Search

Background: The likelihood of menses recovery varies greatly in premenopausal patients receiving adjuvant chemotherapy for breast cancer. Quantifying this probability for each patient could better inform the chemotherapy discussion and individualize fertility counseling. We performed a pooled analysis of the PACS04 and PACS05 adjuvant randomized trials to develop a nomogram to estimate the probability of menses recovery at 3, 6, and 18 months after the end of adjuvant chemotherapy., Patients and Methods: Women who were premenopausal and aged ≤ 50 years at randomization in the PACS04 and PACS05 trials were included in the present analysis. The primary endpoint was the probability of menses recovery within 18 months of chemotherapy completion. Multivariable Cox proportional hazards regression was used to estimate the association of each variable with the likelihood of menses resumption. A nomogram was developed to predict menses recovery at different intervals., Results: The factors associated with menses recovery were assessed for 1210 patients. At a median follow-up of 90 months (range, 3-189 months), 342 of 1210 patients (28.2%) had recovered menses. The probability of menses recovery at 18 months was 25.5% (range, 23.0%-27.9%). After backward elimination, age, final body mass index, type of chemotherapy, and hormone therapy were selected to build the nomogram to predict the probability of menstrual resumption at 3, 6, and 18 months after chemotherapy., Conclusion: An accurate and individualized prediction of menses recovery is feasible for premenopausal patients eligible for adjuvant chemotherapy for early-stage breast cancer. Our nomogram will be externally validated in a large prospective cohort., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To develop a prognostic nomogram to predict freedom from recurrence for patients treated with adjuvant hormonal therapy (HT) for localised breast cancer (BC)., Methods: We performed a retrospective analysis of 142 patients treated with adjuvant HT between 1996 and 2000. Clinical and pathological parameters were analysed., Results: A nomogram that predicts the probability of remaining free of recurrence for 5 years after surgery with adjuvant HT was developed using a Cox proportional hazards regression model. The progesterone receptor status (p < 0.001), nodal status (p = 0.008) and cathepsin-D (p < 0.001) were retained to construct the nomogram (C-index 0.734)., Conclusions: The nomogram we developed may be useful for estimating the probability of successful treatment 5 years after surgery for localised BC.

Background: The detection of synchronous metastases at primary diagnosis of breast cancer (BC) affects its initial management. A risk calculator that incorporates many factors to evaluate an individual's risk of harbouring synchronous metastases would be useful to adapt cancer management., Patients and Methods: Patients with primary diagnosis of BC were identified from three institutional databases sharing homogeneous work-up recommendations. A risk score for synchronous metastases was estimated and a nomogram was constructed using the first database. Its performance was assessed by receiver characteristic (ROC) analysis. The nomogram was externally validated in the two independent cohorts., Results: A preoperative nomogram based on the clinical tumour size (P<0.001), clinical nodal status (P<0.001), oestrogen (P=0.17) and progesterone receptors (P=0.04) was developed. The nomogram accuracy was 87.3% (95% confidence interval (CI), 84.45-90.2%). Overall, the area under the ROC curve (AUC) was 86.1% for the validation set from the Institut Curie-René Huguenin, and 63.8% for the MD Anderson validation set. The negative predictive value (NPV) was high in the three cohorts (97-99%)., Conclusions: We developed and validated a strong metastasis risk calculator that can evaluate with high accuracy an individual's risk of harbouring synchronous metastases at diagnosis of primary BC., Condensed Abstract: A nomogram to predict synchronous metastases at diagnosis of breast cancer was developed and externally validated. This tool allows avoiding unnecessary expensive work-up.

Background: The purpose of the study was to propose a clinical decision-making tool for predicting mortality in patients undergoing emergency abdominal surgery with a palliative intent in the oncology setting., Methods: Identification of all emergency surgical procedures performed in a Department of Oncologic Surgery in a Comprehensive Cancer Center between January 2008 and January 2013. Multivariate logistic and Cox regression models were used to identify factors predicitve of mortality at 3 months and survival probabilities. Models were internally validated using bootstrapping and calibration., Results: The mortality rates were 30% at 1 month, 46.7% at 3 months and 83.3% at the end of the study. One model based on the albumin level and the P-POSSUM score (AUC: 0.725) adequately predicted mortality at 3 months. A survival nomogram predicted mortality with a concordance index (CI) of 0.718, using the following factors: WHO performance status (P = 0.02), albumin level (P < 0.01) and P-POSSUM score (P < 0.01). The origin or the extent of the carcinoma did not own sufficient pronostic impact to be selected in this model., Conclusions: Pre-operative mortality risk scores can be developed in a palliative context. Physicians counselling and surgical decision making should be based on the use of these tools., (© 2014 Wiley Periodicals, Inc.)

Background: Currently, the benefit of chemotherapy (CT) in node-negative breast carcinoma (NNBC) is discussed. The evaluation of classical clinical and histological factors is limited to assess individual outcome. A statistical model was developed to improve the prognostic accuracy of NNBC., Methods: A total of 305 node-negative breast carcinomas who underwent surgery (+/- radiotherapy) but no adjuvant treatment were selected. Putative prognosis factors including age, tumour size, oestrogen receptor (ER), progesterone receptor (PgR), Scarff-Bloom-Richardon (SBR) grading, urokinase plasminogen activator (uPA), plasminogen activator inhibitor 1 (PAI-1) and thymidine kinase (TK) were evaluated. The developed model was internally validated using Harrell's concordance index. A prognosis index (PI) was proposed and compared with Adjuvant! Online program., Results: Age (p < 0.001), pathological tumour size (pT) (p < 0.001), PgR (p = 0.02), and PAI-1 (p ≤ 0.001) were included in the Cox regression model predicting Breast cancer specific survival (BCSS) at 5-years. Internal validation revealed a concordance index of 0.71. A PI score was derived from our nomogram. The PI score was significantly associated with BCSS (hazard ratio (HR): 4.1 for intermediate, p=0.02, HR: 8.8, p < 0.001 for high group) as compared to Adjuvant! Online score (HR: 1.4, p=0.14)., Conclusion: A nomogram can be used to predict probability survival curves for individual breast cancer patients., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Purpose: Our objective was to develop a nomogram to predict individual overall survival (OS) for primary breast cancer, based on pathological and biological tumor parameters., Methods: A retrospective study in a cohort of 180 patients with primary breast cancer was used to build the nomogram. Pathological factors and tumor proteases measured prospectively in primary tumors were used. A multivariate Cox proportional hazards model was used to explore the relationship with OS, and regression coefficients were used to build the nomogram. The nomogram was internally validated with 200 bootstrap re-samples., Results: The final variables included in the nomogram comprised tumor size (P = 0.04), nodal pathological status (P = 0.01), estrogen receptor status (P = 0.04), urokinase plasminogen activator inhibitor-1 (PAI-1; P = 0.02), thymidine kinase (P = 0.03), and cathepsin D (P = 0.004). The predictive accuracy of the nomogram at estimating the probability of OS, at both 2 and 5 years, was respectively 0.874 and 0.832 before and after calibration., Conclusion: A nomogram to predict 2- and 5-year OS in BC, using histological and biological parameters was successfully developed. This prognostic tool should prove useful in decision-making and therapeutic research., (Copyright © 2011 Wiley-Liss, Inc.)

Background and Objective: BI-RADS Category classification is the most powerful predictor of breast cancer (BC). However, BI-RADS Category 4 lesions are associated with a highly variable rate of BC. The purpose of this study was to develop and validate a nomogram for the prediction of individual probability of BC in patients with BI-RADS Category 4 lesions., Methods: The study included all patients with BI-RADS Category 4 lesions at screening mammogram, who underwent diagnostic cytology or biopsy and, as needed, surgery or follow-up. Univariate and multivariate logistic regression analyses were used to develop the model and build the nomogram. This nomogram was evaluated on a training set of 170 patients treated at IGR Cancer Center, Paris, France. Nomogram performance was evaluated on an external independent dataset of 188 patients from MDA Cancer Center, Houston, Texas., Results: A total of 51 (28.5%) patients in the training set and 73 (42.4%) patients in the validation set were diagnosed with BC. The final, most informative, nomogram included information on patient age (P = 0.04), palpable tumor (P = 0.002), menopausal status (P = 0.32), lesion size (P = 0.81), HRT (P = 0.09), and Gail risk (P = 0.58). The predictive accuracy of the nomogram was 0.716, respectively. The concordance index of the model was 0.66 in the validation set., Conclusion: The nomogram based on clinical and radiological findings may help inform the patients before surgical explorations, to decrease the number of missed cancer cases but currently cannot replace FNA or biopsy., (2010 Wiley-Liss, Inc.)

Objective: To develop and validate a nomogram that predicts individual probability of cesarean delivery in cases of macrosomia (>4,000 g)., Methods: The nomogram was built based on the data from 246 patients who delivered macrosomic infants at Conception Hospital (Marseille, France), and was validated on an external population of 206 patients. Logistic regression was used to construct a model to predict the probability of cesarean section. The calculations were based on actual birth weight., Main Outcome Measures: The accuracy of the model was evaluated by area under the receiver operator curve., Results: In the multivariate analysis performed on the training set, maternal age (p=0.002), parity (p=0.003), and maternal height <1.65 m (p=0.01) were found to be significantly associated with the occurrence of cesarean delivery and included in the nomogram. The final variables included in the nomogram were: age (p=0.01), maternal height (p=0.02), parity (p<0.001), and previous cesarean section (p=0.009). Area under the ROCs was 0.80 and 0.78 in the training set before and after bootstrapping, respectively, and 0.88 in the validation set. The calibration of the nomogram was good., Conclusion: We have developed a nomogram based on actual birth weight that accurately predicts the risk of cesarean delivery in cases of macrosomia. This tool might be useful for decision-making.