Validation and update of a lymph node metastasis prediction model for breast cancer

Qiu, Si; Aarnink, Merel; Maaren, Marissa C. van; Dorrius, Monique D.; Bhattacharya, Arkajyoti; Veltman, Jeroen; Klazen, Caroline A. H.; Korte, Jan H.; Estourgie, Susanne H.; Ott, Pieter; Kelder, Wendy; Zeng, Hualin; Koffijberg, Hendrik; Zhang, Guo Jun; Dam, Gooitzen M. van; Siesling, Sabine

doi:10.1016/j.ejso.2017.12.008

Cited by 14 publications

(9 citation statements)

References 35 publications

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“…Among the currently available prediction tools, the nomogram has high accuracy and good discriminability, as well as convenient and important in clinical use (24), which can change the treatment pattern of BC patients (25). Previous nomograms of breast tumors mainly focused on the risk of non-SLN metastasis (26)(27)(28) or total LN metastasis (10,(29)(30)(31) to predict the possibility of axillary lymph node dissection omission to appropriately minimize the scope of axillary surgery. However, few studies have focused on the omission of SLNB.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

Hu¹,

Xue²,

Peng³

et al. 2021

Front. Oncol.

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BackgroundSentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied on postoperative data, thus limiting their application in the preoperative setting. Therefore, it is necessary to find a new model for preoperative risk prediction for SLN metastasis to help clinicians facilitate individualized clinical decisions.Materials and MethodsBC patients who underwent SLN biopsy in two different institutions were included in the training and validation cohorts. Demographic characteristics, preoperative tumor pathological features, and ultrasound findings were evaluated. Multivariate logistic regression was used to develop the nomogram. The discrimination, accuracy, and clinical usefulness of the nomogram were assessed using Harrell’s C-statistic and ROC analysis, the calibration curve, and the decision curve analysis, respectively.ResultsA total of 624 patients who met the inclusion criteria were enrolled, including 444 in the training cohort and 180 in the validation cohort. Young age, high BMI, high Ki67, large tumor size, indistinct tumor margins, calcifications, and an aspect ratio ≥1 were independent predictive factors for SLN metastasis of BC. Incorporating these parameters, the nomogram achieved a robust predictive performance with a C-index and accuracy of 0.92 and 0.85, and 0.82 and 0.80 in the training and validation cohorts, respectively. The calibration curves also fit well, and the decision curve analysis revealed that the nomogram was clinically useful.ConclusionsWe established a nomogram to preoperatively predict the risk of SLN metastasis in BC patients, providing a non-invasive approach in clinical practice and serving as a potential tool to identify BC patients who may omit unnecessary SLN biopsy.

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Section: Discussionmentioning

confidence: 99%

Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

Hu¹,

Xue²,

Peng³

et al. 2021

Front. Oncol.

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Section: Discussionmentioning

confidence: 99%

“…Abilities to identify the presence of clinically relevant residual LN disease through noninvasive MRI without imposing the need for LN biopsy or complete lymphadenectomy could reduce the requirement for current surgical approaches used for cancer staging and cancer treatment. 32,33 Surgical interventions permanently disrupt the lymphatic circulation and are unnecessary when no nodal disease is present, 34 yet may result in substantial quality of life issues for survivors. 35 Further, an imaging signature of LN metastasis and lymphatic vasculature would be useful to validate targeted delivery and efficacy of immunomodulators or chemotherapeutics to LNs 36 where anatomical indicators alone are incomplete.…”

Section: Clinical Applications Of Noninvasive Imaging Of Lymphatic mentioning

confidence: 99%

3.0 T relaxation time measurements of human lymph nodes in adults with and without lymphatic insufficiency: Implications for magnetic resonance lymphatic imaging

et al. 2018

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Purpose: To quantify 3.0T (i) T1 and T2 relaxation times of in vivo human lymph nodes (LNs), and (ii) LN relaxometry differences between healthy LNs and LNs from patients with lymphatic insufficiency secondary to breast cancer treatment-related lymphedema (BCRL). Materials and Methods: MR relaxometry was performed over bilateral axillary regions at 3.0T in healthy female controls (105 LNs from 20 participants) and patients with BCRL (108 LNs from 20 participants). Quantitative T1 maps were calculated using a multi-flip-angle (20, 40, 60 degrees) method with B1-correction (dual-TR method, TR1/TR2=30/130 ms), and T2 maps using a multi-echo (TE=9–189 ms; 12 ms intervals) method. T1 and T2 were quantified in the LN cortex and hilum. A Mann-Whitney U-test was applied to compare LN relaxometry values between patients and controls (significance: two-sided p<0.05). Linear regression was applied to evaluate how LN relaxometry varied with age, BMI, and clinical indicators of disease. Results: LN substructure relaxation times (mean ± standard deviation) in healthy controls were: T1 cortex=1435±391 ms, T1 hilum=714±123 ms; T2 cortex=102±12 ms, and T2 hilum=119±21 ms. T1 of the LN cortex was significantly reduced in the contralateral axilla of BCRL patients compared to the axilla on the surgical side (p<0.001) and compared to bilateral control values (p<0.01). The LN cortex T1 asymmetry discriminated cases vs. controls (p=0.004) in a multiple linear regression, accounting for age and BMI. Conclusion: Human 3.0T T1 and T2 relaxation times in axillary LNs were quantified for the first time in vivo. Measured values are relevant for optimizing acquisition parameters in anatomical lymphatic imaging sequences, and can serve as a reference for novel functional and molecular LN imaging methods that require quantitative knowledge of LN relaxation times.

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“…It is a logistic regression model including the following variables: lymph node diameter, cortical thickness and presence or absence of a hilum as detected by ultrasonography, histological grade, tumor size and ER status of the primary tumor [5]. In a previous validation study the NCR was enriched with the necessary data on imaging [25]. The validation population consisted of 1416 patients with a positive ultrasound diagnosed between 2011 and 2015 with T1–3N0–1 stage breast cancer in one of the six participating hospitals in the Netherlands.…”

Section: Methodsmentioning

confidence: 99%

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

Steenbeek

Maaren

Siesling

et al. 2019

BMC Med Res Methodol

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Background Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform ( https://www.evidencio.com ) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation. Methods Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation. Results Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods. Conclusions This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice . In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation . Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population. Electronic supplementary material The online version of this article (10.1186/s12874-019-0761-5) contains supplementary material, which is available to authorized users.

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Validation and update of a lymph node metastasis prediction model for breast cancer

Abstract: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.

Cited by 14 publications

References 35 publications

Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

3.0 T relaxation time measurements of human lymph nodes in adults with and without lymphatic insufficiency: Implications for magnetic resonance lymphatic imaging

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

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