Bruno Rezende

Dr. Bruno Rezende

Hospital do câncer de londrina, Londrina, Paraná, Brazil
orcid
Oncology surgeon at Londrina cancer hospital

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 November 2023 in Cancers
Verified
Journal Article
Open access
PDF file
Stoyan KostovStoyan Kostov
Stoyan Kostov
PSPavel Sorokin
Pavel Sorokin
Bruno RezendeBruno Rezende
Bruno Rezende
HYHakan Yalçın
Hakan Yalçın
ISIlker Selcuk
Ilker Selcuk
A radical hysterectomy is the standard method of surgical treatment for patients with early-stage cancer of the uterine cervix. It was first introduced more than 100 years ago. Since then, various and many different radical procedures, which diverge in terms of radicality, have been described. Inconsistencies are clearly seen in practical anatomy, which were defined as surgically created artifacts. Moreover, the disparity of the procedure is most notable regarding the terminology of pelvic connective tissues and spaces. Despite these controversies, the procedure is widely performed and implemented in the majority of guidelines for the surgical treatment of cancer of the uterine cervix. However, a different and unique concept of surgical treatment of cervical cancer has been reported. It is based on ontogenetic anatomy and maps any tissue in the mature organism according to its embryologic development. The clinical implementation of this theory in the context of early cervical cancer is total mesometrial resection. The present article aims to describe and compare the anatomical and surgical basics of a radical hysterectomy (type C1/C2) and total mesometrial resection. Discrepancies regarding the terminology, resection lines, and surgical planes of both procedures are highlighted in detail. The surgical anatomy of the pelvic autonomic nerves and its surgical dissection is also delineated. This is the first article that compares the discrepancy of classic anatomy and ontogenic anatomy regarding surgical treatment of cancer of the uterine cervix. Clinical data, oncological outcome, and neoadjuvant and adjuvant treatment regarding both procedures are not the topic of the present article.

19 February 2026 in Cancers
Verified
Journal Article
Open access
PDF file
Bruno RezendeBruno Rezende
Bruno Rezende
BWBenjamin Wolf
Benjamin Wolf
VCVinicius Colman
Vinicius Colman
RORivadavio Antunes Menacho de Oliveira
Rivadavio Antunes Menacho de Oliveira
SKSvetlana Kulikova
Svetlana Kulikova
PSPavel Sorokin
Pavel Sorokin
Background/Objective: The aim of this study was to compare two different staging systems (FIGO 2018 and ontogenetic staging) for cervical cancer in a locally advanced setting and to determine which one provides better prognostic stratification and a stronger association with oncological outcomes. Methods: A multicenter retrospective cohort study was conducted in patients with cervical cancer (FIGO 2018 stages IIB-IVA) primarily treated with chemoradiotherapy. The primary endpoint was the difference in accuracy for predicting cancer-specific survival at 3 years between the ontogenetic tumor staging system and the FIGO 2018 staging system. Secondary endpoints assessed the same difference in prognostic power with respect to recurrence-free survival and overall survival. Results: Of 341 patients with locally advanced cervical cancer, the ontogenetic tumor staging system demonstrated superior prognostic performance compared to the FIGO 2018 system. The ontogenetic system showed clear separation of Kaplan–Meier curves for recurrence-free, overall, and cancer-specific survival, unlike the overlapping FIGO 2018 survival curves. Multivariable Cox models controlling clinicopathological factors revealed a progressive, significant worsening of cancer-specific survival across ontogenetic tumor (oT) stages, which was not observed with FIGO staging. Formal comparisons using Harrell’s C-index, time-dependent areas under the curve (AUC), and Akaike’s information criterion confirmed the ontogenetic model’s significantly better discriminative ability and fit. Furthermore, ontogenetic staging provided significant risk stratification within the FIGO IIIC nodal-positive group and among patients with parametrial invasion, demonstrating its refined prognostic utility. Conclusions: Ontogenetic tumor staging can be effectively applied using imaging alone and was superior to the FIGO 2018 classification in this study.

15 June 2025 in Klinicka onkologie
Verified
Journal Article
Closed access
PSPavel Sorokin
Pavel Sorokin
SKSvetlana Kulikova
Svetlana Kulikova
ASArtem Stepanyan
Artem Stepanyan
HDHasmik Davtyan
Hasmik Davtyan
MKMarek Kuryllo
Marek Kuryllo
Bruno RezendeBruno Rezende
Bruno Rezende
Total mesometrial resection (TMMR) has shown excellent locoregional control for treatment of cervical cancer without adjuvant radiation therapy in highly selected centers. However, this procedure was never evaluated in resource-limited setting. We hypothesized that the procedure can be reproduced outside the university centers without compromising results. This is a retrospective, observational, multicenter cohort study of patients with IB1-IIB cervical cancer who underwent TMMR in developing countries and omitted adjuvant radiation therapy. A total of 124 patients who met the inclusion criteria were identified between 2015 and 2024 in three centers. The median follow-up was 29 months. The relapse rate was 6.1% (2 out of 33) for IB1, 3% (1 out of 33) for IB2, 11.1% (2 out of 18) for IB3, 20% (1 out of 5) for IIA1, and 16% (6 out of 24) for node-positive patients. No relapses were detected among IIA2 and IIB stages (3 and 8 patients, respectively). There was no significant difference (P = 0.36) in the relapse rate between patients who met the Sedlis criteria (2.9%) and those who did not (8.8%). According to the study, TMMR outcomes can be reproduced without compromising oncologic outcomes. However, prospective evaluation, longer follow-up and a larger cohort are needed to confirm these preliminary data.