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Three-Dimensional (3D) Tumor Spheroid Invasion Assay
Published on: May 1, 2015
Three-dimensional pathological size assessment in primary breast carcinoma
Uwe Güth1, Denise Brenckle, Dorothy Jane Huang
1Department of Gynecology and Obstetrics, University Hospital Basel, Basel, Switzerland. ugueth@uhbs.ch
Breast Cancer Research and Treatment
|July 16, 2008
Summary
Maximal tumor diameter (MD), tumor volume (TV), and tumor surface area (TSA) all predict axillary lymph node involvement in breast cancer. However, 3D measurements do not offer superior prognostic accuracy over the traditional 1D MD.
Area of Science:
- Oncology
- Pathology
- Radiology
Background:
- Maximal tumor diameter (MD) is a traditional prognostic factor in breast cancer.
- The predictive value of a single 1D measurement for a 3D tumor is questionable.
Purpose of the Study:
- To compare the prognostic accuracy of 1D and 3D tumor measurements for predicting axillary lymph node involvement (ALNI).
- To evaluate tumor volume (TV) and tumor surface area (TSA) as prognostic indicators in invasive breast carcinomas.
Main Methods:
- Calculated MD, two perpendicular diameters (PDs), eccentricity (EF), TV, and TSA for 395 invasive breast carcinomas (10-40 mm).
- Assessed ALNI as the dependent prognostic variable.
- Performed univariate and multivariate analyses to determine the predictive accuracy of each parameter.
Main Results:
- MD, TV, and TSA were all significant predictors of ALNI.
- Univariate analyses showed P-values of 0.0003 for MD, 0.0009 for TV, and <0.0001 for TSA.
- Multivariate analyses yielded P-values of 0.0018 for MD, 0.0109 for TV, and 0.0009 for TSA.
- Pseudo R-squared values were 0.42 for MD, 0.39 for TV, and 0.39 for TSA.
- Despite variations in tumor shape, 3D measurements (TV/TSA) did not demonstrate superior prognostic precision compared to 1D MD.
Conclusions:
- While MD, TV, and TSA are significant predictors of ALNI, 3D measurements do not offer improved prognostic accuracy over the traditional 1D MD.
- The study questions the sole reliance on 1D measurements for assessing breast cancer prognosis.

