What can we learn from the 10 mm lymph node size cut-off on the CT in advanced ovarian cancer at the time of interval debulking surgery?

Benoit, Louise; Zerbib, J.; Koual, Meriem; Nguyen-Xuan, Huyên-Thu; Delanoy, Nicolas; Frère‐Belda, Marie‐Aude Le; Bentivegna, Enrica; Bats, Anne‐Sophie; Fournier, Laure; Azaïs, Henri

doi:10.1016/j.ygyno.2021.06.025

Cited by 5 publications

(8 citation statements)

References 23 publications

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“…Third, by adding clinical variables, the performance of the CT results was significantly boosted, with an NRI of 0.593 and an IDI of 0.054. CT, the most frequently used imaging technique in the preoperative evaluation of ovarian cancer, achieved an AUC of 0.699, which was similar to that reported in other studies [ 18 , 19 ]. A short-axis diameter threshold of 10 mm failed to consider normal-sized LNs with microscopic or partial infiltration or inflammatory node enlargement, leading to misdiagnosis as well as missed diagnosis.…”

Section: Discussionsupporting

confidence: 87%

“…Computed tomography (CT) remains the first-line imaging method owing to its wide availability, fast data acquisition, and high image resolution [ 15 ]. An LN with a short-axis diameter over 10 mm was considered malignant in previous studies [ 16 , 17 , 18 ]. However, the studies were limited by the low sensitivities (35–50%) and areas under the receiver operating characteristic (ROC) curves (AUCs; 0.57–0.717) [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…An LN with a short-axis diameter over 10 mm was considered malignant in previous studies [ 16 , 17 , 18 ]. However, the studies were limited by the low sensitivities (35–50%) and areas under the receiver operating characteristic (ROC) curves (AUCs; 0.57–0.717) [ 16 , 17 , 18 , 19 ]. A poor diagnostic performance like this can lead to a high proportion of patients being under-staged or over-staged.…”

Section: Introductionmentioning

confidence: 99%

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A Nomogram for Preoperative Prediction of the Risk of Lymph Node Metastasis in Patients with Epithelial Ovarian Cancer

et al. 2023

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Objective: To develop a nomogram for predicting lymph node metastasis (LNM) in patients with epithelial ovarian cancer (EOC). Methods: Between December 2012 and August 2022, patients with EOC who received computed tomography (CT) and serological examinations and were treated with upfront staging or debulking surgery were included. Systematic pelvic and para-aortic lymphadenectomy was performed in all patients. Univariate and multivariate analysis was used to identify significant risk factors associated with LNM. A nomogram was then constructed to assess the risk of LNM, which was evaluated with respect to its area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness. Results: Of 212 patients enrolled in this study, 78 (36.8%) had positive LNs. The nomogram integrating CT-reported LN status, child-bearing status, tumour laterality, and stage showed good calibration and discrimination with an AUC of 0.775, significantly improving performance over the CT results (0.699, p = 0.0002) with a net reclassification improvement of 0.593 (p < 0.001) and integrated discrimination improvement of 0.054 (p < 0.001). The decision curve analysis showed the nomogram was of clinical use. Conclusions: A nomogram was constructed and internally validated, which may act as a decision aid in patients with EOC being considered for systemic lymphadenectomy.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Nomogram for Preoperative Prediction of the Risk of Lymph Node Metastasis in Patients with Epithelial Ovarian Cancer

et al. 2023

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“…Although 10 mm in short diameter is commonly used as a threshold for CT to indicate whether it is a lymph node metastasis or not, its accuracy is still questionable. The presence of ESLN in CT imaging is an indicator of lymph node involvement, accompanied by low sensitivity (12-35%) [7,23]. In this study, we investigated whether the small lymph nodes could affect the prognosis in ESCC patients.…”

Section: Discussionmentioning

confidence: 99%

Quantitative CT evaluation after two cycles of induction chemotherapy to predict prognosis of patients with locally advanced oesophageal squamous cell carcinoma before undergoing definitive chemoradiotherapy/radiotherapy

et al. 2022

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Objective To investigate the performance of quantitative CT analysis in predicting the prognosis of patients with locally advanced oesophageal squamous cell carcinoma (ESCC) after two cycles of induction chemotherapy before definitive chemoradiotherapy/radiotherapy. Methods A total of 110 patients with locally advanced ESCC were retrospectively analysed. Baseline chest CT and CT after two cycles of induction chemotherapy were analysed. A multivariate Cox proportional-hazard regression model was used to identify independent prognostic markers for survival analysis. Then, a CT scoring system was established. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were employed for analysing the prognostic value of the CT scoring system. Results Body mass index, treatment strategy, change ratios of thickness (ΔTHmax), CT value of the primary tumour (ΔCTVaxial) and the short diameter (ΔSD-LN), and the presence of an enlarged small lymph node (ESLN) after two cycles of chemotherapy were noted as independent factors for predicting overall survival (OS). The specificity of the presence of ESLN for death after 12 months was up to 100%. Areas under the curve value of the CT scoring system for predicting OS and progression-free survival (PFS) were higher than that of the RECIST (p < 0.05). Responders had significantly longer OS and PFS than non-responders. Conclusion Quantitative CT analysis after two cycles of induction chemotherapy could predict the outcome of locally advanced ESCC patients treated with definitive chemoradiotherapy/radiotherapy. The CT scoring system could contribute to the development of an appropriate strategy for patients with locally advanced ESCC. Key Points • Quantitative CT evaluation after two cycles of induction chemotherapy can predict the long-term outcome of locally advanced oesophageal cancer treated with definitive chemoradiotherapy/radiotherapy. • A CT scoring system provides valuable imaging support for indicating the prognosis at the early stage of therapy. • Quantitative CT evaluation can assist clinicians in personalising treatment plans.

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“…The presence of enlarged LNs on CT turned out to be a weak indicator of LN involvement in patients undergoing Neoadjuvant Chemotherapy (NACT). However, it could be used to assess the overall prognosis [17]. The study by Sohn et al [18] focuses on evaluating LN metastasis from thyroid cancer using ultrasound.…”

Section: Introductionmentioning

confidence: 99%

Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale

Chudobiński,

Świderski,

Antoniuk

et al. 2024

Cancers

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The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.

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What can we learn from the 10 mm lymph node size cut-off on the CT in advanced ovarian cancer at the time of interval debulking surgery?

Cited by 5 publications

References 23 publications

A Nomogram for Preoperative Prediction of the Risk of Lymph Node Metastasis in Patients with Epithelial Ovarian Cancer

A Nomogram for Preoperative Prediction of the Risk of Lymph Node Metastasis in Patients with Epithelial Ovarian Cancer

Quantitative CT evaluation after two cycles of induction chemotherapy to predict prognosis of patients with locally advanced oesophageal squamous cell carcinoma before undergoing definitive chemoradiotherapy/radiotherapy

Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale

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