapy and standard therapy (Thymosin-α1 group), and 1,156 patients (83.3%) received standard therapy (control group). After propensity score matching (1:1 ratio), baseline characteristics were well-balanced between the Thymosin-α1 group and control group. The proportion of patients that progressed to severe COVID-19 is 2.17% for the Thymosin-α1 group and 2.71% for the control group (p = 0.736). The COVID-19-related mortality is 0.54% for the Thymosin-α1 group and 0 for the control group (p = 0.317). Compared with the control group, the Thymosin-α1 group had significantly shorter SARS-CoV-2 RNA shedding duration (13 vs. 16 days, p = 0.025) and hospital stay (14 vs. 18 days, p < 0.001). No statistically significant difference was found between the Thymosin-α1 group and control group in duration of symptoms (median, 4 vs. 3 days, p = 0.843) and antibiotic utilization rate (14.1% vs. 15.2%, p = 0.768).Conclusion: For non-severe patients with COVID-19, Thymosin-α1 can shorten viral RNA shedding duration and hospital stay but did not prevent COVID-19 progression and reduce COVID-19-related mortality rate.

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329 Prolonged overall survival (OS) in patients with metastatic colorectal cancer (mCRC) in SPICE, a phase I study of enadenotucirev in combination with nivolumab

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0329 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A355-A355

Author(s):

Tom Lillie ◽

Andrew Stone ◽

Sarah Lockwood ◽

Richard Brown ◽

Andrew Fox ◽

...

Keyword(s):

Colorectal Cancer ◽

Propensity Score ◽

Phase I ◽

Metastatic Colorectal Cancer ◽

Propensity Score Matching ◽

Cox Regression ◽

Phase 1 ◽

Control Group ◽

List Type ◽

Link Type

BackgroundSPICE is a phase 1 study of the oncolytic adenovirus enadenotucirev in combination with nivolumab in patients with advanced epithelial tumors (NCT02636036). Preliminary data has indicated a survival advantage in patients with mCRC resulting in a median OS of 14 months.1 To further understand this OS signal, a comparison to historical patient-level data from the placebo arm of the CORRECT study (NCT01103323) was performed using data obtained from Project Data Sphere.MethodsIndividual patients from SPICE were matched with patients in the placebo arm of the CORRECT study in terms of covariates known to be associated with OS (ECOG, presence of liver mets, haemoglobin, albumin, LDH and platelet count). The OS outcomes were then compared between the matched SPICE and CORRECT patients to minimise any bias due to patient selection. The distribution of the covariates was broadly similar between studies with minor differences favouring the SPICE study.ResultsThe mOS in confirmed microsatellite stable mCRC patients (n=25) in the SPICE study was 15.4 months (95% CI; 11.8 m, 21.0 m) as compared to 5.0 months for patients in the placebo arm of the CORRECT study (n=251).Two different statistical analyses were performed to compare the outcomes between studies: (1) A comparison of OS matching each SPICE patient to a maximum of 10 (average of 5.5) placebo patients from CORRECT using M:1 variable nearest neighbour propensity score matching; (2) Multivariate analysis of SPICE vs CORRECT adjusting for all covariates in a Cox regression model.The Hazard Ratio (SPICE:CORRECT) from the regression model was 0.28 with an upper 2-sided 95% confidence limit of 0.48, which was consistent with results using propensity score matching. The upper 95% CL for the HR for method (2) was 0.61.ConclusionsThe results appear promising, particularly in a population that has historically shown little response to PD-1 intervention and warrant further exploration in a randomised study. However, these analyses cannot be regarded as definitive, due to the possible presence of unmeasured confounders between a small phase 1 cohort and a large phase 3 control group.Ethics ApprovalThe study was approved by the Western Institutional Review Board, study approval number 1160755.ReferenceFakih M, Wang D, Harb W, Rosen L, Mahadeven D, Berlin J, Basciano P, Arogundade O, Cox C, Krige D, McElwaine-Johnn H, Brown R, SPICE: A phase I multicenter study of enadenotucirev in combination with nivolumab in tumors of epithelial origin: an analysis of the metastatic colorectal cancer patients in the dose escalation phase. Annals of Oncology 2019 30 (suppl_5)::198-v252. 10.1093/annonc/mdz246

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Therapeutic Impact of Initial Treatment for Chlamydia trachomatis Among Patients With Pelvic Inflammatory Disease: A Retrospective Cohort Study Using a National Inpatient Database in Japan

Clinical Infectious Diseases ◽

10.1093/cid/ciy862 ◽

2018 ◽

Vol 69 (2) ◽

pp. 316-322

Author(s):

Daisuke Shigemi ◽

Hiroki Matsui ◽

Kiyohide Fushimi ◽

Hideo Yasunaga

Keyword(s):

Chlamydia Trachomatis ◽

Propensity Score ◽

Pelvic Inflammatory Disease ◽

Propensity Score Matching ◽

Inflammatory Disease ◽

Initial Treatment ◽

Primary Outcome ◽

Control Group ◽

Short Term ◽

Significant Difference

Abstract Background Pelvic inflammatory disease (PID) is common among women of reproductive age and can be complicated by tuboovarian abscess (TOA), which is a serious and potentially life-threatening disease. However, recent mortality rates from PID on hospital admission and the short-term therapeutic usefulness of initial treatment for Chlamydia trachomatis remain unknown. Methods Using the Diagnosis Procedure Combination database, we identified patients who were diagnosed with PID on admission from July 2010 to March 2016 in Japan. We excluded patients who were pregnant, had cancer, or had missing data. Propensity score–adjusted analyses were performed to compare short-term outcomes between patients administered initial treatment for C. trachomatis and those without this treatment. The primary outcome was surgical intervention (laparotomy, laparoscopic surgery, and/or drainage procedure) during hospitalization. Results In total, 27841 eligible patients were identified. Of these patients, 2463 (8.8%) had TOA on admission. Mortality during hospitalization was 0.56% and 0.28% in the groups without and with TOA, respectively. Propensity score matching created 6149 pairs. A significant difference was observed in the primary outcome between those receiving initial treatment for C. trachomatis and the control group after propensity score matching (11.5% vs 13.4%; risk difference, −1.9%; 95% confidence interval, −3.1 to −0.7). The group that received initial treatment for C. trachomatis also had a significantly lower mortality rate. Conclusions In this retrospective nationwide study, initial treatment for C. trachomatis among hospitalized patients diagnosed with PID had clinical benefits in terms of improved short-term outcomes.

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Propensity score matching analysis of ovarian cancer patients with multiple primary malignant neoplasms using multicenter real-world data.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e17501 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e17501-e17501

Author(s):

Qing-lei Gao ◽

Xiaofei Jiao ◽

Ruyuan Li ◽

Shaoqing Zeng ◽

Yingjun Zhao ◽

...

Keyword(s):

Breast Cancer ◽

Ovarian Cancer ◽

Overall Survival ◽

Propensity Score ◽

Propensity Score Matching ◽

Cancer Patients ◽

Cox Regression ◽

Malignant Neoplasms ◽

Cox Regression Analysis ◽

Multiple Primary

e17501 Background: Multiple primary malignant neoplasms (MPMNs) in patients with ovarian cancer is rare and has not attracted enough attention. It is unclear how the MPMNs affect the prognosis of ovarian cancer (OC) patients. Methods: This is a multicenter retrospective analysis of 5, 268 ovarian cancer patients from six centers who was diagnosed with ovarian cancer from January 1, 1989 to August 21, 2020. Propensity score matching was used to balance the baseline characteristics between patients with and without MPMNs. Cox regression analysis was utilized to analyze the influence of MPMNs on overall survival (OS). Results: After excluding unqualified medical record, totally 4, 848 patients were analyzed and 240 were concurrent at least one MPMNs other than OC. Ten patients had two MPMNs and one patient had three. The most common concurrent cancer was breast cancer (111/240, 46.25%), followed by endometrial cancer (37/240, 15.42%), and cervical cancer (30/240, 12.50%). Patients with MPMNs were elder than those without MPMNs (52 vs. 51, P = 0.03) when ovarian cancer was diagnosed. And the proportion of early-stage cases was lower in patients with MPMNs (25.8% vs. 27.2%, P < 0.001). Patients with breast cancer had a higher proportion of high-grade serous ovarian cancer (HGSOC) than those without MPMNs. After using the propensity score matching method adjusting age, pathological type, grade, and stage, concurrent MPMNs, including breast cancer, had no effect on OS of ovarian cancer patients. Among 240 patients with MPMNs, patients with breast cancer shared similar age and stage compared with the rest patients, while their proportion of HGSOC was higher than patients with other cancer (68.4% vs. 51.1%, P = 0.028). However, the median OS of those two groups were similar (27.3 m vs.27.1 m, P = 0.744). In addition, 94 patients were diagnosed with breast cancer prior to ovarian cancer, seven diagnosed posteriorly to ovarian cancer, four diagnosed simultaneously, and six had no precise diagnosed dates. There was no remarkable difference in clinical characteristics between the prior and posterior groups, however, the median OS of those seven patients was significantly longer than the prior group (76.0 m vs. 25.4 m, P = 0.002). Conclusions: The MPMNs showed no influence on the overall survival of ovarian cancer patients. The order of diagnosis of ovarian cancer and breast cancer might affect the prognosis.

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