ArticleAnnals of surgical treatment and research2018
Prognostic influence of 3-dimensional tumor volume on breast cancer compared to conventional 1-dimensional tumor size.
Article in Annals of surgical treatment and research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dynamic contrast-enhanced magnetic resonance imaging-derived three-dimensional tumor volume improves prognostic stratification in breast cancer: a retrospective cohort study.Gland surgery · 2026Article
- Physician-guided deep learning model for assessing thymic epithelial tumor volume.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Comparison of Tumor Diameter and Tumor Volume in Terms of Aggressive Tumor Behavior and Prognosis in Papillary Thyroid Cancer.Cancers · 2025Article
- The Value of Micro-CT in the Diagnosis of Lung Carcinoma: A Radio-Histopathological Perspective.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Outcomes of surgical resection of super-giant (≥15 cm) hepatocellular carcinoma: Volume does matter, if not the size.Journal of clinical and translational research · 2022Article
- Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer.The British journal of radiology · 2022Article
- Complications and local relapse after intraoperative low-voltage X-ray radiotherapy in breast cancer.Annals of surgical treatment and research · 2020Article
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Abstract
purposeThe prognostic influence of 3-dimensional tumor volume (Tv) on breast cancer compared to conventional 1-dimensional tumor size (T) was investigated.
methodsAnalysis was performed on a cohort of 8,996 primary breast cancer patients who were initially diagnosed with TNM stage I-III. Tumor size was defined as the maximum tumor dimension, and Tv was calculated by the equation of (4π × r1 × r2 × r3)/3; r1, r2, and r3 were defined as half of the largest, intermediate, and shortest dimension of the tumor, respectively. Tv was classified into Tv1, Tv2, and Tv3 according to the cut off values of 2.056 cm
resultsThe survival curves according to both the T and Tv categories were clearly differentiated (all P < 0.001), as were those for staging by T and Tv (all P < 0.001). In T1 and T2 tumors, the Tv1 group showed superior survival over the Tv2 group (T1, P < 0.001; T2, P = 0.001). Univariate and multivariate analysis both indicated that Tv was a significant prognostic factor (both P < 0.001). The receiver operating characteristic curve showed that the area under the curves were 0.712 (P < 0.001) for Tv and 0.699 (P < 0.001) for T. Positive correlations were observed between the number of positive nodes and T (coefficient = 0.325; P < 0.001), and between the number of positive nodes and Tv (coefficient = 0.321; P < 0.001).
conclusionTv classification works well for predicting the prognosis of breast cancer, and it is a better predictor than conventional T classification in several aspects. Further studies are needed to validate the practical usefulness of Tv classification in clinical settings.
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