Background: Accurate preoperative sizing of breast cancer with imaging modalities has a great importance in the surgical planning.
Purpose: To assess the influence of tumor size and histology on the accuracy of measurement of cancer local extension by magnetic resonance imaging (MRI).
Material And Methods: One hundred and eighty-six patients with primary breast cancer, for a total of 221 lesions, were included in this retrospective study. Tumors were divided into five histological groups: invasive ductal carcinoma (IDC), IDC with extensive intraductal component (EIC), invasive lobular carcinoma (ILC), ductal carcinoma in situ (DCIS), and "other histology" (mucinous, papillary, medullary, tubular, and apocrine breast cancer). Microscopic measurement of the largest diameter of tumors at pathology was chosen as reference standard and compared with MRI measurement. Concordance was defined as a difference ≤ 5 mm between MRI and pathology.
Results: The mean size of tumors at pathology was 24.8 ± 19.4 mm, while at MRI it was 29.7 ± 20 mm (P < 0.05), with a significant overestimation of MRI. MRI-pathology concordance was found in 98/221 cases (44.3%), while MRI overestimated the size of 81/221 tumors (36.7%). The extent of overestimation was significantly different among the five histological groups (P < 0.05). At multivariate analysis, DCIS histology was the factor more significantly associated with MRI-pathology discordance (P = 0.0005), while the influence of tumor dimension at pathology was less significant (P = 0.0073).
Conclusion: DCIS histology is strongly associated with discordance between MRI and pathology sizing of breast cancer. Lesion size can also influence the accuracy of MRI measurements, but to a lesser extent.
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http://dx.doi.org/10.1177/0284185114524089 | DOI Listing |
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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