Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers
| Autor: | Gregory J. Czarnota, Lakshmanan Sannachi, Hira Rahman Sha-E-Tallat, Hadi Moghadas-Dastjerdi, Ali Sadeghi-Naini, Laurentius O. Osapoeta |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_treatment
Treatment outcome Breast Neoplasms 02 engineering and technology Machine learning computer.software_genre Machine Learning 03 medical and health sciences 0302 clinical medicine Breast cancer Early prediction 0202 electrical engineering electronic engineering information engineering Humans Medicine Breast Quantitative computed tomography Chemotherapy medicine.diagnostic_test business.industry Cancer Patient survival medicine.disease 030220 oncology & carcinogenesis 020201 artificial intelligence & image processing Artificial intelligence Tomography X-Ray Computed business computer Biomarkers Chemotherapy response |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc44109.2020.9176099 |
| Popis: | Early prediction of cancer response to neoadjuvant chemotherapy (NAC) could permit personalized treatment adjustments for patients, which would improve treatment outcomes and patient survival. For the first time, the efficiency of quantitative computed tomography (qCT) textural and second derivative of textural (SDT) features were investigated and compared in this study. It was demonstrated that intra-tumour heterogeneity can be probed through these biomarkers and used as chemotherapy tumour response predictors in breast cancer patients prior to the start of treatment. These features were used to develop a machine learning approach which provided promising results with cross-validated AUC0.632+, accuracy, sensitivity and specificity of 0.86, 81%, 74% and 88%, respectively.Clinical Relevance- The results obtained in this study demonstrate the potential of textural CT biomarkers as response predictors of standard NAC before treatment initiation. |
| Databáze: | OpenAIRE |
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