CARLOS SHIMIZU

Índice h a partir de 2011
5
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Instituto do Câncer do Estado de São Paulo, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/24 - Laboratório de Oncologia Experimental, Hospital das Clínicas, Faculdade de Medicina

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Agora exibindo 1 - 4 de 4
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    Brazilian Randomized Study - BREAST-MRI Trial - Impact of Preoperative Magnetic Resonance in the Evaluation for Breast Cancer Conservative Surgery: Local recurrence and surgical outcomes
    (2023) MOTA, Bruna S.; REIS, Yedda N.; BARROS, Nestor; CARDOSO, Natalia; MOTA, Rosa S.; SHIMIZU, Carlos; TUCUNDUVA, Tatiana; GONCALVES, Rodrigo; DORIA, Maira T.; FERREIRA, Vera; RICCI, Marcos; TRINCONI, Angela; RIERA, Rachel; BARACAT, Edmund C.; SOARES JR., Jose Maria; FILASSI, Jose Roberto
  • article 0 Citação(ões) na Scopus
    Pathological macroscopic evaluation of breast density versus mammographic breast density in breast cancer conserving surgery
    (2023) REIS, Yedda Nunes; MOTA, Bruna Salani; MOTA, Rosa Maria Salani; SHIMIZU, Carlos; RICCI, Marcos Desiderio; AGUIAR, Fernando Nalesso; SOARES-JR, Jose Maria; BARACAT, Edmund Chada; FILASSI, Jose Roberto
    Correlation between imaging and anatomopathological breast density has been superficially explored and is heterogeneous in current medical literature. It is possible that mammographic and pathological findings are divergent. The aim of this study is to evaluate the association between breast density classified by mammography and breast density of pathological macroscopic examination in specimens of breast cancer conservative surgeries. Post-hoc, exploratory analysis of a prospective randomized clinical trial of patients with breast cancer candidates for breast conservative surgery. Breast mammographic density (MD) was analyzed according to ACR BI-RADS (R) criteria, and pathologic macroscopic evaluation of breast density (PMBD) was estimated by visually calculating the ratio between stromal and fatty tissue. From 412 patients, MD was A in 291 (70,6%), B in 80 (19,4%) B, C in 35 (8,5%), and D in 6 (1,5%). Ninety-nine percent (201/203) of patients classified as A+B in MD were correspondently classified in PMBD. Conversely, only 18.7% (39/209) of patients with MD C+D were classified correspondently in PMBD (p < 0.001). Binary logistic regression showed age (OR 1.06, 1.01-1.12 95% CI, p 0.013) and nulliparity (OR 0.39, 0.17-0.96 95% CI, p 0.039) as predictors of A+B PMBD.Conclusion: Mammographic and pathologic macroscopic breast density showed no association in our study for breast C or D in breast image. The fatty breast was associated with older patients and the nulliparity decreases the chance of fatty breasts nearby 60%.
  • article 5 Citação(ões) na Scopus
    Effects of preoperative magnetic resonance image on survival rates and surgical planning in breast cancer conservative surgery: randomized controlled trial (BREAST-MRI trial)
    (2023) MOTA, Bruna Salani; REIS, Yedda Nunes; BARROS, Nestor de; CARDOSO, Natalia Pereira; MOTA, Rosa Maria Salani; SHIMIZU, Carlos; TUCUNDUVA, Tatiana Cardoso de Mello; FERREIRA, Vera Christina Camargo de Siqueira; GONCALVES, Rodrigo; DORIA, Maira Teixeira; RICCI, Marcos Desiderio; TRINCONI, Angela Francisca; CAMARGO, Cristina Pires; RIERA, Rachel; BARACAT, Edmund Chada; JR, Jose Maria Soares; FILASSI, Jose Roberto
    BackgroundBreast magnetic resonance imaging (MRI) has high sensitivity in detecting invasive neoplasms. Controversy remains about its impact on the preoperative staging of breast cancer surgery. This study evaluated survival and surgical outcomes of preoperative MRI in conservative breast cancer surgery.MethodsA phase III, randomized, open-label, single-center trial including female breast cancer participants, stage 0-III disease, and eligible for breast-conserving surgery. We compared the role of including MRI in preoperative evaluation versus radiologic exam routine with mammography and ultrasound in breast cancer conservative candidates. The primary outcome was local relapse-free survival (LRFS), and secondary outcomes were overall survival (OS), mastectomy rate, and reoperation rate.Results524 were randomized to preoperative MRI group (n = 257) or control group (n = 267). The survival analysis showed a 5.9-years LRFS of 99.2% in MRI group versus 98.9% in control group (HR = 0.72; 95% CI 0.12-4.28; p = 0.7) and an OS of 95.3% in the MRI group versus 96.3% in the control group (HR = 1.37 95% CI 0.59-3.19; p = 0.8). Surgical management changed in 21 ipsilateral breasts in the MRI group; 21 (8.3%) had mastectomies versus one in the control group. No difference was found in reoperation rates, 22 (8.7%) in the MRI group versus 23 (8.7%) in the control group (RR = 1.002; 95% CI 0.57-1.75; p = 0.85).ConclusionPreoperative MRI increased the mastectomy rates by 8%. The use of preoperative MRI did not influence local relapse-free survival, overall survival, or reoperation rates.
  • article 18 Citação(ões) na Scopus
    Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network
    (2022) PETRINI, Daniel G. P.; SHIMIZU, Carlos; ROELA, Rosimeire A.; VALENTE, Gabriel Vansuita; FOLGUEIRA, Maria Aparecida Azevedo Koike; KIM, Hae Yong
    Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a ""patch classifier"" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the ""single-view whole-image classifier"". We propose to make a third transfer learning to obtain a ""two-view classifier"" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use EfficientNet as the basis of our model. We ""end-to-end"" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.9344 using 5-fold cross validation (accuracy, sensitivity and specificity are 85.13% at the equal error rate point of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, as far as we know the highest reported AUC for this problem, although the subtle differences in the testing conditions of each work do not allow for an accurate comparison. The inference code and model are available at https://github.com/dpetrini/two-views-classifier