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Unlocking Precision and Minimizing Morbidity: Sentinel Lymph Node Mapping in...
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Unlocking Precision and Minimizing Morbidity: Sentinel Lymph Node Mapping in Gynecological Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3556

Special Issue Editor

Dr. Petra L.M. Zusterzeel

E-Mail Website
Guest Editor
Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
Interests: gynecological cancer

Special Issue Information

Dear Colleagues,

Sentinel lymph node (SLN) mapping has revolutionized the assessment and management of gynecological cancer by providing crucial prognostic information while minimizing treatment-related morbidity. SLN biopsy has transformed the traditional lymph node evaluation paradigm for early stage vulvar, cervical, and endometrial cancers, where nodal status carries significant prognostic implications. With the goal of removing the primary tumor and accurately staging regional lymph nodes, SLN biopsy offers a promising alternative to extensive lymphadenectomies, which are often associated with substantial morbidity.

For vulvar cancer, level 3 evidence supports the safety of omitting inguinofemoral lymphadenectomy when the sentinel node is negative.

Similarly, for clinical early stage endometrial and cervical cancer, acceptable false-negative rates have been observed, indicating the potential clinical utility and safety of SLN biopsy. But despite its potential in these malignancies, SLN biopsy alone has yet to attain gold-standard status due to the lack of prospective evidence on long-term oncological safety. Ongoing prospective trials are expected to shed light on these unresolved issues, providing crucial insights into the efficacy and safety of SLN biopsy.

SLN is not yet an established and widely accepted procedure in ovarian cancer, and evidence of SLN in early EOC is still scarce. Emerging techniques utilizing infundibulopelvic and proper ovarian ligament injections show promise for successful SLN detection during minimally invasive surgeries.

Sentinel lymph node mapping holds immense potential in gynecological cancer care, offering precision staging with reduced morbidity. As ongoing trials continue to investigate its long-term oncological safety, we anticipate that SLN mapping will become an integral component of the standard of care, improving outcomes and enhancing the quality of life for women facing gynecological malignancies. 

Dr. Petra L.M. Zusterzeel
Guest Editor

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Keywords

  • sentinel lymph node
  • gynecological cancer
  • vulvar cancer
  • endometrial cancer
  • cervical cancer
  • ovarian cancer

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Published Papers (4 papers)

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Research

14 pages, 485 KiB  
Article
Analysis of Predictive Factors Associated with Unsuccessful Sentinel Lymph Node Mapping in Endometrial Carcinoma
by Linas Andreika, Monika Šiaudinytė, Karolina Vankevičienė, Diana Ramašauskaitė and Vilius Rudaitis
Cancers 2024, 16(21), 3680; https://doi.org/10.3390/cancers16213680 - 31 Oct 2024
Viewed by 536
Abstract
Background: Sentinel lymph node (SLN) biopsy is recommended over systematic lymphadenectomy in early-stage endometrial cancer due to its lower morbidity and comparable detection rate. The objective of this study was to identify clinical factors associated with unsuccessful mapping. Methods: Between April 2020 and [...] Read more.
Background: Sentinel lymph node (SLN) biopsy is recommended over systematic lymphadenectomy in early-stage endometrial cancer due to its lower morbidity and comparable detection rate. The objective of this study was to identify clinical factors associated with unsuccessful mapping. Methods: Between April 2020 and June 2024, 120 patients over the age of 18 and diagnosed with early-stage endometrial cancer were enrolled in this prospective study at a single institution. Demographic, clinicopathologic, and treatment data were collected and analyzed using descriptive statistics. Univariate and multiple linear regressions were performed to identify predictors of failed mapping. Results: The mean age of the patient cohort was 62.5 years (range 33 to 83), and the mean body mass index (BMI) was 32 kg/m2 (range 18 to 50). Patients underwent intracervical injections with methylene blue (MB), indocyanine green (ICG), or a combination of both tracers, with 40 patients in each group. A total of 108 patients (90.0%) were diagnosed with endometrioid carcinoma and 12 (10.0%) with non-endometrioid cancers. Additionally, 110 patients (91.7%) were diagnosed in early stages of the disease. The overall SLN detection rate was 73.4%, with bilateral detection at 49.2% and unilateral detection at 24.2%. Univariate analysis showed that older age (p < 0.001), menopause (p = 0.001), the use of MB as the sole tracer (p = 0.006), a shorter tumor-to-serosa distance (p = 0.048), and bulky lymph nodes (p = 0.18) were associated with unsuccessful mapping. Multiple linear regression model analysis identified age (p = 0.007), tracer type (p = 0.013), and enlarged lymph nodes (p = 0.013) as independent predictors of SLN mapping failure. Conclusions: Advanced age, tracer type, and intraoperative detection of enlarged lymph nodes were identified as independent risk factors for unsuccessful mapping in patients undergoing laparoscopic SLN mapping. Full article
(This article belongs to the Special Issue Unlocking Precision and Minimizing Morbidity: Sentinel Lymph Node Mapping in Gynecological Cancer)
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12 pages, 2594 KiB  
Article
Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer
by Ilse G. T. Baeten, Jacob P. Hoogendam, Nikolas Stathonikos, Cornelis G. Gerestein, Geertruida N. Jonges, Paul J. van Diest and Ronald P. Zweemer
Cancers 2024, 16(21), 3619; https://doi.org/10.3390/cancers16213619 - 26 Oct 2024
Viewed by 751
Abstract
Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by [...] Read more.
Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy. This proof-of-principle study evaluated the effectiveness of a deep learning algorithm for SLN metastasis detection in early-stage cervical cancer. Methods: We retrospectively analyzed whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained SLNs from early-stage cervical cancer patients diagnosed with an SLN metastasis with either H&E or IHC. A CE-IVD certified commercially available deep learning algorithm, initially developed for detection of breast and colon cancer lymph node metastases, was employed off-label to assess its sensitivity in cervical cancer. Results: This study included 21 patients with early-stage cervical cancer, comprising 15 with squamous cell carcinoma, five with adenocarcinoma, and one with clear cell carcinoma. Among these patients, 10 had macrometastases and 11 had micrometastases in at least one SLN. The algorithm was applied to evaluate H&E WSIs of 47 SLN specimens, including 22 that were negative for metastasis, 13 with macrometastases, and 12 with micrometastases in the H&E slides. The algorithm detected all H&E macro- and micrometastases with 100% sensitivity. Conclusions: This proof-of-principle study demonstrated high sensitivity of a deep learning algorithm for detection of clinically relevant SLN metastasis in early-stage cervical cancer, despite being originally developed for adenocarcinomas of the breast and colon. Our findings highlight the potential of leveraging an existing algorithm for use in cervical cancer, warranting further prospective validation in a larger population. Full article
(This article belongs to the Special Issue Unlocking Precision and Minimizing Morbidity: Sentinel Lymph Node Mapping in Gynecological Cancer)
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10 pages, 458 KiB  
Article
Optimizing the Sensitivity of a Pelvic Sentinel Node Algorithm Requires a Hybrid Algorithm Combining Indocyanine Green Based Mapping and the Removal of Non-Mapped Nodes at Defined Anatomic Positions
by Michele Bollino, Barbara Geppert, Petur Reynisson, Celine Lönnerfors and Jan Persson
Cancers 2024, 16(18), 3242; https://doi.org/10.3390/cancers16183242 - 23 Sep 2024
Viewed by 720
Abstract
Aim of the study: to investigate the incidence of non-mapped isolated metastatic pelvic lymph nodes at pre-defined anatomical positions. Patients and Methods: Between June 2019 and January 2024, women with uterine-confined endometrial cancer (EC) deemed suitable for robotic surgery and the detection of [...] Read more.
Aim of the study: to investigate the incidence of non-mapped isolated metastatic pelvic lymph nodes at pre-defined anatomical positions. Patients and Methods: Between June 2019 and January 2024, women with uterine-confined endometrial cancer (EC) deemed suitable for robotic surgery and the detection of pelvic sentinel nodes (SLNs) were included. An anatomically based, published algorithm utilizing indocyanine green (ICG) as a tracer was adhered to. In women where no ICG mapping occurred in either the proximal obturator and/or the interiliac positions, defined as “typical positions”, those nodes were removed and designated as “SLN anatomy”. Ultrastaging and immunohistochemistry were applied to all SLNs. The proportion of isolated metastatic “SLN anatomy” was evaluated. Results: A non-mapping of either the obturator or interiliac area occurred in 180 of the 620 women (29%). In total, 114 women (18.4%) were node-positive and five of these women (4.3%) had isolated metastases in an “SLN anatomy”, suggesting a similar lower sensitivity of the ICG-only algorithm. Conclusion: In an optimized SLN algorithm for endometrial cancer, to avoid undetected nodal metastases in 4.3% of node-positive women, if mapping fails in either the proximal obturator or interiliac area, nodes should be removed from those defined anatomic positions, despite mapping at other positions. Full article
(This article belongs to the Special Issue Unlocking Precision and Minimizing Morbidity: Sentinel Lymph Node Mapping in Gynecological Cancer)
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10 pages, 3658 KiB  
Article
Sentinel Lymph Node Mapping by Retroperitoneal vNOTES for Uterus-Confined Malignancies: A Standardized 10-Step Approach
by Daniela Huber and Yannick Hurni
Cancers 2024, 16(11), 2142; https://doi.org/10.3390/cancers16112142 - 5 Jun 2024
Viewed by 978
Abstract
(1) Background: Sentinel lymph node (SLN) mapping represents an accurate and feasible technique for the surgical staging of endometrial and cervical cancer. This is commonly performed by conventional laparoscopy or robotic-assisted laparoscopy, but in recent years, a new retroperitoneal transvaginal natural orifice transluminal [...] Read more.
(1) Background: Sentinel lymph node (SLN) mapping represents an accurate and feasible technique for the surgical staging of endometrial and cervical cancer. This is commonly performed by conventional laparoscopy or robotic-assisted laparoscopy, but in recent years, a new retroperitoneal transvaginal natural orifice transluminal endoscopic surgery (vNOTES) approach has been described and developed by Jan Baekelandt. This technique provides easy visualization of lymphatic afferent vessels and pelvic lymph nodes, early SLN assessment, and a coherent mapping methodology following the lymphatic flow from caudal to cranial. However, only a few publications have reported it. Following the IDEAL (Idea Development Exploration Assessment Long-term follow-up) framework, research concerning this technique is in Stage 2a, with only small case series as evidence of its feasibility. Its standardized description appears necessary to provide the surgical homogeneity required to move further. (2) Methods: Description of a standardized approach for retroperitoneal pelvic SLN mapping by vNOTES. (3) Results: We describe a 10-step approach to successfully perform retroperitoneal vNOTES SLN mapping, including pre-, intra-, and postoperative management. (4) Conclusions: This IDEAL Stage 2a study could help other surgeons approach this new technique, and it proposes a common methodology necessary for evolving through future IDEAL Stage 2b (multi-center studies) and Stage 3 (randomized controlled trials) studies. Full article
(This article belongs to the Special Issue Unlocking Precision and Minimizing Morbidity: Sentinel Lymph Node Mapping in Gynecological Cancer)
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