Biomedical subjects

Annette Molinaro

Publications and source records attributed to Annette Molinaro.

3 recordsLinked to original sources

PubMed2006

Evaluation of apolipoprotein A1 and posttranslationally modified forms of transthyretin as biomarkers for ovarian cancer detection in an independent study population.

BACKGROUND: Although overall 5-year survival rates for ovarian cancer are poor (10-30%), stage I/IIa patients have a 95% 5-year survival. New biomarkers that improve the diagnostic performance of existing tumor markers are critically needed. A previous study by Zhang et al. reported identification and validation of three biomarkers using proteomic profiling that together improved early-stage ovarian cancer detection. METHODS: To evaluate these markers in an independent study population, postdiagnostic/pretreatment serum samples were collected from women hospitalized at the Mayo Clinic from 1980 to 1989 as part of the National Cancer Institute Immunodiagnostic Serum Bank. Sera from 42 women with ovarian cancer, 65 with benign tumors, and 76 with digestive diseases were included in this study. Levels of various posttranslationally forms of transthyretin and apolipoprotein A1 were measured in addition to CA125. RESULTS: Mean levels of five of the six forms of transthyretin were significantly lower in cases than in controls. The specificity of a model including transthyretin and apolipoprotein A1 alone was high [96.5%; 95% confidence interval (95% CI), 91.9-98.8%] but sensitivity was low (52.4%; 95% CI, 36.4-68.0%). A class prediction algorithm using all seven markers, CA125, and age maintained high specificity (94.3%; 95% CI, 89.1-97.5%) but had higher sensitivity (78.6%; 95% CI, 63.2-89.7%). CONCLUSIONS: We were able to replicate the findings reported by Zhang et al. in an independently conducted blinded study. These results provide some evidence that including age of patient and these markers in a model may improve specificity, especially when CA125 levels are >/=35 units/mL. Influences of sample handling, subject characteristics, and other covariates on biomarker levels require further consideration in discovery and replication or validation studies.

Adolescent↗

PubMed2005

Survival ensembles.

We propose a unified and flexible framework for ensemble learning in the presence of censoring. For right-censored data, we introduce a random forest algorithm and a generic gradient boosting algorithm for the construction of prognostic and diagnostic models. The methodology is utilized for predicting the survival time of patients suffering from acute myeloid leukemia based on clinical and genetic covariates. Furthermore, we compare the diagnostic capabilities of the proposed censored data random forest and boosting methods, applied to the recurrence-free survival time of node-positive breast cancer patients, with previously published findings.

Algorithms↗

PubMed2003

Characteristics associated with recurrence among women with ductal carcinoma in situ treated by lumpectomy.

BACKGROUND: Clinical and histopathologic characteristics that may predict risks of recurrence in women with ductal carcinoma in situ (DCIS) have not been consistently identified. We identified factors associated with recurrence as DCIS versus invasive breast cancer and determined the 5-year absolute risks of recurrence as a function of these factors. METHODS: We conducted a population-based cohort study among 1036 women in the San Francisco Bay Area who were aged 40 years or older when diagnosed with DCIS and treated by lumpectomy alone from January 1983 through December 1994. Standardized pathology reviews were conducted to determine disease recurrence, defined as DCIS or invasive breast cancer diagnosed in the ipsilateral breast containing the initial DCIS lesion or at a distant site more than 6 months after the initial diagnosis and treatment of DCIS. Conditional logistic regression models were used to determine factors associated with recurrence. All statistical significance tests were two-sided. RESULTS: During a median follow-up of 77.9 months, 209 women (20.2%) experienced a recurrence. Overall, the 5-year risks of recurrence as invasive cancer and as DCIS were 8.2% (95% confidence interval [CI] = 6.6% to 9.8%) and 11.7% (95% CI = 9.9% to 13.3%), respectively. The 5-year risks of recurrence as invasive cancer and as DCIS were 4.8% (95% CI = 3.7% to 6.8%) and 4.8% (95% CI = 3.8% to 5.8%), respectively, for women with low-nuclear-grade DCIS; 11.8% (95% CI = 9.9% to 14.1%) and 17.1% (95% CI = 15.5% to 18.7%), respectively, for women with high-nuclear-grade DCIS; 11.6% (95% CI = 11.3% to 12.0%) and 8.6% (95% CI = 7.1% to 10.2%), respectively, for women whose initial DCIS lesion was detected by palpation; and 6.6% (95% CI = 6.2% to 7.1%) and 14.1% (95% CI = 11.4% to 17.8%), respectively, for women with DCIS detected by mammography alone. High- (versus low-) nuclear-grade DCIS lesions and detection of the initial DCIS lesion by palpation (versus mammography) were associated with recurrence as invasive cancer. High- (versus low-) nuclear-grade lesions; resection margins that were positive, uncertain, or less than 10 mm disease-free (versus > or = 10 mm disease-free); and age 40-49 years at diagnosis (versus > or =50 years) were associated with recurrence as DCIS. CONCLUSIONS: Nuclear grade is strongly associated with recurrence but not with the type of recurrence. Women with high-nuclear-grade DCIS or DCIS detected by palpation who are treated by lumpectomy alone are at relatively high risk of having an invasive breast cancer recurrence, compared with women with low-nuclear-grade or mammographically detected DCIS, and may be appropriate candidates for additional treatment.

Adult↗