Artificial neural network models to predict nodal status in clinically node-negative breast cancer

Autor: Lisa Rydén, Pär-Ola Bendahl, Mattias Ohlsson, Looket Dihge, Patrik Edén
Rok vydání: 2019
Předmět:
0301 basic medicine
Cancer Research
Logistic regression
Breast cancer
0302 clinical medicine
Sentinel lymph node biopsy
Estrogen Receptor Status
Aged
80 and over
Artificial neural networks
Neovascularization
Pathologic
Middle Aged
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Tumor Burden
medicine.anatomical_structure
Receptors
Estrogen
Oncology
Area Under Curve
Lymphatic Metastasis
030220 oncology & carcinogenesis
Female
Radiology
Research Article
Adult
medicine.medical_specialty
Nodal status
Sentinel lymph node
Breast Neoplasms
Prediction models
lcsh:RC254-282
Young Adult
03 medical and health sciences
Genetics
medicine
Humans
Aged
Retrospective Studies
Receiver operating characteristic
business.industry
Cancer
Retrospective cohort study
medicine.disease
Carcinoma
Lobular
Axilla
030104 developmental biology
Linear Models
Lymph Nodes
Neural Networks
Computer
business
Zdroj: BMC Cancer
BMC Cancer, Vol 19, Iss 1, Pp 1-12 (2019)
ISSN: 1471-2407
Popis: Background Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models. Methods Patients with primary breast cancer were consecutively included between January 1, 2009 and December 31, 2012 in a prospectively maintained pathology database. Clinical- and radiological data were extracted from patient’s files and only clinically node-negative patients constituted the final study cohort. ANN-based models for nodal prediction were constructed including 15 risk variables for nodal status. Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test (HL) were used to assess performance and calibration of three predictive ANN-based models for no lymph node metastasis (N0), metastases in 1–3 lymph nodes (N1) and metastases in ≥ 4 lymph nodes (N2). Linear regression models for nodal prediction were calculated for comparison. Results Eight hundred patients (N0, n = 514; N1, n = 232; N2, n = 54) were included. Internally validated AUCs for N0 versus N+ was 0.740 (95% CI = 0.723–0.758); median HL was 9.869 (P = 0.274), for N1 versus N0, 0.705 (95% CI = 0.686–0.724; median HL: 7.421; P = 0.492) and for N2 versus N0 and N1, 0.747 (95% CI = 0.728–0.765; median HL: 9.220; P = 0.324). Tumor size and vascular invasion were top-ranked predictors of all three end-points, followed by estrogen receptor status and lobular cancer for prediction of N2. For each end-point, ANN models showed better discriminatory performance than multivariable logistic regression models. Accepting a false negative rate (FNR) of 10% for predicting N0 by the ANN model, SLNB could have been abstained in 27.25% of patients with clinically node-negative axilla. Conclusions In this retrospective study, ANN showed promising result as decision-supporting tools for estimating nodal disease. If prospectively validated, patients least likely to have nodal metastasis could be spared SLNB using predictive models. Trial registration Registered in the ISRCTN registry with study ID ISRCTN14341750. Date of registration 23/11/2018. Retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12885-019-5827-6) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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