Prediction of the axillary lymph node status in mammary cancer on the basis of clinicopathological data and flow cytometry

Artificial intelligence in breast cancer histopathology

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.

The implementation of NILS: A web-based artificial neural network decision support tool for noninvasive lymph node staging in breast cancer

Objective

To implement artificial neural network (ANN) algorithms for noninvasive lymph node staging (NILS) to a decision support tool and facilitate the option to omit surgical axillary staging in breast cancer patients with low-risk of nodal metastasis.

Methods

The NILS tool is a further development of an ANN prototype for the prediction of nodal status. Training and internal validation of the original algorithm included 15 clinical and tumor-related variables from a consecutive cohort of 800 breast cancer cases. The updated NILS tool included 10 top-ranked input variables from the original prototype. A workflow with four ANN pathways was additionally developed to allow different combinations of missing preoperative input values. Predictive performances were assessed by area under the receiver operating characteristics curves (AUC) and sensitivity/specificity values at defined cut-points. Clinical utility was presented by estimating possible sentinel lymph node biopsy (SLNB) reduction rates. The principles of user-centered design were applied to develop an interactive web-interface to predict the patient's probability of healthy lymph nodes. A technical validation of the interface was performed using data from 100 test patients selected to cover all combinations of missing histopathological input values.

Results

ANN algorithms for the prediction of nodal status have been implemented into the web-based NILS tool for personalized, noninvasive nodal staging in breast cancer. The estimated probability of healthy lymph nodes using the interface showed a complete concordance with estimations from the reference algorithm except in two cases that had been wrongly included (ineligible for the technical validation). NILS predictive performance to distinguish node-negative from node-positive disease, also with missing values, displayed AUC ranged from 0.718 (95% CI, 0.687-0.748) to 0.735 (95% CI, 0.704-0.764), with good calibration. Sensitivity 90% and specificity 34% were demonstrated. The potential to abstain from axillary surgery was observed in 26% of patients using the NILS tool, acknowledging a false negative rate of 10%, which is clinically accepted for the standard SLNB technique.

Conclusions

The implementation of NILS into a web-interface are expected to provide the health care with decision support and facilitate preoperative identification of patients who could be good candidates to avoid unnecessary surgical axillary staging.

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

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.

Applications of Machine Learning in Cancer Prediction and Prognosis

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Predictors of axillary lymph node metastasis in breast cancer: A systematic review

AIMS

To review the established and emerging techniques in axillary lymph node prediction and explore their potential impact on clinical practice. To reliably identify patients in whom axillary lymph node surgery, including SLNB, can be safely omitted.

METHODS

Searches of PubMed were made using the search terms "axilla" (or "axillary"), "lymph", "node" and "predictor" (or "prediction"). Articles from abstracts and reports from meetings were included only when they related directly to previously published work.

FINDINGS

There are numerous studies in which the predictive utility of biomarkers as determinants of axillary lymph node status have been investigated. Few of these have specifically addressed the attributes of the primary tumour which could offer much potential for the prediction of tumour metastasis to the axillary lymph nodes.

CONCLUSIONS

Currently, no single marker is sufficiently accurate to obviate the need for formal axillary staging using SLNB or axillary clearance.

Vascular endothelial cell growth factor receptor 3-mediated activation of lymphatic endothelium is crucial for tumor cell entry and spread via lymphatic vessels

Lymphangiogenic growth factors vascular endothelial growth factor (VEGF)-C and VEGF-D have been shown to promote lymphatic metastasis by inducing tumor-associated lymphangiogenesis. In this study, we have investigated how tumor cells gain access into lymphatic vessels and at what stage tumor cells initiate metastasis. We show that VEGF-C produced by tumor cells induced extensive lymphatic sprouting towards the tumor cells as well as dilation of the draining lymphatic vessels, suggesting an active role of lymphatic endothelial cells in lymphatic metastasis. A significant increase in lymphatic vessel growth occurred between 2 and 3 weeks after tumor xenotransplantation, and lymph node metastasis occurred at the same stage. These processes were blocked dose-dependently by inhibition of VEGF receptor 3 (VEGFR-3) signaling by systemic delivery of a soluble VEGFR-3-immunoglobulin (Ig) fusion protein via adenoviral or adeno-associated viral vectors. However, VEGFR-3-Ig did not suppress lymph node metastasis when the treatment was started at a later stage after the tumor cells had already spread out, suggesting that tumor cell entry into lymphatic vessels is a key step during tumor dissemination via the lymphatics. Whereas lymphangiogenesis and lymph node metastasis were significantly inhibited by VEGFR-3-Ig, some tumor cells were still detected in the lymph nodes in some of the treated mice. This indicates that complete blockade of lymphatic metastasis may require the targeting of both tumor lymphangiogenesis and tumor cell invasion.