Can machine learning be used to reduce overtreatment of the axilla in breast cancer? Results from a retrospective cohort study (Preprint)

2021 ◽  
Author(s):  
Felix Jozsa ◽  
Rose Baker ◽  
Peter Kelly ◽  
Muneer Ahmed ◽  
Michael Douek
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Axillary Node ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Axillary Ultrasound ◽  
Primary Surgery ◽  
Large Patient ◽  
Specificity And Sensitivity ◽  
Axillary Node Clearance

BACKGROUND Patients with early breast cancer undergoing primary surgery who have low axillary nodal burden can safely forego axillary node clearance (ANC). However, routine use of axillary ultrasound (AUS) leads to 43% of patients in this group having ANC unnecessarily following a positive AUS. The intersection of machine learning with medicine can provide innovative ways to understand specific risk within large patient data sets, but this has not yet been trialled in the arena of axillary node management in breast cancer. OBJECTIVE To assess if machine learning techniques could be used to improve pre-operative identification of patients with low and high axillary metastatic burden. METHODS A single-centre retrospective analysis was performed on patients with breast cancer who had a preoperative axillary ultrasound, and the specificity and sensitivity of AUS were calculated. Machine learning and standard statistical methods were applied to the data to see if, when used preoperatively, they could have improved the accuracy of AUS to better discern between high and low axillary burden. RESULTS The study included 459 patients; 31% (n=142) had a positive AUS, and, among this group, 62% (n=88) had two or fewer macrometastatic nodes at ANC. When applied to the dataset, logistic regression outperformed AUS and machine learning methods with a specificity of 0.950, correctly identifying 66 patients in this group who had been incorrectly classed as having high axillary burden by AUS alone. Of all the methods, the artificial neural network had the highest accuracy (0.919). Interestingly, AUS had the highest sensitivity of all methods (0.777), underlining its utility in this setting. CONCLUSIONS Machine learning greatly improves identification of the important subgroup of patients with no palpable axillary disease, positive ultrasound, and more than two metastatically involved nodes. A negative ultrasound in patients with no palpable lymphadenopathy is highly indicative of low burden and it is unclear if sentinel node biopsy adds value in this situation. CLINICALTRIAL n/a

TP8.1.1 Does ultrasound quantification of suspicious axillary nodes correlate with pathological results?

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Muhammad Abdullah
Keyword(s):  
Breast Cancer ◽  
Fast Track ◽  
Axillary Node ◽  
Retrospective Data ◽  
Breast Cancer Patients ◽  
Axillary Ultrasound ◽  
Axillary Nodes ◽  
Axillary Node Clearance ◽  
Ultrasound Findings ◽  
Abnormal Node

Abstract Aims Fast-track axillary node clearance (ANC) leads to overtreatment of axilla. Improved quantification by axillary US (AUS) is suggested to avoid unnecessary ANC and proceed with ANC or SLNB based on the number of abnormal axillary nodes. This retrospective study was aimed to evaluate whether ANC can be omitted based on AUS quantification in patients with low axillary burden. Methods Retrospective data of breast cancer patients who underwent ANC following a positive pre-operative axillary nodal biopsy between 1 January 2017 and 31 December 2018 were included in this study. The patients who received neoadjuvant chemotherapy, those having ANC following positive SLNB and those with axillary recurrence were excluded. The histopathology results of ANC were correlated with axillary ultrasound findings. Results 45 patients underwent fast-track ANC following positive axillary core biopsy. On pre-operative AUS, 18 of these patients were reported to have a single abnormal node, while 8 had two abnormal nodes and 19 patients had multiple abnormal nodes. The comparison of the number of metastatic nodes following ANC, and the reported abnormal nodes on pre-operative AUS, showed that 57.3% of patients with 1 – 2 abnormal nodes on AUS had 3 or more metastatic nodes and 26.3% of patients with multiple abnormal nodes on AUS had 1 – 2 metastatic nodes following ANC. Conclusions The quantification of the axillary burden with pre-operative AUS does not correlate with the number of metastatic axillary nodes. The reported relevant axillary burden on AUS is not sufficiently specific to form the basis of omission of ANC.

Breast Cancer Prediction Using Deep Learning and Machine Learning Techniques

2020 ◽  
Author(s):  
MONIKA TIWARI ◽  
Rashi Bharuka ◽  
Praditi Shah ◽  
Reena Lokare
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Cancer Prediction ◽  
Learning Techniques

Why I favour axillary node clearance in the management of breast cancer

1995 ◽  
Vol 21 (1) ◽  
pp. 5-7 ◽  
Author(s):  
Tim Davidson
Keyword(s):  
Breast Cancer ◽  
Axillary Node ◽  
Axillary Node Clearance

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

2021 ◽  
Author(s):  
Gediminas Adomavicius ◽  
Yaqiong Wang
Keyword(s):  
Machine Learning ◽  
General Purpose ◽  
Reliability Estimation ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Learning Techniques ◽  
Reliability Indicator ◽  
Machine Learning Approach ◽  
Prediction Reliability

Numerical predictive modeling is widely used in different application domains. Although many modeling techniques have been proposed, and a number of different aggregate accuracy metrics exist for evaluating the overall performance of predictive models, other important aspects, such as the reliability (or confidence and uncertainty) of individual predictions, have been underexplored. We propose to use estimated absolute prediction error as the indicator of individual prediction reliability, which has the benefits of being intuitive and providing highly interpretable information to decision makers, as well as allowing for more precise evaluation of reliability estimation quality. As importantly, the proposed reliability indicator allows the reframing of reliability estimation itself as a canonical numeric prediction problem, which makes the proposed approach general-purpose (i.e., it can work in conjunction with any outcome prediction model), alleviates the need for distributional assumptions, and enables the use of advanced, state-of-the-art machine learning techniques to learn individual prediction reliability patterns directly from data. Extensive experimental results on multiple real-world data sets show that the proposed machine learning-based approach can significantly improve individual prediction reliability estimation as compared with a number of baselines from prior work, especially in more complex predictive scenarios.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

Comparison of multi-frequency bioimpedance with perometry for the early detection and intervention of lymphoedema after axillary node clearance for breast cancer

2015 ◽  
Vol 151 (1) ◽  
pp. 121-129 ◽  
Author(s):  
Nigel J. Bundred ◽  
◽  
Charlotte Stockton ◽  
Vaughan Keeley ◽  
Katie Riches ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Axillary Node ◽  
Axillary Node Clearance
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