A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.
Journal:
Scientific reports
PMID:
32616912
Abstract
Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient basis that would be expected to improve treatment outcomes and patient survival. This study investigated, for the first time, the efficacy of quantitative computed tomography (qCT) parametric imaging to characterize intra-tumour heterogeneity and its application in predicting tumour response to NAC in LABC patients. Textural analyses were performed on CT images acquired from 72 patients before the start of chemotherapy to determine quantitative features of intra-tumour heterogeneity. The best feature subset for response prediction was selected through a sequential feature selection with bootstrap 0.632 + area under the receiver operating characteristic (ROC) curve ([Formula: see text]) as a performance criterion. Several classifiers were evaluated for response prediction using the selected feature subset. Amongst the applied classifiers an Adaboost decision tree provided the best results with cross-validated [Formula: see text], accuracy, sensitivity and specificity of 0.89, 84%, 80% and 88%, respectively. The promising results obtained in this study demonstrate the potential of the proposed biomarkers to be used as predictors of LABC tumour response to NAC prior to the start of treatment.
Authors
Keywords
Adolescent
Adult
Aged
Aged, 80 and over
Antineoplastic Combined Chemotherapy Protocols
Breast Neoplasms
Carcinoma, Ductal, Breast
Carcinoma, Lobular
Female
Follow-Up Studies
Humans
Machine Learning
Middle Aged
Neoadjuvant Therapy
Prognosis
Receptor, ErbB-2
Receptors, Estrogen
Receptors, Progesterone
ROC Curve
Tomography, X-Ray Computed
Young Adult