Comparison of Oncotype DX Recurrence Score by Histologic Types of Breast Carcinoma

2015 ◽  
Vol 139 (12) ◽  
pp. 1546-1549 ◽  
Author(s):  
Philip E. Bomeisl ◽  
Cheryl L. Thompson ◽  
Lyndsay N. Harris ◽  
Hannah L. Gilmore

ContextOncotype DX (ODX) is a widely used commercial assay that estimates the risk of distant recurrence and may predict the benefit of chemotherapy in a subset of breast cancers. Some studies have shown the ability to predict Oncotype DX recurrence score (ODXRS), based on routinely reported pathologic features; however, there are limited data correlating specific histologic type of breast cancer to ODXRS.ObjectiveTo compare ODXRS to specific histologic types of breast cancer.DesignOne hundred eighty-four cases were sent for ODXRS testing and the results were compared with histologic type and grade.ResultsThe highest average ODXRS was seen in invasive ductal carcinoma with micropapillary features (29) followed by invasive ductal carcinoma not otherwise specified (mean = 19.4, SD = 11.6), invasive mucinous carcinoma (mean = 17.2, SD = 5.9), invasive lobular carcinoma (mean = 15.7, SD = 7.2), mixed ductal and lobular carcinoma (mean = 14.1, SD = 7.7), tubular carcinoma (10.0), and mixed ductal and mucinous carcinoma (mean = 8.0, SD = 4.2). Most tumors that had a high ODXRS were grade 3 invasive ductal carcinoma, representing 13 of a total of 20 cases (65%). Interestingly, 3 of the 4 cases of pure invasive mucinous carcinoma had an intermediate ODXRS.ConclusionsAlthough the numbers are small, our findings raise further awareness of the significance between histologic type and grade, and RS in breast cancer. In some special histologic types of breast cancer, particularly those considered to follow either an excellent or poor clinical course by histology alone, it is unclear whether the ODXRS results are as meaningful as in carcinomas of no special type. Further investigation with higher numbers and outcome data is needed.

2013 ◽  
Vol 99 (1) ◽  
pp. 39-44
Author(s):  
Claudia Maria Regina Bareggi ◽  
Dario Consonni ◽  
Barbara Galassi ◽  
Donatella Gambini ◽  
Elisa Locatelli ◽  
...  

Aims and background Often neglected by large clinical trials, patients with uncommon breast malignancies have been rarely analyzed in large series. Patients and methods Of 2,052 patients diagnosed with breast cancer and followed in our Institution from January 1985 to December 2009, we retrospectively collected data on those with uncommon histotypes, with the aim of investigating their presentation characteristics and treatment outcome. Results Rare histotypes were identified in 146 patients (7.1% of our total breast cancer population), being classified as follows: tubular carcinoma in 75 (51.4%), mucinous carcinoma in 36 (24.7%), medullary carcinoma in 25 (17.1%) and papillary carcinoma in 10 patients (6.8%). Whereas age at diagnosis was not significantly different among the diverse diagnostic groups, patients with medullary and papillary subtypes had a higher rate of lymph node involvement, similar to that of invasive ductal carcinoma. Early stage diagnosis was frequent, except for medullary carcinoma. Overall, in comparison with our invasive ductal carcinoma patients, those with rare histotypes showed a significantly lower risk of recurrence, with a hazard ratio of 0.28 (95% CI, 0.12–0.62; P = 0.002). Conclusions According to our analysis, patients with uncommon breast malignancies are often diagnosed at an early stage, resulting in a good prognosis with standard treatment.


Author(s):  
Anak Agung Ngurah Gunawan ◽  
I Wayan Supardi ◽  
S. Poniman ◽  
Bagus G. Dharmawan

<p>Medical imaging process has evolved since 1996 until now. The forming of Computer Aided Diagnostic (CAD) is very helpful to the radiologists to diagnose breast cancer. KNN method is a method to do classification toward the object based on the learning data which the range is nearest to the object. We analysed two types of cancers IDC dan ILC. 10 parameters were observed in 1-10 pixels distance in 145 IDC dan 7 ILC. We found that the Mean of Hm(yd,d) at 1-5 pixeis the only significant parameters that distingguish IDC and ILC. This parameter at 1-5 pixels should be applied in KNN method. This finding need to be tested in diffrerent areas before it will be applied in cancer diagnostic.</p>


2018 ◽  
Vol 29 ◽  
pp. viii68
Author(s):  
F. Namuche ◽  
R.E. Ruiz ◽  
Z.D. Morante Cruz ◽  
D. Urrunaga ◽  
A. Aguilar Cartagena ◽  
...  

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