Primary tumor and patient characteristics in breast cancer as predictors of adjuvant chemotherapy regimen: A regression model

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e11632-e11632
Author(s):  
V. Francescutti ◽  
F. Farrokhyar ◽  
R. Tozer ◽  
B. Heller ◽  
P. Lovrics ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Adjuvant Chemotherapy ◽  
Regression Model ◽  
Hormonal Therapy ◽  
Chemotherapy Regimen ◽  
Univariate Analysis ◽  
Stepwise Logistic Regression ◽  
Adjuvant Radiation ◽  
Tumor Characteristics

e11632 Background: Adjuvant chemotherapy is used to reduce the risk of recurrence of breast cancer. This study was undertaken to determine which patient and tumor characteristics are important in guiding the choice of adjuvant chemotherapy. Methods: A retrospective review was undertaken of patients diagnosed with breast cancer (stages I-III) at a regional cancer center from 2004–7. Patient and tumor characteristics were identified and chemotherapy regimens compared. Binary logistic regression analysis was performed to the choice of FEC/D, CEF, AC/T, or ddAC/T against AC or CMF, or the choice of chemotherapy to hormonal therapy only. Univariate analysis was used to select factors (p<0.1) for entry into a multivariate stepwise logistic regression model using the forward method. Odds ratios with 95% CI were calculated. A p-value of < 0.05 was significant and comparisons were two tailed. Results: Model 1 (n=871) included regimen (AC or CMF vs. aggressive regimen) as the dependant variable. Indicators of choice of aggressive regimen were higher stage [OR 4.7 (CI 3.3, 6.8)], positive nodes [2.5 (1.6, 3.8)], negative PR [2.1 (1.4, 3.1)], higher grade [1.4 (1.0, 1.8)], and age [0.91 (0.88, 0.92)]. Model 2 (n=640) involved choice of any regimen (chemotherapy vs. hormonal therapy only) as the dependant variable. Indicators of choice of chemotherapy were higher stage [7.19 (2.8, 18.4)], higher grade [7.02 (3.3, 14.8)], positive nodes [3.25 (0.98, 10.77)], age [0.85 (0.81, 0.90)], and ER negativity [0.04 (0.004, 0.37)]. Factors not significant in both models were: family history, comorbidities (renal/hepatic dysfunction, diabetes, cardiac history, or neuropathy), treating medical oncologist, histology, Her2/neu, > 3 positive nodes, ratio of positive to total nodes, multicentricity, multifocality, and positive margin status. Conclusions: This study verifies known important factors for choice of chemotherapy regimen as found in current guidelines, quantifies their effects at our center, and excludes others thought to be important. Further studies are required to confirm these results both nationally and internationally, where risk stratification may be different, and if variables predicting adjuvant radiation therapy are similar. No significant financial relationships to disclose.

Searching for predictors markers in renal cell carcinoma: A regression model.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e15542-e15542
Author(s):  
Maria Quindós Varela ◽  
Vanessa Medina Villaamil ◽  
Isabel Santamarina Cainzos ◽  
Guadalupe Aparicio ◽  
Manuel Valladares Ayerbes ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Model ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Univariate Analysis ◽  
Individual Therapy ◽  
Stepwise Logistic Regression ◽  
Regression Equations ◽  
Node Status

e15542 Background: Renal cell carcinoma (RCC) is known to be resistant to chemotherapy. The need for the identification of biomarkers capable of determining RCC prognosis factors and metastatic potential that are obtainable from non-invasive or minimally invasive techniques are desirable. The purpose from this analysis was to derive predictive models which could predict more accurately than any one factor alone. Methods: By tissue micro-array (TMA) technology we assessed 29 markers involved in the cascade of events leading to the formation and progression of this disease. The ability of multivariate logistic regression to improve the diagnostic accuracy for RCC by combining determinations of proteins markers and pathological variables was evaluated. Univariate analysis was used to select factors (p<0.05) for entry into a multivariate stepwise logistic regression model using the forward method. Odds ratios with 95% CI were calculated. A p-value of < 0.05 was significant. Results: The regression equations obtained were the next: for lymphatic vessels invasion, Y(x)=6.815- 0.442 (EGFR value); for node status Y(x)=2.07+ 0.494(NOTCH1 value) - 0.269(GLUT5 value); for veins invasion Y(x)=57.61+ 6.01 (NOTCH2 value)- 9.3 (GLUT1 value)+ 1.73 (C-KIT value); for breaking capsule Y(x)=2.713- 0.261(EGFR value)+ 0.407 (NOTCH1 value)- 0.305(NOTCH2 value)+ 0.463 (GLUT1 value); for pelvis invasion Y(x)=8.624-0.198 (EGFR value)- 0.514 (DLL3 value). Receiver-operator characteristic (ROC) curve analyses were made to analyse the accuracy of the best candidate proteins. Should be noted from this analysis NOTCH1 and GLUT5 for node status prediction (AUC=0.833, 95% CI, 0.744-0.922; p<0.001) and EGFR and DLL3 for pelvis invasion (AUC=0.777, 95% CI, 0.631-0.922; p=0.007). Conclusions: In the age of individual therapy, the approach to percutaneous image-guided RCC biopsy procedures plays an expanded role. Applying a 2 mm punch needle for constructing a TMA, thereby mimicking the percutaneous biopsy technique we could describe for the first time how are combined 29 protein markers in regression equations to predict in the most optimal way a number of pathological variables associated with RCC that may help in the diagnosis and prognosis of this disease.

A STUDY ON PREVALENCE OF POSTPARTUM DEPRESSION AT TERTIARY HOSPITAL, WEST BENGAL.

2021 ◽  
pp. 33-36
Author(s):  
Chandrima Maity ◽  
Debasish Sanyal ◽  
Arati Biswas ◽  
Sudarsan Saha
Keyword(s):  
Logistic Regression ◽  
Postpartum Depression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Depression Scale ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Background Information ◽  
Stepwise Logistic Regression ◽  
Postpartum Mothers

The investigators assessed the prevalence of Postpartum Depression (PPD), its clinical features and relationship of PPD with socio-demographical and obstetrical factors. The samples were selected from the OPD and IPD, of a Medical college in Kolkata.. Observational study was performed on 500(N=500) postpartum mothers who were selected by using Simple Random Sampling Technique within the six weeks of postpartum period. Data were collected by using the Structured Questionnaire for background information, Edinburgh Postnatal Depression Scale (Bengali Version of EPDS) for postpartum depression. Data analysis was performed using Descriptive Statistics, Chi-square, Logistic Regression and Decision Tree. A total of 112 (Prevalence Rate 22.4%) postpartum mothers had PPD. Stepwise logistic regression model correctly classied 92.2% of women who developed PPD. Using logistic regression model, postpartum depression is best predicted by: No. of Postpartum days p< 0.001***, Age of the mother p<0.024**, Religion p<0.003**, Type of family p<0.020**, Education of the mother p<0.001***, Monthly Income of the family p<0.001***, No of other living children p<0.001***, Pregnancy outcome p<0.033**, Any complication during pregnancy / delivery/ postpartum p< 0.001*** and Problems with family members p< 0.001***. The study recommends that evaluation should be carried out for Postpartum Depression and its risk factors to prevent and treat PPD in a timely manner.

Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy

2021 ◽  
Author(s):  
Cuiping Zhou ◽  
Xiaohua Ban ◽  
Huijun Hu ◽  
Qiuxia Yang ◽  
Rong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Partial Hepatectomy ◽  
Logistic Regression Model ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Roc Curve Analysis ◽  
Hepatocellular Carcinomas ◽  
Ct Features

Abstract Background: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy.Methods: The clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis.Results: CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis (P<0.05). Only tumor margin and VI remained independent risk factors in binary logistic regression analysis (OR=6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI：0.827-0.947).Conclusion: Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes.

Does Concomitant Chemoradiotherapy Influence Cosmetic Outcome in Conservative Treatment of Breast Cancer?

1997 ◽  
Vol 83 (4) ◽  
pp. 743-747 ◽  
Author(s):  
Gregorio Moro ◽  
Michele Stasi ◽  
Valeria Casanova Borca
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Conservative Treatment ◽  
Univariate Analysis ◽  
Conservative Surgery ◽  
Negative Influence ◽  
Cosmetic Outcome ◽  
Breast Cancers ◽  
Aesthetic Outcome ◽  
The Aesthetic

Purpose To evaluate retrospectively factors influencing the cosmetic outcome after conservative treatment for breast cancer. Material and methods From 1988 until 1992, 164 patients were treated with conservative surgery (quadrantectomy) and radiotherapy with 60Co (50 Gy on the whole breast) plus 10 Gy on the surgical bed (300 kV photons) for T1–T2 breast cancers; 46 patients (28%) received concomitant adjuvant chemotherapy (CMF schedule). Cosmesis evaluation was carried out after 24 to 108 months (median, 38 months). A logistic regression analysis was performed to identify independent variables influencing the aesthetic outcome. P values of 0.05 or less were considered significant. Results Univariate analysis showed that T2 versus T1 (P = 0.0102), lower quadrants site (P = 0.0002) and concomitant adjuvant chemotherapy (P = 0.0009) produced a worse aesthetic outcome. Multivariate analysis confirmed the same factors: tumor size (P = 0.0020), tumor site (P = 0.0150) and concomitant chemotherapy (P = 0.0024). Conclusions The significant negative influence on the cosmetic outcome of concomitant adjuvant chemotherapy implies questions about the timing of radiotherapy and chemotherapy in breast cancer conservative treatment.

Noncompliance with adjuvant radiation, chemotherapy, or hormonal therapy in breast cancer patients

2008 ◽  
Vol 196 (4) ◽  
pp. 500-504 ◽  
Author(s):  
Aye Moe Thu Ma ◽  
Julie Barone ◽  
Ashley E. Wallis ◽  
Nancy Jade Wu ◽  
Luiza Baez Garcia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Hormonal Therapy ◽  
Adjuvant Radiation ◽  
Breast Cancer Patients

Competing risks of death in NCIC CTG MA.27 adjuvant exemestane versus anastrozole.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 564-564
Author(s):  
Judy-Anne W. Chapman ◽  
Lois E. Shepherd ◽  
James N. Ingle ◽  
Hyman Bernard Muss ◽  
Kathleen I. Pritchard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Cause Of Death ◽  
Performance Status ◽  
Postmenopausal Breast Cancer ◽  
Prior Treatment ◽  
Phase Iii ◽  
Lymph Node Status ◽  
Tumor Characteristics ◽  
All Cause Mortality

564 Background: Our group previously examined if baseline patient/tumor characteristics, or prior treatment affected cause of death in MA.17, a placebo controlled extended adjuvant trial of the aromatase inhibitor (AI) letrozole. We now examine factor effects on all cause mortality in MA.27. Methods: MA.27 was an adjuvant phase III superiority trial of 5 yrs of exemestane vs anastrozole, in ER+ postmenopausal breast cancer accrued between 2003 and 2008; event free survival was similar. We examined by intention-to-treat, the multivariate time-to-breast cancer-specific (BrCa), cardiovascular (Cardio), and other causes (OT) of death with log-normal survival analysis adjusted by treatment and stratification factors (lymph node status, adjuvant chemotherapy, celecoxib, aspirin, and trastuzumab). We tested whether factors were associated with 1) all cause mortality, and if so, 2) cause-specific mortality. We also fit step-wise forward cause-specific adjusted models. Results: 7,576 women (median age 64.1 years; 5417 (71.5%) <70 yrs and 2159 (28.5%) >70 yrs) were enrolled and followed for a median of 4.1 yrs. The 432 deaths comprised: 187 (43.3%) BrCa, 66 (15.3%) Cardiovascular, and 179 (41.4%) OT MA.27 therapy was not associated with mortality (p=0.84). Five baseline factors were differentially associated with cause of death. Older age was associated with greater BrCa (p=0.03), Cardio (p<0.001), and OT (p<0.001) mortality. Pre-existing cardiovascular history led to worse Cardio mortality (p<0.001). Worse ECOG performance status led to worse OT death (p<0.001). T1 tumors were associated with less BrCa mortality (p<0.001). PgR+ tumors were also associated with less BrCa mortality (p<0.001). There were fewer BrCa deaths with Node –ve disease (p<0.001), ER+ tumors (p=0.001) and without adjuvant chemotherapy (p=0.005); there was worse Cardio mortality (p=0.01) with receipt of trastuzumab; worse OT (p=0.03) for non-whites, and without adjuvant radiotherapy (p=0.003). Conclusions: 56.7% of deaths in MA.27 patients were non-breast cancer related. We showed baseline patient and tumor characteristics, and prior treatment differentially affected cause of death. Clinical trial information: NCT00066573.

Radiation therapy practice patterns for elderly patients with early-stage breast cancer.

2013 ◽  
Vol 31 (26_suppl) ◽  
pp. 82-82
Author(s):  
Erin Healy ◽  
Lihong Qi ◽  
Jason Vuong ◽  
Richard J. Bold ◽  
Dominique Rash ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Elderly Patients ◽  
Hormonal Therapy ◽  
Molecular Subtype ◽  
Early Stage ◽  
Treatment Plan ◽  
Margin Status ◽  
Early Stage Breast Cancer ◽  
Categorical Variables ◽  
Tumor Characteristics

82 Background: Controversy exists regarding the benefit of whole breast radiation (RT) after breast conservation surgery (BCS) in elderly early stage breast cancer patients. We investigated the clinical and pathological characteristics that influence referral patterns and consent for RT. Methods: Between 2006 and 2011, 93 women, aged ≥ 70 were treated with BCS at the University of California Davis Medical Center (UCDMC). Electronic medical records were reviewed to identify pathological and clinical tumor characteristics, including stage, nodal involvement, lymphovascular invasion, margin status and molecular subtype. Patient factors including Karnofsky performance status, Charlson comorbidity index, and distance from UCDMC were recorded. Adjuvant therapy recommendations regarding RT, radiation dose and fractionation, hormonal and chemotherapy, and the ultimate treatment plan were noted. Descriptive statistics were used to characterize the referral pattern data. Patient and tumor characteristics were compared between those referred and not referred for RT using chi-square tests for categorical variables and Student’s t tests for continuous variables. Similar comparisons were also conducted for those who consented and declined RT. Results: Of the 93 women eligible for adjuvant whole breast radiation, 79 (85%) were referred to radiation oncology. Sixty five patients had stage I, 16 had stage II, and 4 had stage III breast cancer. Seventy four patients had a luminal A molecular subtype, 11 of whom declined RT. Hormonal therapy was recommended for 78 patients, of which 11 were not referred to RT. The mean age of those referred to RT was significantly less than those not referred, 76 vs. 81 years, P = 0.006. Mean distance to UCDMC was 17.2 miles for those not referred and 34.4 miles for those referred, P = 0.02. There were no significant correlation between stage, molecular subtype, margin status or lymph node involvement and referral to RT. Conclusions: The majority of elderly patients are referred to RT but increased age and comorbidity were associated with non-referral. Patients’ perception of their own comorbidity, previous experience with RT and the decision to pursue hormonal therapy contributed to patients’ decisions to decline RT.

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