Lesion Size on Ultrasonography Predicts Potential Invasion in Ductal Carcinoma in situ Preoperatively Diagnosed by Breast Needle Biopsy

Ductal carcinoma in situ (DCIS) of the breast has no potential to metastasize, but over 20% of cases preoperatively diagnosed as DCIS are upstaged on final pathology. The rates of upstaging and the predictors for invasion on final pathology were evaluated. For 240 primary breast cancers, radiological findings on mammography, ultrasonography, and magnetic resonance imaging were investigated along with pathological and clinical information. Univariate and multivariate analyses were performed to identify predictors of potential invasion. Of the 240 breast cancers, 68 (28.3%) showed invasion on final pathology, and 5 (2.5%) had sentinel node metastasis. The multivariate analysis identified five independent predictors: non-mass lesions >2.4 cm on ultrasonography (odds ratio [OR] 2.84, 95% confidence interval [CI] 1.02-7.95, p=0.047), comedo-type histology (OR 6.89, 95% CI 1.89-25.08, p<0.01), solid-type histology (OR 7.97, 95% CI 2.08-30.49, p<0.01), palpable mass (OR 2.63, 95% CI 1.05-6.64, p=0.04), and bloody nipple discharge (OR 4.61, 95% CI 1.20-17.66, p=0.02). These five predictors were associated with invasion on final pathology and may help select candidates for sentinel node biopsy

Common Peak Approach Using Mass Spectrometry Data Sets for Predicting the Effects of Anticancer Drugs on Breast Cancer

We propose a method for biomarker discovery from mass spectrometry data, improving the common peak approach developed by Fushiki et al. (BMC Bioinformatics, 7:358, 2006). The common peak method is a simple way to select the sensible peaks that are shared with many subjects among all detected peaks by combining a standard spectrum alignment and kernel density estimates. The key idea of our proposed method is to apply the common peak approach to each class label separately. Hence, the proposed method gains more informative peaks for predicting class labels, while minor peaks associated with specific subjects are deleted correctly. We used a SELDI-TOF MS data set from laser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using an anticancer drug on breast cancer patients. The AdaBoost algorithm is adopted for pattern recognition, based on the set of candidate peaks selected by the proposed method. The analysis gives good performance in the sense of test errors for classifying the class labels for a given feature vector of selected peak values

Ductal carcinoma in situ and sentinel lymph node metastasis in breast cancer

<p>Abstract</p> <p>Background</p> <p>The impact of sentinel lymph node biopsy on breast cancer mimicking ductal carcinoma in situ (DCIS) is a matter of debate.</p> <p>Methods</p> <p>We studied the rate of occurrence of sentinel lymph node metastasis in 255 breast cancer patients with pure DCIS showing no invasive components on routine pathological examination. We compared this to the rate of occurrence in 177 patients with predominant intraductal-component (IDC) breast cancers containing invasive foci equal to or less than 0.5 cm in size.</p> <p>Results</p> <p>Most of the clinical and pathological baseline characteristics were the same between the two groups. However, peritumoral lymphatic permeation occurred less often in the pure DCIS group than in the IDC-predominant invasive-lesion group (1.2% vs. 6.8%, p = 0.002). One patient (0.39%) with pure DCIS had two sentinel lymph nodes positive for metastasis. This rate was significantly lower than that in patients with IDC-predominant invasive lesions (6.2%; p < 0.001).</p> <p>Conclusions</p> <p>Because the rate of sentinel lymph node metastasis in pure DCIS is very low, sentinel lymph node biopsy can safely be omitted.</p