Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models.

Objective: The purpose of this study was to build machine learning models to predict surgical upstaging risk of ductal carcinoma in situ (DCIS) to invasive cancer and to compare model performance to eligibility criteria used by the Comparison of Operative versus Monitoring and Endocrine Therapy (COMET) active surveillance trial.

Methods: Medical records were retrospectively reviewed of all women with DCIS at core-needle biopsy who underwent surgery from 2007 to 2016 at an academic medical center. Multivariable regression and machine learning models were developed to evaluate upstaging-related features and their performance was compared with that achieved using the COMET trial eligibility criteria.

Ipsilateral tumor recurrence risk in women with ductal carcinoma in situ: application of the Van Nuys Prognostic Index and the Memorial Sloan Kettering Cancer Center nomogram.

Purpose: To apply the Van Nuys Prognostic Index (VNPI) and the Memorial Sloan Kettering Cancer Center (MSKCC) ductal carcinoma in situ (DCIS) nomogram to DCIS patients with known long-term outcomes.

Methods: A retrospective review was performed of consecutive patients diagnosed with DCIS from 2007 to 2014. Included patients underwent breast-conserving surgery (BCS) and were followed with imaging for at least five years.

Taste of Clindamycin and Acetaminophen.

This study evaluated the taste palatability of liquid clindamycin and acetaminophen products on the market. Subjects rated the palatability of 3 clindamycin suspensions, 1 amoxicillin suspension (tasted twice), an acetaminophen elixir, and an acetaminophen suspension in a randomized blinded fashion on a 0 to 5 scale. Forty-six adults aged 20 to 82 years volunteered for this study.