Predictors of Non-Sentinel Lymph Node Metastasis in Patients with Positive Sentinel Lymph Node in Early-Stage Cervical Cancer: A SENTICOL GROUP Study

Pache, Basile; Tantari, Matteo; Guani, Benedetta; Mathevet, Patrice; Magaud, Laurent; Lecuru, Fabrice; Balaya, Vincent

doi:10.3390/cancers15194737

Cited by 4 publications

(2 citation statements)

References 62 publications

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“…However, no specific analysis was performed for patients with LVM confined to the SLN only. Recently, Pache et al [ 34 ] published an ancillary analysis of SENTICOL 1-2 trials to exploit the risk factors associated with non-SLN metastases in patients with positive SLN. All non-SLNs were analyzed via ultrastaging to avoid missing LVM.…”

Section: Present and Future Challengesmentioning

confidence: 99%

Low-Volume Metastases in Cervical Cancer: Does Size Matter?

Bianchi,

Grassi,

Di Martino

et al. 2024

Cancers

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The implementation of sentinel lymph node (SLN) biopsy is changing the scenario in the surgical treatment of early-stage cervical cancer, and the oncologic safety of replacing bilateral pelvic lymphadenectomy with SLN biopsy is currently under investigation. Part of the undisputed value of SLN biopsy is its diagnostic accuracy in detecting low-volume metastases (LVM) via pathologic ultrastaging. In early-stage cervical cancer, the reported incidence of LVM ranges from 4 to 20%. The prognostic impact and the role of adjuvant treatment in patients with LVM is still unclear. Some non-prespecified analyses in prospective studies showed no impact on the oncologic outcomes compared to node-negative disease. However, the heterogeneity of the studies, the differences in the disease stage and the use of adjuvant treatment, and the concomitant pelvic lymphadenectomy (PLND) make reaching any conclusions on this topic hard. Current guidelines suggest considering micrometastases (MIC) as a node-positive disease, while considering isolated tumor cells (ITC) as a node-negative disease with a low level of evidence. This review aims to highlight the unanswered questions about the definition, identification, and prognostic and therapeutic roles of LVM and to underline the present and future challenges we are facing. We hope that this review will guide further research, giving robust evidence on LVM and their impacts on clinical practice.

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Section: Present and Future Challengesmentioning

confidence: 99%

Low-Volume Metastases in Cervical Cancer: Does Size Matter?

Bianchi,

Grassi,

Di Martino

et al. 2024

Cancers

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“…Comprehensive regional nodal irradiation is recommended for patients with LVI according to the NCCN guidelines 9 . Furthermore, the presence of LVI is related to non-sentinel lymph node involvement in patients with a positive sentinel node biopsy 10 . Currently, LVI status is determined using surgical pathology specimens, which can lead to complications such as delayed treatment, additional surgeries, unnecessary anxiety, and diminished quality of life.…”

Section: Introductionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

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Guidelines of the Brazilian Society of Surgical Oncology for anatomopathological, immunohistochemical, and molecular testing in female tumors

Ribeiro,

Carvalho,

Baiocchi

et al. 2024

Journal of Surgical Oncology

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IntroductionPrecision medicine has revolutionized oncology, providing more personalized diagnosis, treatment, and monitoring for patients with cancer. In the context of female‐specific tumors, such as breast, ovarian, endometrial, and cervical cancer, proper tissue collection and handling are essential for obtaining tissue, immunohistochemical (IHC), and molecular data to guide therapeutic decisions.ObjectivesTo establish guidelines for the collection and handling of tumor tissue, to enhance the quality of samples for histopathological, IHC, genomic, and molecular analyses. These guidelines are fundamental in informing therapeutic decisions in cancer treatment.MethodThe guidelines were developed by a multidisciplinary panel of renowned specialists between June 12, 2013 and February 12, 2024. Initially, the panel deliberated on critical and controversial topics related to conducting precision medicine studies focusing on female tumors. Subsequently, 22 pivotal topics were identified within the framework and assigned to groups. These groups reviewed relevant literature and drafted preliminary recommendations. Following this, the recommendations were reviewed by the coordinators and received unanimous approval. Finally, the groups made the final adjustments, classified the level of evidence, and ranked the recommendations.ConclusionThe collection of surgical samples requires minimum quality standards to enable histopathological, IHC, genomic, and molecular analyses. These analyses provide crucial data for informing therapeutic decisions, significantly impacting potential survival gains for patients with female tumors.

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Predictors of Non-Sentinel Lymph Node Metastasis in Patients with Positive Sentinel Lymph Node in Early-Stage Cervical Cancer: A SENTICOL GROUP Study

Cited by 4 publications

References 62 publications

Low-Volume Metastases in Cervical Cancer: Does Size Matter?

Low-Volume Metastases in Cervical Cancer: Does Size Matter?

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Guidelines of the Brazilian Society of Surgical Oncology for anatomopathological, immunohistochemical, and molecular testing in female tumors

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